Proceedings - NUSYS'2017nusys2017.umt.edu.my/wp-content/uploads/sites/106/2017/09/Procee… ·...

Proceedings

PROCEEDINGS

NATIONAL TECHNICAL SEMINAR ON UNDERWATER SYSTEMTECHNOLOGY (NUSYS) 2017

Published by

School of Ocean Engineering,Universiti Malaysia Terengganu,

21030 Kuala Nerus,Terengganu

eISBN: 978-967-2134-03-9

2017

i

Proceedings NUSYS’17

Editors

Muhamad Zalani bin Daud

Mohammad bin Ismail

Nurul Hayati binti Idris

ii

PREFACE

The National Technical Seminar on Underwater System Technology (NUSYS’17) was held at

the Universiti Malaysia Terengganu (UMT) in Kuala Nerus, Terengganu Malaysia during 11th

and 12th September 2017. The seminar aimed at presenting current research being carried

out in the areas related to underwater system technologies. A total of 39 abstracts and full

papers were reviewed and accepted for presentation at the seminar and 22 papers were

selected to be included in this volume of proceedings. There were 8 technical sessions

devoted to the presentation of the papers accepted and the keynote talks have been delivered

by the following distinguished speakers:

Professor Dr. Omar bin Yaakob, Professor at Marine Technology Centre, Universiti

Teknologi Malaysia (UTM), Johor Bahru Malaysia

Assoc. Prof. Dr. Mohd Zamri bin Ibrahim, Rector, Kolej Universiti TATI, Terengganu

En. Mohd Fairuz bin Nor Azmi, AUV Engineer at Fugro Geodetic, Malaysia Sdn. Bhd.

En. Mohd Nasir bin Abdullah, Chief Engineer METOCEAN at Petronas Malaysia.

In addition to the aforementioned keynote speakers, there were also contributions by the

plenary speakers who have delivered their talks on areas related to underwater system

technologies, marine engineering and oceanography as follows:

Prof. Ir Dr. Mohd Rizal bin Arshad, Head of Underwater Robotics Research Group

(URRG), Universiti Sains Malaysia (USM), Pulau Pinang Malaysia

Prof. Dr. Wan Mohd Norsani bin Wan Nik, Professor in Marine Engineering, UMT

Prof. Wan Izatul Asma binti Wan Tala’at, Professor at INOS, UMT

Assoc. Prof. Dr. Rosmiwati binti Mokhtar, Researcher at URRG, USM

Assoc. Prof. Dr. Mohd Fadzil bin Mohd Akhir, Researcher at INOS, UMT

Assoc. Prof. Ir Dr. Ahmad Fitriady, Senior Lecturer at SOE, UMT

Dr. Ahmad Zaki bin Annuar, Senior Lecturer at SOE, UMT

Dr. Ahmad Faisal Mohamad Ayob, Senior Lecturer at SOE, UMT.

The review processes of the papers would not have been possible without the help of

fellow researchers at School of Ocean Engineering (SOE), UMT that agreed to serve on the

seminar’s technical program committee. In this respect, we are deeply thankful to all the

researchers who have contributed to review of the papers included in this seminar. The

reviewers that had successfully completed the review processes are; Wan Mariam binti Wan

Muda, Ahmad Zaki bin Annuar, Suriani binti Mat Jusoh, Salisa binti Abdul Rahman, Hidayatul

Aini binti Zakaria, Anuar bin Abu Bakar, Muhamad Zalani bin Daud, Mohammad bin Ismail,

Nurul Hayati binti Idris, Wan Hafiza binti Wan Hassan, Ahmad Nazri bin Dagang, Nur Farizan

binti Munajat and Mohd Asamudin bin Abdul Rahman.

On behalf of the organizing committee of NUSYS’17, we thank all TPC members and

reviewers for their hard work. Likewise, we thank the authors for submitting their papers to

the seminar. This volume of Proceedings NUSYS’17 would have never materialized without

their contributions. Therefore, we sincerely hope that the concepts, techniques and results

presented in the 22 papers that we have selected are useful to the reader.

Wan Mariam binti Wan Muda

Muhamad Zalani bin Daud

Mohammad bin Ismail

Nurul Hayati binti Idris

iii

CONTENT

PREFACE ii

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM (ANFIS) BASED ON

MAMDANI MODEL FOR UNDERWATER REMOTELY OPERATED VEHICLE

(ROV)

Muhammad Wahyuddin Nor AzmiMohd Shahrieel Mohd AraMohammad Haniff HarunMuhamad Khairi Aripin

AhmadFaiez Husni @ Rusli

Center for Robotics and Industrial Automation, Faculty of Electrical Engineering,

Universiti Teknikal Malaysia Melaka, Melaka, Malaysia.

1

AN OVERVIEW OF MICRO-CONTROLLER BASED ENERGY

MONITORING SYSTEM FOR ENERGY EFFICIENCY IMPROVEMENT

Tan Rui Lin

Muhamad Zalani Daud

Wan Hafiza Wan Hassan

School of Ocean Engineering, Universiti Malaysia Terengganu, 21030 Kuala

Nerus, Malaysia.

5

COMPARISON PERFOMANCES OF CONTROLLER DESIGN FOR

UNDEREATER REMOTELY OPERATED VEHICLE (ROV)-DEPTH CONTROL

Muhammad Wahyuddin Nor Azmi

Mohd Shahrieel Mohd Aras

Mohammad Haniff Harun

Ahmad Faiez Husni @ Rusli

Alias Khamis

Marizan Sulaiman

Mohd Khairi Mohd Zambri

Postgraduate student, Faculty of Electrical Engineering, Universiti Teknikal

Malaysia, Melaka

Center for Robotics and Industrial Automation, Faculty of Electrical Engineering,

Universiti Teknikal Malaysia Melaka, Melaka, Malaysia.

Center for Robotics and Industrial Automation, Faculty of Technology

Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

9

iv

CONTROL OF UNDERWATER GLIDER WITH 1DOF INTERNAL MOVABLE

SLIDING MASS USING SUPER-TWISTING SLIDING MODE CONTROL

Maziyah Mat-Noh

M.R.Arshad

Rosmowati Mohd-Mokhtar

Underwater, Robotics Research Group (UCRG),School of Electrical and

Electronics Engineering, Engineering Campus, Universiti Sains Malaysia,

Nibong Tebal,14300 Pulau Pinang, Malaysia.

Instrumentation and Control Research Cluster (ICE), Faculty of Electrical and

Electronics Engineering, Universiti Malaysia Pahang, Pekan, 26600 Pahang,

Malaysia

13

DC-DC CONVERTER FOR RENEWABLE ENERGY APPLICATION:

MODELING, DESIGN AND CONTROL

T.X. Pei

A.B. Maulud

A.Z Annuar


Nerus, Malaysıa

16

DESIGN OF ROV FOR AUTONAUT WITH SELF-PROPULSIVE VEHICLE

Ahmad Faris Ali

Mohd Rizal Arshad

Universiti Sains Malaysia, Malaysıa.

20

DEVELOPMENT OF RIVER DRIFTING BUOY INSPIRED BY WATER

STRIDER

Herdawatie Abdul Kadir

Nurul Hasniza Darus

Khalid Isa

Mohd Rizal Arshad

Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn

Malaysia, 86400 Parit Raja, Batu Pahat,Johor, MALAYSIA.

UCRG, School of Electrical and Electronic Engineering, Universiti Sains

Malaysia, Engineering Campus 14300 Nibong Tebal, Seberang Perai Selatan,

Pulau Pinang, Malaysia.

25

EXTRACTING RAW HYDROXYAPATITE POWDER FROM NATURAL

SOURCES

Hamizah Nadia bt Alias@Yusof A.Hamizah

Nik Aziz bin Nik Ali

Haziqah bt Abdul Majid

Afiqah Qayyum

Pusat Pengajian Sains Perikanan & Akuakultur, Universiti Malaysia

Terengganu, Malaysia

28

v

FEASIBILITY STUDY OF WAVE ENERGY SYSTEM IN TERENGGANU

F. S. Burhanuddin

W.M.W. Muda

N.A.S.Salleh

A.Q.Mohd Nor

Special Interest Group of Eastern Corridor Renewable Energy, School of Ocean

Engineering, Universiti Malaysia Terengganu, 20130 Kuala Nerus, Malaysia

31

GEARING SYSTEM ANALYSIS FOR UNDERWATER REMOTELY

OPERATED CRAWLER (ROC)

M.S.M. Aras

Iktisyam Zainal

M.N.Kamarudin

M.K.M. Zambri

M.H. Harun

Muhammad Wahyuddin Nor Azmi

Underwater Technology Research Group, Faculty of Electrical Engineering,

Technical University of Malaysia Melaka (UTeM), 76100 Melaka, Malaysia

Jabatan Teknologi Kejuruteraan Elektrik, Faculty of Technology Engineering,

Technical University of Malaysia Melaka (UTeM), 76100 Melaka, Malaysia

35

HORIZONTAL TARGET STRENGTH OF Oreochromis niloticus and Clarias

gariepinus IN ASPECT OF SIZE

N.A. Nik Aziz

Hisam Fazrul

Ezmahamrul Afreen

S. Hasiah

School of Fisheries and Aquaculture Sciences, Universiti Malaysia Terengganu,

21030 Kuala Nerus, Terengganu.

Center for Foundation and Liberal Education, Universiti Malaysia Terengganu,


39

HORIZONTAL TARGET STRENGTH OF Oreochromis niloticus IN ASPECT OF

TILT ANGLE

Hisam Fazrul

N.A. Nik Aziz

Ezmahamrul Afreen

Amir Saiful Ariff



43

vi

MACROALGAE AS FUEL : TGA AND KINETIC STUDY OF Gracilaria cacalia

AND Halymenia maculata PYROLYSIS

Amira Nabila binti Roslee

Nur Farizan binti Munajat


Nerus, Malaysıa.

46

OPTICAL AND ELECTRICAL STUDY OF BIOPOLYMER BASED ON

METHYLCELLULOSE DOPED WITH Ca(NO3)2

A.M.S. Nurhaziqah

N.A. Nik Aziz

S. Hasiah

A.Hamizah

I.Q. Afiqah


Terengganu, Terengganu.



Center for Foundation and Liberal Education, Universiti Malaysia Terengganu,


50

SCATTERING REGIMES FOR DIFFUSE UNDERWATER OPTICAL

WIRELESS COMMUNICATIONS

Faezah Jasman

Wan Hafiza Wan Hassan

Faculty of Engineering and Computing, First City University College,

Selangor,MALAYSIA

School of Ocean Engineering, Universiti Malaysia Terengganu, Terengganu,

Malaysıa.

54

STUDY OF LED IN UNDERWATER FISH ATTRACTION LAMP AND ITS

LIGHT PROPAGATION

Ahmad Nazri Dagang

Chong Geeng Jun

School of Ocean Engineering, Universiti Malaysia Terengganu, Malaysia

57

THE DEVELOPMENT OF AN AUTONOMOUS FLOATING TRASH

COLLECTOR

Ming-Yi Wong

Khalid Isa

Embedded Computing System Research Group (EmbCoS), Faculty of

Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia,

Malaysıa.

60

THE IMPACT OF ESS SOC ON FUEL ECONOMY, EMISSIONS,

ELECTRICAL CONSUMPTION AND AER FOR PHERB POWERTRAIN

J.S Norbakyah

A.R Salisa

63

vii


Nerus, Terengganu

THE STUDY OF SCHIFF BASE (O-PHENYLENE) AND PEROVSKITE

(CaO-ZnO COMPOSITE) AS THE POTENTIAL SOLAR CELL MATERIAL

I.Q. Afiqah

N.A. Nik Aziz

A. Hamizah

A.M.S. Nurhaziqah


Nerus, Terengganu.



66

TYPE OF SEDIMENT CLASSIFICATION AND BATHYMETRY MAPPING AT

THE PAYAR ISLAND

Razak Zakariya

Mohd Azhafiz Abdullah

Zainudin Bachok

Idham Khalil

School of Marine and Environmental Science

Institute of Oceanography and Environment, Universiti Malaysia Terengganu,

21030 Kuala Nerus, Terengganu, Malaysia

70

WATER SURFACE PLATFORM FOR INTERNET-BASED ENVIRONMENTAL

MONITORING SYSTEM

Nurliyana Kafli

Khalid Isa

Embedded Computing System Research Group (EmbCoS), Faculty of

Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia,

Malaysıa.

77

ZINC OXIDE AND NATURAL ANTHOCYANIN DYE FROM RED FRANGIPANI

FLOWER AS THE ACTIVE MATERIALS FOR THIN FILMS

Wan Almaz Dhafina Che Wan Ahmad

Hasiah Salleh

Mohd Zalani Daud


Nerus, Terengganu,Malaysia

Centre for Fundamental and Liberal Education, Universiti Malaysia Terengganu,

21030 Kuala Nerus, Terengganu, Malaysia

80

1

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM (ANFIS) BASEDON MAMDANI MODEL FOR UNDERWATER REMOTELY

OPERATED VEHICLE (ROV)

Muhammad Wahyuddin Nor Azmi, Mohd Shahrieel Mohd Aras, Mohammad Haniff Harun, Muhamad KhairiAripin, AhmadFaiez Husni @ Rusli

Center for Robotics and Industrial Automation,Faculty of Electrical Engineering, Universiti Teknikal

Malaysia Melaka, Melaka, [email protected], [email protected], [email protected], [email protected]

ABSTRACTThis paper present an Adaptive Neural Network based on Fuzzy Inference System (ANFIS) model generatedfrom the table of rules in Mamdani inference engine for underwater Remotely Operated Vehicle (ROV). ThisROV design focused on depth control. The main objective of this study to analyze the performance of systemresponse between the conventional Mamdani Fuzzy Logic Controller and ANFIS controller using the similardata. This method was verified and validated in MATLAB/Simulink platform. The result shows it was foundthe method proposed is improved the performances of the system response in terms of rise time and steady stateerror.

Keyword: Adaptive Neural Fuzzy Inference System; Mamdani Fuzzy Logic Controller; Remotely OperatedVehicle; depth control.

1. INTRODUCTION

In 1992, Bezdek come out with an idea of Computational Intelligence (CI). From that moment, CI getmore attention as well as picked up an across the board of worry among researcher rising and put asanother field of study. Computational Intelligence use the concept of bionics ideas for reference, it wasbased on emulating intelligent phenomenon in nature. CI endeavors to recreate and return thecharacters of intelligent with the goal that it can be another new research domain in nature andengineering reconstructing. The quintessence of CI is an all-inclusive approximation, and it has theconsiderable capacity of non-linear mapping and optimization. Further study on hybrid algorithms inCI study has been the more interested. It is ending up plainly less basic to peruse around an applicationthat utilization simply neural network, or developmental calculation, or fuzzy logic. There arenumerous potential outcomes for joining the technique [1]. In vast measure, fuzzy logic, neuro network,and probabilistic thinking are reciprocal, not competitive. It is winding up clearly progressiveobvious that by and large it is profitable to join them. A valid example is developing number of"neurofuzzy" purchaser items and systems that utilization a mix of fuzzy logic and neural-networktechniques [2].

System Modeling in view of conventional method (e.g., differential equation) is not appropriate formanaging as well not characterized and uncertain systems. By difference, a fuzzy inference system(FIS) utilizing fuzzy if-then principles can show the subjective parts of human information and thinkingforms without utilizing exact quantitative investigation. Therefore, there are some fundamental partsof FIS which need better understanding. All the more particularly: 1) No standard strategies exist forchanging human information or experience into the control base and database of a fuzzy inferencesystem. 2) There is a requirement for viable strategies for tuning the membership function (MF's) inorder to limit the yield mistake measure or maximize execution record [3].This paper investigated amethod of converting the Mamdani Fuzzy logic to the ANFIS model based on given data in MamdaniFuzzy logic function for the underwater Remotely Operated Vehicle (ROV) depth control system.Through an in deep understanding of ANFIS model structure and comparative analysis between theperformance of conventional Mamdani fuzzy logic and adaptive neuro fuzzy inference system(ANFIS). Experimental result show that this model can surpass the desired targets and performance.

2

2. ANFIS MODEL

ANFIS is utilized for modelling of nonlinear of fuzzy input and output data, and for forecast of outputas per the input. It applies a blend of the minimum squares technique and back propagation angleplunge technique for training fuzzy inference system membership. Practically, it is identical to thecombination of neural network and fuzzy inference system. It consolidates the advantage of neuralnetwork learning algorithm and FIS to guide contributions input to an output [6]. Figure 1 shows thestructure of ANFIS model. By using the data from conventional fuzzy logic rule table to create anANFIS model controller. This mapping is either a linear relationship or a very nonlinear one regardingupon the structure for the system and the capacity for every node. The point is to develop a system foraccomplishing a coveted nonlinear mapping that is controlled by an informational collection thatcomprises of various input– output sets of an objective system. This data index is normally called thetraining data set and the strategy that is taken after is the alteration of the parameters to enhancethe execution of the system and this is regularly referred to as the learning principle or the learningalgorithm. Figure 2 shows the ANFIS platform used in MATLAB.

.

Figure 1: Structure of ANFIS model Figure 2: ANFIS model generator

By collecting the data from conventional mamdani fuzzy rule table and create a variable data thatconsist of at least two row of data. The first row represents the input and the last row representthe output data that automatically recognize by ANFIS platform. For this experiment that using twoinput and one output will have three row of data. Load the data from workshop that created in datavariable, then generate the FIS. The FIS generated with the rule by itself. Figure 3 shows that the ruletable and surface viewer created by generated the ANFIS.

Figure 3: ANFIS Rule and Surface viewer Figure 4: ANFIS structure rule ofmembership

The membership function that created was quite different with conventional mamdani fuzzy logiccontroller which is the input membership function MF was set the range and structure differentlyand the output variable turn to constant parameter. Figure 4 shows the relationship of variable in ANFISmodel controller.

3. RESULTS AND DISCUSSION

3

The structure of ANFIS model controller and conventional mamdani fuzzy logic controller is totallydifferent but by using the same data that got from rule table show that the ANFIS can be generateand the result show the output response of ANFIS controller had increased the performance of systemresponse. A simulation was developed in MATLAB Simulink platform using the same type of functionblock for ANFIS model controller and mamdani fuzzy logic controller model. Figure 5 shows thesimulation block used to gain the output response. The result show that the ANFIS model controllerhave higher rise time and fast steady state error than mamdani fuzzy logic. Figure 6 shows the outputof system response for ANFIS model controller and mamdani fuzzy logic controller model.

Figure 5: Simulation block for Mamdani Figure 6: ANFIS and Mamdani system response

fuzzy logic and ANFIS controller model

The ANFIS model controller able to control the system better than mamdani fuzzy logic controller.The ANFIS model can be tuning using the number of tolerance and epochs function and ANFIS editplatform. ANFIS is a general approximator as a result of its vast approximating ability via training. Allparameters in ANFIS are nonlinear parameters which can be balanced by learning rules. TheANFIS controller have higher rise time,4.565s than the Mamdani Fuzzy logic controller which is4.939s. Both controller approximately same value of overshoot percentage which is 0.919%.

4. CONCLUSION

As conclusion, by using the data from mamdani fuzzy logic controller can generate the ANFISmodel. This method particularly useful to make contribution in improving the performance of systemresponse. An ANFIS model controller has been successful designed and proved a relatively goodperformance during simulation. The future planning to improve the system response with faster risetime by using a simplified or combination of computing intelligence (CI) such as M-ANFIS, singleinput fuzzy logic controller (SIFLC) and etc.

ACKNOWLEDGEMENT

We wish to express our gratitude to honorable University, Universiti Teknikal Malaysia Melaka(UTeM). Special appreciation and gratitude to especially for Underwater Technology Research Group(UTeRG), Centre of Research and Innovation Management (CRIM), Center for Robotics and IndustrialAutomation (CERIA) for supporting this research and to Faculty of Electrical Engineering fromUTeM and Ministry of Higher Education for supporting this research under FRGS(FRGS/1/2015/TK04/FKE/02/F00257).

REFERENCES

1. R. C. Eberhart, Overview of computational intelligence, Proceedings of the 20th Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society, 20(3),2007.

2. L. A. Zadeh, Soft Computing and Fuzzy Logic, IEEE Software,11(6):48–56, 2007.

3. J. S. R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions onSystems, Man, And Cybernetics, 23(3):665–685, 2006.

4. Aras, M. S. M., Abdullah, S. S., Jaafar, H. ., Aziz, M. A. ., & Kasim, A. M. (2013). Tuning process ofsingle input fuzzy logic controller based on linear control surface approximation method for depth control

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of underwater remotely operated vehicle. Journal of Engineering and Applied Sciences, 7.5. Choi, B., Kwak, S., & Kim, B. K. (2000). Design and Stability Analysis of Single-Input, 30(2), 303–309.

Vural, Y., Ingham, D. B., & Pourkashanian, M. (2009). Performance prediction of a proton exchangemembrane fuel cell using the ANFIS model. International Journal of Hydrogen Energy, 34(22), 9181–9187. https://doi.org/10.1016/j.ijhydene.2009.08.096

5

AN OVERVIEW OF MICRO-CONTROLLER BASED ENERGYMONITORING SYSTEM FOR ENERGY EFFICIENCY

IMPROVEMENT

Tan Rui Lin, Muhamad Zalani Daud*, Wan Hafiza Wan Hassan

School of Ocean Engineering, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Malaysia.(E-mail: [email protected], *[email protected], [email protected])

ABSTRACT

The monitoring of building’s energy consumption is vital for a comprehensive energy management relating to

improving energy efficiency of the building. This paper presents an overview of the recent research anddevelopment of systems that utilizes microcontrollers for used as energy monitoring of the building electrical

facilities and appliances. The system is developed for energy efficiency improvement purpose. Microcontrollerbased system configuration and properties of the devices used are highlighted. Of significant importance are the

devices that are used as support for the microcontrollers according to the system’s requirement. Finally,recommendation for future system development is presented, primarily focusing on system that supports Internetof Things technology.

Keywords: Energy Monitoring System, Sensor and Monitoring Protocol, Energy Efficiency

1. INTRODUCTION

Energy efficiency is strongly related to the building’s electrical energy consumption. Currently, theconventional building’s electrical loads are not controllable and most of the time, the consumption aredepend on human factor. For example, an air conditioning system, that is in standby mode, stillconsuming electricity until its main switch is switched off by the user. A web survey in 2015 amongthe casual user shows that around 29% of the user are not aware that appliances are still consumingenergy in standby state and about 80% of respondent still connects their appliances to the power sourcewhen not in use [1]. These standby state appliances actually consumes electricity of approximately 10%of the overall energy consumption [2]. To optimize and reduce energy consumption, many energymonitoring system have been proposed in the literature [3-5]. Author in [3] purposed an Arduino basedwireless power meter to monitor energy usage and send the information to the user Wishield. Thesystem that detects human activities has been designed in [4] which utilizes PIR, temperature and lightsensors and programmed with machine learning algorithm to intelligently help consumers reduce totalelectricity payment without much involvement of consumers. Later, an improved system has beenproposed in [5] with the energy monitoring and automated control system that is modified with facedetection, GSM module and a controller. This paper highlights the system configuration of the existingenergy monitoring system technologies with some discussions on improving the state of the art of theexisting devices.

