international journal of engineering technology...

INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS)

The International Journal of Engineering Technology and Sciences is an open access peer-reviewed international

journal that welcomes global submissions. Authors are encouraged to submit articles for the dissemination of knowledge on topics relevant to Engineering Technology and Safety and Health Sciences

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International Editorial Board

Professor Dr.Tetsuro MIMURA

Kobe University, Japan Associate Professor Dr.Omar Ghrayed

Northern Illinois University, USA Professor Dr. K. Prasad Rao University of Utah, Salt Lake City, USA.

Proessor Dr. Cliff Mirman Northern Illinois University, USA

Associate Professor Dr. A. K. M. Sadrul Islam Islamic University of Technology (IUT), Bangladesh Professor Dr. Kim Choon Ng

National University of Singapore, Singapore Professor Dr. Fereidoon P. Sioshansi Walnut Creek CA, USA

Professor Dr. Wan Mansor Wan Muhamad University Kuala Lumpur, Bangi, Malaysia

Professor Dr. Shamsuddin Bin Sulaiman University Putra Malaysia, Serdang, Malaysia Professor Dr. V. Vasudeva Rao

University of South Africa (UNISA), South Africa Associate Professor Dr. A. Kumaraswamy Defence Institute of Advanced Technology (DIAT), India

Professor Dr. T. Srinivasulu Kakatiya University, India

Professor Dr. P.Padhamanabham, JNTU, India Professor Dr. G. V. Rao

SNIST, India

Assistant Professor Kevin B Martin Northern Illinois University, USA

Professor Promod Vohra Northern Illinois University, USA

Advisor

Professor Dato’ Dr.DaingNasir Ibrahim

Vice Chancellor, Universiti Malaysia Pahang, Kuantan,

Malaysia

Editor-in-Chief Professor Dr. Zularisam Bin Abd Wahid Dean, Faculty of Engineering Technology, University

Malaysia Pahang, Kuantan, Malaysia Managing Editor Dr. Ramaraju Ramgopal Varma

Senior Lecturer Faculty of Engineering Technology, Universiti Malaysia Pahang, Kuantan, Malaysia Editors Professor Dr. Mimi Sakinah Binti Abdul Munaim Faculty of Engineering Technology, University Malaysia

Pahang, Kuantan, Malaysia Dr. Muhamad Arifpin Mansor Senior Lecturer

Faculty of Engineering Technology, Universiti Malaysia Pahang, Kuantan, Malaysia Associate Professor Dr.Che Ku Mohammad Faizal Che Ku

Yahya Faculty of Engineering Technology, Universiti Malaysia Pahang, Kuantan, Malaysia

Dr. Hadi Manap Senior Lecturer Faculty of Engineering Technology, Universiti Malaysia Pahang, Kuantan, Malaysia

Dr. Norazura Binti Ismail Senior Lecturer Faculty of Engineering Technology, Universiti Malaysia

Pahang, Kuantan, Malaysia

Assistant Editors Dr.Lakhveer Singh Senior Lecturer Faculty of Engineering Technology, University Malaysia

Pahang, Kuantan, Malaysia Dr.Azrina Abd Aziz Senior Lecturer

Faculty of Engineering Technology, Universiti Malaysia Pahang, Kuantan, Malaysia

1. Effect of Temperature on Diffusivity of Monoethanolamine (MEA) on Absorption

Process for CO2 Capture

E. E. Masiren, N. Harun, W. H. W. Ibrahim; F. Adam 1

2. Design of Selectable Modems for MC-CDMA Based on Software Defined Radio

Ali Kareem Naha and Yusnita Rahayu 7

3. Simulation and Fabrication of Open-Type Boiler of Fish Cracker Production Line Mohd Zaidi Sidek, Mohd Syahidan Kamarudin, Mohamad Nafis Jamaluddin 15

4. Photocatalytic conversion of CO2 into methanol: Significant enhancement of the

methanol yield over Bi2S3/CdS photocatalyst M. Rahim Uddin, Maksudur R. Khan, M. Wasikur Rahman, Abu Yousuf, Chin Kui Cheng 23

5. A New Neuron Ion Channel Model with Noisy Input Current

Ahmed Mahmood Khudhur, Ahmed N Abdalla 29

6. An Analysis of Beliefs among UMP International Students towards English Oral

Presentation “Pilot Study”

Abdelmadjid Benraghda, Zuraina Binti Ali, Noor Raha Mohd Radzuan. 36

7. Computational Fluid Dynamics study of Heat transfer enhancement in a circular tube

using nanofluid.

Abdolbaqi Mohammed Khdher, Wan Azmi Wan Hamzah, Rizalman Mamat 40

8. Development of Solar Oven Incorporating Thermal Energy Storage Application

Roziah Binti Zailan, Amir Bin Abdul Razak, Firdaus Bin Mohamad 47

9. Examination of selected Synthesis parameters for composite adhesive-type Urea-

Formaldehyde/activated carbon adhesives

Tanveer Ahmed Khan, Arun Gupta , S. S. Jamari, Rajan Jose 52

10. Maximum system loadability based on optimal multi facts location

Maher A. Kadim and Yahya N. Abdalla 59

11. The effect of filler ER4043 and ER5356 on weld metal structure of 6061 aluminium

alloy by Metal Inert Gas (MIG) Mahadzir Ishak, Nur Fakhriah Mohd Noordin, Ahmad Syazwan Kamil Razali

, Luqman Hakim

Ahmad Shah 66

12. Effect of strong base during co-digestion of petrochemical wastewater and cow

dung

Md. Nurul Islam SIddique and A.W. Zularisam 74

http://www.sciencedirect.com/science/article/pii/S0016236113011526


INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015

Effect of Temperature on Diffusivity of Monoethanolamine (MEA) on

Absorption Process for CO2 Capture

E. E. Masiren

Faculty of Chemical and Natural Resources Engineering,

Universiti Malaysia Pahang

Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang,

Malaysia

W. H. W. Ibrahim Faculty of Chemical and Natural Resources Engineering,



Malaysia

N. Harun Faculty of Chemical and Natural Resources Engineering,



Malaysia

[email protected]

F. Adam Faculty of Chemical and Natural Resources Engineering,



Malaysia

Abstract— Diffusion coefficient study gains an

interest to know the mass transfer properties of

molecules especially in study of the absorption process.

The main objective of this study is to investigate the

effect of temperature on diffusivity of MEA absorption

process for CO2 capture. Three different values of

process temperature were chosen for simulation in this

study, 25oC, 40oC and 45oC. The MD simulation was

carried out at NVE (200ps) and NPT (1ns) ensemble in

Material Studio 7.0 software. Mean Square

Displacement (MSD) analysis was done to compute the

self-diffusion coefficient of molecules in tertiary system

(MEA+H2O+CO2). The results show that the rate of the

diffusion coefficient is increased as temperature

increased. Diffusion coefficient at 45oC is the highest

compared to others temperature. MD simulation is used

to study details about absorption process and capture

CO2 acid gases. The simulation diffusivity result

obtained from this work shows higher compared with

theoretical results.

Index Terms— Molecular Dynamic, Simulation,

Amine Absorption Process, Monoethanolamine,

Carbon Dioxide, Mean Square Displacement

I. INTRODUCTION

Recently, the increment of CO2 composition in air

will be contributing the increment of the global

temperature. IPCC (Intergovernmental Panel on

Climate Change) is an international agency, whose

plays a role in preparing a report about the global

climate change [1]. IPCC is one of agencies in this

world which extensively conduct the research to

overcome the global climate change problem. At

present, there are many CO2 capturing chemical

materials are known such as amine-solvent, organic

molecular cages, ionic liquids, metal-organic

framework, zeolite and carbon-materials [2]. In this

study, amine-solvent is used to determine the

diffusivity of CO2 in amine-based absorption

process. Absorption process is a process in the

scope of post-combustion capture process. This

technology has been established over the year since

1930’s [3]. MEA, monoethanolamine (C2H7NO) is

a primary amine. Which has been used as solvent in

CO2 capture due to higher performance in the

absorption process [4].

Molecular Dynamic (MD) simulation was run on

molecules species to calculate the diffusion

coefficient. This computational method has become

an effective tool to explore more deeply about

absorption process and also CO2 capture. MD

simulation used to study the molecular properties

such as the diffusion coefficient [5]. This

computation technique can also study others

thermodynamic condition at atomic level that

cannot be study by doing experimental. Moreover,

this technique has many advantages compare to

chemical experimental study such as environmental

friendly and money saving [6]. In CO2 capture cost

process, more than half is distributed to the

absorbent regeneration part [7]. Study on the

thermodynamic properties is essential before

operating the absorption process in pilot plant. In

addition, cost for experimental research to study the

diffusion coefficient is high particularly at

operating condition of higher pressure and

temperature [8]. Besides that, the equipment used

for study diffusion coefficient of solute in liquid

solvent in low concentration is expensive. MD

simulation also is an option to study the diffusion

coefficient of solute in supercritical fluid because

it’s difficult to run by experimental [9]. Therefore,

the computation measurement study offers better

approach to do the research on diffusivity.


Factors that affected the diffusion are concentration

gradient, pressure gradient and temperature

gradient [10]. Diffusion coefficient, density and

viscosity are used to calculate the mass transport

properties of molecule in a system [11]. Diffusion

coefficient is usually seen as DAB in equation which

represent as the flux of a diffusing component A

and B with unit m2/s. In MD simulation, mean

square displacement (MSD) analysis or the mean

square of the distance molecule move is used

calculate the rate of diffusion coefficient. The

theories which can be used to study MSD analysis

are the Fick’s laws, Einstein-Smoluchowski theory

[5], [12] and the Maxwell-Stefan theory [13]. In

MD simulation, the Maxwell-Stefan theory will be

used in MSD analysis. This theoretical can be used

to calculate the diffusion of mixture system [14].

Besides MSD analysis, velocity auto-correlation

function analysis also used to study transport

diffusion system [12].

The literature study on diffusion coefficient by

using MD simulation is very limited in open

literature. Result diffusion coefficients of MEA,

CO2 and H2O in various systems were reported in

the literature such as study diffusion coefficient of

H2, H2O and CO in various n-alkanes by using MD

simulation [15], calculate diffusion coefficient of

pure water by MD simulation [16], calculate

diffusion coefficient of MEA, DEA, MDEA and

DIPA [17] by experimental, calculate diffusion

coefficient of PZ and MDEA by Tylor dispersion

method experiment [18].

The temperature and pressure as operating

conditions in actual absorption process in pilot

plant are in ranges 38oC-60

oC and 1 bar,

respectively [19]. The aim of this paper is to

discuss the effect of temperature on diffusivity of

CO2 and MEA in MEA solution during absorption

process.

II. . SIMULATION METHODOLOGY

MD simulation was done by using software of

Material Studio (version 7.0) which installed on HP

Z420 workstation. This software is licensed

software manufacture by Acceryls (San Diego,

USA) [20]. The MD simulation was started with

replicate the structure of single molecule. The

structure of molecules is obtained from Royal

Society of Chemistry database [21]. There are three

phases in running MD simulation which are the

relaxing phase, the equilibrium phase and the

sampling phase [5]. Geometry optimization step

were carried out on each of the molecules to ensure

the stable molecular geometry to be used in further

simulation steps. The default algorithm used is the

Smart Algorithm and Fine convergence level. The

simulation boxes are developed using the

amorphous cell calculation model in Material

Studio software. Type of forcefield used is

COMPASS and summation method used is Ewald

to calculate electrostatic energy or electrical

interaction [22]. Once the simulation box is

developed, this model is simulated for box energy

minimization. The simulation is started with

equilibration of the system under constant number

moles, volume and energy (NVE) ensemble for 200

ps with random initial velocities. The simulation

process is continued in dynamic mode under

constant number of moles, pressure and

temperature (NPT), isothermal-isobaric ensemble

for 1ns. Within this time step, the integration of

equation algorithm is going through. The time step

choose to be used is 1 fs. 1 fs time step is reported

to enough for ensure the molecules in amorphous

cell box does not overlapping [22]. Pressure is kept

constant at 1 atm to achieve an equilibrium density.

The simulation box consists of 300 molecules of

MEA, 300 molecules of CO2 and 1000 molecules

of H2O. Fig 1 shows the molecular structure of

molecules CO2, H2O and MEA. Table 1 shows the

simulation parameters to represent MEA absorption

process. This study is an the extension of previous

study [23]. This model is simulated at three

different temperature, 25oC, 40

oC and 45

oC.

(a)

(b)

(c)

Fig. 1: Schematic labelling of molecules (a),

Monoethanolamine (b) Water (c) Carbon

Dioxide

The Einstein relation is used to calculate MSD in

MD simulation. Equation 1 shows equation to

determine coefficient of molecular diffusion, D in

MD simulation [13]. The slope of MSD graph is


diffusion coefficient value, D. The value have

divide with 6 as the system is in 3-dimensional

system and do conversional unit (Å2/ps to m

2/s) as

shown in equation 1.

Di,self =1

6Nilimm.δt

1

m.δt∑ < (rl,i(t + m. δt) −Ni

l=1

rl,i(t) >2 (1)

Ni = the number of the molecules of component i

δt = the time step used in the simulation

m = the total number of the time steps

rl,i(t) = the position of the lth molecule of

component I at time t

The Stokes-Einstein relation also can be used in

calculation of MSD. Equation 2 shows the Einstein

equation over the time interval [24]:

6Dt =< |r(t) − r(0)|2 >= 𝑀𝑆𝐷(𝑡) (2)

Where r(t) = [x(t), y(t), z(t)] show the

coordinates atoms at time t. Equation 3 is used to

calculate the diffusion coefficient as a function of

MSD results [25].

D =1

6

d

dtMSD(t) = constant (3)

III. RESULT AND DISCUSSION

MD simulation is used to calculate CO2 and MEA

diffusivity in MEA solution [13]. MEA is classified

as bases with the presence of nitrogen atom which

has unshared electron pair. It is consists of

hydroxyl group which help for the solubility in

water and amino group is used to assist the

alkalinity in water to absorb the acid gas [26].

Table 2 shows the diffusion result in simulation. As

shown in table 2, as temperature increased, the

value of diffusion coefficient also increased.

Molecule is colliding with each other in periodic

boundary result the repeat motion which called as

diffusion. When give a heat, the atom will be

vibrational motion and collide with other neighbour

atom [27].Moreover, increasing in temperature

condition will be affect the rate constant of reaction

[28]. The diffusion coefficient of CO2 is higher

than MEA and H2O as depicted in Table 2. The

molecular mass of MEA, CO2 and H2O are 61, 44

and 18, respectively. As the molecular mass (size

of the molecule) increase, the diffusion rate will be

slower.