2. CONFIGURATION OF ENERGY MONITORING SYSTEM

A complete and systematic energy monitoring system (EMS) is made from few parts of devices thatcan be divided into measuring device, recognition device, communication device and display device.

2.1. Measuring devices

The main purpose in EMS is to monitor the current consumption or current flow through the wire.Current transformer (CT) sensor is normally used as a measuring device. It is a non-invasive sensor andwith simple clamp onto the main lines without interrupting power into the circuit breaker to monitorthe current flow through the appliances [3,6]. Table 1 compares few types of current sensors used and

6

their sensitivity properties. From the table, different types of sensor are considered which depends onthe application needs, cost and quality of the sensors.

Table 1. Different models of current sensors recently used in researchNo. Sensor Sensitivity Remarks1 CR3110 CT Maximum output voltage 3.3V, 48.2A [3]2 YHDC STC 013-030 CT Maximum output voltage 1V, 30A [6]3 IC WCS 2705 CT 255mV/A [7]4 ZMPT107 voltage sensor 1-2mA [8]5 HX 03-P/SP2 CT DC output voltage of 2.5±0.625 V, ±9 A [9]

Besides CT sensor, the light detecting resistor sensors are used to monitor the luminance and can beprogrammed with automatic switch to turn on or off the lamp [2,4,6]. The temperature sensor, forexample SEN 23292P can be used to detect the ambient temperature [6], whereas in [10], DHT11temperature and humidity sensor is connected to the system to measure the room temperature andhumidity.

2.2. Recognition devices

This part is also known as microcontroller used to exploit the measured data and recognize the workingappliances with running some algorithm to extract fact about the appliances. Arduino based monitoringsystem is the most popular. There are several papers [2,3,6-8,10] discussing about Arduino as themicrocontroller of their system. Besides that there are some researches utilizes Raspberry Pi as themicrocontroller. The Raspberry Pi is a low cost small computer that support Internet of Things whichis designed to run variations of Linux and the primary programming language is Python [9]. In addition,there are some studies using LPC2138 [4] and LPC2148 [5] field-programmable gate array (FPGA) asmicrocontroller.

2.3. Communication devices

Communication technology mainly divided into wired and wireless with their advantages anddisadvantages. Table 2 shows the characteristics of different types of communication method. FromTable 2 it clearly can be seen that there are wide variety communication devices with the diverse rangeof functions can be chosen depending on the application needs.

Table 2. Comparison of communication methods [7,11]Function ZigBee WIFI BluetoothData Rate 20,40&50 Kbit/s 54Mbit/s 1Mbit/s

Range 10-100m 50-100m 10mOperating Frequency 900-928MHz 2.4&5GHz 2.4GHzPower Consumption Very Low High HighTypical Application Industrial Control Wireless LAN Wireless Communication

Data Protection 16-bit CRC 32-bit CRC 16-bit CRCNode Wake-up Time 15ms 1s 3s

From Table 2, although power consumption of WIFI is high, however it has advantages in terms of datarate, range of function, operating frequency and data protection compared to ZigBee and Bluetooth [1-3,8,10].

7

2.4. Display devices

Most systems require real-time display of the energy consumption of the building. The display devicesuch as computer monitors, LCD, smart phone and tablet PC can provide the visual display of energyconsumption to the user in the form of graph or depending on the need of the load monitor [11]. Recenttrend on display device may be equipped with interactive touch display features to enable the user to domonitoring and control of the energy consumption in real time.

3. FUTURE DIRECTION OF RESEARCH AND DEVELOPMENT OF SYSTEM

From the literature, different types of sensor, microcontroller, and communication way are neededaccording to the system’s requirement. As for the brain of the system, Arduino is found to be dominant.However, with the advent of Raspberry Pi, an interesting more creative system development is feasibledue to its flexibility, easily operates and fully integrated with IoT technology through WIFI andBluetooth services. For low energy consumption of the system communication, using ZigBee is muchpreferred due to its efficient and low energy consumption [7]. With the IoT technology, developmentof an interactive real time energy monitoring and management system is feasible.

4. CONCLUSION

The paper has presented an overview of the recent literatures discussing about EMS for the purpose ofenergy efficiency improvement. The technical aspects of the devices used are presented to give a generalview about the state of the art of the system. Some future research and development have beenhighlighted with particular stress on the system that supports IoT technology.

ACKNOWLEDGEMENT

Thanks to Universiti Malaysia Terengganu, Malaysia and Ministry of Higher Education Malaysia(MOHE) for the financial support. This research is supported by MOHE under the FundamentalResearch Grant Scheme (FRGS), Vot No. 59418 (Ref:FRGS/1/2015/TK10/UMT/02/1).

REFERENCES

1. Putra, L., & Kanigoro, B. (2015). Design and Implementation of Web Based Home Electrical ApplianceMonitoring, Diagnosing, and Controlling System. Procedia Computer Science, 59, pp. 34-44.

2. Anastasi, G., Corucci, F., & Marcelloni, F. (2011, November). An Intelligent System For Electrical EnergyManagement in Buildings. In Intelligent Systems Design and Applications (ISDA), 2011 11th InternationalConference on, pp. 702-707. IEEE.

3. McNally, C. (2010). Arduino Based Wireless Power Meter (Doctoral dissertation, Cornell University).4. Kedar, A., & Somani, S. B. (2015). Hardware Prototype of Smart Home Energy Management System, pp.

2319-2322.5. Mounika, V., & Ravitheja, T. (2016). Design and Development of Energy Monitoring and Automated

Control System Using Labview for Energy Conservation. vol. VI, no. 6, pp. 327-332.6. F. E. Barnicha. (2015). Smart Home Energy Management System Monitoring and Control of Appliances

Using an Arduino Based Network in the context of a Micro-grid.7. Baviskar, J. J., Mulla, A. Y., Baviskar, A. J., Panchal, N. B., & Makwana, R. P. (2014, March).

Implementation of 802.15. 4 for Designing of Home Automation and Power Monitoring System.In Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students' Conference on, pp. 1-5.IEEE.

8. David, N., Anozie, F. N., Ebuka, F. O., & Nzenweaku, S. A. (2016, September) Design of An ArduinoBased Wireless Power Meter.

9. Fletcher, J., & Malalasekera, W. (2016). Development of A User-friendly, Low-cost Home EnergyMonitoring and Recording System. Energy, 111, pp. 32-46.

10. Dahlan, N. Y., Aris, A. A. M., Saidin, M. A., Nadzeri, M. J. M., Nawi, M. N. M., Abbas, W. F., ... &Arshad, P. (2016). Development of Web-based Real-time Energy Monitoring System for CampusUniversity. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(10), pp. 157-

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164.11. Abubakar, I., Khalid, S. N., Mustafa, M. W., Shareef, H., & Mustapha, M. (2017). Application of Load

Monitoring In Appliances’ Energy Management–A review. Renewable and Sustainable EnergyReviews, 67, pp. 235-245.

9

COMPARISON PERFOMANCES OF CONTROLLER DESIGN FORUNDERWATER REMOTELY OPERATED VEHICLE (ROV)-

DEPTH CONTROL

Muhammad Wahyuddin Nor Azmi1, Mohd Shahrieel Mohd Aras2, Mohammad Haniff Harun3, Ahmad Faiez

Husni @ Rusli1, 2Alias Khamis, 2Marizan Sulaiman, 2Mohd Khairi MohdZambri

1Postgraduate student, Faculty of Electrical Engineering, Universiti Teknikal Malaysia, Melaka2Center for Robotics and Industrial Automation, Faculty of Electrical, Engineering, Universiti Teknikal

Malaysia Melaka, Melaka, Malaysia.3Center for Robotics and Industrial Automation, Faculty of Technology Engineering, Universiti Teknikal Malaysia Melaka,

Melaka, Malaysia([email protected] [email protected], [email protected])

ABSTRACTThis paper present controller design using in controlling the ROV depth control system which involved SingleInput Fuzzy Logic Controller (SIFLC), Adaptive Neural Fuzzy Inference System (ANFIS), MamdaniFuzzy Logic Controller (M-FLC) and Proportional Integrated Differential (PID) controller. The model of ROVwas generated by using MATLAB System Identification Toolbox’s to gain a transfer function represent theROV model. This ROV design focused on depth control. The main objective of this study to analyzethe performance of system response between the Controller designs. This method was verified andvalidated in MATLAB/Simulink platform. The result shows it was found the analysis performances of thesystem response in terms of rise time and percentage of overshoot.

Keywords: Single Input Fuzzy Logic Controller (SIFLC); Adaptive Neural Fuzzy Inference System;Mamdani Fuzzy Logic Controller; Proportional Integrated Differential (PID); Remotely Operated Vehicle; depthcontrol.

1. INTRODUCTION

An underwater Remotely Operated Vehicle (ROV) is a submarine like robotic device which controlledby onboard computer. The main purpose of developing the underwater ROV is eliminated humanlimitless such as dangerous underwater task, for underwater surveillance task, surveying, inspection,recovery, maintenance and repair [1]. The ROV are maneuverable in three dimension x, y and z axiswhich can be programmed to float passively or actively to desired location and swim in differencelevel of depth. For that purpose, the controller need to be design which able to fulfill the requiredperformance. This paper investigated the performances of the controller design in controlling anunderwater Remotely Operated Vehicle (ROV) depth control system. Through an in deepunderstanding of controllers of the proportional integrated differential (PID), fuzzy logic controller,ANFIS controller and single input fuzzy logic controller in the performance analysis in term ofpercentage of overshoot and rise time. Experimental result show that this model can surpass the desiredtargets and performance.

2. CONTROLLERS DESIGN

Controller design use the concept of bionics ideas for reference, it was based on emulating intelligentphenomenon in nature. CD endeavors to recreate and return the characters of intelligent with the goalthat it can be another new research domain in nature and engineering reconstructing [2]. The controllerdesign using the computer such as PID, Fuzzy Logic controller and many more. The controller use incontrolling the underwater ROV are stated below

2.1 Proportional Integrated Differential (PID) controller

PID is an aerodyne for the mathematical terms of proportional, integral and derivative. The controllerused to improve the dynamic response which is to reduce the steady state error. The derivative give

10

a finite zero to an open loop plant and improve the transient response while the integral adds a pole atthe origin to increasing system type by one thus eliminate the steady state error due to step functionto zero [3].

2.2 Mamdani Fuzzy Logic Controller (M-FLC)

The Mamdani fuzzy logic controller consist of a fuzzifier, rule base, fuzzy inference engine anddefuzzifier were employed. In this paper, multiple input single output (MISO) type of controllerdesigned and use in formulation of control law [4]. The fuzzy logic controller was designed in orderto control the depth of an underwater ROV.

Figure 2: Fuzzy logic controller structure Figure 3: ANFIS controller structure

2.3 Adaptive Neural Fuzzy Inference System (ANFIS) controller

Adaptive Neural Fuzzy Inference System (ANFIS) architecture consist of two input, two rule and oneoutput for the sugeno type of fuzzy logic controller. The ANFIS controller was generated by using“anfisedit” function in MATLAB Sugeno fuzzy platform. The data was gained from the rule tableof Mamdani type of fuzzy logic controller as discuss above. The ANFIS generated use to convertfrom the Mamdani type to Sugeno type of fuzzy logic controller. The controller designed usingMATLAB Simulink platform as shown in figure 3.

2.4 Single Input Fuzzy Logic Controller (SIFLC)

The Single Input Fuzzy Logic Controller (SIFLC) was first introduced by Choi B.J et al [5] proposea simplified method in fuzzy logic controller by using “signed distance” method that reduced the ruleand membership function. There are formulae proposed in using this method as shown in equation (1).

d e e (1)

1 2

The controller was designed based on the formulae and the controller block simulation as shownin figure 3. The controller was designed using MATLAB Simulink platform.

Figure 4: Simulation block for controller design

11


The underwater ROV model was represent by a first order transfer function. The CI controllers wasdesigned to control the depth stability control system. Firstly, the PID controller able to controlthe model with good performance and high rise time but there is an overshoot in the system response.The Mamdany type of fuzzy logic controller was designed to have lower the percentage of overshootthan the PID controller but the controller has slower rise time. The third controller which is ANFIScontroller designed to get the higher rise time than the Mamdani type of fuzzy logic controller. Theresult shown in figure 4 that the ANFIS controller have an improvement in term of rise time.The fourth controller which is Single Input Fuzzy Logic Controller (SIFLC) designed based on theformulae (1) have good performances. The result shown in figure 4 that the SIFLC has higher rise timethan the PID controller and there is lower percentage of overshoot. In other mean that the SIFLC ableto control the underwater ROV depth control system nicely. All controllers were designed byusing MATLAB Simulink Platform and validate the system performance in the simulation. All CIcontroller performance as shown in table 1. The performance analysis was in term of higher rise timeand lower percentage of overshoot.

Figure 5: System response for CI controllers

4. CONCLUSION

As conclusion, the performance of the controllers has been recorded and analyses. The PID controllerable to control nicely since the model was a first order transfer function but the SIFLC controller ableto gain better performance than PID controller in term of percentage of overshoot. The PID controllerable to control the lower order of transfer function while the fuzzy type controller able to control higherorder of transfer function. The future planning to improve the ANFIS controller performance in termof rise time by designing hybrid algorithm to have higher or equal to SIFLC controller performance.

ACKNOWLEDGEMENT

We wish to express our gratitude and appreciate the support granted by Universiti Teknikal MalaysiaMelaka (UTeM) in pursuing this research especially for Underwater Technology Research Group(UTeRG), Center for Research and Innovation Management (CRIM) and Center for Robotics andIndustrial Automation (CeRIA).

REFERENCES

1. Ishaque, K., Abdullah, S.S., Ayob, S.M. and Salam, Z., 2010. Single input fuzzy logiccontroller for unmanned underwater vehicle. Journal of Intelligent and Robotic Systems,59(1), pp.87-100.

2. J. S. R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEETransactions on Systems, Man, And Cybernetics ,23(3):665–685, 2006.

3. Gaing, Z.L., 2004. A particle swarm optimization approach for optimum design of PID controller inAVR system. IEEE transactions on energy conversion, 19(2), pp.384-391.

12

4. Shahrieel, M. and Aras, M., 2015. Adaptive simplified fuzzy logic controller fordepth control of underwater remotely operated vehicle (Doctoral dissertation, UniversitiTeknikal Malaysia Melaka).

5. Choi, B., Kwak, S., & Kim, B. K. (2000). Design and Stability Analysis of Single-Input, 30(2), 303-309.

13

CONTROL OF UNDERWATER GLIDER WITH 1DOF INTERNALMOVABLE SLIDING MASS USING SUPER-TWISTING SLIDING

MODE CONTROL

Maziyah Mat-Noh1,2, M.R.Arshad1, Rosmowati Mohd-Mokhtar1

1Underwater, Robotics Research Group (UCRG),School of Electrical and Electronics Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong

Tebal,14300 Pulau Pinang, Malaysia.2Instrumentation and Control Research Cluster (ICE),

Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, 26600 Pahang, Malaysia(E-mail: [email protected]*, [email protected], [email protected])

ABSTRACTThis paper presents the evaluation of super-twisting sliding mode control (STSMC) implementationin autonomous underwater glider (AUG) with 1DOF internal moving mass. The controller is designedbased on the linearized longitudinal plane of an AUG. The controller is designed and tested for the glide pathmoving from 45o to 30o downward and upward. The performance of the STSMC is compared to conventionalsliding mode control (SMC) with boundary layer based on its response time specifications. The STSMC asexpected demonstrates a better performance as compared to conventional sliding mode control (SMC).

Keywords: Autonomous underwater glider, boundary layer, super-twisting sliding modecontrol

1. INTRODUCTION

The dynamic models of the gliders are multi-input multi-output (MIMO) nonlinear systems.Furthermore, the gliders are considered as underactuated systems and difficult to maneuver as well asits dependent on the complex ocean environment. Several control strategies have been reported. In[1][2][3], the proportional-integral-derivative (PID) controls have been designed due its simplearchitecture and controller tuning parameters. The optimal control so called Linear Quadratic Regulator(LQR) is another approach has been used in [4][3][5], where in this method only two tuning knobs (Qand R) need to be varied in order to obtain an optimal gain that minimize the cost function and be asolution for Ricatti equation. In [6][7][8], the model predictive control strategies have beendiscussed where Francesco Tatone et al in [6] using Model Predictive Control (MPC) to control theattitude of Slocum glider. Ian Abraham and Jingang Yi in [7] use the MPC technique to integrate withthe path-following technique to tune the desired vehicle velocity online along with the trajectory andtherefore validate the 3D motion dynamics of the Slocum glider. The conventional SMC with boundarylayer approach has been used in [9][10] using linear model and Yanh Hai and Ma Jie in [13] usinginverse system to design the nonlinear SMC in a type of composite controller.

2. METHOD

In this section, the model linearization and the steps to design the controller algorithm are described.The linear model is formulated at an equilibrium gliding path. Then controller is designed based onthe linear model.

2.1. Model linearization

The linearization about a steady glide path (ξ) is determined for the nonlinear motion equation as explained in [9]. We adopt the method to calculate the equilibrium glide path from [11] .Define the state vector,

14

x z' , ,2 ,v1 ,v3 ,rp1 , Pp1 ,mb T and input vector, u u1 u4 T

where u1 and u4 are the force applied on the internal mass moving in x-axis and ballastpumping rate respectively. For acceleration control input, the state variables, x are set tobe and input variables, where w1and w4is the control acceleration on the internal massmoving in x-axis and ballast pumping rate respectively. Define the state vector,

x z' , ,2 ,v1 ,v3 ,rp1 ,rp1 ,mb T and input vector u w1 w4 T .

2.2. Controller design

The relative degree of super-twisting control sliding variable to the control input is one, thusthe control law is developed based on the super-twisting control algorithm to avoid chattering.The control law u(t) is defined as a combination of two terms. The first term is defined as itscontinuous function whereas the second term is the discontinuous time derivative of the slidingvariable which is present during the reaching phase only. The control law of super-twistingSMC is defined as

1 t

u ( SB )1 SAx( t)-S 2 sign( S ) sign( S ) (1)0

Where S

3. RESULT AND DISCUSSION

In this section, the proposed control scheme is implemented and tested within the simulationenvironment of the glider system and the corresponding results are presented. Figure 1 to Figure 4shows the selected outputs of the glider. All the figures are plotted for comparing nonlinear open loopsystem with super- twisting SMC.. The super-twisting SMC also reduce the chattering effect.

Figure 1: Pitching angle (Down) Figure 2: Velocity v1 (down)

15

Figure 3: Pitching angle (Up) Figure 4: Velocity v1 (Up)

4. CONCLUSION

In this paper, the STSMC is successfully designed. Based on the results and analysis, a conclusionhas been made that STSMC is capable of controlling the 1DOF autonomous underwater gliderwith acceptable errors. Simulation results show that STSMC can be further improved by introducingnonlinear controller and the optimization approach to improve the control capability.

REFERENCES

1. A. Bender, D. M. Steinberg, A. L. Friedman, and S. B. Williams, “Analysis of an AutonomousUnderwater Glider,” 2006.

2. N. Mahmoudian and C. Woolsey, “Underwater glider motion control,” 2008 47th IEEE Conf.Decis. Control, pp. 552–557, 2008.

3. M. M. Noh, M. R. Arshad, and R. M. Mokhtar, “Depth and pitch control of USM underwaterglider:performance comparison PID vs. LQR,” Indian J. Geo-Marine Sci., vol. 40, no. 2, pp. 200–206, 2011.

4. N. E. Leonard and J. G. Graver, “Model-based feedback control of autonomous underwater gliders,”IEEE J. Ocean. Eng., vol. 26, no. 4, pp. 633–645, 2001.

5. M. G. Joo and Z. Qu, “An autonomous underwater vehicle as an underwater glider and its depthcontrol,”Int. J. Control. Autom. Syst., vol. 13, no. 5, pp. 1212–1220, 2015.

6. F. Tatone, M. Vaccarini, and S. Longhi, “Modeling and Attitude Control of an AutonomousUnderwater Glider,” pp. 217–222, 2009.

7. I. Abraham and J. Yi, “Model predictive control of buoyancy propelled autonomous underwaterglider,” 2015 Am. Control Conf., pp. 1181–1186, 2015.

8. Y. Shan and Z. Yan, “Model Predictive Control of Underwater Gliders Based on a One-layerRecurrent Neural Network,” pp. 328–333, 2013.

9. M. Mat Noh, M. R. Arshad, and R. Mohd-mokhtar, “Control of 1 DoF USM Underwater Glider(USMUG),” in 4th International Conference on Underwater System Technology: Theory andApplications 2012, 2012, no. 1.

10. M. Mat-Noh, M.R.Arshad, and R. Mohd-Mokhtar, “Jurnal Teknologi THE EVALUATION OFCONTROLLER TRACKING PERFORMANCE BASED ON TAYLOR ’ S SERIES,” vol. 9, pp. 175–181,2015.

11. J. G. Graver, “Underwater Gliders: Dynamics, Control and Design,” Princeton University,2005.

16

DC-DC CONVERTER FOR RENEWABLE ENERGY APPLICATION:MODELING, DESIGN AND CONTROL

T.X. Pei, A.B. Maulud, A.Z Annuar*

School of Ocean Engineering, Universiti Malaysia Terengganu21300 Kuala Terengganu, MALAYSIA

(E-mail:*[email protected])

ABSTRACTModeling, design and control of a DC-DC converter for a renewable energy application is presented. A proposed DC-

DC converter topology is based on two half bridge DC-DC converters which control the power between the battery,energy storage system (ESS) of ultra-capacitor, and an output load resistance. Two well-known control schemes basedon small-signal ac model are used to design converter control system. The average voltage mode control is used forthe boost converter operation and the two-loop average current mode control for the bidirectional operation. Controlschemes are modeled and designed, the performance and responses were analyzed using MATLAB/Simulink. It isanticipated that the proposed power converter system can be used to regulate the power flow between two DC energystorages for renewable energy application.

Keywords: DC-DC converter, mathematical model, small-signal linearization, voltage and current mode control.

1. INTRODUCTION

DC-DC converter along with energy storage system (ESS) has become a promising option for many powerrelated systems, including hybrid vehicle, fuel cell vehicle, renewable energy and so forth. One key featurewithin most renewable energy converters is the bidirectional dc-dc converter, which connect the DC sourcesto the energy storage system. Its bidirectional operation allows the power flow in either direction and notonly can reduces the cost and improves efficiency, but also increase the performance of the system byallowing better control of the input and output of the voltage and current of the system. This variability alsopermits different ESS to be used within the system such as battery, ultra-capacitor and fuel-cell, which havediversity of applications in future energy conservation [1-3].

To realizing a bidirectional power flow between two power sources, two paths current flow convertertopology normally is required. However through one path current flow topology, the bidirectional operationcan be done by interleaving the multiple phases or legs of the converter. The proposed interleaved bi-directional dc-dc converter consists of three ports, where the input components of the converter arethe DC source (can be solar panels, DC grid or battery) which served as a main power source, ultra -capacitor (UC) served as ESS source to the output component of the converter, resistive

Figure 1. (a)The proposed system (b) Interleaved DC-DC Converter topology

DC GridDC-DC

Resistive Load

Ultra-Capacitor

17

load. Figure 1 shows the block diagram of the system and the DC-DC Converter topology used in the study.During normal condition, the DC-DC converter will transfer power from the DC source; where in this studyis battery, to the load by performing step-up (boost) operation. To charge UC, the input power from thebattery will pass two stages, step-up (boost) the battery’s voltage to the desired load voltage and step-down(buck) the output voltage of the load to the desired UC input voltage. During power contingency, theadditional power is requested by the output load, thus the converter needs to transfer the power both fromthe battery and UC to the converter’s output. During this period, UC acts as an auxiliary power sources tothe load.

1. MATHEMATICAL MODEL OF DC-DC CONVERTER

Interleaved two-legged DC-DC converter, Figure 1b, have a specific task of operation. Leg 1 consists ofelectronics switches S1a and S1b alternately conduct to step-up (boost) the battery voltage into the desiredhigher output voltage level than the battery input voltage. The current flowing through this path is controlledso that it flows in one direction which is from the battery to the load or UC. Leg 2 which consists of switchesS2a and S2b are responsible to perform the bidirectional power control operation at DC bus (point P).Whenever the UC needs to be charged, the Leg 2 will act to step down (buck) the UC voltage to the requiredvalue. However, if the load demanding an extra power, Leg 2 will conduct to step-up (boost) the UC voltageto the desired voltage level and surplus the deficit power at the load.

Equations (1)-(2) express the converter control-to-output voltage )( svdG and control-to-ultra capacitor

current )(sidG during a boost and buck operations where D is the steady state duty cycle of power switches

[4-5]. Both of these equations then are used in averaged voltage mode and averaged current mode control.

)(ˆ

)(ˆ

sd

sv

)()(

)()(

)1()(()(

2

222

cc

cc

ccc

g

rDRrR

DRCDRrLsrRLCs

sRCDRrrRsLDRDRrD

V

(1)

)(ˆ)(ˆ

sd

siuc

)()()(

)(

1)(

2

sp

spspsp

spspg

rR

rRCrRLsrRLCs

rRCsrRCV

(2)

a. Average Voltage Mode Control To Regulate the Load Output Voltage

The input control variables for the average voltage mode control is the load output voltage while the outputcontrol variables is the transient duty cycle )(td , in which is applied to the S1a while the switches signal

)()( tdtd 1 to the S1b. Figure 2 shows the block diagram of average voltage mode control of boost

converter. The voltage loop gain of the Leg 1 converter is defined by (3).

mcvdv

VsHsGsG

sd

svsT

1)()()(

)(ˆ

)(ˆ)( (3)

b. Average Current Mode Control To Regulate the Ultra Capacitor Voltage

The UC charge voltage is controlled through outer voltage and inner current control. As can be seen inFigure 3, the desired UC voltage is compared with its reference signal and the error is forwarded to the

Figure 2.. Average voltage mode control ofboost converter

18

outer voltage control and is used to the reference current for inner mode control. The current loop gain forLeg 2 of the converter can be written as (4). In order to control the UC voltage, closed-loop inner currentcontrol is added to the converter controller. When UC needs to be charged, more current is needed, thereforethe battery has to supply more current to fulfill both UC and resistive load power requirement.

miidi

VsRsGsT

1)()()( (4)

2. SIMULATION RESULTS

The proposed system is modelled and simulated in Simulink MATLAB environment and theircorresponding output waveforms were generated. Figure 4 shows the results of the output voltage, )(tv

when various condition of load voltage is set (pu). As can be seen, the output voltage successfully controlled

to its reference voltage. Figure 5 shows the UC voltage, )(tvuc when it is being charged and discharged

through the UC current, )(tiuc .

3. CONCLUSION

The DC-DC converter can be controlled by adjusting the duty cycle through the linearized average modelof Leg 1 and Leg 2 as described in previous section. The system model is considered working successfullysince the simulation results obtained were compared and verified with the mathematical model of theconverters. A control algorithm objectives were also achieved as the power flow within the interleaved DC-DC converter was controlled accordingly.

REFERENCES

1. S. Lu, K.A. Corzine, and M. Ferdowsi, “A new battery/ultracapacitor energy storage system design and itsmotor drive integration for hybrid electric vehicles, “ IEEE Transactions on Vehicular Technology, vol.56,no.4, pp.1516-1523, July 2007.

2. M.Marchesoni, and C.Vacca. “New dc-dc converter for energy storage system interfacing in fuel cell hybridelectric vehicles,” IEEE Transactions on Power Electronics, vol.22, no.1, pp.301-308, Jan. 2007.

3. Zhang. J, “Bidirectional DC-DC Power Converter Design Optimization , Modeling and Control,” M.Phil.Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University.

4. M.H. Rashid, “Power Electronics Handbook”, 1st ed. Academic Press Series in Engineering. 2002

0 1 2 3 4 5 6 7 8 90

0.2

0.4

0.6

0.8

1

Time (seconds)

Vo

lta

ge

Lo

ad

(pu

)

ref v(t)

v(t)

0 1 2 3 4 5 6 7 8 9

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Time (seconds)

UC

vo

lta

ge

(pu

)

vuc ref

vuc(t)

Figure 3.. Average current mode control ofbuck converter

Figure 4.. Variation of load voltage level)(tv through boost operation

Figure 5.. The charges and discharges of theUC voltage )(tvuc

19

5. R.W.Erickson, D. Maksimovic (2004), “ Fundamentals of Power Electronics”, 2nd ed, Kluwer AcademicPublishers.

20

DESIGN OF ROV FOR AUTONAUT WITH SELF-PROPULSIVEVEHICLE

Ahmad Faris Ali1, Mohd Rizal Arshad1

1Universiti Sains Malaysia, MALAYSIA.(E-mail: [email protected], [email protected])

ABSTRACTIn this paper, A self-propulsive Remotely Operated Vehicle (ROV) is design for operate together with the AutoNaut.It is use for the mapping and visual inspection of the sea bottom over a wide area. Called D50-ROV, the under-actuatedvehicle with the simple design of the body and have three thrusters; two thrusters for forward, reverse, left and right,and one propeller at the center for submerge and rise. The vehicle with dimensions of 0.5m x 0.46m x 0.22m andweighing 15kg in air is design for a speed around 3knots at battery on-board 16VDC. The umbilical transmit andreceive the data through the 100 m length of buoyant cable communicates with Surface Control Unit (AutoNaut) andthe UHF antenna to communicate with the operator at the control room. A real time command from a control room tocontrol the vehicle’s functions. The self-propulsive system activated by an operator to control the speed to get equallyspeed for surface vehicle and underwater vehicle at the same acceleration.

Keywords: Remotely Operated Vehicle, AutoNaut, Self-Propulsive Vehicle, Design

1. INTRODUCTION

Nowadays there are so many Unmanned Underwater Vehicle (UUV) have been developed for theunderwater survey, surveillance, inspection etc. Such an Unmanned Surface Vehicle (USV) namelyAutoNaut. It is generally carry more sensors for longer durations due to their larger size and powercapabilities. Furthermore, they can combine collection of both surface and sub-surface measurements. Forthese reasons, USVs have been recognised globally as a solution for long-term scientific monitoring.AutoNaut is propelled by wave motion and use solar energy to power their sensors. It is commanded locally,using RF or remotely by using satellite communications. AutoNaut are used in various project such asmetocean, passive acoustic monitoring, surveillance, marine survey, water quality, communicationgateway, marine life monitoring. The various sensors and cameras were deployed on the AutoNaut to trackmarine litter, seabirds, marine mammals, fish tagged area and in a littoral environment.

Based on the summary of functional and system of AutoNaut, there are some limitation on the AutoNautto observe sea bottom in visual as there is no underwater camera. To solve the problem, ROV-D50 wasdesigned attach at the center of the body AutoNaut. It is portable compartment combined with cable winchto release and recover the ROV-D50. There are some successful underwater vehicle was used widely aroundthe world like Seaeye Falcon [1], BlueROV2 [2],Ginora 500 [3]. Remotely Operated Vehicle (ROV) usedto help human to do the underwater job that allows the vehicle’s operator to remain in a safe environmentwhile the ROV works in the hazardous environment. As ROV using the cable to transmit and receive datafrom surface, to avoid free-swimming and bending cable a self-propulsive system for the ROV can beguided to ensure the same acceleration between the AutoNaut and D50ROV. This paper organized asfollow: Mechanical System Design in Section 2. Electronics System Design in Section 3.

2. MECHANICAL SYSTEM

In this section, the mechanical system design will be discussed. First, extension frame design will bepresented, following the design of body D50-ROV and electronics components.

21

a. Extension Frame Design

The extension frame is designed 100cm long by 213cm wide and 81.5cm height. Material used in design isaluminum plates which not reacting to seawater by rust. The electronics components like microprocessor,sensors and battery installed in the watertight compartment. The UHF antenna used to communicate withthe surface operator. The winch cable mounted in the middle of the frame to launch and recover the tethercable where the signal and data from the D50-ROV passes through it.

Figure 1. Extension Frame Design

b. D50-ROV Body Design

The ROV was designed is 60cm x 46cm x 21cm with the cover to reduce dragged in the water (Figure 2).The material of the body is from PVC cylinder with water blocking system. Seal system of the enclosure isusing o-ring between cylinder door and acrylic dome. It is suitable to put an electronics component such asmicroprocessor, sensors, motor driver and etc. The design of the cover to reduce the water drag by usinghydrodynamic shape. The suitable material for the cover is fiberglass, it is more strong and lightweight andalso not react with salt water. This vehicle is under-actuated with three thrusters to maneuvering throughunderwater. Two surge thruster is used to move forward, reverse, left, and right. There is one heave thrusterat the center of the body to submerge, raise the vehicle. All thruster fitted using bold and nut to ensure thethruster is completely fixed. Those thrusters will maintain the vehicle at desired heading and depthdetermined by a trained operator at the surface. Hook system use in this D50-ROV to ensure the cable andthe connector is not too tense and will cause cable disconnect to the surface, and it is also used to rocoveror pull up the D50-ROV. The exploded view for the body D50-ROV and the cover was designed in (Figure3).

22

Figure 2. D50-ROV

Figure 3. Exploded view of body ROV and the cover.

3. ELECTRONICS SYSTEM DESIGN

The microprocessor used for control system is Arduino Mega 2560, it reads data from all sensors and givesinstruction to three thrusters. The D50-ROV fitted by lighting system to increases the brightness of theimages and video taken by the camera. Laser pointer fitted identify to the orientation object in front ofvehicle. Besides, IMU sensor are used to measure the orientation of the vehicle in the position pitch androll. For the heading, underwater compass are used, it gives more accurate data than other compass. Somecompass does not work properly in underwater. [5] Water leakage sensor, temperature sensor, currentsensor are used in security system of the vehicle. They will give a warning indicator at surface (operator)if the water leakage, overheat, short-circuit and it will terminate the power source if necessary. D50-ROVis communicate with surface control unit using tether cable with 100 meters length. The cable is embeddedwith water blocking fibers to seal any leaks. Diameter of the cable is 7.66mm carries four unshielded twistedpairs (UTP) of 26AWG wire. The cable also colored same with Cat5 networking cable including cross-talkresistance. This high-quality tether with naturally buoyant reduce the dragged by water current and curvedcable in water. Tether cable also contains Kevlar strands for strength around 350lb or 159kg suitable forrecover the vehicle from underwater. The overall electronics components in the D50-ROV and extensionframe was shown in below (Figure 4).

23

Figure 4. Overall electronics components

4. Application D50-ROV

D50-ROV is designed to contribute the visual inspection for AutoNaout system. AutoNaut can becommanded locally,using RF, or remotely, and using satellite communications. A pilot can task anAutoNaut to complete a waypoint track, a servey grid or hold station. When the AutoNaut in hold stationmood at specific location, the D50-ROV released into underwater using the cable reel installed at the centerof the extension frame. The reel cable fitted by a DC motor to control the rotation of reel either to releaseor recover the cable. During the location need inspect by visual, the D50-ROV will submerged into waterin parallel position with Autonaut at the same movement. After finish the task the D50-ROV will recoverrecover to the parking station on extension frame and AutoNaut continue the mission at the next hold stationto get an underwater visual for servey and inspection. The design of application is a portable compartment,it can be install as required or remove if the task does not require the visual inspection. This system fittedby side buoyancy to ensure the AutoNaut stable during strong waves.

Figure 2. Final Design of D50-ROV for AutoNaut

24

REFERENCES

1. Limited, K. (n.d.). Seaeye - Falcon - Options & Accessories. Retrieved July 11, 2017, fromhttp://www.seaeye.com/falconaccessories.html

2. BlueROV2. (n.d.). Retrieved July 13, 2017, from http://www.bluerobotics.com/store/rov/bluerov2/3. D. Ribas, N. Palomeras, P. Ridao, M. Carreras, and A. mallios, “Girona 500 AUV: From Survey to

intervention,” IEEE/ASME Transactions on mechatronics, Vol. 17, No. 1, pp. 46-53, February 2012

25

DEVELOPMENT OF RIVER DRIFTING BUOY INSPIRED BY WATERSTRIDER

Herdawatie Abdul Kadir1*, Nurul Hasniza Darus1, Khalid Isa1, Mohd Rizal Arshad2

1, 2 Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu

Pahat,Johor, MALAYSIA.(E-mail: [email protected])

3 UCRG, School of Electrical and Electronic Engineering Universiti Sains Malaysia, Engineering Campus14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, MALAYSIA.

(E-mail: [email protected])

ABSTRACTEcosystem of a freshwater from river or lake can be extremely fragile. Assesment on flows and quality in water systemis critical as fresh water mostly come from rivers and lakes. The used of river drifting buoy is new to estuarial andriverine studies compared to the oceanography. The collected river parameters from the drifters benefits the communityin term assesment of water quality, usage and stormwater control planning. In this work, the river difting buoydeveloped were inspired by water-walking arthropods which is water strider. The design difting buoy were able tocontrol their heading and have the ability to withstand the impact from a deployment at the river banks under severalobstacles. The design introduce power efficiency and agile movements on the water surface for deep or very shallowwater surfaces. The performance of the buoy were recorded with real data collected at several local area near BatuPahat, Johor.

Keywords:buoy,drifting buoy, river measurement, water strider,water quality.

1. INTRODUCTION

River conditions poses challenges due to constantly changing current speeds, water level, presence ofvegetation, precipitation, as well as other environmental factors [1]. River drifting buoys has widely beenused in many countries, making it one a popular platform which is used to collect parameter which isproduced by rivers such as the river water flow direction of travel as well as water quality [2-3]. Despite tolower construction cost, the river drifter owns a high density level and provide good float level. Thus attractinterest of researchers to create and produce an improvement to the drifting buoy design [4-8]. The collectedparameter such as turbidity gives an idea of the sediment being transported in the river system, while thechange in turbidity as the drifter travels down the river shows the locations of sediment input. Moreover,the data gather will provide better understanding to the dynamic features of ecosystem of freshwater andriverine environments.

2. SYSTEM DESCRIPTION

The river drifting buoy is a cost effective floating sensor consisting of a microcontroller, GPS module,heading controller and several sensors. Measurement data are regularly transmitted to local ground stationwhich allows for near real-time deployment tracking from any location. The drifting buoys design wereinspired by water strider as shown in Figure 1. The drifter were released at the surface of the water andallowed to move in two modes which are free-flow or use the legs that controlled by four servo motors.Then the data were recorded via data logger and transfer to the ground station when available.

26

(a) (b)

Figure 1. River difting buoy design (a) water strider (b) designed buoy

3. HARDWARE DEVELOPMENT

The design mimic the water-walking arthropods to introduce stable buoyancy capability. The design usedavailable and low cost material which are waterproof plastic container, aluminum L-Chanel, polystyrenefoam, plastic bottle and screw and nuts. The selected waterproof plastic container were used as the mainbody of river drifting buoy because it has the length and width that match the actual body weight ratio 2:4of the water strider anatomy. The main body is used to secure modules consists of controller, sensors, GPS,wireless device, data logger, power supply and propulsion system. The legs were made of aluminum becauseit is lighter and strong. In addition, easy for machining and good corrosion resistance. Polystyrene were usedas the legs padding due to the ability to float on water. Overall, the river drifter are made waterproof andthe electronics module are enclosed. The developed drifting buoy is illustrated in Figure 2.

Figure 2. Water Strider inspired river difting buoy

27

4. SAMPLE DEPLOYMENT

Several experiment has been performed at area near Batu Pahat, Johor. Figure 3 marks the location of thetested area. More results will be display in the full paper.

Figure 3. Experiment locations

For a sample: The tested area has a gravity-fed supply of flow up to 26.22 mph. For the experiment thedesign buoys were deployed for 87 seconds.

Figure 4. Experiment Sample

REFERENCES

1. L. Emery, R. Smith, D. McNeal, B. Hughes, W. Swick, and J. MacMahan (2010). Autonomous collection ofriver parameters using drifting buoys. MTS/IEEE Seattle, Ocean, pp 1-7

2. L. Emery, R. Smith, D. McNeal, and B. Hughes (2009). Drifting Buoy for Autonomous Measurement ofRiver Environment, Ocean, pp 1-8.

3. J. Macmahan (2009). Drifter Trajectories in Riverine Environments. Distribution, pp. 1–7.4. Y. S. Song, S. H. Suhr, and M. Sitti (2006). Modeling of the supporting legs for designing biomimetic water

strider robots, Proc. - IEEE Int. Conf. Robot. Autom, pp. 2303–2310.5. O. Ozcan, H. Wang, J. D. Taylor, and M. Sitti (2014). STRIDE II: A Water Strider-inspired Miniature Robot

with Circular Footpads, Int. J. Adv. Robot. Syst., 11(1), pp. 1-116. J. A. Austin and J. A. Barth (2002). Drifter behavior on the Oregon–Washington shelf during downwelling-

favorable winds. J. Phys. Oceanogr., 32(11), pp. 3132–3144.7. J. Austin and S. Atkison (2004). The Design and Testing of Small, Low-cost GPS-tracked Surface Drifters.

Estuaries, 27(6), pp. 1026–1029.8. D. Boydstun, M. Farich, J. M. C. Iii, S. Rubinson, Z. Smith, and I. Rekleitis, (2015). Drifter Sensor Network

for Environmental Monitoring, Proc. -2015 12th Conf. Comput. Robot Vision, CRV, pp. 16–22.

0

50

100

0 100 200 300He

adin

g,d

eg

Tiime,s

28

EXTRACTING RAW HYDROXYAPATITE POWDER FROM NATURALSOURCES

Hamizah Nadia bt Alias@Yusof A.Hamizah*1, Nik Aziz bin Nik Ali N.A. Nik Aziz2, Haziqah bt Abdul Majid A.M.Haziqah3, Afiqah Qayyum4

1, 2, 3, 4 Pusat Pengajian Sains Perikanan & Akuakultur, UMT, MALAYSIA.(E-mail: [email protected], [email protected], [email protected], [email protected])

ABSTRACTThe production of Hydroxyapatite (HA) powder from synthetic process involves many chemicals with complicatedprocedures. Due to this matter, the raw HA powder was extracted from natural sources (selayang fish bones) in thisstudy. The accessible selayang fish-bones waste was used to produce HA powder and the whole process is notcomplicated with low cost in total. Extraction process was started with boiling fish bones for two hours in order toeasily eliminate the adherent fish meats from bones. After cleaning process, fish bones were then dried in roomtemperature for at least two days. This is important in order to remove the excessive water and organic portions duringboiling and cleaning process. Fish bones were then crushed finely using grinder mortar to produce the powder. Thecharacterization of raw HA powder was done via X-ray Diffraction method and Fourier Transform Infraredspectrometer to analyze the raw powder as compared to other references.

Keywords: Hydroxyapatite powder, natural sources, waste fish bones, Selayang fish.

1. INTRODUCTION

Hydroxyapatite (Ca10(PO4)6(OH)2, HA) is the main inorganic calcium phosphate mineral with excellentosteoconductivity, good bioactivity and biocompatibility [1]. Due to their similar composition to teeth andbones, HA powder has been extensively used in implant technology especially clinical applications andalso in biosensor applications [2]. Researches worldwide have used different synthetic methods to synthesiscalcium phosphate such as heat treatment, sol-gel, hydrothermal and polymer-assisted [3]. Moreover, theproduction of raw HA powder were mostly used chemical agents as the main Ca sources either through wetor dry process. The process is more complicated and costly. As revealed by chemical analysis, naturalsources such as oyster shells, eggshells, corals and bovine bones are abundant sources of calcium phosphateand suitable to be used as an economic source for HA synthesis with low cost and ease production [4].Since keropok production warehouse in local area are using fish as main sources the significant amount offish waste is a lot. In addition, waste fish bone are composite materials made of carbonated HAp, type Icollagen, non-collagenous protein and water [5]. As a consequence, utilizing these natural sources biowastein large scale can possibly support sustainable environmental development thus reducing environmentalissues. In this study, selayang (Trachurustrachurus) fish-bones waste was used as natural sources to extractraw HA powder. The powder was then characterized by using X-ray Diffraction method (XRD) and FourierTransform Infrared spectrometer (FTIR).

2. METHODOLOGY

Selayang fish-bones waste was purchased at keropok warehouse production. The raw powder preparationwas started by boiling selayang fish-bones waste in 2 hours duration. This process was done in order toremove the excessive fish meats easily during cleaning process later. The cleaning process was done usingclean water. Then, all fish bones were dried in room temperature for at least 2 days. The water and organicmatter from boiling and cleaning process were dried out during those 2 days. The fish bones were then

29

crushed into powder using mortar grinder. The characterization of selayang fish-bones waste powder wasdone using XRD and FTIR to analyze the raw hydroxyapatite powder.