In this simulation, MEA solution is selected as

chemical solvent were used to absorb CO2 gas. CO2

gas will diffuse from gas to liquid phase then

dissolve in liquid phase to do interaction and

reaction [29].

Table 2: Diffusion result for simulation

Simulation

Components/ Temperature 45oC 40

oC 25

oC

CO2 (m²/s) 9.0897E-09 8.6780E-09 7.2200E-09

H2O (m²/s) 6.6975E-09 6.6600E-09 5.8200E-09

MEA (m²/s) 5.2770E-09 5.3830E-09 4.6400E-09

Fig 2 to 4 shows the plot of the MSD versus time at

temperature 45oC, 40

oC and 25

oC, respectively.

These graph used to calculate diffusion coefficient

have a straight line with a constant slope. The slope

was the MSD value increase linearly with time.

From these graphs, can be seen that the slope were

increased with the increased of temperature. The

results obtain from this work show some different

with the theoretical calculation.

Fig. 2: Prediction of diffusion coefficient graph for MEA, H2O and CO2 at 45

oC



oC


oC

COMPARE WITH MATHEMATICAL EQUATION

In order to validate the accuracy of simulation

result, theoretical calculation is done. Two type of

theoretical calculation is used which are Wilke-

Chang equation [30] and based on Versteeg and

van Swaaij (1988) study [17]. Versteeg and van

Swaaij (1988) study is used because related to CO2

diffusion in amine solution.

Theoretical calculation of diffusivities in liquids

(Wilke-Chang equation)

The Wilke-Chang equation is used to calculate the

diffusivity of MEA and H2O [30]. Equation 4

shows the Wilke-Chang equation.

𝐷𝐴𝐵 = 1.173 × 10−16(∅𝑀𝐴)1/2𝑇

𝜇𝐵𝑉𝐴0.6 (4)

MA = the molecular weight of solvent B

𝜇𝐵 = the viscosity of B

VA = the solute molar volume at the boiling point

= the association parameter for solvent, 2.6 for

water

T = the temperature of system

Theoretical calculation of diffusion CO2 in MEA

aqueous from Versteeg and van Swaaij (1988)

The diffusion of CO2 in MEA aqueous can be

calculate by using the N2O analogy in literature

[17] [31]. Equations 5 to 9 are shows the way how

CO2 diffusivity calculated on aqueous MEA amine.

𝐷𝐶𝑂2= 𝐷𝑁2𝑂 (

𝐷𝐶𝑂2

𝐷𝑁2𝑂

) 𝑖𝑛 𝑤𝑎𝑡𝑒𝑟 (5)

𝐷𝐶𝑂2(𝑚2. 𝑠−1) = 2.35

× 10−6𝑒𝑥𝑝 {−2119

𝑇(𝐾)}

(6)

𝐷𝑁2𝑂(𝑚2. 𝑠−1) = 5.07

× 10−6𝑒𝑥𝑝 {−2371

𝑇(𝐾)}

(7)

𝐷𝑁2𝑂

= (5.07 + 0.865𝐶𝑀𝐸𝐴 + 0.278𝐶𝑀𝐸𝐴2 )

× 10−6exp (−2371 − 93.4𝐶𝑀𝐸𝐴

𝑇(𝐾))

(8)

𝐶𝑀𝐸𝐴 =10𝐶%𝑤/𝑤𝑑

𝑀𝑤

(9)

Table 3 shows the comparison of simulation results

with theoretical results. Based on this table, the

value of exponent (E-09) is same for all results. But


when compared to diffusivity value, the simulation

result is quite larger compared to theoretical results.

The simulation result obtained from this work

shows bigger than theoretical result. The reasons

are these two systems were different in number of

molecules, operating conditions and simulation

approximation (force field, summation method,

ensemble, algorithm etc.) as previous simulation

work. In this simulation work, it applied the

molecular mechanics principle. Its means, MD

simulation consider the physical interaction and

without chemical interaction such in quantum

mechanics and experimental study. Furthermore, a

reliable result of diffusion coefficient of solutes in

solution can be obtained if longer MD simulation

need to run expect up to 3 ns of NPT ensemble [5],

2 ns of NVT ensemble [22] and 30 ns of NVT

ensemble [15]. [25] Also literature shows the

calculation of diffusion coefficient by using

numerical computation need to run over a long time

periods and/or used large ensemble size for

statistical reasons. However, this present simulation

work only can be done NPT ensemble part until 1

ns due to limited time and lower performance of

computer processor used. Different type of

ensemble for production phase contribute to

different result of diffusion coefficient. [12], [22],

[9] proposed the simulation procedure by using the

canonical equilibrium ensemble (NVT) and [25]

used PVT ensemble (the temperature-pressure-

volume) in order to compute the diffusion

coefficient.

Table 3: Comparison simulation results with theoretical results

Simulation

Components/ Temperature 45oC 40

oC 25

oC

CO2 (m²/s) 9.0897E-09 8.6780E-09 7.2200E-09

H2O (m²/s) 6.6975E-09 6.6600E-09 5.8200E-09

MEA (m²/s) 5.2770E-09 5.3830E-09 4.6400E-09

Theoretical (used Wilke-Chang equation)

CO2 (m²/s) in water 3.3893E-09 3.1661E-09 1.1034E-09

H2O (m²/s) in water 5.1372E-09 4.7989E-09 3.3537E-09

MEA (m²/s) in water 1.9655E-09 1.8360E-09 1.2831E-09

Theoretical (used Versteeg and van Swaaij (1988))

CO2 (m²/s) in water 3.0001E-09 2.6972E-09 1.9183E-09

H2O (m²/s) in water 2.9303E-09 2.6013E-09 1.7766E-09

MEA (m²/s) in aqueous MEA 2.2954E-09 2.0577E-09 1.4467E-09

CO2 (m²/s) in aqueous MEA 2.4002E-09 2.1181E-09 1.4214E-09

IV. CONCLUSIONS

Mean square displacement calculation is used to

calculate the diffusivity of molecules in MEA

solution. The rate of the diffusion coefficient is

increased as temperature increased. Rate of

diffusion coefficient at 45oC is the highest

compared to 40 o

C and 25 o

C. The diffusion

coefficient of CO2 is larger than H2O and MEA in

liquid state due to small molecular weight. Even

though the values of diffusion coefficient of this

simulation work are higher than experimental and

theoretical data, the trend follows the theoretical

with CO2 has the highest diffusion coefficient.

While, it is good effort to study the diffusion

coefficient by MD simulation. The main reason of

different diffusivity value is probably due to MD

simulation used molecular mechanic principle that

may ignore some factors for cheaper calculation.

MD simulation basically involved the physical

interaction and based on molecular mechanic

principle. Besides that, this simulation is run in

condition of 1 ns NPT ensemble and on lower

performance of computer processor. Further study

need to carry out in order to study deeply about

CO2 absorption in MEA solution.

V. ACKNOWLEDGMENTS

The author would like to give appreciation to

University Malaysia Pahang and the Accelrys Asia

Pacific for fully cooperate to complete this work. In

addition, for financial support from the Higher

Education Ministry of Malaysia through

fundamental research grant scheme (FRGS) on

RDU130109.

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Design of Selectable Modems for MC-CDMA Based on Software

Defined Radio

Ali Kareem Nahar a,b

a Faculty of Electrical and Electronic

Engineering, University Malaysia Pahang ,

26600Pekan, Pahang, Malaysia

Yusnita Rahayu a

b Universities of Technology, Department of

Electrical Engineering, Baghdad, Iraq

MC-CDMA technique is the combination of

Orthogonal Frequency Division Multiplexing (OFDM)

technique and Code Division Multiple Access (CDMA)

technique and collects the benefits of both techniques to

provide higher data rates and greater flexibility for

voice, data, video and internet services for future

wireless systems. In this paper MC-CDMA system based

on Software Defined Radio (SDR) was proposed. The

proposed data spread model consists of gold code and

Selectable six modulation types (BPSK, QPSK, 8QAM,

16QAM, 32QAM and 64QAM). In addition, OFDM is

designed by both FFT and IFFT for detecting ideal

channel. The programming is done by using MATLAB-

Simulink tool as well as M-files presented for each

modem. Matlab 13A. The transmitter send 4, 3 and 2 bit

to the receiver in which the system indicate is too big

for 4 and 3 bit therefore the transmitted but reduced to

two bit for successfully system work. To achieve

optimum encoding and decoding signal the all

modulation techniques use 5 MHz to 20 MHz spectrum

frequency. Moreover the bandpass signal generation

has optimal utilized area to satisfy the required

sampling rate

I. INTRODUCTION

Recently, the growth of video, voice and data

communication, the users demanded high date rate

over the Internet wireless environment where the

spectral resource is scarce. To fulfill the

requirements SDR-CDMA is very efficient way to

overcome inter-symbol interference (ISI) on

frequency selective channels [1].

Many research focus on OFDM scheme which has

severed disadvantages such as nonlinear

amplification, sensitivity to frequency offset and

difficulty in subcarrier synchronization [2]. MC-

CDMA is a combination of CDMA and OFDM and

has the benefits of both systems [4, 5]. Thus, the

parameters of OFDM become the basic parameters

of MC-CDMA. In [3,4] proposed OFDM based on

wavelet, where both FFT and IFFT blocks are

replaced by an inverse discrete wavelet transform

(IDWT) and discrete wavelet transform

(DWT)respectively. In [5,6] propose MC-CDMA

system based on a combination of OFDM and

CDMA system for better robustness against

multipath, interference rejection, and impulse noise

frequency reuse, etc. In [7] proposed OFDM for

broad-band local area wireless based on standards

IEEE802.11a [8, 9].

In this paper focus inn analyze the parameters of

OFDM selectable modulation in MC-CDMA. The

simulation parameters considered are: guard time

interval, sampling rate, symbol duration, and

number of data subcarriers. The analysis carried out

using MATLAB. The OFDM and MC-CDMA

analyze under different parameters to determine the

better of the two for the modern wireless

communications.

II. RELATED WORK

In the recent past, a number of study projects in the

field of SDR networking have been presented. In

[10] proposed a new design of CDMA digital

transmitter for a multi-standard SDR base band

stage. The platform involves of reconfigurable and

reprogrammable hardware platform which provide

different standards with a common platform, and

implemented with FPGA by create VHDL model

of CDMA transmitter. In [11] introduce a basic

acquisition system for finding and classifying Base

Stations (BSs) in visibility in the framework of a

CDMA wireless positioning system, based on IS-

95 cellular standard. In [12], concentration on the

importance of MC-CDMA and use adaptive

modulation, the exploitation of fluctuations channel

quality, so that they can exchange more traffic

multimedia using the same bandwidth, a high

efficiency in bandwidth and diversity inherent to

the channel fading as compared with OFDM and

DS-CDMA in the Fig. 1 shown

Fig. 1 MC-CDMA and DS-CDMA use the whole

bandwidth

Each of these blocks was tested using FPGA

advantages 7.2 software during design process; the

same process was done at the receiver part where

using each of the modules was experienced during

design process [13]. Moreover, Mahbub, [14]


proposed an implementation of DS-CDMA

transmitter. In [15] was a show implementation

topic of a digital transmitter for an OFDM through

adjusted VHDL in contradiction of system

generator results. Canet’s work is absorbed on

solutions for the OFDM signal generation in IF and

base-band. Implementation of SDR implies more

specific design and analysis procedures than the

implementation of conventional transceiver

systems. Selection of hardware components for

transceiver implementation, that follows the SDR

concept, is the first and crucial step necessary for

its implementation. All selected hardware

components together form a hardware platform for

SDR creation. During the process of forming a

hardware platform it is necessary to achieve a

compromise between desired, scalability,

flexibility, modularity and performance of the SDR

system [16]. Scalability is related to modularity,

and it allows the system to be enhanced to improve

capability such as increasing number of channels

that a base station could handle. In addition,

flexibility is the capability of a system to switch

variety of air-interfaces and protocols, even if they

have yet to be defined. Also, modularity of a

system allows easy replacement or progress of sub-

systems to take advantage of new technologies

[16]. In [17], that they discussed the M-QAM for

forward link of MC-CDMA schemes with

interference dissolution to support high data rate

service, and provided an analytical BER

performance of the system. In [18], emphasizes the

suitability of high level design tools when

designing sophisticated systems, and the

importance to design FPGA systems rather than

ASIC for accomplishing one day the SDR idea and

give a high level overview of the FPGA

implementation, that work emphasizes the packet

detection, synchronization, preamble correlator,

channel estimation and equalization; that is

primarily at the OFDM receiver for the

IEEE802.11. In [19], developed a SDR networking

is platform using GNU Radio and the USRP. They

integrated a Tun/Tap device into their solution and

additionally studied the impact of channel quality

and different modulation schemes. In [20], based

on their previous observations, MacKenzie et al.

developed a split functionality approach in order to

overcome the communication delays introduced

through SDR and the USRP. Moving time sensitive

functionality closer to the radio promises better

performance in terms of delay. The drawback,

however, is the decreased flexibility and higher

implementation complexity.

III. THE PROPOSED SYSTEM

The general layout for proposed system is shown in

Fig. 2. The main parts and functions of the

implemented proposed system are:

1. Transmitter: The transmitter is responsible for

generating the symbols of the transmitted data

which is transmitted over a wireless channel. Six

modems are used in this transmitter, these are

BPSK, QPSK, 8QAM, 16QAM, 32QAM and

64QAM that can be select which type of these

modems above is turned on and the others are

turned off by the response of the selectable modem

unit.

2. Receiver: This is responsible for data reception

and demodulation of the received data. The

selectable modem unit is used in the receiver

section to decide which demodulation and decision

circuit are used to demodulate the received

modulated signal and received the data signal.

Fig.2. Proposed system layout


Fig. 3 describes the design and implementation

procedure used for the proposed SDR system. The

SDR parameters are set up according to

IEEE802.16e CDMA standard. Then, the design is

implemented as a model using MATLAB

(combination MATLAB-Simulink and M-file) and

functional simulation is performed to performance

evaluation.

Fig. 3 The proposed SDR system implemented in MATLAB

IV. RESULT AND DISCUSSION

The system parameters setting includes specifying

the different types of modulation/demodulation and

other related system operations that the SDR could

handle [21]. Table 1. shows the proposed design

system parameters. The SDR system is very

flexible and can change its parameters easily.