The FTIR spectra of raw HA powder is shown in Figure 1. The bending mode B-type carbonatehydroxyapatite can be consigned to at peak 875cm-1 [6]. Due to the substitution of PO4

3- by CO32-, the peaks

at 1460 cm−1 and 1419 cm−1 are associated with bending in B type carbonate. The FT-IR shows similarspectra in both samples at around 3000 cm−1 to 3700 cm−1 which are characteristic of the OH- stretching ofH2O. The PO4

3- bending mode contains in the second region at peaks 1093 cm−1 to 1047 cm−1. In the bandbetween 579 cm−1 and 607 cm−1 are the characteristic peaks of PO4

3- bending mode [6, 7, 8]. The bandsaround 1635–1648 cm1 , 1558–1564 cm1 and 1256 cm1 were attributed to amide I, II and III bands foundin fish bone [9]. The FTIR pattern confirm that the extract powders are raw HA powder.

Figure 1. FTIR spectra of raw HA powder

The purity, stability and the crystallization of raw HA powder can be characterized using XRD analysis.Figure 2 shows the XRD pattern of raw HA powder extracted from selayang fish-bones waste. There areseveral peaks exist at 25, 30, 40 and around 50 θ respectively. However, the peaks shown are low and rough which indicating a weak crystallization of raw HA powder. Besides the pattern in Figure 2 is similar to thepattern of raw HA powder extracted from various fish-bones waste [4, 9, 10].

Transmitance, %

Wavenumber, cm-1

amide II

amide III

PO43-

PO43-

CO32-

B type

CO32-

B type

CO32-

A type

H2O

OH-

amide I

30

Figure 2. XRD pattern of raw HA powder

4. CONCLUSION

In this study, selayang fish-bones waste was used as a natural sources to extract raw HA powder. The FTIRspectra have shown the presence of PO4

3-, CO32-, OH- and H2O bending mode in respective peaks. In

addition, peaks emerge in XRD pattern confirms the characteristics of raw HA powder. This indicates thatselayang fish-bones waste is suitable to be used as natural source to extract raw HA powder.

ACKNOWLEDGEMENTThe author would like to acknowledge Pusat Pengajian Sains Perikanan dan Akuakultur, UMT.

REFERENCES

1. H. Cao, L. Zhang, H. Zheng and Z. Wang (2010). Hydroxyapatite nanocrystals for biomedical applications.J. Phys. Chem. C, 114, pp. 18352–18357.

2. P. N. Kumta, C. Sfeir, D. H. Lee, D. Olton and D. Choi (2005). Nanostructured calcium phosphates forbiomedical applications: novel synthesis and characterization. Acta Biomater, 1, pp. 65–83.

3. M. S. Shojai, M. T. Khorasani, E. D. Khoshdargi and A. Jamshidi (2013). Synthesis methods for nanosizedhydroxyapatite with diverse structures. Acta Biomater, 9, pp. 7591–7621.

4. P. O. Weeraphat, S. Panan, C. Narattaphol, T. Jirawan, K. Nateetip and I. M. Tang (2016). Hydroxyapatitefrom fish scale for potential use as bone scaffold or regenerative material. Materials Science and EngineeringC, 62, pp. 183–189

5. J. Toppe, S. Albrektsen, B. Hope and A. Aksnes (2007). Chemical composition, mineral content and aminoacid and lipid profiles in bones from various fish species. Comp. Biochem. Physiol. B Biochem. Mol. Biol.,146, pp. 395–401.

6. P. N. Kumta, C. Sfeir, L. Dong-Hyun, D. Olton and D. Choi (2005). Nanostructured calcium phosphates forbiomedical applications: novel synthesis and characterization. Acta Biomater, 1, pp. 65–83.

7. M. Boutinguiza, J. Pou, R. Comesaña, F. Lusquiños, A. de Carlos and B. León (2012). Biologicalhydroxyapatite obtained from fish bones. Mater. Sci. Eng. C, 32, pp. 478–486.

8. A. Shavandi, A. El-Din, A. Bekhit, A. Ali and Z. Sun (2015). Synthesis of nano-hydroxyapatite (nHA) fromwaste mussel shells using a rapid microwave method. Mater. Chem. Phys., 149-150, pp. 607–616.

9. P. Anindya, P. Sudeep, R. C. Amit, K. B. Vamsi, D. Mitun and S. Arijit (2017). Synthesis of hydroxyapatitefrom Lates calcarifer fish bone for biomedical applications. Materials Letters, 203, pp. 89–92.

10. N. Mustafa, M. H. I. Ibrahim, R. Asmawi and A. M. Amin (2015). Hydroxyapatite Extracted from WasteFish Bones and Scales via Calcination Method. Applied Mechanics and Materials, 773-774, pp. 287-290.

11. M. Z. Daud, A. Mohamed and M. Hannan (2013). An improved control method of battery energy storagesystem for hourly dispatch of photovoltaic power sources. Energy Conversion and Management, 73 (1), pp.256–270.

2θ, (degree)

31

FEASIBILITY STUDY OF WAVE ENERGY SYSTEM IN TERENGGANU

F. S. Burhanuddin2, W. M. W. Muda1, 2, N. A. S. Salleh, A. Q. Mohd Nor1 Special Interest Group of Eastern Corridor Renewable Energy.

2 School of Ocean Engineering, Universiti Malaysia Terengganu, 20130 Kuala Terengganu, Malaysia(E-mail: [email protected])

ABSTRACTMalaysia has started encouraging renewable energy technology since 1996 in 7th Malaysia Plan. However, inadequatepower distribution in the island make diesel generator an important power source which cause pollution. Someinitiative have been made by installing wind turbine and solar panel to reduce the dependency on diesel generator inthe island. The hybrid photovoltaic (PV)/wind system is a good combination in Terengganu as solar radiation is highfrom February to October, and strong wind from November until January. Since the wind speed and wave height hasa strong tie, during the Northeast Monsoon, the wave height can go up to two meters. However, the feasibility of waveenergy as power supply has not yet been explored. This paper focus on the feasibility of off-grid hybrid wave energysystem to supply electricity to a chalet in Terengganu. A simulation-based study is conducted in HOMER. Theobtained results show that a hybrid wave energy system has low potential in generating electricity with cost ofelectricity (COE) is RM 1.122/kWh compared to the most optimal configuration which is PV-battery generator systemwith COE RM 0.716/kWh.

Keywords: off-grid, HOMER, hybrid-RE system

1. INTRODUCTION

Malaysia is ideal for large scale solar power installations due to its location in the equatorial region. Thecontinuous supply of sunlight, low maintenance cost, independent of fuels source, and contributions tolower carbon emissions, made solar is the best choice for future energy power generation [1]. Anotherpotential sources is wind energy. The wind speed in Malaysia is light and varies from season to season inthe range of 2 m/s to 13 m/s [2]. The Northeast Monsoon which is from September to March recorded thestrongest wind comes from the South China Sea to the East Coast. Despite from that, ocean wave energyalso has high power density and generating significant wealth [3]. But, the energy has not yet been exploredto any significant extent in Malaysia.

Terengganu is popular with beautiful islands. Most of well-known islands that can be found at PeninsularMalaysia on the east side are Pulau Perhentian, Pulau Redang and Pulau Kapas. Due to the popularity ofthis place, tourists attract to go there. Therefore, the power demand increases. So far, the chalet in the islandsusing diesel generator to supply electricity. İt causes pollution especially to the underwater ecosystem and the price of diesel is unstable. With the abovementioned potential of renewable energy and disadvantagesof diesel generator, an off-grid simulation-based hybrid renewable energy system to supply electricity inTerengganu is developed in HOMER.

2. PULAU PERHENTIAN SYSTEM CONFIGURATION

The proposed system configuration illustrates in Fıgure 1. It consists of four sources of electivity which are photovoltaic (PV), wind turbine (W), hydro power plant to represent wave generator (WG) as used in [4]and diesel generator (DG). Load in the system is the electrical appliance used in a chalet. The hourly averageload profile in the chalet is shown in Figure 2. Battery (B) is needed to store the excess electricity fromsources to be used later especially at night.

32

A selected area for this study is Pulau Perhentian, Terengganu. Solar radiation and wind speed data werecollected at Universiti Sultan Zainal Abidin (UniSZA) weather station and the monthly average of solarradiation and wind speed data can be referred in Figure 3 of [5]. According to the figure, the highestradiation is in March with radiation of 6.447 kWh/m2/day and lowest on December with 2.493 kWh/m2/day.The decreasing of solar radiation from September to December is due to monsoon season as Terengganu islocated at East of Peninsular Malaysia facing to South China Ocean. Otherwise, the wind speed data canbe seen in Fıgure 4 of [5], shows the highest reading during December at 4.175 m/s. The significant wave height and wave period were obtained from Institute of Oceanography and Environment (INOS), UniversitiMalaysia Terengganu (UMT). From the data, flow rate can be calculated using equation (6) of [4] and theannual wave flow rate is depicted in Figure 3. For the cost of components in this research were referred tothe previous research. For PV panel, Wind turbine Bergey XL 1, and converter are obtained from [6] whilediesel generator from [7], and battery Surrette 4KS25P from [8].

Figure 1. System configuration of proposedsystem

Figure 2. Daily load profile in a chalet

Figure 3. The annual wave flow rate data

3. RESULTS AND DISCUSSIONS

The simulation results generated by HOMER which rank all possible configurations for the proposedsystem from the least to highest net present cost (NPC) shown in Figure 4. PV/DG/B system is the mostoptimal system with configuration of 5 kW PV modules, 3 kW diesel generator, 25 units of battery and3kW converter. The total NPC is RM 87 777.00 with cost of electricity (COE) RM 0.716/kWh which is thelowest than other configurations.

From the result, PV is the most potential renewable energy component to be developed as the initial cost toinstall the PV is cheaper compared to wind turbine [6]. In addition, the solar radiation in Terengganu ishigh while the wind speed is still considered low. For the use of wave generator in generating electricity,Figure 4 shows that the best combination is PV/WG/DG/B with the total NPC is RM 137 542 and COERM 1.122/kWh.

0

0.5

1

Flow Rate (L/s)

33

The production of electricity for different configurations is shown in Figure 5. DG system produces 9125kWh/yr, while the consumption of electricity is 4780 kWh/yr. Thus, excess electricity for the system is sohigh up to 48%. Similar trend in PV/B configuration which shows high electricity production by PV at9092 kWh/yr. With the same amount of consumption, the excess electricity for this system is 28%. ForPV/DG/B system, 95% of electricity is generated by PV with excess electricity is low at 6%. ForPV/W/DG/B system, 90% of electricity is supplied from PV, 6% from DG and the balance is from windturbine. For PV/WG/DG/B configuration, 87% comes from PV, while WG contributes 12% from the totalof electricity production and the least comes from DG.

When the system is a hybrid with diesel generator, surely the system produced emission. Yet, with thepresence of renewable energy components, the emission factor can be reduced. Figure 6 shows the emissionreleased by each system configurations. As expected, the DG system produces the highest carbon dioxide(CO2). PV/B system has zero CO2 as it is 100% green technology. The other three configurations producelow CO2 based on the percentage of electricity produced by DG. As PV produce very high percentage, thusthe CO2 can be reduced significantly.

Figure 4. Optimization results of off-grid hybrid-RE system

Figure 5. Electrical production of system Figure 6. Carbon emission of system

REFERENCES

1. Mekhilef, S., et al., Solar energy in Malaysia: Current state and prospects. Renewable and SustainableEnergy Reviews, 2012; 16(1): p. 386-396.

2. Darus, Z.M., et al., The Development of Hybrid Integrated Renewable Energy System (Wind and Solar) forSustainable Living at Perhentian Island, Malaysia European Journal of Social Sciences, 2009; 9(4): p. 557 -563.

3. Duckers, L., Wave Energy, in Renewable Energy, G. Boyle, Editor. 2004, Oxford University Press.4. Jones S. Silva, Alexandre Beluco, and L.E.B.d. Almeida, Simulating an ocean wave power plant with

HOMER. International Journal of Energy and Environment, 2014; 5(5): p. 619-630.5. Salleh, N.A.S. and W.M.W. Muda, Techno-economic and sensitivity analysis for grid-connected renewable

energy electric boat charging station in Terengganu. MATEC Web Conf., 2017; 90: p. 01016.

0

5,000

10,000

Electrical Production (kWh/yr)

PV DG W WG

34

6. Singh, A. and P. Baredar, Techno-economic assessment of a solar PV, fuel cell, and biomass gasifier hybridenergy system. Energy Reports, 2016; 2: p. 254 - 260.

7. Razak, N.A.A., M.M. Othman, and I. Musirin, Optimal sizing and operational strategy of hybrid renewableenergy system using homer. Power Engineering and Optimization Conference (PEOCO), 2010 4thInternational, 2010: p. 495-501.

8. Jacobus, H., et al., Evaluating the impact of adding energy storage on the performance of a hybrid powersystem. Energy Conversion and Management, 2011; 52: p. 2604 - 2610.

35

GEARING SYSTEM ANALYSIS FOR UNDERWATER REMOTELYOPERATED CRAWLER (ROC)

M.S.M. Aras1, Iktisyam Zainal1, M. N. Kamarudin1, M.K.M. Zambri1, M.H. Harun2 , 1Muhammad Wahyuddin

Nor Azmi

1Underwater Technology Research Group, Faculty of Electrical Engineering, Technical University of MalaysiaMelaka (UTeM), 76100 Melaka, Malaysia

2Jabatan Teknologi Kejuruteraan Elektrik, Faculty of Technology Engineering, Technical University ofMalaysia Melaka (UTeM), 76100 Melaka, Malaysia

*Corresponding e-mail: [email protected]

ABSTRACT

This paper presents the development of gearing system for Underwater Remotely Operated Crawler (ROC). Thedevelopment of ROC allows for underwater intervention by staying a direct contact with any condition of theunderwater environment. The wheel mechanism is the chain type wheels. To increase the torque of the ROCgearing system is applied. The mathematical design of gearing system also covered in this paper. The output torqueis higher than the input torque when input gear is smaller than the output gear.

Keywords: Gearing system; remotely operated crawler.

1. INTRODUCTION

Remotely Operated Vehicle (ROV) mostly being used in underwater operations such as conductingresearch, observation and more. In order to conduct work or survey operations, ROVs need to move andwithstand the reaction force of operations. Furthermore, ROVs sometimes need to remain stationary andkeep their posture stable during operations [1-2]. Thus, in order to tackle this requirements, RemotelyOperated Crawler (ROC) is developed as shown in Figure 1. Since the ROC will operates underwater,it should be designed to tackle all the conditions of the underwater environments. There are a fewnecessary elements to consider on developing ROC which are; water pressure and environment, sinkingmechanism, power supply, crawl technique and controlling method [3]. It is import that the ROC havea chassis that can withstand the underwater pressure and have a hydrodynamic design in order to reducedrag [4]. This paper presents the crawl technique using gearing system. This technique is more suitablefor any underwater conditions.

(a) (b)Figure 1: ROC View

2. GEAR CONFIGURATION

The configuration for sprocket and chain ratio can be determined from the calculation of gear ratioand configuration. The basic rules and idea for both mechanical power transmitter is the same since bothhave a number of teeth, diameter and mechanism of their function. Gear is one the power transmissionelement between an energy source and desired output motion besides belt, levers and screw drivers. Thepower transmission is always included a Gear Ratio. A Gear Ratio can either increase the output torqueor the output speed of a mechanical mechanism. A gear ratio cannot improve both, torque and speed atthe same time. The input gear is rotating counter clockwise with an angular velocity, and the outputgear rotating clockwise with an angular velocity, ௨௧. An input torque, τin, is applied by the motor onto

36

the input gear, and an opposing output torque, τout, onto the output gear is applied by the machine. Theradius of the gear is taken at the pitch circle of the gear which contact occurs between two gears. Thedevelopment and optimizing the gear shape will help to reduce friction loss, reduce noise and even makea smooth power transfer. By including a gear ratio in the design, it will help to increase the output speedor torque of the power transmission. Figure 2 shows the gearing system for ROC.

Figure 2: Gear System of ROC

2.1 Gear Ratio calculation

Gear Pair Equation: The relationship between number of teeth and torque.

௨௧

=

௨௧

(1)

Gear Pair Equation: The relationship between radius and torque.

௨௧ݎݎ

=௨௧

(2)

Gear Pair Equation: The relationship between number of teeth and speed.

௨௧=௨௧

(3)

Gear Pair Equation: The relationship between radius and speed.

ೠ=

ఠೠ

ఠ(4)

In this project, the crawler needs a high torque power transmission instead of high speed to operate. Thisis due to the load and the environment that the crawler will operates. Thus, the equation (3) and (4) isneglected for further analysis. Higher torque is needed because the weight of the crawler itself is quiteheavy to avoid the crawler floating on the water surface and help it to operate it on the underwater. Then,the underwater environment has not always had a flat, hard and even seabed. Sometimes, the seabed hasa muddy surface, rocky and uneven surface. Thus, it is essential for underwater crawler to gain morepower to move on the seabed in any type of surface. From equation (1)

37

Input teeth =33

Output teeth =18

Input torquefrom the motor= 0.9N.m

18

33=

௨௧

0.9

௨௧ = 0.4909 .

Input teeth =18

Output teeth =33

Input torque from themotor = 0.9N.m

33

18=

௨௧

0.9௨௧ = 1.65 .

The output torque produced 1.65Nm to the crawler can climb up to 9.5 cm obstacles and it is themaximum height it can climb. This is because the bottom base of the crawler stuck to the edge of theobstacles. Spikes on chain help the crawler to have greater tractions and pull the crawler up. Thiscondition can be overcome by having larger sprocket that tied to the chain or weld longer spike to thechain. Then, by using Equation (3) or (4), the speed of Crawler can be calculated. The measurement forspeed of ROC also tested. The time taken for the crawler to climb the obstacles of the height of 9.5 isrecorded. Wooden platform is used for this test. The results as shown in Table 1.

Table 1: The ROC to crawl over wooden platform.Test Height (cm) Time Taken (s) Descriptions

1 9.5 25.28 Able to climb




3. CONCLUSION

As a conclusion, when the input gear is smaller than the output gear, the output torque is higherthan the input torque. Thus, in designing the power transmission for the underwater crawler, the inputgear is having the least number of teeth compare to the output gear to gain higher torque. The designcould have 18 teeth for the input gear and 33 teeth for the output gear. The value of input torque dependson the type of motor use later on. The output velocity of the rotation will be lower than the input velocitysince the transmission has higher torque.

Nomenclature:

Input AngularVelocity

Input Torque

Input GearRadius

Output AngularVelocity

38

Output Torque Output GearRadius

Number of InputTeeth

௨௧ Number ofOutput Teeth

REFERENCES

1. Iktisyam Zainal, Mohd Shahrieel Mohd Aras, Muhammad Nizam Kamarudin, design process andhydrodynamics analysis of underwater remotely operated crawler, Submitted to ICEI 2016 Conferences.

2. Wood, S., Harris, W., Ismail, T., Malone, J., Nanney, M., Ojeda, J., Vandedrinck, S. (2013). Hybrid RobotCrawler / Flyer for use in Underwater Archaeology. 1-11, 2013.

3. M Welling, D., & B. Edwards, D. Multiple Autonomous Underwater Crawler Control for MineReacquisition, IMECE2005, 2005.

4. M. S. M. Aras, Iktisyam Zainal, S.S. Abdullah, A.M. Kassim, and H.I. Jaafar, Development of an UnmannedUnderwater Remotely Operated Crawler (ROC) for Monitoring Application, International Journal onMechanical Engineering, 2016.

39

HORIZONTAL TARGET STRENGTH OF Oreochromis niloticus and Clarias

gariepinus IN ASPECT OF SIZE

N.A. Nik Aziz1, Hisam Fazrul1, Ezmahamrul Afreen1, S. Hasiah2

1School of Fisheries and Aquaculture Sciences, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,Terengganu.

(E-mail: [email protected], [email protected], [email protected])2Center for Foundation and Liberal Education, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,

Terengganu.(E-mail: [email protected])

*Corresponding Author. Tel: +609 668 4318 Fax: +609 668 3991. Email: [email protected]

ABSTRACTHydroacoustics is a technique that measure the reflected sound wave or echo from a transmitting sound wave devicecalled transducer. The capacity of an object to reflect sound is target strength (TS). Horizontal TS are used to estimatefish population abundance and density. To calculate fish biomass and density, relationship species TS and fishbiological parameters (length and weight) are necessary to know. In this study, the experiment has been carried outby the ex-situ measurement in tank with cages for the species of Oreochromis niloticus and Clarias gariepinus withdifferent size using echo sounder SIMRAD EK15 that operate at frequency of 200 kHz. The aim of this study is toidentify and compare the TS data with the body size and species of fish. The experiment has been carried out in fibreglass fresh water tank. Echo sounder were run for 2 minutes and data were analysed using Sonar 4 and SPSS software.Fish size for this experiment were 15.5 to 25.5 cm for Oreochromis niloticus and 28.5 to 38.5 cm for Clariasgariepinus. The results of Oreochromis niloticus TS was higher than Clarias gariepinus. The average TS ofOreochromis niloticus and Clarias gariepinus were found to be -54.04 dB and -54.07 dB. TS for Oreochromis niloticusand Clarias gariepinus has shown directly proportional with length and weight. Determination coefficients, (r2) wereobserved to be 0.8668 and 0.838 for length and 0.778 and 0.7228 for weight.

Keywords: Target Strength, Echosounder, SIMRAD, Oreochromis niloticus, Clarias gariepinus.

1. INTRODUCTION

Acoustic is a technique that measure reflected sound waves. Sound waves is emit into water and when ithit an object or organism, sound waves will reflect back as echos. Hydroacoustics technique were first usedin military, for example submarine. After Second World War, hydroacoustics technique then use byfisheries science to estimate biomass and distribution of the commercial fish [1]. One of the function ofacoustics is used to evaluate density of fishes [2]. Target strength (TS) or acoustic backscattering of a fishis obtain by using acoustic methods need to convert species-specific biomass. Capacity to reflect sound isthe TS of a fish and it is vary on factors such as size of the fish [3, 4], morphology [5, 6] and acoustic devicefrequency [7].

Horizontal measurement of TS has been proven to be a significant tool to estimate the density and biomasscompared to vertical measurement [8] that also can be used in shallow water body or pelagic layer in largedeep water body [9]. In this study, the experiment carried out by the ex-situ measurement in tank with cagesfor the species of Oreochromis niloticus and Clarias gariepinus with different size and length using echosounder SIMRAD EK15 with frequency of 200 kHz. The aim of this study is to identify and compare theTS data with the body size and species of fish.

2. EXPERIMENTAL

2.1 Equipment and instrument

40

The acoustic equipment that has been used in the TS study are SIMRAD EK 15 single beam echo sounderwith colour monitor, processor unit (personal computer), Ethernet switch, transceiver unit and transducer

2.2 Experiment designThis study were conducted in a freshwater fiberglass tank at the freshwater hatchery in Universiti MalaysiaTerengganu. The dimension of fiberglass tank are 3.0 meters length, 1.2 meters height and 3.0 meters width.Fiberglass tank was filled with freshwater for the study. Fish cage dimension were of 30 cm length, 30 cmwide and 30 cm height. Cage were installed in the tank with 2.5 meters length from the transducer, theillustration of the experiment set up is shown in Figure 1. Before the Echo Sounder was run, the fish lengthand weight was measured and recorded. Fish were separately transfer into the cage according to the size.Transducer were run for 2 minutes for each fish. Data were saved in personal laptop using EK15 softwareand for further analyse using Sonar4 and Sonar5.

Figure 1: Block Diagram Illustrating the Application of Transducer Mounted in a Tank for TargetStrength Measurement.

3.RESULTS AND DISCUSSION

3.1 Target Strenght versus length-weight relationship

The TS distribution of Oreochromis niloticus and Clarias gariepinus are shown in Figure 2 as scatter plotsof TS (dB) versus fish total length (TL). Figure 3 show the plots of TS (dB) versus weight relationship.Results had shown correlation between fish TS against fish total length and weight. The study shows thatthe TS of Oreochromis niloticus and Clarias gariepinus increases linearly with the increment of length andweight. According to [10], horizontal measurement of TS were influenced by the length of the fish and thehydroacoustics instrument frequency. The smallest TS of the Oreochromis niloticus was 15.9 cm with -55.31 dB. The largest fish was 22.8 cm with -52.91 dB. While, for the Clarias gariepinus, smallest fish was27.2 cm with -55.79 dB and the largest fis was 39 cm with -52.47 dB. From the Figure 3, the smallest TSof Oreochromis niloticus was 67 g with -55.51 dB and the largest fish weight was 207 g with -52.91 dB.For Clarias gariepinus, the smallest fish shown 109 g with -55.79 dB and 355 g with -52.47 dB for thebiggest size. Based on the results, TS (dB) of the smaller sizes fish seem to be more consistent comparedto the larger fish. According to [6,11], the TS (dB) of fish were different even same species of fish, therewere several reason such as morphological differences, condition and state of maturity between similar sizefish. Coefficient determination (r2) of TS and length of Oreochromis niloticus and Clarias gariepinus wereobserved to be 0.8668 and 0.838. While, the Coefficient determination (r2) of TS and weight ofOreochromis niloticus and Clarias gariepinus were 0.778 and 0.7228.

41

Figure 2: (a) Target strength-Length relation of Oreochromis niloticus (b) Target strength-Length relationof Clarias gariepinus

NPar test have shown P > 0.05 forthis indicated the data was normally distributed, p=1.000 >0.05 werefound in the T-test. Results of the two fish species were significant. The TS of Oreochromis niloticus werehigher than Clarias gariepinus, because of the morphology of the swim bladders of the fish. Clariasgariepinus has bi-lobed swim bladder that is connected to narrow phneumatic duct, the swim bladder sizewas reduced for its buoyancy.

Figure 3: (a) Target strength-Weight relation of Oreochromis niloticus (b) Target strength-Weigthrelation of Clarias gariepinus

The air in the swim bladder was expelled when the fish need to swim downwards (DeMoor and Bruton,1988). Eventually, the swimbladder size of Clarias gariepinus were smaller compared to Oreochromisniloticus. According to [12], TS of fish were depend on the size of the swimbladder that increase with thefish size. These study of fish TS (dB) in aspect of sizes at least give reliable indicate of expected fish TSand TS dependence on sizes.

4. CONCLUSION

The single beam echo sounder is suitable and reliable for studies in the laboratory (ex-situ) and shallowwater body fish target strength that can be used to estimate fish population. The target strength ofOreochromis niloticus and Clarias gariepinus were found to be -54.04 dB (15.5 to 25.5 cm) and -54.07 dB(28.5 to 38.5 cm). The target strengths of Oreochromis niloticus and Clarias gariepinus shown that thetarget strength are size and species dependent.

(a) (b)

(a) (b)

42

REFERENCES

1. Devolt, F. (1950). Herring Cruise with “G.O.Sars” in the Norweigian Sea. Fiskets Gang 36: 464-466.2. Simmonds. J and MacLennan. D. (2005). Fisheries Acoustics: Theory and Practice, 2nd ed., Blackwell Science.

Fish and Aquatic Resources Series 10.3. Love, R. H. (1977). Target strength of a fish at any aspect. Journal of the Acoustical Society of America 62(6):

1397–1403.4. Nakken, O., Olsen, K., (1977). Target strength measurements of fish. Rapp. Proc. Verb. Reun. Cons. Int.

Explor. Mer. 170, 52–69.5. Jones, F.R.H., Pearce, G., (1958). Acoustic reflexion experiments with Perch (Perca fluviatilisLinn) to

determine the proportion of the echo returned by the swimbladder. J. Exp. Biol. 35, 437–450.6. Foote, K. G. (1980). Importance of the swimbladder in acoustic scattering by fish: a comparison of gadoid and

mackerel target strengths. Journal of the Acoustical Society of America, 67: 2084e2089.7. Horne, J.K., Jech, J.M., (1998). Quantifying intra-species variation in acoustic backscatter models. In:

Proceedings of the 135th Meeting of the Acoustical Society of America, Seattle, August 20–26, pp. 1821–1822.

8. Knudsen, F.R., Sægrov, H. (2002). Benefits from horizontal beaming during acoustic survey: application tothree Norwegian lakes. Fish. Res. 56, 205–211.

9. Encina, L., Rodríguez, A., Granado-Lorencio, C. (2006). The ecology of the Iberian inland waters: homage toRamon Margalef. Limnetica 25 (1–2), 349– 368.

10. Sanchez. V. R., Encina. L. E., Ruiz. A. R., Monteoliva. A., Carmona. R. S. (2016). Horizontal target strengthof Cyprinus Carpio using 200 kHz and430 kHz split-beam systems. Fisheries Research, 174; 136–142.

11. Jones, F.R.H., Pearce, G., (1958). Acoustic reflexion experiments with Perch (Perca fluviatilisLinn) todetermine the proportion of the echo returned by the swimbladder. J. Exp. Biol. 35, 437–450.

12. Blaxter, J. H. S, and Batty, R. S. (1990). Swimbladder “behaviour” and target strength. Fish behaviour andphysiology with respect to target strength. Mer, 189: 23-244.

43

HORIZONTAL TARGET STRENGTH OF Oreochromis niloticus IN

ASPECT OF TILT ANGLE

Hisam Fazrul1, N.A. Nik Aziz1, Ezmahamrul Afreen1, Amir Saiful Ariff 2

1School of Fisheries and Aquaculture Sciences, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,Terengganu.

(E-mail: [email protected], [email protected], [email protected])*Corresponding Author. Tel: +609 668 4533 Fax: +609 668 3991. Email: [email protected]

ABSTRACTMeasurement of Target Strength (TS) is the measurement of echo reflected back by swimbladder’s fish. However,the transducer tilt angle and fish position can affected target strength value. This study were measured TS ofOreochromis nilloticus which also known as invasive species in Malaysia. This study will emphasize on how tiltingposition of this species could affect reflected TS . The target strength were measured with of 0o, 45o, and 90o tilt angle.The TS of fish (n=5 total) was measured using a Simrad EK15 portable scientific echosounder at 120 kHz. The sizeof the tank is about 3 meter length x 3 meter width x 1.2 meter depth. We evaluated the correlation between TS andTA of this species. The fish sizes from this experiment varied from 19.5 - 22.5 cm total lenght. The significantdifference can be seen from mean TS value obtain at 3 different angle tested 0o, 45o, and 90o that is -62.73, -62.58 and-60.50 dB respectively. The result indicated that angle 90o is the best angle for tilt angle experiment in horizontaltarget strength studies with regression, R > 0.800 which makes the data obtained is acceptable.

Keywords: Target Strength, Echosounder, SIMRAD, Oreochromis niloticus, Tilt Angle.

1. INTRODUCTION

Different species of fish have different Target strength. Because the swim bladder has different size andchamber. The target strength is measured by method of reflection of sound by transducer of echo sounderin the water unit decibel (dB). It is necessary to measure the target strength of target species because todifferentiate between disturbances causes by the external factors such as rock, plankton, algae or othersthing than targeted species.

The target strength of fish is significant parameter in determining the abundance and biomass of fishpopulation. Horizontal hydroacoustics is a useful tool to study fish in shallow waters and complete thedensity and biomass estimates calculated from vertical hydroacoustic samplings (1). Most of the commonmethod in measuring target strength are (a) immobilized and unconscious fish, (b) active but confinedwithin a cage, or (c) wild and free to behave normally in their natural environment (d).

In this study, the experiment carried out by the ex-situ measurement in tank with cages for the species ofOreochromis niloticus with different angle tilt by using echo sounder SIMRAD EK15 with high frequencyof 200 kHz. The aim of this study is to identify the best position of transducer position to detect fish.

2. EXPERIMENTAL

2.1 Equipment and instrument

The acoustic equipment that has been used in the TS study are SIMRAD EK 15 single beam echo sounderwith colour monitor, processor unit (personal computer), Ethernet switch, transceiver unit and transducer

2.2 Experiment design

44

Laboratory experiment on target strength was conducted at Freshwater Hatchery at University MalaysiaTerengganu, in a fiberglass tank. A rectangular fiberglass tank filled with freshwater was used in thisexperiment. The size of the tank is about 3 meter length x 3 meter width x 1.2 meter depth. The singlebeam transducer was mounted on a stainless steel rod which clamped to specially design frame set at oneside of the tank supported by two wooden plank. The main beam was set horizontally for adjustment. Thefish is anaesthetized with transmore to prevent injury and less movement. Transducer were run for 2 minutesfor each fish. Data were saved in personal laptop using EK15 software and for further analyse using Sonar4and Sonar5.

Figure 1: The setup of the experiment on measuring the target strength of targeted species with differenttilt position


3.1 Correlation TS and Length in different tilt angle

Figure 2 indicated correlation O. niloticus TS with different tilt angle; a) 00 , b) 450 and 900. The highestcorrelation were found in 900 tilt angle with R2=0.886. All correlation indicated high significant value P <0.001. The best angle for transducer position are 900. In this postion, all TS reflected were fully acceptedby transducer before processing it into backscatter (1). In 900 position, swimbladder of O. niloticus fullyhit by echoes compare in 00 and 450 tit angle. Furthermore, Oreochromis nilloticus have one chambered ofswim bladder which make it can reflected more echoes compare two-chambered fish (3). One chamberswimbladder are full with gas and less fat compare with two-chambered.

45

(a) (b)

(c)

Figure 2: (a) Target strength-Length relation of Oreochromis niloticus in different tilt angle (a) 0 0 (b) 450 and (c) 900.

CONCLUSION

As the conclusion, the best position of transducer to detect single fish in high frequency are 900 tilt angle.The transducer position are very crucial in accuracy of TS.

REFERENCES

1. Simmonds. J and MacLennan. D. (2005). Fisheries Acoustics: Theory and Practice, 2nd ed., Blackwell Science.Fish and Aquatic Resources Series 10.

2. Sanchez. V. R., Encina. L. E., Ruiz. A. R., Monteoliva. A., Carmona. R. S. (2016). Horizontal target strengthof Cyprinus Carpio using 200 kHz and430 kHz split-beam systems. Fisheries Research, 174; 136–142.

3. Knudsen, F.R., Sægrov, H. (2002). Benefits from horizontal beaming during acoustic survey: application tothree Norwegian lakes. Fish. Res. 56, 205–211.

46

MACROALGAE AS FUEL : TGA AND KINETIC STUDY OF Gracilariacacalia AND Halymenia maculata PYROLYSIS

Amira Nabila binti Roslee1*, Nur Farizan binti Munajat2*

1, 2 School of Ocean Engineering, University Malaysia Terengganu, 21030 Kuala Terengganu, MALAYSIA.(E-mail: [email protected], [email protected])

ABSTRACTGracilaria cacalia and Halymenia maculata are aquatic plants that can be found at Malaysia coastal areas andhave a potential as source of renewable energy . Thermal analysis and kinetic study of the macroalgae pyrolysisprocess have been conducted by using thermogravimetric analysis (TGA). The corresponding kinetic parameterswere calculated using model-free methods: Kissinger, Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa(FWO) methods. Gracilaria cacalia and Halymenia maculata released 49.11% and 45.9% of total volatiles thatcould be converted into useful energy carrier via pyrolysis process. The activation energy calculated fromKissinger method for H. maculata was 281.75kJ/mol and 138.17kJ/mol for G. cacalia, respectively. The apparentactivation energies for KAS and FWO methods are increased with increasing the pyrolysis conversion withaverage activation energies of 223.32kJ/mol and 234.64kJ/mol for H. maculata while for G. cacalia (157.87kJ/moland 167.84kJ/mol). These data provide information for further application for designing and modeling inthermochemical conversion system of macro algae biomass.

Keywords: macroalgae biomass, thermochemical conversion, thermal analysis, model-free methods

1. INTRODUCTION

The utilization of biomass for energy carriers for power generation, transport fuel, and chemicalcommodities have been increased due to the limitation of fossil fuels and has been developed as afeedstock during the past 4-5 decades. However, land-based biomass needs large scale of land space.Recently, macroalgae has been attracted as another potential fuel and has some outstanding advantagesand also can effectively reduce the environmental problems and greenhouse effect. Macroalgae can beconverted to useful energy carriers in similar ways of terrestrial biomass via thermochemical processtechnology (1). Pyrolysis is a process where the conversion of biomass to biofuels in a direct pathwayas well as first steps for combustion and gasification process (2). Thermogravimetric analysis (TGA)has been widely used to study the pyrolysis behavior and calculate the kinetic parameters of many fuelsand provides information concerning the amount of weight loss and decomposition rate of the fuelsagainst temperature and time in a specific condition. (3). The information then can be utilized to designthe system for pyrolysis process and other thermochemical systems The determination of the kineticparameters is based on iso-conversational method where the decomposition rate at a constant fractionis a function of temperature and the activation energy of a process is a function of the conversion values,α. As the activation energy is depends on α, it is useful in giving information about the reaction mechanism of the conversion process (4). The pyrolysis characteristics among different species ofmacroalgae biomass are important for a successful design and operation of its thermochemical process.The objective of this study is to investigate the pyrolysis characteristic and kinetic parameters of twodifferent macroalgae collected from the different region of Peninsular Malaysia via TGA analysis. Thisstudy provided useful information on the pyrolytic behavior of macroalgae biomass for fuel production.

2. METHODOLOGY

2.1 Materials and sample preparations

The macroalgae biomass used in this study, G. cacalia and H. maculata were collected from differentplaces in the Southern part of Malaysia. The samples were identified and cataloged at Marine AlgalReference Collection (MARC) in Central Laboratory, Universiti Malaysia Terengganu (UMT). Prior to

47

analysis, the samples were washed, dried in an oven at 60°C for 24 hours, milled and sieved to a particlewith an average size between 100-120µm.

2.2 Materials characterization

The proximate analysis for ash, moisture content (MC), volatile matter (VM) and fixed carbon (FC)determination is conducted according to JIS M8812. The ultimate analysis of carbon (C), hydrogen (H),nitrogen (N) and sulphur (S) is done using an Elemental Analyzer Vario MacroCube-CHNS.

2.3 Thermal analysis by thermogravimetric analysis (TGA)

The pyrolysis characteristic was performed by using thermal analyzer (TGA Q500 V20.13 Build 39).In each experiment, 10mg of the sample mass was heated at heating rates of 10, 20 and 30°C/min undera nitrogen flow of 100 ml min-1. This experiment was conducted from ambient temperature to 800°C.

2.3.1 Tables, Images and Figures

Figure 3. (a) Gracilaria cacalia and (b) Halymenia maculate

Figure 4. TGA and DTG curves for (a) G. cacalia and (b) H. maculata at different heating rates

48

Figure 5. Activation energies calculated by (a) Kissinger (b) KAS and (c) FWO methodsTable 1. Characterization of G. cacalia and H. maculata

SampleUltimate analysis Proximate analysis

Ref.C H N S Oa Ash

Volatilematter

MoistureFixed

carbon

G. cacalia 28.5 4.86 2.68 1.92 62.04 11.45 49.11 14.03 25.41 This work

H. maculata 30.98 5.17 3.33 2.29 58.23 11.18 45.9 10.21 32.71 This work

Ulva prolifera 37.44 7.01 1.87 2.88 50.8 24.46 57.87 9.92 7.77 (5)

Poplar wood 45.5 6.26 1.04 - 47.2 3.7 75.54 9.6 11.15 (6)a Calculated by difference C, H, N, and S

49

Table 2. Thermal degradation characteristics of G. cacalia and H. maculata at different heating ratesHeating

rate[oC/min

]

Temperature (°C)DTGmax

[%/oC]Mass

lossa[wt.%]Mass [mg]

Tp1 T1 Tp2 Tp3 T2

G.cacalia

10 60.7 162.9 244.5 711.2 750.1 3.102 52.14 8.0620 71.4 166.9 253.9 733.5 755.8 6.547 52.92 8.1830 77.4 170.9 261.8 742.9 761.3 9.389 53.19 8.23

H.maculat

a

10 54.1 167.9 274.3 682.7 730.4 3.603 51.16 6.3320 57.2 172.9 280.1 689 735.8 5.824 52.18 6.4630 69.8 176.7 283.9 699.7 742.9 14.86 53.2 6.59

a Mass loss at main degradation stage

Table 3. The activation energies calculated by Kissinger, KAS, and FWO methodsKAS

α G. cacalia H. maculata

E[kJ/mol]

A[min-1]

R2 E[kJ/mol]

A[min-1]

R2

0.1 125.78 8.27x108 0.9992 182.94 1.59x1014 0.99220.2 145.41 7.86x1010 0.9975 193.24 1.52x1016 0.99430.3 151.31 6.10x1011 0.9925 198.72 1.4x1017 0.99830.4 155.21 1.90x1012 0.9944 202.82 1.04x1018 0.99940.5 159.36 7.27x1013 0.9957 211.43 6.73x1019 0.99640.6 164.43 3.86x1014 0.9972 229.91 1.51x1021 0.99890.7 168.09 1.43x1016 1 244.70 1.23x1022 0.99880.8 173.31 8.39x1017 0.9988 258.25 9.02x1022 0.99790.9 177.95 4.6x1018 0.9981 287.86 1.19x1024 0.9989Av. 157.87 223.32

FWO0.1 133.66 2.04x1016 0.9993 190.90 2.79x1020 0.99280.2 153.80 2.58x1017 0.9978 193.60 4.15x1021 0.99480.3 160.23 1.37x1018 0.9933 204.78 9.22x1023 0.99640.4 164.66 4.88x1018 0.9950 212.93 2.97x1024 0.99840.5 169.31 2.04x1019 0.9963 229.97 7.44x1025 0.99950.6 174.92 1.12x1020 0.9975 241.46 5.62x1027 0.99970.7 179.10 4.94x1020 1 265.28 5.37x1028 0.99900.8 184.85 3.18x1022 0.9990 279.56 4.4x1029 0.99820.9 190.02 1.91x1024 0.9993 293.25 9.8x1030 0.999Av. 167.84 234.64

Kissinger138.17 5.66x107 0.9907 281.75 18.86X1020 0.9993

REFERENCES

1. P. D. Filippis, B. D. Caprariis, M. Scarsella, N. Verdone, Double Distribution Activation Energy Modelas Suitable Tool in Explaining Biomass and Coal Pyrolysis Behavior. Energies 8, 1730-1744 (2015).

2. A. V. Bridgwater, Renewable fuels and chemicals by thermal processing of biomass. Chem. Eng. J. 91,87-102 (2003).

3. Z. Shuping, W. Yulong, Y. Mingde, L. Chun, T. Junmao, Pyrolysis characteristics and kinetics of themarine microalgae Dunaliella tertiolecta using thermogravimetric analyzer. Bioresource Technology 101,359-365 (2010).

4. A. Khawam, Application of Solid State-kinetics to Desolvation Reactions. Iowa University DoctorialThesis, (2007).

5. S. Ceylan, J. L. Goldfarb, Green tide to green fuels: TG–FTIR analysis and kinetic study of Ulva proliferapyrolysis. Energy Conversion and Management 101, 263-270 (2015).

6. K. Slopiecka, P. Bartocci, F. Fantozzi, paper presented at the Third International Conference on AppliedEnergy, 2011.

50

OPTICAL AND ELECTRICAL STUDY OF BIOPOLYMER BASEDON METHYLCELLULOSE DOPED WITH Ca(NO3)2

A.M.S. Nurhaziqah1, N.A. Nik Aziz2, S. Hasiah3, A.Hamizah1, I.Q. Afiqah1

1School of Ocean Engineering, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu.2School of Fisheries and Aquaculture Sciences, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,

Terengganu.(E-mail: [email protected], [email protected], [email protected], [email protected])

3Center for Foundation and Liberal Education, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,Terengganu.

(E-mail: [email protected])*Corresponding Author. Tel: +609 668 4318 Fax: +609 668 3991. Email: [email protected]

ABSTRACTThere are three types of polymer which are natural polymers, semi-synthetic polymers, and synthetic polymers.To avoid environmental desecration and more towards green technology, through this research focus on the naturalpolymers known as biopolymer. The intention of this research is to study effect of the several different weightpercent (wt%) of calcium nitrate, Ca(NO3)2 (CN) salt into biopolymer of Methylcellulose (MC). MC-CN solidpolymer electrolyte was prepared by using casting technique. The concentrations of CN salt were preparedbetween 5 to 25wt%. The optical and electrical characteristic of the samples was studied by using FourierTransform-Infrared (FTIR) spectroscopy, X-ray Diffraction (XRD) and Electrical Impedance Spectroscopy (EIS).The highest conductivity from the samples is at 25 wt% of CN which is 2.21X10-7 Scm-1. The optical study wasmeasured using FTIR and it is shows that the CN salt acts as the conducting ions devoid of effect the molecularstructure of the polymer. While the XRD data revealed that MC-CN an amorphous phase.

Keywords: Polymer

1. INTRODUCTION

The environmental problems causes by synthetic polymer, which are non-biodegradable. However,natural polymers have been used as common polymer in electrolyte due to their various of chemicalstructure, richness in nature and their economical and principally biodegradable[1]. While thebiopolymer electrolytes are good substitute for chemical device application.