The variation of the BER are performed according

to the variation ratio for energy of data bit to the

power spectrum density (Eb/ No). Fig. 4 shows the

performance of modulation over channel. Table 2.

shows the representation of data which is greatly

generated. Fig. 5 represents I and Q-symbol which

is multiplied by PN-I and PN-Q respectively. Fig. 6

represents the I and Q signals with 64-QAM

modulation transmitted in MC-CDMA.

The variation of the BER are performed according

to the variation ratio for energy of data bit to the

power spectrum density (Eb/ No). Fig. 4 shows the

performance of modulation over channel. Table 2.

shows the representation of data which is greatly

generated. Fig. 5 represents I and Q-symbol which

is multiplied by PN-I and PN-Q respectively. Fig. 6

represents the I and Q signals with 64-QAM

modulation transmitted in MC-CDMA. The

performance of system using 64-QAM modulation

system will be evaluated by plotting the BER

versus the (Eb/No) in the presence of channel for

different values of Doppler frequency. Fig. 7 shows

the effect of AWGN over 64- QAM modulation,

fig. 8 shows the effect of channel on the system.

Table. 1: Design system parameters

paramter Selected types or values explain

Modlation type BASK,QPSK,8QAM,16Q

AM,32QAM,64QAM

BPSK, QASK and M-QAM

used in this system to increase

data rate of transmitssion

IF frequency 5-20MHz Moderate frequency can be used

to implement SDR system

Sampling frequency 100MHz This value is selected for better

simulation results

FFT size 256

8-inputs and the values of the

twiddle factor, each equation

as paths even and odd

Sprading cods Gold code 1.2288Mp/s


Fig. 4 Performance of different modulation schemes

Table. 2 Representation of 32-QAM, 16-QAM, 8QAM, QPSK andBPSK signals

symbo

l

32QAM 16QAM 8QAM QPSK BPSK

I-

Channe

l

Q-

Channe

l

I-

Channe

l

Q-

Channe

l

I-

Channe

l

Q-

Channe

l

I-

Channe

l

Q-

Channe

l

I-

Channe

l

Q-

Channe

l

0 -3 5 -3 3 -3 1 1 1 1 0

1 -1 5 -3 1 -3 -1 -1 1 -1 0

2 -1 -5 -3 -1 -1 1 -1 -1

3 -3 5 -3 -3 -1 -1 1 -1

4 -5 3 -1 3 1 1

5 -5 1 -1 1 1 -1

6 -5 -1 -1 -1 3 1

7 -5 -3 -1 -3 3 -1

8 -3 3 1 3

9 -3 1 1 1

10 -3 -1 1 -1

11 -3 -3 1 -3

12 -1 3 3 3

13 -1 1 3 1

14 -1 -1 3 -1

15 -1 -3 3 -3

16 1 3

17 1 1

18 1 -1

19 1 -3

20 3 3

21 3 1

22 3 -1


23 3 -3

24 5 3

25 5 1

26 5 -1

27 5 -3

28 3 5

29 1 5

30 1 -5

31 3 -5

Fig. 5 I and Q-symbol multiplied by PN-I and PN-Q with 64QAM

Fig. 6 MC-CDMA using 64-QAM transmitted signal.


Fig. 7 Simulation results of MC-CDMA by using 64-QAM modulation

Fig. 8 Simulation results of MC-CDMA by using 64-QAM modulation

V. CONCLUSIONS

In this paper, selectable six models were proposed

to enhance the performance of OFDM scheme.

Performance of proposed MC-CDMA systems

enhanced with increasing processing gain, but with

large processing gain the performane of systems

degraded. Multimode soft decision circuit to

determine the regions of the received signal

acceptable to define the final output data. The

decision circuit includes 8, 16, 32 and 64 regions.

Division of input data by the variable factor

according to number of bit per symbol. The

variable factor is 2,3,4,5 and 6 and is determined by

selectable circuits. Generation of bandpass signal

for six modems in order to set the IF signal

required by SDR systems, as well as the generation

of the bandpass signal which has optimal utilized

area with satisfied the required sampling rate. SDR

will have a key role to play, in the cognitive

systems. We have suggested the SDR algorithms

for successful data transmission in bandwidth

obtainable. The performance of proposed MC-

CDMA schemes enhanced through increasing

processing gain, but with large processing gain the

performance of systems degraded.

References

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Analysis of Multi-Carrier Code Division

Multiple Access System Under Clipping

Noise. European Journal of Scientific

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Fundamentals and Applications. Prentice

Hall, 2nd Edition, 2001.

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Eng. Department, State University of New

York, November 1999.

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OFDM and DWT-OFDM on Different

Transmission Scenarios. ICITA

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Multimedia Communication. Artech

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Layer (PHY) specifications. 2012, IEEE

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Evaluation of Wi-MAX Network with

AMC and MCCDMA for Mobile

Environments. International Journal of

Multimedia and Ubiquitous Engineering

October, 2012; 7 (4): 107-118.

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implementation of OFDM using FPGA.

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Transmitter Implementation Using DS-

CDMA Technique in FPGA Using

Verilog HDL. International Conference

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Approach to Radio Engineering. Prentice

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Simulation and Fabrication of Open-Type Boiler of Fish Cracker

Production Line

Mohd Zaidi Sidek

Faculty of Manufacturing Engineering,

Universiti Malaysia Pahang, 26600 Pekan,

Pahang [email protected],

Mohd Syahidan Kamarudin



Pahang

[email protected],

Mohamad Nafis Jamaluddin



Pahang [email protected]

Boiling process is performed at the final stage in fish

cracker processing and it is a longest process. It creates

a bottleneck and limits the daily production of fish

cracker. Traditionally, fire woods are used to heat the

boiler. The invention of diesel-fired boiler has improved

the process but there are still some issues at the station.

Therefore, a new boiler is designed to improve the

boiling process by using LPG burner which has 2.9 %

higher calorific value than diesel. The boiler designed

was simulated to study the heat convection inside the

boiler by using SolidWorks Flow Simulation to analyze

the temperature distribution inside the new boiler.

Multiple layers bottom plate of the boiler consist of an

aluminium plate sandwiched between two stainless steel

plates is used to increase the rate of heat transfer from

the flame into the water inside the boiler. The result

from the simulation proves that the multiple layers

bottom plate of the boiler has a higher rate of heat

transfer than the single layer plate where the time taken

for water to boil is 42.2% shorter than the single

stainless steel layer bottom plate boiler.

I. INTRODUCTION

Fish cracker is one of the famous and highly

relished snack foods in Malaysia and it is

originated from east coast of peninsular Malaysia

[1]. It is well known and highly demanded due to

its crispy on the outside but tender on the inside if

it is fried. Besides, fish cracker can be eat by just

boil it which gives fishier flavour according to

some people. Both fried and boiled fish cracker is

best to be eaten with special fish cracker chilli

sauce. The main ingredient of fish cracker is fish,

sago flour, salt and water. The high requirement of

fish cracker in the market urge entrepreneurs to

increase their production but they face a lot of

problem to fulfil the market demand. In the

production of fish cracker, most manufacturers are

still using traditional manufacturing practices with

low competitiveness and poor efficiency which

limit the daily production of the fish cracker. As a

result, these manufacturers cannot meet the demand

of the customer. Thus, there is a necessity to

employ a standard processing procedure in order to

keep the quality while meeting the high production

to provide the consumer demands of the delicious

fish cracker. There are several stages of processing

that are needed to be taken to make fish cracker as

shown in Figure 1.

Fig. 1.Process sequence in fish cracker

manufacturing

Previously, fish cracker manufacturers carry out the

processes manually such as the process to roll the

dough into huge sausage-like fish cracker and

mailto:[email protected]



shaping square fish cracker is still being done

manually. In addition, the process to boil the fish

cracker is done traditionally by boiling water from

burning firewood. This method is not very efficient

where it is difficult to control the fire and it also

produces smoke and soot to the surroundings.

Therefore, at that moment, the manufacturers

cannot produce high production of fish cracker to

meet the market demand.

Nowadays, Small and Medium Industry (SMI)

sector has improved very well in the term of their

manufacturing method. The technology that is used

in other food processing industry such as nugget,

burger and meat ball has influenced many local

foods including fish cracker to be commercialized

[2]. The fish cracker manufacturers have improve

their processes hence increase the production of

fish cracker by the use of automated machines to

perform a certain task such as the processes to

mince and mixing the kneaded fish meat. Other

than that, automatic diesel fire-tube burner is

installed to the boiler to perform the task boiling of

fish cracker. Automated processes can help fish

cracker manufacturers to increase the production to

meet the market demand.

Fish cracker industry has caught the eye of the

Malaysian government therefore it is included into

East Coast Economic Region (ECER) as the east

coast of peninsular Malaysia is rich in resources

and the raw material for fish product food. The fish

industry is put under food and halal product

initiatives.

Traditionally, the process of boiling fish cracker is

by burning firewood. This method is not suitable as

it is hard to control the fire, efficiency is low where

a lot of firewood is used and the burning of

firewood produces smoke and soot on the

surrounding. Currently, the invention of fire tube

burner by using diesel fuel has improved the

boiling process. This burner does not need a worker

to control the fire as the diesel injection is

automatically control via temperature sensor that

sense the temperature of the water inside the tank.

Furthermore, the smoke from burning diesel is

channelled away from the working area. This is a

lot better than the previous one as the water boils

faster by using this method.

Apart from that, the burner still has few things to

be improved because the uneven temperature

distribution as the temperature of water near the

diesel burner is high but it decrease as it moves far

from the burner. The result of this problem is the

fish cracker that are put on the side near the burner

cook faster than the fish cracker that are placed

further from the burner which lead to the bottleneck

on the production.

The aims for this study are to design a new boiler

and to simulate the heat convection inside the new

boiler by using SolidworksFlow Simulation.

II. LITERATURE REVIEW

A. Fish Cracker Industry

Malaysia is unique country of different cultures

that has led to in varieties of foods. It is important

that these traditional foods are preserved for future

generations. By using modern technologies and

traditional techniques, manufacturers could

produce more hygienic way of processing and

preserving food [3]. Thus, there is an urgent need

to refine the processing of traditional foods in

response to new societal needs. Refining and

sustaining traditional foods are essential in facing

the forces of globalization [4].

In the production of fish cracker, most producers

are still using traditional manufacturing practices

with low competitiveness and poor efficiency.

Therefore, there is a need to employ a standard

processing procedure in order to maintain the

quality while meeting consumer demands for

safety, quality and nutritional value of these foods.

Traditionally, fish cracker is precooked by boiling

in water. Study by Bakar (1983) reviewed the

boiling and steaming methods in processing fish

cracker. The researcher found that steaming of fish

cracker does not prove to be feasible and the study

suggested several modifications in the processing

steps in fish cracker preparation are essential. On

the other hand, [1] reviewed in terms of sustaining

and promoting of this local food, more publicity

should be performed continuously and producers of

fish cracker must achieve consistent quality and

safety as it represents Malaysia’s identity.

Traditional fish cracker production methods result

in products of poor quality, with uneven expansion

characteristics, dark objectionable colours and

varying shapes, sizes and thicknesses as well as

low hygiene [6]. Siaw, Idrus, & Yu (2007) have

attempted to upgrade product quality. They have

introduced mechanization and standardization into

fish cracker making. Their process is less time

consuming and gives a better-quality product

compared to the traditionally produced fish cracker.

The two essential ingredients in fish cracker

making are starch and fish. Fish such as ‘Ikan

Parang’ (Chirocentrus dorab), ‘Ikan Tamban

beluru’ (Clupea leiogaster) and ‘Ikan Selayang’

(Decapterus macrosoma) are preferred although

other fishes are also used for making fish cracker.

Tapioca or sago starch is used but sago starch is

said to give the best product in terms of texture and

flavor [8]. Fish cracker can be eaten as soon as it is

boiled and together with chili sauce.

B. Bottleneck In Fish Cracker Processing

The purpose of boiling fish cracker is to precook

for further processing although it is palatable with

chili sauce for some people. According to [5], only

15 minutes of boiling required to achieve complete

cooking of fish cracker while 3 hours is needed to


achieve the same result by steaming. Processing

conditions such as boiling of product can reduce

microbial levels, although recontamination takes

place during post- processing and handling of food

[9].

C. Boiler Specifications

There are several factors that must be studied

before designing a boiler such as material, heating

configuration, temperature distribution and heat

transfer rate [10]. Designing a boiler without a

proper research on the topic will lead to a failure

and will waste lots of money if the design is

fabricated. Therefore, appropriate study on the

boiler specifications should be done before

designing it to ensure the new boiler will produce

good heating characteristics and improved the

production in the boiling station in fish cracker

processing.

Temperature distribution plays an important role

for a boiler in the boiling station as it will affect the

cooking time for the fish cracker [11]. Uneven

temperature distribution will lead to bottleneck

where 10 to 15 minutes are taken to check whether

all fish cracker are properly cooked. Therefore, the

shape and the geometry of the boiler must be able

to allow even temperature distribution inside the

boiler. It is very crucial for the boiler to have the

characteristic because it will solve the problem of

different cooking time of the fish cracker.

Another important factor in boiler specifications is

the material selection for the boiler. Different

material has their own characteristics such as

mechanical properties, thermal properties,

corrosion resistance and durability. Important

aspect such as hygiene is vital in fish cracker

processing as poor hygiene may lead to health

illness such as food poisoning to the consumers.

The contamination of surfaces by spoilage and

pathogenic micro-organisms is a cause of concern

in the food industry. One of the decisive arguments

when choosing materials for processing line

equipment, along with their mechanical and

anticorrosive properties, has become hygienic

status (low soiling level and high cleanability). Of

these materials, stainless steel, which is widely

used for constructing food process equipment, has

previously been demonstrated to be highly hygienic

[12]. However, stainless steel can be produced in

various grades and finishes, affecting bacterial

adhesion because of their various topographies and

physic-chemical properties [13]. The main

difference between commercially available grades

is their relative composition in iron, chromium and

nickel. Austenitic stainless steels containing

chromium and nickel, such as AISI 304, are widely

used in the food industry because of their high

resistance to corrosion by food products and

detergents.

Other elements may be added to improve

anticorrosive properties, such as molybdenum in

AISI 316, often used in dairies. Other materials

such as ferritic stainless steel are used in various

applications because of how easily they can be

formed and welded (catering). Moreover, one grade

can be obtained in more or less rough finishes such

as pickling finish (2B) and bright annealed (2R),

depending on their final steel making process [14].