Otherwise, polymer electrolytes have very poor mechanical properties due to their dual nature[2].Through this research, use Methylcellulose (MC), biopolymer act as polymer electrolyte. MC is one ofthe simplest cellulose derivatives [3]. Besides, MC is also good solubility in water with low temperaturewhile act as biodegradable cellulose ether[4].

In addition, with this research, the biopolymer of MC as the host polymer is mixed with CN salt toincrease the conductivity and achieve the standard characterization of electrolyte which help in thesystem of application such as solar cell, battery and others.

2. EXPERIMENTAL

2.1 Preparation of biopolymer electrolyte films

Solid biopolymer electrolyte thin films were prepared by using cast technique. MC from Sigma Aldrichwere dissolved into different amount amounts of wt% of CN salt from Merck KGaA in distilled waterand stirred continuously until the salt was dissolved completely. Then, the salt and polymer complexwere homogenously mixed and were poured into several polystyrene petri dishes and expose toevaporate the mixtures under room atmosphere.

51

2.2 Characterization

In this research the optical study were analyzed using Fourier Transform Infrared (FTIR), and X-RayDiffraction (XRD).While electrical characterization was used Electrical Impedance Spectroscopy (EIS).


3.1 FTIR analysis

FTIR analysis has been performed to investigate the compositional, structural and interaction bondingbetween MC and CN in BPE system [5]. Besides, the FTIR spectra of MC-CN biopolymer electrolytesshown in Fig.1 with wavenumbers between 4000 to 500 cm-1. From the figure, the peak at 1055 cm-1 isdescribed as the antisymmetric stretching of asymmetric oxygen bridge in the cyclohexane ring withrange between 1150 to 1000 cm-1 which is C-O-C bond [6]. Besides, the Fig.1 show 1055 cm-1 and1049 cm-1 peaks are increase in the per cent of transmittance as the weight per cent of salt is increased.Increasing the concentration of salt in MC causation the concentration of N become higher. Thus, resultare withdrawn toward MC via C-O-C bond to form C-N bond which can prove by shifting of C-O-Cstretching to the lower wavenumber [7].

Figure 1. FTIR analysis

3.2 XRD analysis

The amorphous on crystallinity of the polymer film with respect to pure MC and CN complex have beenexamined by x-ray diffraction. XRD is a multi-purpose tool to monitor the transition in structuralproperties to both crystalline and amorphous region of BPEs system. The pattern of six differentsamples which are 0, 5, 10, 15, 20, and 25 wt% with pure MC based on an x-ray diffraction at roomatmosphere from 2θ = 5° to 80° are shown in Fig.2. Based on that figure, the broad diffused band is at 2θ=21° while the starter peak is at 2θ=8°. The two peaks, at ߠʹ ൌ ͺ ι with 21° was appeared and showedas pure MC pattern. The existence based on peak at 8° is declaration with cellulose adaptation[8].

Figure 2. XRD analysis

3.3 EIS analysis

52

Fig.3 show that the value of conductivity occurs escalation correlative with incorporation of the CN andreaches maximum value of 2.21 × 10 Scm-1 for sample with 25 wt%. The decrease in the highfrequency semi-circular part consequent to doping CN from 0 to 25 wt%. In this case can relate betweenions by the current carriers with the ion conduction result from the total conductivity [9].

Moreover, increment of the conductivity continuously with increasing of wt% CN configurationwhile it claimed to the FTIR analysis supported by the ion association. In MC-CN, the CN interactedwith the O at C-O-C bond of the ether group of the MC host. The increasing of salt may have beenincreasing the number of ions or established new conducting pathway which increase the mobility ofthe protons [10].

Figure 3. EIS analysis

CONCLUSION

The discovery of new BPE has been consummate in this work by incorporating various concentrations(0-25 wt%) of CN with biopolymer materials MC via solution casting technique. The conductivitybiopolymer electrolyte of MC-CN increase due to function of wt% CN. The highest conductivityobtained of this polymer-salt complex was 2.21 × 10 Scm-1, for sample 25 wt% CN. The enhancementof conductivity was affected by increment in mobility ions of proton and malleable structure. Itsevidence that effect of complexation of CN in MC polymer common, influenced by the charge carrierconcentration and confirms composite nature of electrolyte from FTIR analysis and as well as XRDanalysis.

REFERENCES

1. Shukur, M. and M. Kadir, Electrical and transport properties of NH4Br-doped cornstarch-based solidbiopolymer electrolyte. Ionics, 2015. 21(1): p. 111-124.

2. Samsudin, A. and M. Isa, Characterization of carboxy methylcellulose doped with DTAB as new types ofbiopolymer electrolytes. Bulletin of Materials Science, 2012. 35(7): p. 1123-1131.

3. Lin, S.-Y., et al., Temperature effect on water desorption from methylcellulose films studied by thermalFT-IR microspectroscopy. Surface science, 2007. 601(3): p. 781-785.

4. Kanimozhi, K., S.K. Basha, and V.S. Kumari, Processing and characterization of chitosan/PVA andmethylcellulose porous scaffolds for tissue engineering. Materials Science and Engineering: C, 2016. 61:p. 484-491.

5. Monisha, S., et al., Investigation of bio polymer electrolyte based on cellulose acetate-ammonium nitratefor potential use in electrochemical devices. Carbohydrate Polymers, 2017. 157: p. 38-47.

6. Abiddin, J.F.B.Z. and A.H. Ahmad, Fourier transform infrared spectroscopy and electricalcharacterization of methylcellulose based solid polymer electrolyte doped with sodium iodide. JurnalTeknologi, 2015. 76(3): p. 41-45.

7. Samsudin, A., H. Lai, and M. Isa, Biopolymer materials based carboxymethyl cellulose as a protonconducting biopolymer electrolyte for application in rechargeable proton battery. Electrochimica Acta,2014. 129: p. 1-13.

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8. Nik Aziz, N., N. Idris, and M. Isa, Solid polymer electrolytes based on methylcellulose: FT-IR and ionicconductivity studies. International Journal of Polymer Analysis and Characterization, 2010. 15(5): p. 319-327.

9. Jacob, M., S. Prabaharan, and S. Radhakrishna, Effect of PEO addition on the electrolytic and thermalproperties of PVDF-LiClO 4 polymer electrolytes. Solid State Ionics, 1997. 104(3): p. 267-276.

10. Majid, S. and A. Arof, Electrical behavior of proton-conducting chitosan-phosphoric acid-basedelectrolytes. Physica B: Condensed Matter, 2007. 390(1): p. 209-215.

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SCATTERING REGIMES FOR DIFFUSE UNDERWATER OPTICALWIRELESS COMMUNICATIONS

Faezah Jasman*1, Wan Hafiza Wan Hassan2

1, Faculty of Engineering and Computing, First City University College,Selangor,MALAYSIA2, School of Ocean Engineering, University Malaysia Terengganu,Terengganu ,MALAYSIA.

(E-mail: [email protected], [email protected])

ABSTRACTOptical wireless communications (OWC) which can offer high bandwidth (~GHz), but only over short ranges (<200m) is seen as a complementary technology to the acoustic technology in underwater applications. As an alternativemethod to collimated links based on laser source, a diffuse links based on LED are investigated in this paper. Thus,Monte Carlo simulation is used to estimate the scattering regimes for diffuse underwater optical wirelesscommunications. This is done by evaluating the attenuation of unscattered light in three types of underwaterenvironment. It is observed that by finding the distance at which the unscattered light drops to zero, the transition pointfor scattering regimes for diffuse links can be estimated.The transition point for diffuse links in coastal water and turbidwater can be estimated to be around 22 m and 3 m respectively. Further analysis of the frequency responses show thatthe channel bandwidth significantly reduces as the operating region changes from unscattered to multiple scatteringregion.

Keywords: optical wireless communications, Monte Carlo.

1. INTRODUCTION

Historically, research on using lasers for undersea communications has started as early as the 1970s [1].Specifically, wavelengths in the blue/green region are used as they exhibit the minimum attenuationunderwater. Recently data rate of up to 4.8 Gbps has been demonstrated over the range of 5.4 m [2]. Despitethe high data rates achieved by laser based links, they face a considerable challenge to maintain accuratepointing and tracking since laser beams are highly collimated. Thus, several efforts to develop diffusesystems based on LED to ease the strict pointing requirements have been reported. An experiment using anomnidirectional transmitter at 40 Mbps over 10 m has been conducted in [3]. One of the main challenges toimplement underwater OWC is the effect of scattering that cause temporal dispersions that eventually limitthe bandwidth of the links. Much works has been done to understand and characterize the effect of scattering.An equation is used to estimate the transition point from unscattered region to multiple scattering region,which is beneficial to predict the distance where the bandwidth will be limited [4]. An experimental workhas been conducted in [5] to measure temporal dispersions for collimated beam has shown that the transitionpoint obtained matched the work in [6]. However, none of this work investigates the scattering regime fordiffuse links. Hence, this paper will focus on the simulation of diffuse links to estimate the transition pointsfor the scattering regimes. Additionally, the frequency response is calculated to estimate the channelbandwidth supported by the links. The remainder of this paper is organized as follows. In Section 2 webriefly present underwater optical properties and the simulation model. Next, we present numerical resultsin Section 3. Finally, Section 4 concludes the work.

2.BACKGROUND

a. Optical properties

The two major properties that attenuate water are absorption and scattering. Absorption is a process whenthe photon energy is lost due to transfer of energy during the interaction with water molecules and particles.

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Scattering is a process where a photons path is deflected due to the interaction with particulate matter inwater, but there is no change in energy [7].This process causes spatial dispersion that reduces the signal-to-noise ratio (SNR) due to the light from the source spreads out in space and causes a reduction of the numberof photons collected at the receiver. Secondly, it causes temporal dispersion due to photons that reach thereceiver at different times. This will limit the channel bandwidth available for communication due to theintroduction of inter-symbol-interference [8]. Both of these effects are wavelength dependent and aregenerally represented by the absorption coefficient a, scattering coefficient b, and the attenuation coefficientc. Values of the coefficients have been established in the literature and are shown in Table 1 [7].

Table 1. Optical Properties for three types of water

Water type a (m-1) b (m-1) c (m-1)Clean water 0.114 0.037 0.151

Coastal water 0.179 0.219 0.398Turbid water 0.366 1.824 2.19

b. Simulation setup

Monte Carlo simulation is used to model the diffuse links where the light beam is modelled as the continuouspropagation of a large group of photons. A set of probability rules and random variables are used to modelthe initial beam distributions, path length and scattering angle. For a detailed description of Monte Carlosimulation, we refer readers to [4,9]. Three types of water are considered in this simulation: clear water,coastal water and turbid water. The distance between the source and the receiver was varied between 1 mto 50 m to investigate the attenuation of the light. Other simulation parameters were set as following: thewavelength, = 514 nm and the initial beam width = 2 mm. A relatively large receiver aperture of 10 cmwas chosen to increase the number of the scattered photons that was collected. Two sizes of diffuse beamsare used; 15 and 30. To simplify the simulation, we neglected the effect of turbulence, backgroundradiation, and surface waves.

3.RESULTS

c. Percentage of unscattered light

Figure 1 shows the percentage of the unscattered light that contributed to the total power reception as thedistance is varied. From this plot the distance at which the unscattered light ceased to be collected isproposed to be used as the transition point between the minimally scattered and the multiply scattered region.It can be seen that the transition point in coastal water and turbid water is 22 m and 3 m respectively. Itshould be noted that the transition point in clear water cannot be obtained as the simulation is conducted upto 50 m only. Next the channel bandwidth that can be supported by the links is calculated by estimating thefrequency at which the signal drops to half in the frequency response plot (i.e. drops by 3 dB). Two distancesare chosen to represent unscattered region and multiple scattering regions. Figure 2(a) and (b) shows thefrequency response of the diffuse links in coastal water and turbid water. In both types of water, it can beseen that the bandwidth reduces from GHz range to several hundreds of MHz when the the operating regimechange from unscattered to multiple scattering region.

CONCLUSIONThis paper uses Monte Carlo simulation to investigate the transition points for scattering regimes in diffuselinks. It can be said that diffuse links can operate in unscattered regimes up to distance 22 m in costal waterand 3 m in turbid water. From the frequency response plot, it can be seen that the unscattered region cansupport bandwidth up to several GHz and reduces to several MHz in multiple scattering region.

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Figure 1. The percentage of the unscattered light that contributes to the total power reception

Figure 2. Frequency response in (a) coastal water and (b) turbid water

REFERENCES

1. S. Karp, “Optical Communications Between Underwater and Above Surface (Satellite) Terminals,” IEEETrans. Commun., vol. 24, no. 1, pp. 66–81, 1976.

2. H. M. Oubei, J. R. Duran, B. Janjua, H.-Y. Wang, C.-T. Tsai, Y.-C. Chi, T. K. Ng, H.-C. Kuo, J.-H. He, andM.-S. Alouini, "4.8 Gbit/s 16-QAM-OFDM transmission based on compact 450-nm laser for underwaterwireless optical communication," Opt Express, vol. 23, no. 18, pp. 23302-23309, 2015

3. G. Baiden and Y. Bissiri, “High bandwidth spherical optical wireless communication for subsea teleroboticmining,” in OCEANS MTS/IEEE KONA, pp. 1–4, 2011.

4. W. C. Cox Jr, "Simulation, Modeling, and Design of Underwater Optical Communication Systems," Ph.D.thesis, Dept. Elect. and Comp. Eng, North Carolina State Univ, NC, USA, 2012.

5. B. Cochenour, "Experimental Measurements of Temporal Dispersion for Underwater Laser Communicationsand Imaging," Ph.D. thesis, Dept. Elect. and Comp. Eng, North Carolina State Univ, NC, USA, 2012.

6. G. Mooradian, M. Geller, L. Stotts, D. Stephens, and R. Krautwald, "Bluegreen pulsed propagation throughfog," Appl Opt, vol. 18, no. 4, pp. 429-441, 1979.

7. S. Arnon, J. Barry, G. Karagiannidis, R. Schober, and M. Uysal, Advanced optical wireless communicationsystems, Cambridge University Press, 2012.

8. B. Cochenour, L. Mullen, and J. Muth, “Temporal response of the underwater optical channel for high-bandwidth wireless laser communications,” IEEE J. Ocean. Eng., vol. 38, no. 4, pp. 730–742, 2013.

9. R. A. Leathers, T. V. Downes, C. O. Davis, and C. D. Mobley, "Monte Carlo Radiative Transfer Simulationsfor Ocean Optics: A Practical Guide," Naval Research Lab., Washington, DC, Tech.Memo. NRL/MR/5660—04-8819, Sept 1, 2004.

57

STUDY OF LED IN UNDERWATER FISH ATTRACTION LAMP AND ITSLIGHT PROPAGATION

Ahmad Nazri Dagang*, Chong Geeng JunSchool of Ocean Engineering, Universiti Malaysia Terengganu, Malaysia

(E-mail: [email protected])

ABSTRACTArtificial light in fisheries has been one of the most advanced and successful methods to control fish behavior forcapture purposes. This study was conducted with the aim of investigate the lighting efficiency of the LED fish attractinglamp compare to the traditional light sources like halogen lamp and incandescent light bulb that use for fishing. 3colours of LED tubes as the LED fish attracting lamp prototype was constructed and the optical and electricalcharacteristics of LEDs had determined. The LEDs tubes consumed electric power of 7.25 W (blue), 6.56 W (green),3.22 W (red), the incandescent lamp 7.05 W and the halogen lamp 150 W of electric power. The spectrometer shownthat LEDs only emit light in a narrow range of wavelengths but light emitted from traditional light sources such ashalogen and C4 incandescent dive lamp covered all wavelengths in visible spectrums and also non-visible spectrums.The measurement result in pool measurement shown that the light emitted from the LED tubes distributed evenly toall directions and the effective illuminance distance were in range of 2 m to 4 m. The blue light attenuate coefficientvalue is 1.5 m-1 , green light is of 1.89 m-1 and red light has the highest at 2.93 m-1. However, the performance of LEDsin this study shown that it have the potential to become future underwater illuminance light source useful and worth toinvestigate in future.

Keywords: LED, Underwater Lamp, Fish Attraction Lamp, Light Attenuation

1. INTRODUCTION

Artificial lighting of light source had plays an important role in daily life. Most of the economic activitycannot progress without light source especially at night or in dark environments. Hence artificial light sourcehad invented for that purpose. Aquaculture and fisheries industry are the two examples that rely on artificiallight sources. Artificial light in fisheries has been one of the most advanced and successful methods tocontrol fish behaviour for capture purposes [1]. At the early days, fishermen realize that fish are more likelyto attract by light. Hence torch fishing was the common fishing method during nighttime at the old times.The visual of marine fish have peak sensitivity at the range 490 – 510 nm, while fresh water species havepeak sensitivities within the range 500 – 550 nm [2,3]. The threshold sensitive of fish to light have the rangeof 4.9 10-12 lux to 0.001 lux [4,5]. As the lighting technology getting advance, the lighting method hadchange from torch to incandescent lamp and then light emitting diode (LED). Most of the light source usestoday for fishing are fluorescent type electrical discharge lamp and filament type of incandescent lamp. Bothtype of lamp have high power consumption but low energy conversion where most of the electrical energyreleased as heat. Solid state lighting (SSL) such as LED had come out for the solution, SSL has lower powerconsumption, less heat generated and higher light output, therefore it is energy saving and efficient [6]. Thisstudy will focus on the electrical properties and optical properties of the commercial LED. The emissionspectral and power consumption of LED will be studied. The attenuation of different wavelength of lightunderwater will be investigated as well as the light field distribution and the effective illuminance length ofLED.

2. EXPERIMENTAL SETUP

a. Prototyping

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The prototypes of LED fish attracting lamp or LED tubes were constructed using 5 mm high brightnessLED of different colours (red, green and blue), acrylic tube, expandable polyethylene (EPE) foam tube,Polyvinyl Chloride (PVC) pipe and some 1 Ω resistors. The acrylic tube are the outer shell (cover) of the LED fish attracting lamp which act as a protection layer. The EPE foam tube act as a diffusion cover todiffuse the light from LEDs to all directions in horizontal (360°). The prototypes are powered by 18650lithium-ion battery with voltage of 3.7 V.

2.2 Measurements

Each prototype LED tubes were measured for their input current, voltage, heat generated, and light output.The same measurement also carried out for other artificial light soures for comparison. A digitalthermometer (Model: Ebro TFN 520) was used to record the temperature rose on surrounding air of theartificial light sources including prototypes. The change of temperature in surrounding of artificial lightsources were recorded every 10 minutes until 1 hour. The light output of each LED tubes were measuredwith Tecpel 531 light meter and Li-Cor 193SA underwater spherical quantum sensor connected with Li250A light meter. The radiation spectrum characteristics of each LED tubes and artificial light sourcesinvolve in this study were determined using spectrometer (USB4000-UV-VIS, Ocean Optics).

2.3 Operational Test

The prototypes were test for their operational test in a 0.3 m long × 0.2 m height × 0.15 m width aquariumwhich fill with fresh water before deploy to real environment test. This is the small scale experiment thatenable us to simulate the experiment in real environment and also test for the water proof ability of the LEDtubes. The pool measurement was conducted after the prototypes had passed the operational test. Themeasurement was conducted at night with all other light sources (street lights & spot lights) must beswitched off to reduce any possible background illuminance that will contribute error to the measurement.The intensity of the light emitted from each LED tubes were measured by underwater spherical quantumsensor to determine the light field distribution and effective illuminance range of the prototypes. Theattenuation of light of different wavelengths will be revealed through the Photosynthetic Photon FluxDensity (PPFD) readings obtained from the measurement.


From the electrical properties and optical evaluation, LED shown that it has higher lighting efficiencybecause all of its energy had convert into light of a narrow wavelengths especially in blue color range.Where halogen and incandescent emitted spectrums covered all visible spectrum and also small portionUltra-Violet (UV) spectrums and significant Infra-Red (IR) spectrums which are not effective in attractcommercial species, this imply that most of the energy is wasted on generating red wavelengths and IRwavelengths. The attenuation coefficient of light greatly deviate from values obtained in other researcheswhich estimates the due attenuation coefficient are 0.16 ~ 0.57 m-1 [7,8] to a few factors: insufficient datapoints obtained from pool experiment are insufficient due to low resolution of instrument, scattering andabsorption of light by the foreign particles due to impurities of water [9], flaw of the model that used toestimate the attenuation coefficient [10], in effective mapping design of LEDs in the prototypes.

The light emitted from halogen lamp and incandescent lamp has covered all visible spectrums and non-visible spectrums which are UV and IR. The red wavelength spectrums and IR spectrums emitted occupymore than 60% of the total emitted spectrums in both halogen and C4 incandescent dive lamp. This indicatedthat most of the electric energy of the lamp consumed had wasted into generate wavelengths that are stronglyabsorbed by water and not effective to attract the commercial species. Unlike halogen and C4 incandescent

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dive lamp, LEDs emitted light in highly directional and that have narrow range of spectrum and pure colourof light. It basically do not emit other wavelengths other than the colour of wavelength that it designed toemit and it consume total 7.25 W (for blue LEDs) of electric power. In additions, LED does not radiateexcessive heat to surrounding environment like traditional light sources. The light emitted prototype LEDtubes has effective illuminance distance ranging from 2 m to 4 m and distributed evenly to all direction. Thewavelength and intensity of the light emitted are fulfill the requirement to induce the aggregation of fish tothe light source in theoretically.

4. CONCLUSION

This study had investigated the relation of wavelength and the penetration power of light, that is: the shorterthe wavelengths of light, the penetration distance of light will be further. Besides, the light emitted prototypeLED tubes has effective illuminance distance ranging from 2 m to 4 m and distributed evenly to all direction.The wavelength and intensity of the light emitted are fulfill the requirement to induce the aggregation offish to the light source in theoretically. The values of attenuation coefficient are for red, green and bluewavelengths were 2.93 m-1, 1.89 m-1 and 1.5 m-1; respectively. The values estimated had deviated greatlyaway from values obtained from other resources due to there are a lot of factors that do not take in accountsuch as the scattering process of light occur in the system.

REFERENCES

1. M. Anongponyoskun, K. Awaiwanont, S. Ananpongsuk, S. Arnupapboon (2011). Comparison of differentlight spectra in fishing lamps. Kasetsart Journal - Natural Science, 45(5), 856–862.

2. N. J. Marshall, K. Jennings, W. N. Mcfarland, E. R. Loew, G. S. Losey, G. S. (2003). Visual Biology ofHawaiian Coral Reef Fishes. III. Environmental Light and an Integrated Approach to the Ecology of ReefFish Vision. Copeia, 2003(3), 467-480.

3. R. H. Douglas, M. B. Djamgoz (1990). The Visual system of fish. Chapman and Hall. London.4. A .P. Alekseev. (1971) Fish Bhavior and Fishing Techniques. PP 85. Keter Press, Wiener Bindery Ltd.,

Jerusalem.5. Y. Imamura, A. Koike (1959). Study on the disposition of fish towards light 3. The strength of illumination

comfortable to Cololabissaira. J. Tokio Univ. Fish. 45, 179–183.6. T. Okamoto, K. Takahashi, H. Ohsawa, K. Fukuchi, K. Hosogane, M. Kobayashi, K. Moniwa, H. Sasa,

H.Yoshino, M. Ishikawa, K. Harada, H. Asakura, H. Ishii (2008). Application of LEDs to Fishing Lights forPacific Saury. J. Light & Vis. Env. Journal of Light & Visual Environment, 32(2), 88-92.

7. A. C. Brito (2013). Measuring Light Attenuation in Shallow Coastal Systems. Journal of Ecosystem &Ecography, 03(02), 1–4.

8. C. L. Gallegos (1994). Refining habitat requirements of submersed aquatic vegetation: role of optical models.Estuaries 17:198-219.

9. H. Buiteveld, J. H. M. Hakvoort, M. Donze (1994). Optical properties of pure water. Proceedings of the SPIE,2258(JANUARY 1994), 174–183.

10. J. D. Ingle, S. R. Crouch (1988). Spectrochemical Measurements. Spectrochemical Analysis. New Jersey:Prentice Hall: 13‐25.

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THE DEVELOPMENT OF AN AUTONOMOUS FLOATING TRASHCOLLECTOR

Ming-Yi Wong1, Khalid Isa*1, 2 Embedded Computing System Research Group (EmbCoS), Faculty of Electrical and Electronic Engineering,

Universiti Tun Hussein Onn Malaysia, MALAYSIA.(E-mail: [email protected], [email protected])

ABSTRACTThis project presents the development of an autonomous floating trash collector (FTC) to reduce the water pollution.The autonomous floating collector is able to collect small floating rubbish and navigate itself from a location to anotherlocation. The system is controlled by NI myRIO which interfaces to a compass sensor, Global Positioning Systemmodule, DC motors and an infrared sensor. The graphic user interface (GUI) was created using LabVIEW softwarewhich let the user read the data during navigation. This paper covers the methods and functionality tests of theautonomous floating trash collector components and prototype.

Keywords: Navigation, Global Positioning System, Compass, Autonomous vehicles, Artificial Intelligence.

1. INTRODUCTION

This project is about the development of an autonomous floating trash collector (FTC). This idea is inspiredby Seabin that was invented by two Australian in 2015 [1]. In this project, the collector will be movingaccording to the destination that had been set previously and collect small floating rubbish such as plasticbag and paper.

Water pollution is a major global problem and damage has done to ocean, river, lakes, or other water sources[2]. Water pollution usually is caused by garbage and trash that come from the human activity such as fishingor participate in other forms of water-related recreation. Although some solution to the water pollutionproblem already exists, they are either too expensive to run or require too much manual work. This projectis helping create a better way to clean the floating rubbish on the water such as lakes and ponds. Anautonomous navigation system consists of a self-piloted vehicle that does not require any operator tonavigate and accomplish its tasks. Therefore, the autonomous floating trash collector can navigate itselfindependently from a location to another location.

2. METHODOLOGY

The FTC uses GPS module to locate the current position and calculated the distance as well as the bearingdegree to the destination point. Then the compass uses to give the FTC needed heading between the FTCcurrent location and next waypoint location. After that, use the two channels motor driver module to controlPWM and rotate the direction of two thrusters at the back of FTC meanwhile an infrared sensor is placedon the FTC to avoid the obstacle. The main controller of this project, myRIO which is used to connect allelectronic devices so they can communicate with each other and perform various operations in the realworld. Figure 1 shows the system design of the project.

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Figure 1. Block diagram of the autonomous floating trash collector

3. PROTOTYPE DEVELOPMENT

The 3D drawing design of the hull for the FTC is created by using Sketchup software. It can help to visualizethe potential problem in the final prototype of the project. It efficiently to reduce uncertainty or error in thefinal prototype as well as prevent wasted materials and resources. Figure 2 shows the 3D drawing designfor the FTC.

Figure 2. 3D design of the FTC. (a) Top view (b) Bottom view (c) Side view (d) Back view

Figure 3 and 4 demonstrate the new hull design with the deployment mechanism and a small surface vessel.The FTC in this project is designed to support up to 1.6 kg of the payload including existing component.This design is expected to be robust enough for reliable payload capacity and enough stability for the FTC.Hence, the fishing float balls are add on the both side of the vessel and Polystyrenes are attached on thebottom of the hull to increase the FTC’s buoyancy and stability.

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Figure 3. Top view of the actual FTC prototype

Figure 4. Front view of the actual FTC prototype

4. RESULTS

The first prototype and application GUI have been developed and tested, and it satisfies the systemrequirements in the project. The important task of this project was to calculate the accurate distance andheading between two points (starting/current location and desired destination) on the surface. Meanwhile,it also can collect the floating rubbish. Further implementing an algorithm that can store multiple coordinateswhich will navigate the FTC to reach the final location and can return to the starting point.

REFERENCES

1 Kimberly Mok, “Seabin: Floating invention filters plastic pollution out of marine waters (Video) : TreeHugger,” 2015. [Online]. Available: http://www.treehugger.com/sustainable-product-design/seabin-floating-invention-filters-plastic-pollution-out-water.html. [Accessed: 10-Oct-2016].

2 Chris Woodford, “Water pollution,” 2016. [Online]. Available:http://www.explainthatstuff.com/waterpollution.html. [Accessed: 23-Apr-2017].

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THE IMPACT OF ESS SOC ON FUEL ECONOMY, EMISSIONS,ELECTRICAL CONSUMPTION AND AER FOR PHERB POWERTRAIN

J.S NORBAKYAH & A.R SALISASchool of Ocean Engineering, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu

Email: [email protected], [email protected]

ABSTRACTThis paper presents a study of different initial state of charge (SOC) of battery in energy system storage (ESS) on thefuel economy, emissions, electrical consumption and all-electric range (AER) using different water drive cycles forplug-in hybrid electric recreational boat (PHERB). PHERB is a new conceptual and innovation from conventional boatwhich is an environmental friendly boat. This work started with the development of PHERB model which derivednumerically in MATLAB/SIMULINK environment with a special energy management strategy. Pulau Kapas (PK)driving cycle and Tasik Kenyir (TK) driving cycle are used in order to determine the different traffic condition. Theinfluence of different initial SOCs of the battery in ESS on fuel economy, emission, electrical consumption and AERduring the PK driving cycle and TK driving cycle is tested using the PHERB powertrain.

Keywords: PHERB; electrical consumption; fuel economy; emission; AER

1. INTRODUCTION

To develop the sustainable plug-in hybrid electric vehicle (PHEV), the most important part is the energystorage system (ESS). The problem nowadays is to develop batteries that are able to meet the bothrequirements imposed by a PHEV system such as market expectation of the system cost and length of life.In this context, a vehicle systems approach is needed to investigate the operational requirements specific toPHEV technology [1]. This paper was introduced the implementation PHEV on recreational, which is plug-in hybrid electric recreational boat (PHERB).

In general, PHEV powertrain used two separate electric machine (EM), which are used as the motorand generator, respectively, and ESS with no ultracapacitor (UC) bank. The Figure 1 illustrate thepowertrain of PHEV [2].

Figure 1. General PHEV powertrain Figure 2. PHERB powertrain [4]

This paper represents the work done of investigation the impacts of initial SOC of battery in ESS on fueleconomy, emission, electrical consumption and AER. To achieve the goals, the PHERB model with aspecial EMS is assembled in MATLAB/SIMULINK environment. The fuel economy, emission, electricalconsumption and AER were calculated and analysed.

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2. MODEL DEVELOPMENT

In this research, the model development was divided two parts which are PHERB and EMS development.The boat type selected is a recreational boat.

2.1 PHERB development

PHERB model development started with the calculations of boat energy and power requirements [5-8] fortypical driving conditions based on the parameters and target specifications of the boat based on PHERBspecification, parameter and requirement. In simulation, the length of boat used are 12.4 m and density ofwater are 1000 kgm-3. The performance requirement PHERB needed is over 30 km/h for maximum speedand electric vehicle (EV) range is 10 km. The size and capacity of each boat component are then determinedthrough a power flow analysis accordingly to meet the requirements.

2.2 EMS development

EMS is responsible of choosing in which mode that the vehicle is functioning. Several operating modes ofthe proposed EMS to control the dispensation of power amongst the components, including the mechanicalbraking, regenerative braking, motor only, engine recharge, engine and motor assist, and engine only modeaccording to the boat power demand in acceleration and deceleration and the SOC level of ESS [9].


This study is focused on the on fuel economy, emission, AER and electrical consumption at different initialSOCs of the battery in ESS on the PHERB powertrain using PK driving cycle and TK driving cycle.

3.1 Electrical consumption and AER analysis

For the AER and electrical consumption of PHERB analysis, each test is started in EM only mode with ahigh SOC. The high SOC was applied at 90 % and the battery is allowed to discharge to a low SOC level.Before the start of the simulated boat test, the PHERB powertrain used several cycles of the different waterdrive cycle. The battery was charged to a high SOC level. The boat was run in the EM mode only until theICE was on. This phenomena called as AER where the boat used 100 % EM mode only. For the differentinitial SOCs analysis, the boat was run with different initial SOCs from 90 % to 50 %, with an interval of10 %. Table 2 shown electrical consumption and AER for different initial SOC for PK driving cycle andTK driving cycle.

Table 2. Electrical consumption and AER for different initial SOC used water driving cycleDrivingCycle

InitialSOC

Electrical consumption(Wh/km)

AER(km)

PK 0.90 988.8 7.30.80 1083.6 6.60.70 1093.0 5.50.60 1023.5 3.10.50 1373.2 2.8

TK 0.90 1340.7 10.00.80 1404.9 8.40.70 1352.8 6.2

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0.60 1278.0 4.90.50 1321.4 4.0

3.2 Fuel economy and emission analysisThe high and lower SOC for the battery is based on the effect on battery life of operation at different SOCs.The fuel economy and emissions were calculated for a different initial SOC set at 90 %, 80 %, 70 %, 60 %and 50 % after an electric-only discharge of the battery. The PHERB model is simulated using a speciallydeveloped EMS. SOC are important part in EMS although not related to the component sizing but it givethe impact in fuel economy and emission.Table 3 listed the impact of different initial SOCs of the batteryon the boat fuel economy and emissions using different water driving cycle.

Table 3. Fuel economy and emission for different initial SOC used water driving cycleDrivingCycle

Initial SOC Fuel economy(mpg)

Emission (g/s)HC CO NOx

PK drivingcycle

0.90 51.0 0.296 0.151 0.0000.80 45.9 0.326 0.166 0.0000.70 38.6 0.327 0.167 0.0000.60 21.9 0.307 0.157 0.0000.50 19.4 0.411 0.210 0.000

TK drivingcycle

0.90 151.9 0.643 0.328 0.0000.80 127.3 0.674 0.344 0.0000.70 93.9 0.663 0.339 0.0000.60 73.9 0.626 0.332 0.0000.50 61.2 0.648 0.330 0.000

4. CONCLUSION

As a conclusion, the chosen initial SOC of the battery has significant effects on the boat on fuel economy,emission, AER and electrical consumption Different initial SOCs are analysed by the PHERB simulationmodel using water drive cycle and the best initial SOC of battery is 90 % is obtained by optimizing the onfuel economy, emission, AER and electrical consumption of the boat.

REFERENCES

1. Rousseau, A., Shidore, N., Carlson, R.,Karbowski, D.,. (2008). Impact of Battery Characteristics on PHEVFuel Economy, presented at AABC, Tampa, FL, May 2008.

2. Minami, S., Hanada, T., Matsuda, N., Ishizu, K., Nishi, J., and Fujiwara, J. (2013). Performance of a NewlyDeveloped Plug-in Hybrid Boat. Journal of Asian Electric Vehicles, 11:2:1653.

3. Salisa A.R., Norbakyah J.S., and Atiq W. H. (2015). A Conceptual Design of Main Components Sizing ForPHERB Powertrain. Jurnal Teknologi. 76:8: 107–111.

5. Minami, S., Hanada, T., Matsuda, N., Ishizu, K., Nishi, J., and Fujiwara, J. (2010). A Newly Developed Plug-in Hybrid Electric Boat (PHEB). Journal of Asian Electric Vehicles. 8:1:1383-1392.

6. Luttenberger, L. R., Ančić, I., Šestan, S., and Vladimir. N. 2013. Integrated power systems in small passenger ships. Plug Boat 2013World Electric & Hybrid Boat Summit Nice. France.

7. Nóbrega, Juraci., Dan, T. C.,. and Rubanenco, I. (2013). Electric Propulsion Applied for Research Vessel.International Conference on Renewable Energies and Power Quality). Spain.

8. Freire, T., Sousa, D. M., and Branco, P. J. C. (2010). Aspects of Modeling an Electric Boat Propulsion System.IEEE Region 8SIBIRCON. Irkutsk Listvyanka, Russia

9. Abdul Rahman S, Walker P.D., Zhang N., Zhu. J.G, and Du H. (2012). A Comparative Study of VehicleDrive Performance and Energy Efficiency. Sustainable Automotive Technologies: 319-324.

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THE STUDY OF SCHIFF BASE (O-PHENYLENE) AND PEROVSKITE(CaO-ZnO COMPOSITE) AS THE POTENTIAL SOLAR CELL

MATERIAL

I.Q. Afiqah1, N.A. Nik Aziz2, A. Hamizah1, A.M.S. Nurhaziqah1

1School of Ocean Engineering, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu.2School of Fisheries and Aquaculture Sciences, University Malaysia Terengganu, 21030 Kuala Nerus, Terengganu.

(E-mail: [email protected], [email protected], [email protected], [email protected])*Corresponding Author. Tel: +609 668 4318 Fax: +609 668 3991. Email : [email protected]

ABSTRACT

Hybrid solar cells are a mixture of nanostructures of both organic and inorganic materials. They combine uniqueproperties of inorganic semiconductor nanoparticles with properties of organic or polymeric materials. Hybrid solarcells can be manufactured using different conceptssuch as bulk heterojunction concept with different nanoparticles.This study focused on the Schiff base (O-Phenylene) and perovskite (CaO-ZnO composite) as the potential solar cellmaterials. The aims are to investigate the composition, functional group of the composite and energy band gap of theO-Phenylene.CaO-ZnO composite was prepared by mechanochemical treatment of CaO and ZnO powder mixture withthe addition of deionized water necessary for the formation of corresponding mixed hydroxides. O-Phenylene is oneof the most valuable type of material that easily afford variety of useful electrical and physical properties such aselectrical conductivity mobility and power conversion efficiency. These easily synthesized materials are consideredthe future of solar cells, as their distinctive structure makes them perfect for enabling low-cost, efficient photovoltaics.Optical and electrical properties were analyzed by using Fourier Transforms Spectroscopy (FTIR), X-RayDiffractometer (XRD), Ultraviolet Visible Spectroscopy (UV-Vis) and Four Point Probe (4PP). Overall, the materialsSchiff base and metal oxide composite were suitable and had great potential for solar cell application.

Keywords:Renewable Energy, Solar cell, Perovskite, Schiff base

1. INTRODUCTION

Over the past decade, the thrust for renewable energies have came in limelight due to the world's everincreasing demand for energy. Different forms of renewable energies are recognized as alternatives fortraditional sources. Among these renewable energies, solar energy is a key technology obtained fromsunlight which includes utilization of sun energy in different manner.The current photovoltaic landscape isdominated by silicon solar cells, which have benefited from recent advances leading to reducedmanufacturing cost [1]. In any case, this develop innovation is obliged by some key cost boundaries, forexample, temperature handling.In order to achieve it, perovskite solar cell has being seen as a considerableattention because of its unique properties and potential application in solar energy. The term perovskite isused to describe a group of compounds characterized by the general formula ABX3 where A contains anorganic cation, B is a metal cation and X is a halide anion. Furthermore, the nonstoichiometry of transitionmetal oxides has been demonstrated to exert prominent effect on the performance of differentelectrochemical systems such as lithium-ion batteries and fuel cells [2]. The use of perovskite affords severaladvantages : excellent optical properties that are tuneable by managing chemical composition, ambipolarcharge transport and very long electron-hole diffusion length [3].

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2. EXPERIMENTAL METHOD

2.1 Sample PreparationPreparation of Indium Tin Oxide (ITO) Glass Substrate was suggested by N. A Nik Aziz to clean up theITO glass by using Ultrasonic bath machine [4]. O-phenylene solution were prepared by dissolving O-phenylene powder with dimethyl sulfoxide (DMSO). Preparation of CaO-ZnO composite were done byreacting 2g of Zink (II) Acetate with the purify of 98+%, A.C.S reagent (Sigma Aldrich) and 2g of CalciumAcetate monohydrate with the purify of 99% (Sigma Aldrich) which were dissolved separately in 4g ofCitric acid anhydrous (C2H6O8) with the purify of 99.5% (Alfa Aesar). The mixture of CaZn was dried for29 hours at 100°C to obtained CaZn powder. Next, the powder were burnt in the furnace with differenttemperature which were 700°C, 800°C and 900°C to obtained CaO-ZnO composite.

2.2 Sample Fabrication

The technique used to fabricate CaO-ZnO composite was spin coating. In this work, perovskite CaZnO3 wasdissolve in ethanol and spin coated onto cleaned ITO substrates at 2000 rpm for 20s.

2.3 Sample Characterization

Perkin Elmer Lambda 25 Ultraviolet-Visible spectroscopy was used to measure and observed the absorptionof light spectrum of Schiff base O-Phenylene. Functional group ofCaO-ZnOcomposite was determined byusing NICOLET 380 FT-IR Spectroscopy. RigakuMiniFlex IIX-ray diffractormeter was performed todetermine the nature of the films. These samples were scanned at 2θ angles between 10° and 80°. The Electrical conductivity was measured by using four point probe.

3. RESULTXRD patterns displayed in Figure 1 shows the pattern of CaO-ZnO composite after calcination at differenttemperatures 700°C, 800°C and 900°C.

Figure 1. XRD Patterns of CaO-ZnO Composite After Calcination at Different Temperatures, 700°C,800°C and 900°C.

The structural changes caused by the temperature were observed through FTIR spectra. Infrared studieswere carried out in order to ascertain the purity and nature of the CaO-ZnO composite

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Figure 2. Infrared (IR) Spectra of CaO-ZnO Composite

Energy band gap of Schiff Base O-Phenylenewas measured by using Ultraviolet Visible Spectroscopy (Uv-Vis).

Figure 3. Energy Band Gap of Schiff base (O-Phenylene)

The electrical conductivity of CaO-ZnO composite was analyzed using four point probe. The conductivityresults at different temperature are shown in Figure 4.

Figure 4. Graph of Electrical Conductivity of CaO-ZnO Composite with Different Intensities versusTemperature

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4. CONCLUSION

The patterns of XRD shown the crystallinity of the CaO-ZnO composite. The IR spectra shows thefunctional group of CaO-ZnO composite. The energy band gap of the O-Phenyleneis 3.5 eV based on UV-Vis spectra. The value of electrical conductivity of CaO-ZnO composite increase with increasing of lightintensity.

REFERENCES

1. Green, M.A., A. Ho-Baillie, and H.J. Snaith, The emergence of perovskite solar cells. Nature Photonics, 2014.8(7): p. 506-514.

2. Du, J., et al., Nonstoichiometric perovskite CaMnO3− δ for oxygen electrocatalysis with high activity.Inorganic chemistry, 2014. 53(17): p. 9106-9114.

3. Jeon, N.J., et al., Solvent engineering for high-performance inorganic–organic hybrid perovskite solar cells.Nature materials, 2014. 13(9): p. 897-903.

4. Aziz, N.N., M. Isa, and S. Hasiah, Electrical and Hall Effect Study of Hybrid Solar Cell. Journal of CleanEnergy Technologies, 2014. 2(4).

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TYPE OF SEDIMENT CLASSIFICATION AND BATHYMETRY MAPPINGAT THE PAYAR ISLAND

Razak Zakariya1, Mohd Azhafiz Abdullah 1*, Zainudin Bachok 2, Idham Khalil 1

1School of Marine and Environmental Science2Institute of Oceanography and Environment

Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia*Corresponding author: [email protected]

ABSTRACT

Type of sediment classification and bathymetry mapping was evaluated for area shipwreck at Payar Island. Thesediment samples were collected 31 samples. The sediment means size range between -1.18 to 7.24 Phi for shipwreck.Generally, the sediment in the study area consists mostly of silt and sand particles. The major portions of the sedimentare negative skewed. This indicates that the study area is under the influence of the rather strong wave and currents.The Multibeam bathymetry survey that was conducted the study area. The Bathymetry data is a mapping features andlocated structures below the surface of the seafloor. Bathymetric survey is an activity used to get the depth. The seabeddepth was then corrected using qimera software for tide, sound velocity profile and patch test (roll, pitch and yaw).The aim of this study was to determine the water depth (bathymetry) and analysis type of sediment surface in theshipwreck area. From seabed different depth and sediment, seabed morphology can be classified in 2 parts flat areaand steep area.

Keywords: bathymetry; Sediment; GIS

1. INTRODUCTION

Pulau Payar and other smaller island was gazetted as Marine Parks under the Marine Parks Malaysia order1989 is located in kedah state.There are various types of ecosystems and exciting aquatic life available inthe Payar Island including coral reef, artificial reefs and shipwreck.

Much research on distribution sediment (grain size) of distribution has been widely carried out bysedimentologists to classify the sedimentary environment and explain the dynamics of transporting certainareas. Sediment distribution is mainly influenced by several factors such as distance from coastline distancefrom source (rivers and estuaries) topographic source material and transport mechanism (Abuodha, 2003).

According Pethick (1984), the factors that cause sediment distribution are cyclic properties and includewinds, current, waves and tide. Sediment distribution occurs because the sediment is experiencing asignificant movement and spread between the water bases. Distribution of sediment which occurs can beclassified in the form of distribution patterns based on the size and type of sediment existing in the waters.

Addition of the information on deep sediment a layer has been gained from multibeam sonar to productbathymetry map. The advent of multibeam sonar technology in the last decade, providing 100% sea floorcoverage, has revolutionized geological mapping (Courtney & Shaw 2000)

The objective of this study is to determine the bathymetry and analysis type of sediment surface in theshipwreck area. The relationship between bathymetry and sediment for areas around the shipwreck area isspecified.