Higher heat conductivity of the cooper used as

heating plate can result in short recovery time [15].

Currently, the invention of fire tube burner by

using diesel fuel has improved the boiling process.

This burner does not need a worker to control the

fire as the diesel injection is automatically control

via temperature sensor that sense the temperature

of the water inside the tank. Apart from that, the

burner still has few things to be improved because

the uneven temperature distribution. The result of

this problem is the fish cracker that are put on the

side near the burner cook faster than the fish

cracker that are placed further from the burner

which lead to the bottleneck on the production.

In addition to the burner, it also has a blower to

circulate the heat along with vents that remove the

by-products of combustion and allow fresh air to

flow into the burner for a steady burn rate. One of

the most important aspects in making a good

heating system is the design of the heating system

and its tank. Therefore, factors such as better

temperature distribution and control, faster

cooking, less energy, safer operation, better

sanitation and flexibility must be taken into account

in considering the design. Plus, the shape of the

heating unit is also an important design

consideration. Ekundayo (1994) stated that the

optimum configuration to achieve the most steady-

state rate of convection was with the heating

element placed in the lower half of the tank.

Research Methodology

In this analysis, the current boiler design and

application are recorded to be analysed. Then, a

new design fish cracker boiler created to overcome

the issues of the current boiler.

The design of the new fish cracker boiler is based

on several considerations and factors which will be

explained next section. The drawing of the new

boiler is as shown in Figure 2.

To verify the simulations, an experimental test

method is used to examine if a correlation between

the test method and the simulations exist. The aim

is to find a parameter that can be evaluated in the

simulation. In the experiment, a Liquefied

Petroleum Gas (LPG) burner is use to heat 1.15 L

of water in a pot until the point of boiling as shown


in Figure 3. The same parameters from the

experiment will be used in the simulation and the

result of the simulation is compared with the

experimental result to see whether the simulation is

valid or not.

With the validated simulation, the design of the

new boiler can be simulated to analyse the heat

convection characteristics inside the new boiler by

using the similar method that is used in the

simulation validation in order to determine whether

the new design is capable to solve the problem of

bottleneck hence increase the production of fish

cracker in the factory.

Fig. 2. Design of fish cracker boiler

Fig. 3.Experiment apparatus

III. RESULT AND DISCUSSION

A. . Boiler Design

There are several considerations that must be

included in designing the new boiler .The

temperature distribution and material selection in

fabricating the boiler were the main issues to be

concerned and as an innovation of the boiler

design, an insulation system of the boiler was

created.

To avoid the uneven distribution ot the

temperature, a centred-stove boiler concept was

selected with a big cylindrical shape replacing the

u-shape heating system. The current u-shape, the

water boils faster on the nearest side to the blower

and vice versa on the side further from the blower.

Figure 4 shows the drawing of the tank that is used

to boil fish cracker at the factory. The fire from

diesel burning flows inside the hollow tube inside

the tank which heat is transferred to the water. To

prove that the temperatures are varies inside the

tank; thermocouples are used to measure the water

temperature at point A, point B and point C. The

temperature is measured and the graph of the

temperature at the points is shown in Figure 5.

Fig. 4. Tank drawing

Fig. 5. Temperature distribution in tank

4.2. Simulation Validation

For the validation purpose of the simulations, an

experimental test method is used to examine if a

correlation between the test method and the

simulations exist. The aim is to find a parameter

that can be evaluated in the simulation. In the

experiment, an LPG burner is used to heat 1.15 L

of water in a pot until the point of boiling. The

same parameters from the experiment will be used

in the simulation and the result of the simulation is

compared with the experimental method. The

measured temperature of the flame from LPG

burning in the experiment is 820 °C. This

parameter will be used at the bottom plate of the

pot in the simulation validation as wall temperature

boundary condition as shown in the Figure 6.

Graph in the Figure 6 shows the water temperature

(°C) versus time (minutes) for the experiment. The

temperature steadily increased from 27.32 °C until

maintained at 84.97 °C after 7.23 minutes of heat

applied.


Fig. 6. Measured flame temperature

Fig. 7. Experimental result

Figure 8 shows the graph of temperature over time

for the simulation on the pot. The graph is

automatically plotted by Flow Simulation software.

The temperature of water increased steadily from

26.72 °C until maintain at 87.74 °C after 6.33

minutes.

From both graphs, we can see that the temperature

of water for both experiment increased steadily

until maintain at temperature around 84 °C to 87 °C

after 820 °C of flame temperature is applied at the

bottom surface of the pot. The simulation takes

shorter time which is 6.33 minutes while the

experiment takes 7.23 minutes. This is due to the

ideal condition in the simulation such as the purity

of water in the simulation differs slightly with the

water that is used in the simulation. Plus, the flame

temperature in the simulation is maintained at 820

°C from the first second until the end of the

simulation. Meanwhile, the flame temperature in

the experiment takes a few seconds to reach 820

°C. Therefore, the slight variation in both result can

be tolerate thus validate the simulation that is done

in SolidWorks Flow Simulation software and the

same simulation can be applied at the new boiler to

simulate the finite volume analysis to study the heat

convection at the inside of the boiler.

Fig. 8. Simulation result graph

4.3. Simulation Result

4.3.1. Result of Multiple Layer Bottom Plate Boiler

The result of the finite volume simulation on the

new boiler is illustrated in the Figure 9 which show

the temperature contour cut plot from the front

plane of the boiler. This cut plot is the temperature

distribution inside the boiler after 15.83 minutes of

heating. From the cut plot contour, temperature

distribution inside the boiler range from 97.36 °C

to 100.16 °C.

Fig. 9. Temperature contour cut plot

Fig. 10.Flow trajectory of water inside the boiler

The new boiler has a better temperature distribution

which may improve the boiling process of fish

cracker and increase the productivity. The time

taken to ensure all fish cracker is fully cooked after

15 minutes can be reduced as all fish cracker inside

the boiler are cooked at temperature range from

97.36 °C to 100.16 °C. Even temperature

distribution is achieved in this boiler because of the

shape of this boiler and the heat is applied at the

bottom part of the boiler which makes the water to

circulate to all part inside the boiler.

From the flow trajectory of the water inside the

boiler, the circulation of water can be determined

as shown in the Figure 10. The circulation is due

the buoyancy effect inside the boiler where hotter

water moves upwards due to lower density and vice

versa for cooler water. Although the temperature of


water is just slightly varied, the difference in

density will determined the movement of the water

which creates the flow inside the boiler. The line

with arrows in the Figure 10 shows the direction of

the water flow. In the middle part of the boiler have

hotter water flowing upward. As it reaches the top

surface, the temperature of the water will drop a

little and move downward by the side part of the

boiler and the cooler water will be heated again as

it reaches the bottom part where the heat is applied.

This result proves the finding by Jullien, Bénézech,

Carpentier, Lebret, & Faille, (2003) that is the

optimum configuration to achieve the most steady-

state rate of convection was with the heating

element placed in the lower half of the tank.

B. Comparison of Single Layer Plate and Multiple

Layer Plate

Figure 11 shows the temperature over time of

boiling process that is simulated on the boiler with

multiple layers bottom plate. The time taken for the

water to reach 100 °C is 15.3 minutes.

The graph in Figure 12 shows the temperature over

time of boiling process that is simulated on the

boiler with single layer bottom plate. The time

taken for the water to reach 100 °C is 26.47

minutes.

From the graphs shown in Figure 11 and Figure 12,

the boiler with multiple layers bottom plate will

boils the water faster than the boiler with single

layer bottom plate. This proves that the multiple

layer bottom plate has a higher rate of heat transfer

which transfers the heat faster from the LPG flame

into the water through the multiple layers bottom

plate.

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒

=Single layer time − Multiple layer time

𝑆𝑖𝑛𝑔𝑙𝑒 𝑙𝑎𝑦𝑒𝑟 𝑡𝑖𝑚𝑒 𝑋 100%

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 = 42.2% The new boiler with multiple layer bottom plates

has higher rate of heat transfer by 42.2 % compared

to single layer bottom plate. Thus, the multiple

layer bottom plate is capable able to increase the

daily rate of fish cracker production.

Fig. 11. Multiple plies plate graph

Fig. 12. Single ply plate graph

IV. CONCLUSION

New boiler design can improved the heating rate

and temperature distribution of water inside the

boiler thus improving the boiling process of fish

cracker where the time taken to ensure that all fish

cracker is fully cooked can be reduced thus solving

the bottleneck problem which is due to 10 to 15

minutes taken to check for all fish cracker to fully

cook. All fish cracker that are boil inside this boiler

will take 15 minutes to fully cooked as the

temperature is even distributed inside this boiler

which is around 97.36 °C to 100.16 °C.

Multiple layers bottom plate is better than single

layer because it has 42.2 % higher rate of heat

transfer by single layer bottom plate boiler as the

multiple layers configuration reduced the total

thermal resistance. The layer of aluminium in

between two stainless steel layer has improved the

rate of heat transfer. This layer configuration is

preferable to be used since the stainless steel has

low conductivity but the boiler must be made of

corrosion resistance material and provide good

hygiene.


V. RECOMMENDATION FOR FUTURE RESEARCH

Based on the findings of the present investigation,

the following recommendations are made for

further research:

The further improvement for the new boiler can be

made to increase the performance of the boiler such

by using higher thermal conductivity material to

replace the aluminium with copper that has 401

W/mK which is almost twice as large as the value

of thermal conductivity of aluminium. The study on

the boiler with copper plate in between stainless

steel at the bottom plate can be done by using the

same method used in this simulation.

The system to control the LPG burner can be

developed to save the fuel consumption at the

boiling station in keropok ikan industry. This

system also will eliminate the need of worker to

monitor the LPG flame. Furthermore, this system

will also reduce the fuel cost for boiling fish

cracker.

ii. Samples with fiber volumetric ratios of 1.5

kg.m-3 indicated better corrosion resistance

compared to the other samples.

iii. In this research, using coral aggregate for

producing concrete samples showed that this

concrete composition was not a practical

composition. Corrosion rate in this concrete was at

least twice that was shown in siliceous concrete.

iv. The results show that 6 mm length fibers were

not the suitable size to be used in concrete. The

result of using fibers with length of 12 and 19 mm

was approximately the same, with the optimum size

being 12 mm.

v. Apart from increasing corrosion resistance, the

presence of polypropylene fibers decreased the

permeability, volumetric expansion and contraction

of concrete, which in turn had reduced the chance

of concrete cracking.

VI. ACKNOWLEDGMENTS

This research was made possible with a scholarship

from Ministry of Highr Education, Malaysia and

support from University Malaysia Pahang (UMP).

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Photocatalytic conversion of CO2 into methanol: Significant

enhancement of the methanol yield over Bi2S3/CdS photocatalyst

M. Rahim Uddin, Maksudur R. Khan*, M.

Wasikur Rahman, Chin Kui Cheng

Faculty of Chemical and Natural Resources

Engineering, Universiti Malaysia Pahang,

26300 Gambang, Pahang, Malaysia [email protected]

Abu Yousuf

Faculty of Engineering Technology, Universiti

Malaysia Pahang, 26300 Gambang, Pahang,

Malaysia

ABSTRACT The present work is a significant approach

to explore the photo-conversion of carbon dioxide

(CO2) into methanol on Bi2S3/CdS photocatalyst under

visible light irradiation. In this perspective, Bi2S3

nanoparticles have been successfully synthesized via

corresponding salt and thiourea assisted sol–gel

method. An innovative hetero-system Bi2S3/CdS has

been proposed to achieve methanol photo evolution and

its photocatalytic activities have been investigated. The

photocatalysts are characterized by X-ray diffraction

(XRD), ultraviolet-visible spectroscopy (UV-Vis)

instruments. Results show that the photoactivity and

visible light response of commercial CdS loaded Bi2S3

is higher than that of synthesized CdS. The

photocatalytic activity of Bi2S3/CdS photocatalyst was

enhanced and the highest yield of methanol was 590

μmol/g when the weight proportion of Bi2S3 to CdS

was (2:1).

Key Words : CO2 reduction Photocatalyst,Bi2S3/CdS,

Visible light; Methanol

I. INTRODUCTION

The continuous increase in atmospheric CO2 leads

to climate change, which is one of the major threats

of times. The rapid consumption of fuel resources

and the undergoing concerns over the emissions of

CO2 have stimulated research objectives on the

conversion of CO2. It is urgent to reduce the

accumulation of CO2 in the atmosphere. There are

three effective ways to reduce CO2 emissions:

reducing the amount of the produced CO2, using

CO2 and storing CO2, where transformation of CO2

into chemicals is an attractive option and fulfils the

recycle use of CO2 [1, 2]. Photocatalytic process

for CO2 reduction provides a suitable approach for

clean and environmental friendly production of

hydrocarbon by visible light. However, in order to

harness sunlight to produce hydrocarbons from

CO2 conversion, there are different fundamental

requirements that must be satisfied [3-8]. Firstly,

light must be efficiently absorbed to generate

electron-hole pairs for the electron transfer from

one conduction band to other. Secondly, either the

recombination of the photo-generated electron-hole

pairs like to be prevented for the CO2 adsorption on

catalyst surface. Thirdly, undesirable reactions or

products, such as photocorrosion or degradation of

the photocatalyst, as well as environmental

unfriendly products, must be prohibited by

adjusting the pH before suspending the catalyst onto

reaction medium. To develop suitable

photocatalysts, these fundamental key factors and

the aims of photocatalytic reduction of CO2 need to

be satisfied [3, 9-11].

As for photocatalytic conversion of CO2 to

methanol, CdS is the most popular photocatalyst

due to its excellent stability, innocuity and low

price. In addition, due to its larger surface and

regular structure has also been brought to much

attention in the field of photocatalytic conversion

of CO2 [12, 13]. The band-gaps of CdS and Bi2S3

were narrower and their conduction bands were

more negative than those of other photocatalysts

[12, 13], therefore, CdS and Bi2S3 have been

hugely used to the photocatalytic conversion of

CO2. CO2 bubbled in water was converted to

HCHO, HCOOH and CH3OH over various

semiconductor photocatalysts, such as

CuFe2O4,CdS, TiO2, ZnO, GaP and SiC under

photo irradiation of their suitable reaction medium

maintaining required PH value [3, 6, 9, 14-19].

In this study, Bi2S3 was modified by CdS and the

obtained Bi2S3/CdS was used for the photocatalytic

conversion of CO2 with water under visible light

irradiation. The Bi2S3/CdS photocatalyst was

characterized by X-ray diffraction (XRD),

ultraviolet visible (UV-Vis) spectroscopy. The

photocatalytic activities of Bi2S3/CdS photocatalyst

for the conversion of CO2 to CH3OH under visible

light irradiation have been investigated.

II. MATERIALS AND METHODS

2.1. Materials

The Bi(NO3)3·5H2O, thiourea and CdS were

obtained from R&M Chemicals. All chemicals

used in this work were laboratory standard and

used as purchased.




2.2. Preparation of photocatalyst

Bi2S3 was synthesized by the reactions between the

corresponding salt and thiourea, 3.05 g

Bi(NO3)3·5H2O and 0.71 g thiourea was dissolved

in 400 ml water and retained for 3 h under

continuous stirring at room temperature [13]. The

solution was then heated under stirring at 95oC for

3 h. When cooled and settled down, the precipitate

was filtered off, and washed with distilled water

and dried in vacuum at 60 ◦C overnight. At last,

Bi2S3 was heat treated at 250 ◦C for 3 h. To prepare

the hetero-system Bi2S3/CdS photocomposite, the

mass ratio of Bi2S3 to commercial CdS was taken

1:0.5. The starting materials were mixed randomly

after grinding them and the system was heated at

250 oC for 3 h in tubular furnace under N2

atmosphere.

2.3. Characterization

The XRD patterns were obtained at room

temperature using Rigaku MiniFlex II. The UV-Vis

diffuse reflectance spectrum (DRS) in

the range of 200−800 nm was measured with a

Daojin UV-2550PC diffuse reflectance

spectroscope. The liquid products were analyzed by

using gas chromatography-flame ionization

detector (GC-FID), and the analysis was performed

with Shimadzu, GC- 14B series gas chromatograph

equipped with FID detector and the capillary

column DB-WAX (60 m × 0.25 mm, 0.25 μm). The

carrier gas was nitrogen at a flow rate of 1 mL/min.

The injector and detector temperature were

maintained at 250 and 260°C, respectively;

consisting of split less.

2.4. Photocatalytic activity

Photocatalytic conversion of CO2 into methanol is a

process in which photons are absorbed with higher

energies than its band-gap energy (Eg) to create

electron-hole pairs. The photogenerated electrons

(e-) and holes (h+) participate in various

photoreduction processes to produce final products

[20]. However, if the electrons fail to find any

trapped species (e.g. CO2) on the semiconductor

surface or their energy band-gap is too small, then

they recombine immediately and release

unproductive energy as heat[21, 22]. Photocatalytic

absorption of photons creates photoelectrons in the

conduction band (CB) and holes in the valence

band (VB) of the semiconductor, as schematically

depicted in Figure 1a. In the Figure 1b, the

photogenerated electron-hole pairs must separate

and migrate to the surface (paths a and b in Figure

1b) competing effectively with the electron-hole

recombination process (path c in Figure 1b) that

consumes the photo charges generating heat. The

photo-induced electrons and holes reduce and

oxidize adsorbed CO2 to hydrocarbons [13, 20, 23,

24].

The photo reaction was performed in a continuous-

flow reactor system as shown in Figure 2 [12]. A

500 W Xe lamp located in the quartz cool trap was

the irradiation source. The catalyst concentration of

the prepared Bi2S3/CdS photocatalyst was

maintained at 1 gL-1

. The pH was adjusted to the

desired value by adding KOH (1.2 gm), 2.0 M

sodium nitrite, and absolute sodium sulphite (3.78

gm) was dissolved in 300 mL distilled water. This

solution was then put into a photocatalytic reactor.

Before irradiation, ultrapure CO2 was bubbled

through the solution in the reactor for at least 1 h to

ensure that all dissolved oxygen was eliminated,

then, 300 mg of catalyst powder was added into

300 mL of prepared solution, and the irradiation

process was started. The CO2 was continuously

bubbled through the solution in the reactor during

the whole irradiation (6 h). The liquid sample was

analyzed by the GC-FID and UV method describe

above [12, 21]

III. RESULTS AND DISCUSSION

3.1. XRD analysis

The XRD pattern of the Bi2S3/CdS photocatalyst is

showed in Figure 3. It was observed from Figure 3,

According to the XRD diffraction peaks of CdS,

these three significant peaks were consistent with

the peak positions of CdS as spinel-type (JCPDS

111, 220, 311) respectively. It can be seen from the

XRD patterns of the Bi2S3 in Figure 3, that sharp

peaks were in good matching with the standard

diffraction peaks of Bi2S3 corresponded with the

crystal planes of (JCPDS 130, 211, 221, 431, 351)

phase Bi2S3, respectively.

3.2. UV-Vis spectroscopy analysis

The UV–Vis DRS of the as-prepared Bi2S3/CdS has

been presented in Figure 4. As shown in Figure 4,

the photo absorption of the Bi2S3/CdS photocatalyst

was clearly higher than that of CdS and increased

with the proportion of Bi2S3 in the photocatalysts.

This proves that the addition of CdS can effectively

enhance the absorbance of Bi2S3 under visible light.

Therefore, it clearly shows the Bi2S3/CdS

photocatalyst is more suitable for applying under

visible light.. A band gap of 1.72 eV was obtained

from UV-Vis DRS analysis (Figure 4). The

required wavelength to make the photocatalyst

active can be calculated using the following

equation [25]:

Wavelength,λ(nm)≤ 1240

𝐵𝑎𝑛𝑑 𝑔𝑎𝑝 𝑜𝑓 𝑠𝑒𝑚𝑖𝑐𝑜𝑛𝑑𝑢𝑐𝑡𝑜𝑟 (𝑒𝑉)

3.3. Photocatalytic conversion of CO2

The mechanism of photocatalytic conversion of

CO2 with H2O to CH3OH is shown in figure 1, and


the experimental set up is shown in figure 2. The

formation of CO2 photocatalytic conversion

products was examined over a period of 6 h on

Bi2S3/CdS photocatalyst. The evolution products

were obtained as the functions of the irradiation

time for the Bi2S3/CdS (2:1) catalyst. The yield of

methanol is higher than that of any other

hydrocarbons. The yield of methanol was measured

for the 50% commercial CdS loaded Bi2S3 catalyst

during 6 h irradiation. The photo-reactivity of

Bi2S3/CdS increases with the increase of time,

when the active sites of the catalyst decrease the

production of methanol was stopped. The yields of

methanol production in the photocatalytic

conversion of CO2 over Bi2S3/CdS photocatalysts

under visible light irradiation are shown in Fig. 5.

The highest methanol yield (590μmol/g) obtained

for CdS loaded Bi2S3 (2:1) catalyst

IV. CONCLUSIONS

The photocatalytic conversion of CO2 into

methanol on Bi2S3/CdS catalyst surface under

visible light has been carried out quite effectively.

The Bi2S3/CdS photocatalyst for CO2 conversion

has been studied but for the commercial CdS has

not been studied yet. The activity is attributed due

to the increased active site on the surface area of

Bi2S3/CdS. The modification of Bi2S3 with

commercial CdS can increase its photocatalytic

activity and visible light response. The highest

methanol yields was found over Bi2S3/CdS

photocatalyst and the yield was 590μmol/gcat that

proved the loading of commercial CdS on Bi2S3

cause the significant increase in methanol yield

respectively.

V. ACKNOWLEDGMENTS

We would like to thank the Malaysian Ministry

of Education under the Fundamental Research

Grant Scheme (RDU120112) and Universiti

Malaysia Pahang for funding this research

(GRS140330), and providing all the facilities for

our research work.

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Fig. 1. Photocatalytic water splitting: (a) photoelectron excitation in the photocatalyst-generating electron

hole pairs and (b) processes occurring on photocatalyst for CO2 reduction.

Fig. 2. Schematic presentation of experimental setup for photoreduction of CO2 through splitting of H2O

[12]


Fig. 3. XRD patterns of the Bi2S3/CdS photocatalyst prepared via sol–gel approach and calcined at 240oC

Fig. 4. The UV–Vis DRS of the as-fabricated Bi2S3/CdS, Bi2S3 photocatalyst at CdS loading ratio 2:1.

.

Fig. 5. The methanol yield in the photocatalytic conversion of CO2 over Bi2S3/CdS (2:1) photocatalyst

under visible light irradiation (6 h).

10 20 30 40 50 60 70 80

0

10

20

30

40

50

60

351

431

130,111

221

211

311

220130111

Intensit

y (a.u.)

2-Theta (Degree)

400 600 800 1000 1200 1400

0.0

0.3

0.6

0.9

1.2

1.5

Bi2S

3/CdS

Absor

bance

Wavelength, nm

1 2 3 4 5 6

0

100

200

300

400

500

600

Bi2S

3/CdS

Yield

(micr

o mol/

g cat)

Time(h)


A New Neuron Ion Channel Model with Noisy Input Current

Ahmed Mahmood Khudhur

Faculty of Engineering Technology, University

Malaysia Pahang, Kuantan 26300, Malaysia

Ahmed N Abdalla

Faculty of Engineering Technology, University

Malaysia Pahang, Kuantan 26300, Malaysia

Abstract: The data processing fundamental problem

affects all aspects of nervous-system function by the

noise of ion channels. The conducting and non

conducting of ion channels depends on random

transitions of channel noise, which affect the states of

several numbers of gates in every single individual ion

channel. This paper, introduce a new ion channel

model in the neuron with noisy input current as

approximations of the HH model. It briefly introduces

the ion channel based on stochastic Hodgkin-Huxley

model. The method is able to fully constrain the HH

model and obtain all models capable of reproducing the

data. Therefore, this method overcomes the limitations

of other parameter estimation methods. The stochastic

Markov process method is simply applied to simulate

each gate individually to determine the relationship

between channel noise and the spike frequency. The

proposed model shows the sequence of colored noise

experiments described efficiently compared with

microscopic simulations. In addition, the spiking rate

generated from the proposed model very close to

microscopic simulations and doesn’t effect by the

membrane size.

Keywords: Ion Channel, Noisy, Hodgkin-Huxley,

Microscopic.

I. INTRODUCTION

The nerve cell theoretical foundation in the

building block of the nervous system was

introduced by Hodgkin and Huxley (1952). It

processes information and sends, receives the

ultimate control signal as control functions such as

our breathing, complex memory, and different body

activity (Andersen et al. 2007). Although all

neurons share the same basic structure still the

neuron in nervous system has many different forms

depending on its occupied area and its function.

The ideas of the patch-clamp technique permitted

to determine experimental approaches of the

possibility of measuring ion currents through

individual ion channels which development by

Neher and Sakmann (1976). The channel

fluctuations can become critical close the action

potential threshold, even if the numbers of ion

channels are large (Schneidman et al. 1998;

Rubinstein 1995); in the action potential threshold

that has small numbers of ion channels and that are

open, the timing accuracy was determined. In

addition, the bursting or spiking in the ion channels

in the numerical simulations and theoretical

investigations of channel dynamics caused by the

internal noise (DeFelice and Isaac 1992;

Strassberg, and DeFelice 1993; Fox and Lu 1994;

Chow and White 1996; Rowat and Elson 2004).

Channel noises in the patch-clamp experiments are

producing large voltage fluctuations to affect the

propagation of action potentials, and timing,

initiation (Diba et al. 2004; Dorval and White

2005; Jacobson et al. 2005; Kole et al. 2006). The

membrane channel dynamics which have

represented by Markov models was utilized

(Kienker, P. 1989; Rudy, Y., and Silva, J. 2006).

Many researchers work in this field to produce

accurate enough statistics of spike generation in the

stochastic HH (Mino, Rubinstein, & White, 2002;

Zeng & Jung, 2004; Bruce, 2009; Sengupta,

Laughlin, & Niven, 2010). These studies suggest

that Fox and Lu’s stochastic extension to the HH

equations may not be suitable for accurately

simulating channel noise, even in simulations with

large numbers of ion channels. The method that

proposed using more stochastic terms and avoids

the expense, complex matrix operations (Orio &

Soudry, 2012). The gating variables that contain

Gaussian white noise in the stochastic HH equation

was proposed (Güler, 2013). However, a complete,

comprehensive analysis of spike generation in the

stochastic HH this model is needed, that

additionally includes the generation of the database

on the estimation.

In this paper, the proposed model directly

determines a set of maximal functions of voltage

parameters to fit the model neuron from the

Hodgkin-Huxley equations. The behaviors of the

theoretical relationship between neural behavior

and the parameters that specify a neuronal model

are described in detail. The simulation model

doesn't only depend on the fluctuations in the

number of open gates, but additionally on the

existence of several numbers of gates in individual

ion channels.

II. THEORETICAL BACKGROUND

A. The Gaussian white noise (GWN)

The stochastic Hodgkin-Huxley models responded

by a Gaussian white-noise process with zero mean

and unit variance. (Rowat, P. 2007; Sengupta, B.

2010). The additive white-noise term can be

interpreted as a clear method for representing the

combined effect of numerous synaptic inputs that


neurons in cortex and other networks receive in

vivo; (Abbot, D. P. (2002), distribution, is

additionally recognized as the Gaussian

distribution, and the values that the noise can take

on being Gaussian-distributed. A special case is

white Gaussian noise, in which the values in each

pair of times are statistically independent. In

applications, Gaussian noise is most usually

utilized as additional white noise to yield additive

white Gaussian noise.

B. Ionic Mechanisms of Action Potentials

An action potential is bounded by a region

bordered on one extreme by the K+ equilibrium

potential (-75 mV) and on the other excessive by

the Na+

equilibrium potential (+55 mV). The

resting potential is -60 mV. Note that the resting

potential is not equal to the K+ equilibrium

potential because, as discussed previously, there is

a small resting Na+ permeability that makes the cell

slightly more positive than EK. In principle, any

point along the trajectory of action potential can be

obtained simply by varying alpha in the Hodgkin-

Huxley equation. If alpha is very large, the Na+,

terms dominate, and according to the Hodgkin-

Huxley equation, the membrane potential will

move towards the Na+ equilibrium potential. The

peak of the action potentials' approaches but does

not quite reach ENa, because the membrane retains

its permeability to K+ (Wilfred D. S., Thomas L.,

2015).

C. The Hodgkin-Huxley equations

Hodgkin and Huxley deduced that the ionic

membrane conductances are variable with time and

voltage-dependent, and gave the form of this

voltage-dependence (Sahil Talwar, Joseph W.