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2. STUDY AREA

The Payar Island is situated off the state of Kedah, between Pulau Langkawi and Pulau Penang figure 1shows the Payar Island. The Marine Park is commonly accessed through the ports at Kuala Kedah, PulauLangkawi and Pulau Pinang, each of which are 15, 19 and 32 nautical miles respectively (Lim, 1998). Thereis One Island within the jurisdictional area of the Marine Park (Figure 1); Pulau Payar is the largest with anarea of (2.9 km²) hectares and an approximate length of 1.6 km. The remaining Islands in order of decreasingland area are, with their areas in brackets; Pulau Lembu (0.67 km²), Pulau Segantang (0.15 km²) and PulauKaca (0.01 km²).

Figure 1: study area of Payar Island, kedah

3. MATERIAL AND METHOD

The description of methods employed in this study is divided into two major parts; the first describes thesediment sampling, and the second is based on the multibeam survey (Fig. 1). The sediment samplingcomprises both the shipwreck area. Meanwhile, for the multibeam survey, this was applied for the shipwreckof the Payar Island. Figure 1 describes the location of the sampling station for shipwreck area, respectively.The shipwreck area sampling site was located nearby Payar Island.

3.1 Sediment sampling and analysis

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The sediment samples were collected from 31 stations in the artificial reef area and shipwreck area,respectively. The point station sediment Taken close to artificial reef and shipwreck as the purpose ofcollecting the sample was to obtain the sediment distribution in the study areas, a Ponar grab was usedbecause it is compatible and easy to handle. Samples ranging from 1 to 7 cm thick were taken from thesurface layer, then were placed in plastic bags, labelled and brought back to the laboratory for analysis.

Sediment analyses were carried out to find the grain size of the sediments using the standard of dry and wetsieving techniques. For the coarse fraction, particles with a diameter greater than 63 lm were analyzed usingdry sieving techniques. Approximately 100 g of the samples were passed through a set of sieves arrangedconsecutively finer downwards. Sieving time was between 15 and 20 min per sample using a sieve shaker.For silt and clay (particles with diameters lesser than 63 lm), the laser diffraction method using a ParticleSize Analyzer (PSA) CILAS 1180 was employed to obtain the statistical value. The classification betweencoarser and finer particles was based on Went- worth’s scale (1922). Based on the data obtained, statisticalmeasures were calculated, and computation of mean size (phi), sorting (phi) and skewness was carried outusing the moment method of Folk and Ward (1957).

3.2 Multibeam survey

MBES R2 Sonic 2020 was used in this study for the collected bathymetry image and backscatter image.The average speed boat was 5 knot. This instrument produces 256 beams equiangular arrayed over an arcof 130 ° and frequency sonar was set 400 kHz. Differential global position system (DGPS) Trimble 461used was for navigation system and heading (gyro compass). The motion sensor DMS10 provided sub meterand 0.01° accuracy for heave, roll and pitch accuracy. Real time speed of sound for surface used veleportMini svs. The software Qinsy v8.0 used during survey to data logging, real time quality control, displayand navigation. The sound velocity profile (SVP) used for measured during data acquisition because tocorrect for the effect of sonar beam refraction caused by changes in water density.


Based on the statistical (mean and sorting) values, sediment populations can be related to the spatialvariation. The mean size value for the shipwreck area ranged between 3.74 and 7.24 phi (Ø), and the averagevalue was 1.12 Ø (Table 1). It was noted that the highest mean size value was obtained from station 8followed by station 20 as the second highest, whereas station 15 indicated the lowest mean size value. Themean diameter shows that sediment in the shipwreck area consists of fine, medium, coarse and very coarsesilt with more prevalence of medium silt and find silt size according to Wentworth’s classification. Most ofthe sediments were classified as medium and coarse sand, with fewer of them found to be very fine (station8). It was also noted that the grain-size was distributed unequally and showed great variation in space andtime.

Table 1: statistical parameter of the sediment in the shipwreck areaStation Mean (ø) Classification Sorting Type of sorting

1 5.67 Coarse silt 2.16 Very poorly sorted2 5.77 Coarse silt 2.15 Very poorly sorted3 6.47 Medium silt 1.90 poorly sorted4 5.54 Coarse silt 2.06 Very poorly sorted5 6.82 Medium silt 1.66 poorly sorted6 6.23 Medium silt 1.95 poorly sorted7 4.72 Very coarse silt 2.10 Very poorly sorted8 7.24 Fine silt 1.66 poorly sorted

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9 5.44 Coarse silt 2.26 Very poorly sorted10 4.81 Very coarse silt 2.24 Very poorly sorted11 6.17 Medium silt 2.03 Very poorly sorted12 6.77 Medium silt 1.65 poorly sorted13 6.22 Medium silt 2.02 Very poorly sorted14 5.91 Coarse silt 2.10 Very poorly sorted15 3.74 Very fine sand 1.87 poorly sorted16 5.03 Coarse silt 2.16 Very poorly sorted17 5.03 Coarse silt 2.24 Very poorly sorted18 5.26 coarse silt 2.14 Very poorly sorted19 5.28 Coarse silt 2.15 Very poorly sorted20 6.88 Medium silt 1.91 poorly sorted21 6.02 Medium silt 2.03 Very poorly sorted

Figure 2: sediment mean size and Sorting value shipwerck area

Transportation of sediment sorts the materials by size and by density. Sediment with material of manydifferent sizes (or densities) is known as poorly sorted, whereas sediment composed primarily of materialof very similar size (or density) is called well sorted. The process of transportation also sort andmechanically weathers material so that well sorted sediments are typically composed of well-rounded grains(abraded during transport), whereas poorly sorted sediments will have angular (sharp corners) shapes.Sediments in the shipwreck area have a range of sorting from 2.66 to 1.15 Ø. Majority of the total sampleshad a sorting between 2 and 4 Ø (very poorly sorted), while the other was poorly sorted sediment (1– 2 Ø).The lowest value was found at station 12 (1.65 Ø), while the highest value occurred at station 9 (2.26 Ø).

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Figure 3: Sediment map using interpolation of shipwreck area

Meanwhile, the geophysical study was carried out along the area shipwreck of ground truth sedimentstations in Payar Island (Fig. 4) by applying multibeam sonar technique using the R2 sonic 2020 instrument.By using this technique, the system sends out a broad swath of sound pulses, bouncing over a wide area ofseafloor the vessel. Calculated the two ways time it take sound pulse to reach the seafloor, bounce off, andreturn of the sound pulse, the system determined the depth of water beneath the vessel.

The maximum depth at shipwreck area reached -30 m, and the minimum was -0.5m (Fig. 4). Most of thereservoir depth was ranging from-21 to -23 m. the cross section from point a until b Can be summarizedthat the close island shallow and slope. This is because the cross section distance 1 meter to 100 meters thedepth increased from -9 to -23 meters and the middle area is more horizontal.

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Figure 4: Bathymetry map and Cross section of shipwreck area

CONCLUSION

Based on data obtained type sediment in the study area is a very fine sand, coarse silt, medium silt, fine siltand very fine silt. The majority of sediment is coarse silt and very poorly sorted values by the type of sorting.The distribution pattern is occurs in parallel shoreline where the pattern of the distribution of the moresmooth the grain size of sediment increasingly heading to the open sea. Based on bathymetry data in theshipwreck area the dominated by depth is a -20 to -23 meter. The shape of the seafloor can be explained tothe two is a flat area and steep area by this study area. The result hoped that this survey will provide apreliminary study in order to map the sediment and their seabed properties. These data also canassist other researchers and other agencies in accessing the impacts of sediment runoff into thecoastal area, along with human and environmental factors

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REFERENCES

1. Abuodha, J. O. Z. (2003). Grain size distribution and composition of modern dune and beach sediments,Malindi Bay coast , Kenya, 36, 41–54. http://doi.org/10.1016/S0899-5362(03)00016-2

2. Courtney RC, Shaw J (2000) Multibeam bathymetry and backscatter imaging of the Canadian continentalshelf. Geosci Can 27:31–42

3. Folk RL, Ward WC (1957) Brazos river bar: a study on significance of grain size parameters. J SedimentPetrol 31:514–529 Guelorget

4. K.R. Dyer. Wiley, Chichester (1986) Coastal and estuarine sediment dynamics5. Lim, L.C., 1998, Carrying Capacity Assessment of Pulau Payar Marine Park, Malaysia - Bay of Bengal

Programme. BOBP/REP/79. Bay of Bengal Programme, Madras, India.6. Wentworth CK (1922) a scale of grade and class terms for clastic sediments. Geol J 30:377–392

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WATER SURFACE PLATFORM FOR INTERNET-BASEDENVIRONMENTAL MONITORING SYSTEM

Nurliyana Kafli1, Khalid Isa*1, 2 Embedded Computing System Research Group (EmbCoS), Faculty of Electrical and Electronic Engineering,

Universiti Tun Hussein Onn Malaysia, MALAYSIA.(E-mail: [email protected], [email protected])

ABSTRACTThis project presents a water surface platform to monitor the environment at the rivers such as air quality and waterquality by using the Internet. This project consists of several sensors that attached to the platform; temperature andhumidity sensor, carbon monoxide sensor, Global Positioning System (GPS) sensor, pH sensor, and water level sensorthat act as an input. This paper only focuses on the input part. Through this input, the platform collects the data todetermine the quality of air and water as well as to detect the water level of the river for flood warning to the users.All the sensors data are saved into an onboard data logger and sent to the Internet clouds. In order to get an accuratedata of the environment, the data are collected three times a day. Besides that, the data also can be monitored in thereal-time through Internet by the users either on their mobile phone or personal computer by using the Internet cloud.The platform operates on the surface of the river for long periods of time in diverse sensing environment. The long-term goal of this research is to ensure the users can monitor the changes happen at the site without needed to be at thesite themselves.

Keywords: Water surface platform, air quality, water quality, flood detection.

1.INTRODUCTION

This paper presents a water surface platform to monitor the environment at the river. The platform is placedon the surface of the river and left for several days to collect the data of temperature, humidity, carbonmonoxide and pH of the surrounding environment as well as air and water quality. In addition, the platformdetects the water level of the river for flood warning system. Several sensors are attached to the platform asthe input part of the project. Temperature and humidity sensor senses the surrounding temperature. On theother hand, pH sensor obtains the acidity of the water. All the recorded data from each sensor are saved intothe data logger. Furthermore, the water level sensor is used to measure the water level of the river, and thedetected value is used for flood warning. According to [1], 98 percent of the total water started from therivers and needed to be deal with the current conditions of the river. Hence, the pollution control needs tofocused on monitoring the quality of the river water. In this project, the ARM microcontroller is used as themain controller that recorded all the sensors data and sent it to the Internet could. Then, all the recorded dataand analysis can be viewed by the users in a real-time manner through Internet. Thus, it is called Internet ofThings (IoT). Through numerous technology advances, society is moving towards an “always connected”paradigm [2]. GSM module also helps to connect both system and human through Short Message Service(SMS).

The main objective of this project is to develop a water surface platform for the Internet-basedenvironmental monitoring system. Furthermore, to design and develop a durable and reliable prototype of awater surface platform to monitor air quality and water quality of a river as well as the water level of theriver. Finally, to evaluate the effectiveness of the platform and the collected data.

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2. EXPERIMENTAL METHOD

Figure 1 shows the overall planning for this project. The process starts with the input part. Several sensorsare attached to the platform; temperature and humidity sensor, carbon monoxide sensor, GPS module, pHsensor and water level sensor. Temperature and humidity sensor senses the temperature of the environmentof the river to determine the temperature is high or low. It is a critical water quality parameter since itdirectly influences the amount of dissolved oxygen (DO). Carbon monoxide sensor is used to analyse thecarbon monoxide in the air at the surrounding of the river. Based on Malaysia Ambient Air Quality Standard,the standard value for carbon monoxide is 10.0 µg/m3 for 8-hour averaging time. While GPS module isused to locate the platform and inform the users about the current position of the river that has been tested.pH sensor measures the water pH value of the river to verify the acidity of the water. pH is a measure of theconcentration of hydrogen ions in water. The value collected indicates the acidity of the river water eitherit is safe to use or not. Water level sensor is used to detect the water level of the river to alert about theflood. A message will be sent to the user to warn if the water level exceeds the benchmark flood. Figure 2shows the platform that has been sketched by using Google SketchUp. All the sensors are placed on theround platform that attached with the bobber floater. On the other hand, Figure 3 shows the first prototypethat has been developed.

Figure 6. System design of the project

Figure 7. Top and side view of the sketched platform

Temperature and

humidity sensorWater level sensor

Carbon monoxide sensor

Bobber floater

ARM microcontrollerGPS module

GSM module

pH sensor

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Figure 8. First prototype that has been developed

3.RESULTS AND DISCUSSION

İn general, there are some standard ways of measuring water quality that was set by Department of Environment Malaysia (DOE). Several parameters were measured as a standard to determine water qualityusing a Water Quality Index (WQI) [3]. The sensors were tested to analyse the input part. The sensors weresuccessfully tested by using ARM microcontroller (LPC 1768). Temperature and humidity sensor was ableto senses the temperature and humidity value near the river. Besides that, pH sensor also was able to detectthe acidity of the water river. According to National Water Quality Standards (NWQS), several parametersneeded to classify the class of water pollution [4]. For this project, Class IV which is for irrigation waschosen. Carbon monoxide sensor was able to operate, but the results are still in progress to collect the exactamount of the carbon monoxide. New Ambient Air Quality Standards has set the minimum value for carbonmonoxide that can be released into the air to determine the quality of the air [5]. Based on Malaysia AmbientAir Quality Standard, the standard value for carbon monoxide is 10.0 µg/m3 for 8-hour averaging time.GPS module was successfully operated by giving the position of the platform in latitude and longitudecoordinate. İn addition, GSM module was able to access real-time data by viewing the collected data on the cloud. Water level sensor is still in progress for monitoring the water level of the river. All the collecteddata is saved in the data logger and cloud for the future used. Hence, the input part was done by connectingto the ARM microcontroller.

CONCLUSION

As a conclusion, this paper was focusing on the sensor part which is input part for monitoring the water andair quality. Then, the collected data is used to monitor the river environment in real-time by theauthorities. The data can be viewed either on the mobile phone or personal computer. These process will beapplied to the next step for future works.

REFERENCES

1.Huang, Y.F., Ang, S.Y., Lee, K.M. and Lee, T.S., 2015. Quality of Water Resources in Malaysia.2.L. Coetzee and J. Eksteen, “The Internet of Things – Promise for the Future? An Introduction,” Conf. Proc.,pp. 978–1, 2011.3.Department of Environment Malaysia, “Development of Water Quality Criteria and Standards for Malaysia”,1985.4.“Benchmarking River Water Quality in Malaysia,” no. February, pp. 12–15, 2010.5.“Table 1 : New Malaysia Ambient Air Quality Standard.” p. 1989, 2015.

Bobber floater

Round container

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ZINC OXIDE AND NATURAL ANTHOCYANIN DYE FROM REDFRANGIPANI FLOWER AS THE ACTIVE MATERIALS FOR THIN

FILMS

Wan Almaz Dhafina Che Wan Ahmad1, Hasiah Salleh2* and Mohd Zalani Daud3

1, 3School of Ocean Engineering, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,Terengganu,Malaysia

(E-mail: [email protected], [email protected])2Centre for Fundamental and Liberal Education, Universiti Malaysia Terengganu, 21030 Kuala Terengganu,

Terengganu, Malaysia(E-mail: [email protected])

ABSTRACT

Dye-sensitized solar cell is a third generation of solar cell that uses dye or sensitizer as photon absorbent andtransfer electron in light harvesting process to produce electricity. Dye-sensitized solar cell is divided into threeparts which are photoanode, electrolyte and cathode. In this work, zinc oxide nanorods and red frangipani dyewere employed as active materials in the thin film for photoanode. Zinc oxide nanorods was synthesized viahydrothermal method and after that the red frangipani dye were sensitized on to zinc oxide nanorods layer bydipping it in the dye solution. In characterization part, the morphology of zinc oxide nanorods were observedthrough scanning electron microscope (SEM) and crystal structure of the grown zinc oxide nanorods wereinvestigated by employing x-ray diffraction method. Meanwhile the optical characteristics of the photoanode werecarried out by employing Uv-Vis spectroscopy and the energy level of red frangipani dye were studied usingcyclic-voltammetry method. The grown zinc oxide nanorods has (002) diffraction peak as dominant peak whichindicates that it grown perpendicular to indium tin oxide glass substrate which is good because it provides directpassageway for electrons moving into anode. From Uv-Vis spectroscopy study, the band gap of red frangipani dyeis 2.29eV and such value is acceptable to be applied as sensitizer in natural based dye dye-sensitized solar cellbecause its only require low energy of photon to excite an electron. Therefore it is proposed that the zinc oxidenanorods/red frangipani thin film as a feasible and reasonable photoanode for dye-sensitized solar cell accordingto the requirement of the efficiency of employed zinc oxide nanorods and zinc oxide thin films at the same time.

Keywords: zinc oxide nanorods, red frangipani dye, dye-sensitized solar cell, photoanode

1. INTRODUCTION

Dye sensitized solar cell (DSSC) is a new generation solar cell that uses the same concept withphotochemical cell which depends on the photon from sunlight to make chemical reaction and generateenergy. DSSC was invented by Gratzel’s group which its architecture consist of nanoparticle of titania,ruthenium based dye and iodide species electrolyte [1]. Until now N3 dye shows the constant highefficiency compared to the other kind of dyes. However the ruthenium-based dye is expensive due tothe lack of ruthenium resource [2], complex synthetic route and requirement of purification. There aretwo alternative dyes that are low cost and rather environmental friendly which are organic and naturaldyes. Organic dye-based DSSC shows rather high efficiency but still below than ruthenium-based one,unfortunately the synthetic route is also very complicated and the yield is very low. In other hand, naturaldye can be produced by simple extraction method on parts of plant [3] (i.e. flower, leaf, tuber and fruits)to extract pigment (i.e. chlorophyll, betacyanin, cucumin, caratenoide, anthocyanin etc.) [4]. Typically,metal oxide of choice for DSSC is titania, but several studies has attempted to use zinc oxide (ZnO) asan alternative to the titania motivated by their lower in cost, rich family of nanostructures [5] (i.e.nanoparticles, nanowires, nanorods, nanobelts, nanotetrapods, nanoflowers, nanowhiskers etc which isinteresting to be applied in DSSC because of the large surface area due to high aspect ratio). Besides,ZnO also has higher electron mobilities, higher conduction band energy level that prompt the better

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electron injection resulting better efficiency. ZnO is the first material used in experimental design toproof the irreversible electron injection from organic molecules into the conduction band of metal oxide[6]. In this paper, we discussed about the combination of ZnO nanorods (NRs) and natural dye from redfrangipani flower as the active materials for photoanode of DSSC. To the best of our knowledge, thereis no research has been done to investigate these two material combination that potentially to be appliedin natural dye-based DSSC.

2. EXPERIMENT METHOD

2.1 Synthesis of ZnO nanorods

The ZnO NR arrays were grown on indium tin oxide (ITO) substrate via hydrothermal method [9].

2.2 Dye sensitizing on ZnO nanorods

The ZnO NRs were sensitized by soaking it in red frangipani dye (RFD) solution in dark environment.

2.3 Characterization

The ZnO NRs/RFD thin film were characterized in several aspects. Uv-Vis spectrometer (ModelLambda 25 with UV Winlab V2.85 software, Perkim Elmer) was employed to characterize opticalproperties of ZnO and RFD, the functional group of RFD were investigated via Fourier transforminfrared (FTIR), the structural characterization on ZnO were carried via x-ray diffraction (XRD, RigakuMiniflex II, desktop) while the surface morphology of ZnO were viewed via scanning electronmicroscope (SEM, Jeoul JSM-6360 LA).


3.1 Uv-Vis absorption of RFD and ZnO NR

The optical absorption of ZnO NR increase when sensitized with RFD (Figure 1(b)). Through the Uv-Vis plot, the absorption peak of RFD was higher than that of ZnO NR, however after the ZnO NR wassensitized with dye the absorption peak higher than absorption peak of dye but still maintained the Uv-Vis pattern of ZnO, therefore the band gap of the sample was still the same to that of ZnO NR. Thissuggested that RFD molecule have absorbed into ZnO and have interaction with ZnO NR’s surface bychelating hydroxyl functional group of RFD with Zn(II) sites. In this combination of ZnO NR and RFDthin film photoanode, ZnO NR served as stable elongated structures with desirable exciton dissociationand charge transport [7] while RFD served as additional absorbent of incident photons that also partiallyabsorbed by ZnO NR. Apparently RFD contributed in enhancing the absorption of photon from lightsince with the combination of these two materials in photoanode more range of light spectrum can beharvested.

82

(a) (b)

400 500 600 700 800 900

0.0

0.1

0.2

0.3

0.4

0.5

Maximum peak: 541.25, 0.40878

Abs.

(a.u

.)

Wavelength (nm)

300 400 500 600 700 800

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Abs.

(a.u

.)

Wavelength (nm)

ZnO NRZnO NR+RFD

Figure 1. Uv-Vis spectra of (a) RFD and (b) ZnO NR and sensitized ZnO NR

3.2 Elecrical conductivity of ZnO/RFD thin film

The conductivity of sensitized ZnO NRs had higher conductivity value. This suggested that RFDenhance the absorption of photon from light and assisted in light harvesting process which meant moreexciton dissociated and more electron injection and collected.

0 50 100 150 2000.10

0.15

0.20

0.25

0.30

0.35

0.40 ZnO NRZnO NR+RFD(2h)ZnO NR+RFD(4h)

Con

ductivity

(S/m

)

Light intensity (W/m2)

Figure 2. Electrical conductivity comparison between plain ZnO NRs and ZnO NRs that has beensensitized with RFD in two different duration

CONCLUSION

In summary, ZnO NR and RFD thin film were successfully fabricated on ITO substrate as activematerials for thin film photoanode part of DSSC. ZnO NRs were grown via low-cost and robust methodof hydrothermal and acted as electron collector from oxidized RFD. By sensitizing ZnO NRs with RFD,wider range of electromagnetic wavelength was absorbed and the absorption intensity was enhanced.The adsorbed dye into the ZnO NR arrays interacted with ZnO NR’s surface by chelating hydroxylfunctional group of RFD with Zn(II) sites. Furthermore, the electrical conductivity of the sensitized ZnONRs was higher than the sample of ZnO NRs alone. This proofed that RFD enhanced the absorption ofphoton and assisted in light harvesting process which lead to more exciton dissociated and more electroninjection and collected.

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Penerbit UMT,Aras 2, Bangunan Canselori,Universiti Malaysia Terengganu,21030 Kuala Nerus, Terengganu

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