Lynch, 2015). By treating a segment of the axon as

a simple electric circuit, Hodgkin and Huxley

arrived at equations describing the electric activity

of the axon. The cell membrane, which separates

the extracellular medium from the cytoplasm of the

cell, acts as a capacitor with capacitance C

(Hodgkin and Huxley used a value, based on

laboratory measurement, of 10 _F/𝑐𝑚2 for C). The

ion current channels offer parallel pathways by

which charge can pass through the cell membrane.

Hodgkin and Huxley use three ionic currents in

their description of the squid giant axon; potassium

current𝐼𝐾 , sodium current𝐼𝑁𝑎, and a leakage

current𝐼𝐿 . The potassium and sodium currents have

variable resistances that represent the voltage gated

conductances associated with the membrane ion

channels. The total current I is the sum of the ionic

currents and the capacitive current which represents

the rate of accumulation of charge on opposite

sides of the cell membrane. The capacitive current,

from electrical circuit theory, is 𝐶 𝑑𝑉

𝑑𝑡 ,where v is

the membrane potential. Hodgkin and Huxley take

v = 0 to represent the neuron's resting potential, and

the equations below follow this convention.

𝑑𝑉𝑚

𝑑𝑡+ 𝐼𝑖𝑜𝑛 = 𝐼𝑒𝑥𝑡 (1)

𝐼𝑖𝑜𝑛 = ∑ 𝐼𝑖𝑖 (2)

𝐼𝑖 = 𝑔𝑖(𝑉𝑚 − 𝐸𝑖) (3)

𝐼 = �̅� 𝑚𝑝ℎ𝑞(𝑉 − 𝑉𝑟𝑒𝑣) (4)

The number of independent activation gates was

represented by the integer power p in the equation

(4), which was introduced by Hodgkin and

Huxley. In addition, they measured a time delay in

the rise of the potassium and sodium currents when

stepping from hyperpolarized to depolarize

potentials, but when stepping in the opposite

direction, there is no such delay. At the outset when

the axon is depolarized with a delay, there is the

difficulty to increase the conductance of both

potassium and sodium, but when the axon is

depolarized but falls with no appreciable inflection

when it is depolarized. If 𝑔𝑘, is used as a variable

the end of the record can be fitted with a first-order

equation, but a third- or fourth-order equation is

needed to describe the beginning. A useful

simplification is achieved by supposing that 𝑔𝑘 , is

proportional to the fourth power of a variable

which obeys a first-order equation. In this case the

rise of potassium conductance from zero to a finite

value is described by 1 − exp (−𝑡))4 , while the

fall is given by exp (-4t). The rise in conductance

therefore shows a marked inflection, while the fall

is a simple exponential. A similar assumption using

a cube instead of a fourth power describes the

initial rise of sodium conductance (Sudha C.,

2015).

The ionic currents are given by Ohm's law (I = gV

):

𝐼𝑖𝑜𝑛 = 𝐼𝑁𝑎 + 𝐼𝐾 + 𝐼𝐿 (5)

𝐼𝑁𝑎 = 𝑔𝑁𝑎(𝑉𝑚 − 𝐸𝑛𝑎) (6)

𝐼𝐾 = 𝑔𝐾 (𝑉𝑚 − 𝐸𝐾) (7)

𝐼𝐿 = 𝑔𝐿 (𝑉𝑚 − 𝐸𝐿) (8)

Where 𝐸𝑖𝑜𝑛 is the reversal potential, and 𝑔𝑖𝑜𝑛 is

the ionic membrane conductance.

These conductance’s, in the case of the sodium and

potassium currents, are variable and voltage-

dependent, representing the voltage-gating of the

ion channels. Hodgkin and Huxley deduced from

experiment the following forms for the ionic

membrane conductances:


𝑔𝑘 = �̅�𝑘𝑛4 (9)

𝑔𝑛𝑎 = �̅�𝑛𝑎𝑚3ℎ (10)

Where, (n, m, h), are ion channel gate variables

dynamics, �̅�𝑖 is a constant with the dimensions of

conductance per cm2 (mention that n between 0

and 1).

In order to normalize the result, a maximum value

of conductance(�̅�𝑖), is required.

Thе n, m, and h dynamic are listed below:

ṅ =𝑑𝑛

𝑑𝑡= 𝛼𝑛(1 − 𝑛) − 𝛽𝑛𝑛 (11)

ṁ =𝑑𝑛

𝑑𝑡= 𝛼𝑚(1 − 𝑚) − 𝛽𝑚𝑚 (12)

ḣ =𝑑ℎ

𝑑𝑡= 𝛼ℎ(1 − ℎ) − 𝛽ℎℎ (13)

Where 𝛼𝑥 and 𝛽𝑥 , are rate constant that the

changes happened with voltage changes, but not

affected by time, while the value of dimensions

variable n can take place between 0 and 1, also its

stand for of a single gate probability that is in

permissive state.

Hodgkin and Huxley measured constantly 𝛼𝑖 𝛽𝑖 as

functions of V in the following:

𝛼𝑖 =𝑥∞(𝑉)

𝜏𝑛(𝑉) (14)

𝛽𝑖 = 1−𝑥∞(𝑉)

𝜏𝑛(𝑉) (15)

D. Dynamics of the Membrane

The HH model was considered in this study. The

analysis is applicable to each conductance based

model with ion channels governed by linear,

voltage dependent kinetics. The equation below

described the membrane potential of the neuron.

𝐶 𝑑𝑉

𝑑𝑡= −𝑔𝐾 𝜓𝑘 (𝑉𝑚 − 𝐸𝐾) − 𝑔𝑁𝑎𝜓𝑛𝑎(𝑉𝑚 −

𝐸𝑛𝑎) − 𝑔𝐿(𝑉𝑚 − 𝐸𝐿) + 𝐼

(16)

V above is the transmembrane voltage, and 𝜓K is

the dynamic variable in the formula represents the

ratio of open channel from potassium which is the

proportional number of open channels to the

complete numbеr of potassium channel in the

membrane; also 𝜓Na is an open sodium channels

ratio, and 𝐼 is externally current. All of the two

channel variables 𝜓K and 𝜓Na in the Hodgkin–

Huxley (HH) equations is taken as their

approximated deterministic value, 𝜓K= n4 and 𝜓Na=

m3h; while the potassium channel have four n-gates

and sodium channel have thrее m-gatеs and one h-

gatе. In case the channel is considered open, all the

gatеs of that channel have to be open, and the

gating variable for potassium is n and for sodium is

m and h.

The rate functions that found to be as:

𝛼𝑛(𝑉) =0.01(10−𝑉)

exp(10−𝑉

10)−1

, (17)

𝛽𝑛(𝑉) = 0.125 exp (−𝑉

80), (18)

𝛼𝑚(𝑉) =0.1(25−𝑉)

exp(10−𝑉

10)−1

, (19)

𝛽𝑚(𝑉) = 4 exp (−𝑉

18), (20)

𝛼ℎ(𝑉) = 0.07 exp (−𝑉

20), (21)

𝛽ℎ(𝑉) =1

exp(30−𝑉

10)+1

(22)

The functions 𝛼𝑉 and 𝛽𝑉 have dimensions of

[1/time] and govern the rate at which the ion

channels transition from the closed state of the

open state (α) and vice versa (β).

I. THE PROPOSED MODEL

The proposed model in eq. (23), is a new

modification of the Hodgkin-Huxley equations by

adding calcium channel (𝐶𝑎+2), and GWN with the

mean zero (ξ (t)) to the equations. In addition, it

calculates the potassium and sodium channels when

there are more than one n-gate and m-gate, in the

dynamic variable by considering the membrane

potential to have a large number of channels, and

that’s enough to satisfy both 𝜓K, and 𝜓Na. The

differential equations for the activation and

inactivation variables in the proposed model can be

solved at any instant in time, and the values of all

the activation and inactivation variables are known

at any instant by inspection of the voltage trace.

This proposed model allows for estimation all

parameters and functions of voltage precisely.

More specifically, the numbers of the gating

variables, the conductance, and the steady states

and time constant estimated as functions of voltage.

The regular states are using mathematical

modifications on data collected using four voltage

clamp protocols. The equations that describe the

proposed model shown as follows:

𝐶�̇�= − 𝑔𝑘 ∑ 𝑛4𝑖 (V − 𝐸𝐾 ) − 𝑔𝑁𝑎= ∑ 𝑚3ℎ𝑖 (V −

𝐸𝑁𝑎) − 𝑔𝐶𝑎𝜓𝐶𝑎(V − 𝐸𝐶𝑎) − 𝑔𝐿(V − 𝐸𝐿) + 𝐼+ ξ (t)

(23)


𝜓𝐾 = n4

is an open potassium channels ratio.

𝜓𝑁𝑎=𝑚3ℎ is an open sodium channels, ratio.

If we have more than one channel the dynamic

variable (𝜓𝐾), will be as follows:

𝜓𝐾= ∑ 𝑛4𝑖

𝜓𝑁𝑎= ∑ 𝑚3ℎ𝑖 , 𝑖 =number of channels.

𝜓𝐶𝑎, is an open calcium channels, ratio depends on

the concentration of 𝐶𝑎+2 .

[𝜓𝐶𝑎] = {

[𝐼𝑜𝑛𝐶𝑎]𝑜𝑢𝑡

[𝐼𝑜𝑛𝐶𝑎]𝑖𝑛, 𝑖𝑓 𝐼𝑜𝑛𝐶𝑎 ≥ 1𝑚𝑉

𝑜 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(24)

If the concentration of the calcium is high the

channel will open otherwise close.

Here [𝜓𝐾], [𝜓𝑁𝑎], is the ratio of open potassium

and sodium channels, computed across all

achievable order of the membrane getting 4XKn ,

3XNam, XNah, open n- gates, as shown below:

[𝜓𝐾] =

{(4𝑋𝐾𝑛)3 (4𝑋𝐾𝑛)2(4𝑋𝐾𝑛)1𝑛

(4𝑋𝐾)3(4𝑋𝐾)2(4𝑋𝐾)1 , 𝑖𝑓 𝑋𝐾𝑛 ≥ 1

𝑜 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(25

)

[𝜓𝑁𝑎] =

{ (3𝑋𝑁𝑎𝑚)2(3𝑋𝑁𝑎𝑚)1𝑚

(4𝑋𝑁𝑎)2(4𝑋𝑁𝑎)1 ℎ, 𝑖𝑓 𝑋𝐾𝑛 ≥ 1

𝑜 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(26

)

If the membrane size is small then 𝜓𝐾=𝑛4, and

𝜓𝑁𝑎=𝑚ℎ3, in the limit of infinite membrane size,

the proposed model’s value 𝜓𝑘= [𝜓𝐾]=𝑛4 , and

𝜓𝑁𝑎= [𝜓𝑛𝑎]=𝑚ℎ3 , applies at any times.

Where, [𝜓𝐾], [𝜓𝑁𝑎], reads as:

𝜓𝑘=𝑛4+𝜎𝑘𝑞𝑘

𝜓𝑛𝑎=𝑚ℎ3+𝜎𝑁𝑎𝑞𝑁𝑎

The equations that describe the dynamics of qK are:

𝜏𝑞�̇� =𝑝𝐾 (27)

𝜏𝑝�̇� = − 𝛾𝐾𝑝𝐾 −𝑤𝐾2[ 𝛼𝑛(1 − 𝑛) + 𝛽𝑛𝑛]𝑔𝐾

+ 𝜉𝐾(28)

The equations that describe the dynamics of 𝑞𝑁𝑎

are:

𝜏�̇�𝑁𝑎 = 𝑝𝑁𝑎 (29)

𝜏�̇�𝑁𝑎 = − 𝛾𝑁𝑎𝑝𝑁𝑎 − 𝑤𝑁𝑎2 [𝛼𝑚(1 − 𝑚) +

𝛽𝑚𝑚]𝑔𝑁𝑎 + 𝜉𝑁𝑎

(30) In which (𝐷𝑛 , 𝐷𝑚), is identical to:

𝛼𝑛(1 − 𝑛) + 𝛽𝑛𝑛, and 𝛼𝑚(1 − 𝑚) + 𝛽𝑚𝑚

(31)

The standard deviation of 𝜓𝑘, 𝜓𝑛𝑎, will be as

follows:

𝜎𝑘 = √𝑛4(𝑛4)−1

𝑋𝐾𝑞𝐾 (32)

𝜎𝑁𝑎 = √m3(𝑚3)−1

𝑋𝑁𝑎ℎ𝑞𝑁𝑎 (33)

The complete model for the dynamic variable

(𝜓𝐾),( 𝜓𝑛𝑎), is:

𝝍𝑲 = 𝒏𝟒 + √𝒏𝟒(𝒏𝟒)−𝟏

𝑿𝑲𝒒𝑲 (34)

𝜓𝑁𝑎 = 𝑚3ℎ + √m3(𝑚3)−1

𝑋𝑁𝑎ℎ𝑞𝑁𝑎 (35)

The gate noise model is:

ṅ =𝑑𝑛

𝑑𝑡= 𝛼𝑛(1 − 𝑛) − 𝛽𝑛𝑛 + 𝜉𝐾 (36)

ṁ =𝑑𝑛

𝑑𝑡= 𝛼𝑚(1 − 𝑚) − 𝛽𝑚𝑚+ 𝜉𝑛𝑎 (37)

ḣ =𝑑ℎ

𝑑𝑡= 𝛼ℎ(1 − ℎ) − 𝛽ℎℎ𝜉ℎ (38)

II. RESULT AND DISCUSSION

This section consists of the series of experiments

that actually defined efficiency of the noise by

comparing the proposed model with the

microscopic simulations. In addition, a simple

stochastic method has been used as the microscopic

simulation scheme (Zeng, 2004). The simulation

model in equations (34, 35) numerically was

developed by using C++ programing language and

MATLAB. The input current was time

independent, which was modified based on the

program to handle time dependent current and the

noise variance in this simulation were a periodic sin

wave under noise variance, as shown below:

I(t)=𝐼𝑏𝑎𝑠𝑒+ξ(t) (39)


Where, 𝐼𝑏𝑎𝑠𝑒 indicates the current situation, and the

GWN with mean zero is (ξ (t)). A series of

experiments has been used to examine the

effectiveness of the noise in the proposed model in

a comparative manner with the Microscopic

simulation, as mentioned above.

The experiments applying by using parameter

values of the membrane with including Gaussian

white noise in the proposed model and in the

Hodgkin-Huxley equations as described in formula

(39). Hence, it can be seen that the performance of

the proposed model was quite similar to the

microscopic simulations. Thus, whatever figures

have been driven out as a result, there is a

difference between the spike frequency of the HH

equations and the proposed model, which is

actually containing the spikes from microscopic

simulation. In addition, the difference between

spike frequencies becomes smaller when the noise

variance increases.

The Gaussian white noise terms with zero means

which used in the numerical experiments shown

below:

⟨𝜉𝐾(𝑡)𝜉𝐾(𝑡 ,)⟩ = 𝛾𝐾𝑇𝐾[𝛼𝑛(1 − 𝑛) + 𝛽𝑛𝑛]𝛿(𝑡 − 𝑡 ,) (40)

⟨𝜉𝑁𝑎(𝑡)𝜉𝑁𝑎(𝑡 ,)⟩ = 𝛾𝑁𝑎𝑇𝑁𝑎[𝛼𝑚(1 − 𝑚) +𝛽𝑚𝑚]𝛿(𝑡 − 𝑡 ,) (41)

⟨𝜉𝐾(𝑡)𝜉𝐾(𝑡 ,)⟩ =𝛼𝑛(1−𝑛)+𝛽𝑛𝑛

4𝑋𝐾𝛿(𝑡 − 𝑡 ,)

(42)

⟨𝜉𝑛𝑎(𝑡)𝜉𝑛𝑎(𝑡 ,)⟩ =𝛼𝑚(1−𝑚)+𝛽𝑚𝑚

3𝑋𝑁𝑎𝛿(𝑡 − 𝑡 ,)

(43)

⟨𝝃𝒉(𝒕)𝝃𝒉(𝒕,)⟩ =𝜶𝒉(𝟏−𝒉)+𝜷𝒉𝒉

𝑿𝑵𝒂𝜹(𝒕 − 𝒕,)

(44)

The phenomenological methods through numerical

experiments estimate the values of the parameters.

Both these values can calculate an approximation

by phenomenological means, as given in table 1.

Table 1: Constant parameters of the models

𝛾𝐾=10 𝛾𝐾=150 𝑇𝐾=400 𝛾𝑁𝑎=10 𝑤𝑁𝑎

2 =200 𝑇𝑁𝑎=200

The parameter’s value of the membrane which used

in Eq. (23) shows in the table 2. Where XK, XNa,

XCa corresponds for potassium and sodium and

calcium complete numbers of channels, and

multiplied the XK by 4n for potassium to get 4XKn

and also for sodium, calcium resulting 3XNam, XNah

to get open channels with the total number. In

addition, the Markov process has been put into the

gate’s dynamics. The probability of the time t and

time t+∆t is exponential (−αn∆t), which means the

n-gate is closed or becomes open, and the

probability of time t, and time t + ∆t is exponential

(−βn∆t) which means the n-gate is open, and the all

of the parameters αn , βn are the rate of voltage get

at the opening and closing of n-gates. Furthermore,

the same process is applied for the m-gate and h-

gate.

Table 2: Parameter values of the membrane

Ionic

current

Reflection

potential

(mV)

The

conductance

(mS/𝐜𝐦𝟐)

Sodium 𝐸𝑁𝑎 = -115 x1 = 120

Potassium 𝐸𝐾 = 12 x2 = 36

Leakage 𝐸𝐿 = -10.613 x3 = 0.3

Calcium 𝐸𝐶𝑎=136 x4 = 40

Figure 1, the membrane size for potassium is 300,

for sodium is 1000 and 𝐼𝑏𝑎𝑠𝑒 = 4, threshold=0. 005.

The averages are computed in 30 seconds time

window. The comparison between the three curves

used different noise variance, it can be seen that the

proposed model was quite close to the microscopic

simulations and the spike frequency increase and

will be more accurate when the noise variance

increasing (Hodgkin, & Huxley, 1952). The

numbers of the sodium channel calculated as

follows:

No of sodium channel =No. of potassium

channel/3*10.

Fig 1: Mean spiking rates against the noise

variance.

Figure 2, shows the membrane size for potassium is

300, for sodium is 1000, and 𝐼𝑏𝑎𝑠𝑒 = 4, threshold=0.

008. The simulation time window is 30 seconds.

This Figure shows how the speed of spike

frequency as the noise variance increases for both

the proposed model and HH model. In addition,

different noise variance used to show the

comparison between the three curves, it can be seen

also that the proposed model was quite close to the

microscopic simulations.


Fig 2: Shows the relationship between noise

variance and the spike frequency.


300, for sodium is 1000, for calcium is 150, and

𝐼𝑏𝑎𝑠𝑒 = 8. The averages are computed in 30 seconds

time window. The comparison between the three

curves used different noise variance in the

simulations. In addition, the difference between

spike frequencies becomes smaller after the noise

variance increases.

Fig 3: Present the mean spiking rates against the

noise variance.

Figure 4, shows how the speed of spike frequency

as the noise variance increases for both the

proposed model and HH model. The membrane

size for potassium is 1710, for sodium is 5700, and

𝐼𝑏𝑎𝑠𝑒 = 7.25, threshold=0. 005. The simulation time

window is 30 seconds. In addition, different noise

variance used to show the comparison between the

three curves, and the proposed model was quite

close to the microscopic simulations when

increasing the noise variance.

Fig 4: Provides the relationship between noise


In Figure 5, the membrane size for potassium is

1710, for sodium is 5700, for calcium is 1520, and

𝐼𝑏𝑎𝑠𝑒 = 9. The averages are computed in 30 seconds

time window, different noise variance used to show

the comparison between the three curves. The

proposed model was quite close to the microscopic

simulations. In addition, after the noise variance

increases, the difference between spike frequencies

becomes smaller (Hodgkin, & Huxley, 1952). In

the table, 3 different parameter’s value of the

membrane used in Figure 5.

Table 3: Different parameter values of the

membrane

Ionic

current

Reflection

potential

(mV)

The

conductance

(mS/𝐜𝐦𝟐)

Sodium 𝐸𝑁𝑎 = 110 x1 = 130

Potassium 𝐸𝐾 = -15 x2 = 40

Leakage 𝐸𝐿 = 10.5 x3 = 0.2

Calcium 𝐸𝐶𝑎=126 x4 = 36

Fig 5: Mean spiking rates against the noise

variance.


3525, and for sodium is 11750, 𝐼𝑏𝑎𝑠𝑒 = 11,

threshold=0. 005. The averages are computed in 30

seconds time window, and different noise variance

used in the simulations to show the comparison

between the three curves. The proposed model was

affected by noise variance more than the HH

model.

Fig 6: Is the mean spiking rates against the noise

variance.


In Figure 7, the membrane size for potassium is

5676, for calcium is 3525, for sodium is 18920, and

𝐼𝑏𝑎𝑠𝑒 = 10.50, threshold=0. 005. The simulation

time window is 30 seconds. In addition, different

noise variance used to show the comparison

between the three curves (Hodgkin, & Huxley,

1952).

Figure 7: Shows the relationship between noise


Figure 8, shows how the speed of spike frequency

as the noise variance increases for both the

proposed model and HH model. The membrane

patch composed of 10002 of potassium channels

and 33340 of sodium channels, and 𝐼𝑏𝑎𝑠𝑒 = 15,

threshold=0. 005. The averages are computed in 30

seconds time window, and different noise variance

with large membrane size used to show the

comparison between the three curves. It is seen that

the spike frequency increase and will be more

accurate when the noise variance increasing, and

the proposed model were affected by noise

variances more than the HH model.

Figure 8: Provides the relationship between

noise variance and the spike frequency.

III. CONCLUSION

The Hodgkin-Huxley type models accept a set of

parameters as input and generate voltage data

describing the behavior of the neuron. Proposed

model solving the Hodgkin-Huxley equations for a

set of input parameters refers to integrating the

equations in order to obtain the resulting simulated

Gaussian noise and the voltage (potassium, sodium,

calcium) channels. In addition, the channel noise

neuron model was studied well under the influence

of varying input signal, and it has been discovered

that to be the main cause in the unusual increases in

the cell excitability, and in spontaneous firing

membrane size should be small enough. Moreover,

it was discovered that the proposed model keeps on

advancing the spontaneous firing even if membrane

size is larger, wherever the gate of noise is

insufficient for activating the cell. According to

the experimental results, the spiking rate generated

from the model is extremely close to the one from

the actual simulation, doesn’t effect by the

membrane size. In difference, the rate generated

through an increase in noise variance, the stochastic

HH equation was almost similar as compared to the

spikes from the model, and it will be more

accurate. Experimental results also highlight the

mean spiking rates against noise, Which was

introduced by a different membrane size, 𝑰𝒃𝒂𝒔𝒆,

and noise variance, in which three curves represent

the competition between the microscopic

simulation with the proposed model and stochastic

HH equation, Which showed that the proposed

model has worked quite similar to the microscopic

simulations. Overall, the motivation for this work is

to clarify a proposed model, deliberative, and

rigorous methodology for parameter estimation for

the Hodgkin-Huxley models that overcomes all the

limitations of current parameter estimation

methodologies. An important outcome of this

methodology is that the proposed model allows

researchers to study hypotheses that could not have

been studied using any other parameter estimation

method.

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T.,O’Keefe, J. (2007). The hippocampus book.

London: Oxford University Press.

3. Bruce, I.C. (2009). Evaluation of stochastic

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8. Fox, R.F.,& Lu, Y.N. (1994). Emergent

collective behavior of large numbers of

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channels. Physical Review E, 49, 3421-3431.

9. Güler, M. (2013). Stochastic Hodgkin-Huxley

equations with colored noise terms in the

conductances. Neural Computation, 25, 46–

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quantitative description of membrane current

and its application to conduction and

excitation in nerve. Journal of Physiology

(London. Print), 117, 500-544.

11. Jacobson, G.A. et al. (2005). Subthreshold

voltage noise of rat neocortical pyramidal

neurons. Journal of Physiology, 564, 145-160.

12. Kienker, P. (1989). Equivalence of aggregated

Markov models of ion-channel gating. Proc.

R. Soc. Lond. B 236, 269-309.

13. Kole, M.H., Hallermann, S., & Stuart, G.J.

(2006). Single Ih channels in pyramidal

neuron dendrites: properties, distribution, and

impact on action potential output. Journal of


14. Linaro, D., Storace, M., & Giugliano, M.

(2011). Accurate and fast simulation of

channel noise in conductance-based model

neurons by diffusion approximation. PLoS

Computational Biology, 7, e1001102.

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(2002). Comparison of algorithms for the

simulation of action potentials with stochastic

sodium channels. Annals of Biomedical


16. Orio, P., & Soudry, D. (2012). Simple, fast

and accurate implementation of the diffusion

approximation algorithm for stochastic ion

channels with multiple states. PLoS one, 7,

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17. Rowat, P.F., & Elson, R.C. (2004). State-

dependent effects of Na channel noise on

neuronal burst generation. Journal of

Computational Neuroscience, 16, 87-112.

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011903.

http://www.mitpressjournals.org/author/Rowat%2C+P

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An Analysis of Beliefs among UMP International Students towards English

Oral Presentation “Pilot Study"

Abdelmadjid Benraghda*

Centre for Modern Languages and Human Sciences

(CMLHS), Universiti Malaysia Pahang, Malaysia

(UMP)

E-mail: [email protected]

Zuraina Binti Ali, Noor Raha Mohd Radzuan

Centre for Modern Languages and Human Sciences

(CMLHS), Universiti Malaysia Pahang, Malaysia

(UMP)

Abstract—The current study addresses the issue of beliefs among

UMP international university students towards performing oral

presentation in English language. The objective of this study is to

identify their beliefs on speaking and performing oral

presentation in English as foreign language in order to

determine what kind of belief of UMP international students do

possess. The respondents were 30 male and female students. A

survey was used in this study that was adapted from Beliefs

About Language Learning Inventory (BALLI) as well as

personal demographic background questionnaire. The findings

show that the international university students held certain

beliefs which would be detrimental to their speaking and

performing oral presentation in English language as well

Index Terms— Beliefs, Speaking, Oral presentation, English

language

I. INTRODUCTION

English plays a very essential role as a global language.

English has now become a global language; it is used by

speakers from different linguistic and cultural backgrounds

for intercultural communication [10]. English language is

rapidly becoming the world’s most powerful global

language. [11] stated that the dominance of The United

States of America in the 20th

and twenty first centuries have

truly led to the spread of English language [06] as the

international language of communication, tourism,

technology, science, medicine, and many other fields at the

present time [01, 05, 03, 07]. It is considered to be the

essential mode of communication for people from many

different nations [04].

This high degree of importance and concern to English

language is supposed to create a very strong stimulus for

students to use English as an actual means for

communication and oral presentation. This motivation is

strong enough to help students to regulate themselves in

acquiring a language. [09] reported that the language

acquisition becomes easier if learners have more self-

confidence, high motivation and low anxiety. However, oral

communication tests can inherently threaten self-esteem,

particularly for students, is worth considering [04].

Ariogul, Unal and Onursal (2009) posited in their research

which addressed the variances and similarities among

German, English and French language groups in beliefs

towards language learning. The researchers posited that the

three groups held a certain belief about learning foreign

language. In this study, French learners appeared to perceive

a higher motivation, confidence and positive expectations in

language learning among the groups, to speak and learn the

language. French group has been found more perfectionist

than the two groups (English and German learning peers),

also three group students agreed about the importance of

excellent pronunciation in terms of speaking foreign

language.

II. METHODOLOGY

The present research is conducted at Universiti Malaysia

Pahang (UMP) among international university students. The

respondents were 30 male and female university students.

The students were selected randomly from different

faculties like Faculty of Civil Engineering, Faculty of

Computer Science and Faculty of industrial management

and technology.

A. The instrument

In this study, a questionnaire was used to identify the

international university students’ beliefs towards oral

presentation in English language. The questionnaire consists

of two parts. The first part is about demographic

information and the second part is about Students’ beliefs

towards Oral presentation in English language. It was

adapted by Horwitz’s [08] Beliefs About Language

Learning Inventory (BALLI). It consists of two main factors

such as foreign language aptitude and Speaking and

Communication Strategies. The Linkert Scale questions

used a scale ranging from strongly disagree (1) to strongly

agree (5). The reliability of the questionnaire was measured

using the Statistical Package for Social Science (SPSS)

version 20. Cronbach’s alpha was calculated in order to

measure the reliability of the instrument and the percentage

was found to be accepted for the objective of the study

(0.77). Twenty two male and 08 female international

students were conducted in Universiti Malaysia Pahang

(UMP), Malaysia.




A. Foreign language aptitude

(BALLI) Beliefs about language learning Inventory

statements in this study aim at understanding the responses

of students’ beliefs towards speaking and performing oral

presentation in English language. About forty percent of the

international students agreed on the easiness of speaking

and performing oral presentation in English. Thirty-six

percent of university students were quite neutral of

possessing a special ability for presenting and

communicating, here, the students showed no idea on the

statement “I have a special ability for presenting and

communicating. However, when they were asked, which

one is better in terms of speaking and communicating in

English “Women are better in terms of speaking and

communicating in English", twelve to forty-percent neither

agreed nor disagreed on the statement. Forty-percent

disagree on the item “Students who are good at

Mathematics, Science or technology are not good at

speaking English language”. Additionally, 4/6 of the

students both strongly agree and agree on the statement

“Everyone can learn to speak the English language” this

item, particularly, indicates that the students clearly believe

in their abilities of learning to speak the English language.

The rest of the statements were almost consistence with the

answers provided.

B. Speaking and Communication Strategies

60% of the students between strongly agree and agree about

the way you speak in English, according to the statement “I

should not say anything in English until I can say it

correctly”, the students realized the correct process to speak

English in a right way. However, almost 50% respondents

answered on the item of the excellent pronunciation “It is

easy for international students to speak English with

excellent pronunciation” neutral view, so it demonstrates

that students have a certain belief towards pronunciation,

since speaking skills are highly related with pronunciation

so as to perform better way of comprehensive speaking, and

a way for students to deliver a clear and comprehensive

ideas and opinions.

According to the table.01, the finding demonstrates that the

students aged between (21-25) have a positive beliefs than

the students age (17-20) and (25-35). i.e. the second group,

according to the table, indicates that they have positive

beliefs pertaining to speaking and performing oral

presentation in English language (M=3.10, SD .54).

However, the students’ ages (17-20) and (25-35) have

negative beliefs. (M= 2.70, SD .79), (M= 2.63, SD .87)

respectively.

One-way between groups analysis of variance (ANOVA)

was conducted to identify the three groups of students’

beliefs pertaining to oral presentation in English language.

The international university students were divided into three

groups according to their ages (group one: 17-20; group2:

21-25; and group3: 26-35). The table.02 shows ANOVA

output of analysis that statistically there is no significant

difference at the p. < .05 level among the three groups as the

value indicates [(F =1.5), p = .23] and the degree of freedom

(df =2). Despite the real variances between the age groups

in mean scores was slightly small.

IV. CONCLUSIONS

The result that can be found from this study is that the UMP

international students believed of

their ability of speaking English as well as delivering an

English oral presentation. Moreover, they intend to possess

a better correct pronunciation before speaking the English

language. Besides, there is no statistically significant

difference between three groups of the UMP international

students’ beliefs in delivering English oral presentation with

regards to their ages.

Ultimately, UMP international students’ beliefs were found

to be negative pertaining to speaking and performing oral

presentation in English language.

References

1. Altbach, P., Note on the future of AQU:

Comperative perspectives. In towards A Long

terms Strategic Plan for Sultan Qaboos University

Press. 2010

2. Arigol, S., Unal, C., D., Onursal, Foreign language

learners’ beliefs about language learning: a study

on Turkish university students. Procedia Social

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3. Bistong, J. Language choice and cultural

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4. Cheng, C.C., Communication apprehension,

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5. Crystal, D., An Encyclopaedic Dictionary of

Language and Languages. Oxford Blackwell

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6. Crystal, D., English in the New world. Babylonia

2002; 1: 16-17.

7. Graddol, D., English Next: Why global English

may mean the end of “English as a foreign

language” 2006.

8. Horwitz, E. k., Surveying Students’ beliefs about

language learning. In A.Wenden, & J. Rubin

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129

9. Krashen, S., Principles and Practice in Second

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global identities: Perspective from four second

language learners of English in Hong Kong.

Linguistics and Education 2014; 26: 31 39.

11. Friedman, G., The next 100 year: A forecast for the

21st Century. New York: Anchor Books. 2009

Table .1: Descript result

Scale groups

N

Mean Std.

Deviation

Std. Error 95% Confidence Interval for

Mean

Lower

Bound

Upper Bound

beliefs 17-20 6 2.70 .79 .32 1.86 3.53

21-25 15 3.10 .54 .14 2.80 3.41

25-35 9 2.63 .87 .29 1.95 3.30

Total 30 2.88 .71 .13 2.61 3.15

Table .2: ANOVA result for groups of the students’ beliefs towards English oral presentation

ANOVA

Scale Source of

Variance

Sum of Squares df Mean

Square

F Sig

Beliefs Between Groups 1.51 2 .75 1.51 .23

Within Groups

13.50

27 .50

Total 15.02 29

http://www.sdkrashen/


Computational Fluid Dynamics study of Heat transfer

enhancement in a circular tube using nanofluid

Abdolbaqi Mohammed Khdher.*

Faculty of Mechanical Engineering, 26600,

Pekan, Pahang,University Malaysia Pahang,

Malaysia

E-mail address: [email protected]

Wan Azmi Wan Hamzah, Rizalman Mamat

Faculty of Mechanical Engineering, 26600,

Pekan, Pahang,University Malaysia Pahang,

Malaysia

Abstract—A study of computational fluid dynamics

has been conducted to study the characteristics of the

heat transfer and friction factor of CuO/ water &

Al2O3/water nanofluid flowing inside straight tube.

The three dimensional realizable k–e turbulent model

with enhanced wall treatment was utilized. As well as

were used Temperature dependent thermophysical

properties of nanofluid and water. The evaluation of

the overall performance of the tested tube was

predicated on the thermo-hydrodynamic performance

index. The obtained results showed that the difference

in behaviour depending on the parameter that has been

selected to compare the nanofluid with the base fluid.

In addition, the friction factor and the heat transfer

coefficient increases with an increase of the

nanoparticles volume concentration at the same

Reynolds number. The penalty of pressure drop is

negligible with an increase of the volume concentration

of nanoparticles. Conventional correlations that have

been used in turbulent flow regime to predict average

heat transfer and friction factor are Gnielinski

correlation and Pak & Cho correlation, for tubes are

also valid for the tested nanofluids which consider that

the nanofluids have a homogeneous fluid behave.

Index Terms— Nanofluid; Heat transfer; Straight

circular tube: CFD; ANSYS FLUENT

I. INTRODUCTION

Using heat transfer enhancement techniques, can

improve thermal performance of a tubes. The heat

transfer techniques can be classified in to three

broad techniques: Passive techniques that do not

need external power such as rough surfaces, swirl

flow devices, treated surfaces, extended surfaces,

displaced enhancement devices, surface tension

device, coiled tube and additives such as

nanoparticles: Active technique that need external

power to enable the wanted flow modification for

increasing heat transfer such as electrostatic fields,

mechanical aids, jet impingement, suction,

injection, surface vibration, and fluid vibration:

Compound technique is the mix of two or more of

the techniques that mentioned above at one time.

There are many applications of heat transfer

augmentation by using nanofluids to get the

cooling challenge necessary such as the photonics,

transportation, electronics, and energy supply

industries [1-5]. Examined the effect of volume

fraction and temperature on the TiO2/ water

nanofluid viscosity. Results recorded and that have

been analyzed within a temperature range of 25 to

70oC and volume fraction 0.1, 0.4, 0.7 and 1%.

Viscosity of the nanofluid was experimentally

measured by [6]. Utilizing a rheometer. It obtained

as a function of the nanoparticles shear rate and

mass fraction. A double tube coaxial heat

exchanger heated by solar energy using Aluminum

oxide nanofluid presented experimentally and

numerically by [7]. Results showed that the heat

transfer performance of nanofluid higher than base

fluid. Water already used as a base fluid and two

non-similar materials titanium dioxide (TiO2) and

single wall carbon nanohorn (SWCNH). Results

showed empirical correlation equations of

viscosity. Forced convection turbulent flow of

nanofluid (Al2O3 / water) with variable wall

temperature inside an annular tube has been

experimentally investigated by [8]. The results

shown due to the nanoparticle presence in the fluid

the heat transfer has been enhanced. Horizontal

double-tube heat exchanger counter turbulent flow

studied numerically by [9, 10]. Studied the effect of

the SiO2 nanofluid on the automotive cooling

system. The study has been included both

experimental and simulation by FLUENT software.

The results showed that significant of the nanofluid

in heat transfer enhancement and also, good

agreement with other experimental data. The

turbulent flow of nanofluids (TiO2, Al2O3 and

CuO) with different volume concentrations flowing

through a duct under constant heat flux condition

with two-dimensional model has been analysed

numerically [11].

In the current study, the enhancement of heat

transfer in the straight tube is carried out. The CFD

analysis by ANSYS FLUENT software using the

finite volume method is adopted. The heat flux,

Reynolds numbers and the CuO volume

concentration are (5000W/m2), (10

4-10

6) and (1-

3%) respectively. The nanofluids (Al2O3 and CuO)

dispersed to water are utilized. Results were

validated by comparison with experimental data in

the literatures.



II. THERMAL PROPERTIES

The thermal properties of nanofluid such as density

(nf ), specific heat capacity (Cnf), thermal

conductivity (knf) and viscosity (nf) of nanofluid is

obtained by the relation [12].

The assumption of a problem undertaken is that the

nanofluid comports as a Newtonian fluid for

concentration less than 4.0%. For conditions of

dynamic similarity for flow of the two media,

nanoparticles and base liquid water in tube, the

ratio of friction coefficients can be written as

follows:

pnf

mf

f

nfr

Re

Re

C

C

f

ff

3

2 (5)

For base fluid water [13].

250

3160.f

Re

.f (6)

The system of governing criteria can be written as:

f

nf

f

nf

f

nf

r ,Ff

ff

(7)

Number of investigators derive the empirical

correlation from experimental data. [14-18].

1248.0514.0

078.1f

nf

f

nf

f

nf

rf

ff

(8)

Forced convection heat transfer coefficient under

turbulent flow may be estimated by Dittus-Boelter

correlation (Eq. (9)) for pure water in the range of

Reynolds number 104 < Re < 10

5.

40800230 ..

f

fPrRe.D

k

hNu (9)

The modified Dittus-Boelter (Eq. (10)) is

applicable for both water and nanofluids with

spherical shaped nanoparticles dispersed in water

as [14].

23.0012.0

4.08.0

1Pr1

PrRe023.0

nf

f

nf

nf

nf Dk

hNu

(10) (10)

Reynolds number depending on the diameter of the

tube can be defined as:

nf

nf uD

Re (11)

The properties of the solid particles are taken to be

steady in the present operating temperature of 298

K to 320 K. Thus, the properties of nanofluids are

temperature dependent in simulations of this study.

The nanofluids properties at the tube inlet section

shows in table (1).

III. COMPUTATIONAL METHOD

A. Physical model.

Cartesian geometry coordinates of problem

undertaken showed in Fig. (1). The assumption of

this study limited to be steady state, Newtonian and

incompressible turbulent fluid flow, no effect of

gravity, constant thermophysical properties of

nanofluid, heat conduction in the axial direction

and the tube wall thickness was neglected.

High Reynolds number as input parameter

estimated; pressure treatment adopted High

Reynolds number as input parameter estimated;

pressure treatment adopted with 3D realizable k–e

turbulent model with enhanced wall treatment

employed, for all the governing equations the

converged solutions considered for residuals

lower than 25000. The simulation results for

nanofluid were compared with the equations of

Blasius (Eq. (12)) for friction factor and Dittus-

Boelter equation (Eq. (13)) for Nu as:

250

3160.Re

.f (12)

4.08.0eff

f

fPrRe023.0D

k

hNu (13)


A. Governing equations

The single phase approach adopted for nanofluid

modelling according to the assumption of solid

particles (less than 100 nm). The dimensional

conservation equations for steady state mean

conditions for all these assumptions, are as

follows: continuity, momentum and energy

equations [19].

B. Boundary conditions.

Volume concentration nanofluids (1, 2 and 3%) at

25oC base temperature used for nanofluid as input.

while water used as the working fluid for

comparison purposes. CFD studies were conducted

with uniform inlet velocity profile and pressure

outlet condition used at the tube outlet. For an

initial guess of turbulent quantities (k and ε), the

turbulent intensity (I) was specified. Where the

turbulent intensity for each case can be calculated

based on the formula.

8/1Re16.0 I (17) (17)

The wall of tube assumed to be perfectly smooth

with constant heat flux condition of 5000 W/m2

specified on the tube wall. Reynolds number varied

from 1x104 to 1x10

6 at each step of iterations as

input data. The friction factor and Nu introduced as

output data.

C. Grid independence test.

Grids independence in Ansys software for tube as

(20x100) cells and subdivisions in the axial length,

and surface face, respectively tested. To find the

most suitable size of mesh faces, grid independent

test has been performed for the physical model. In

the current study, triangular cells used to mesh the

wall and surfaces of the tube. The grid

independence verified by using different grid

systems and three meshes face of (20x100, 40x100

and 20x200) for pure water. Nusselt number

estimated for all four mesh faces and results were

proper. Nevertheless, in this study, mesh faces

with (20x100) has been adopted to be the best in

terms of accuracy.

A. CFD simulation.

CFD simulations used ANSYS software with solver

strategy and analyse problems. To make numerical

solution possible for governing equations, used

control volume approach to solve the single phase

conservation equations then converting them to

algebraic equations. Simulation results tested by

comparing the predicted results [11, 20& 21], that

used circular heated tube in experimental work.

CFD modelling region might be classified into few

important steps: pre-process step, the problem

geometry that undertaken has been constructed as

flat narrow and the computational mesh was

generated in Ansys 15. It pursued by the physical

model, boundary conditions and supplementary

parameters appropriate described in models setup

and solving stage. All velocity constituents and all

scalar values of the problem computed at the centre

of control volume interfaces whereas the grid

schemes intensively utilized. Throughout the

iterative process accurately monitor of the residuals

done. At the point when the residuals for all

governing equations were less than10−6

, all

solutions assumed to be converged. At last, the

results could be acquired when the iterations of

Ansys Fluent lead to converged solution defined by

a set of converged criteria. The friction factor and

Nusselt number inside the tube might be acquired

all through the computational domain in the post-

procedure stage. It can be seen in Fig. (2).

The grid independent test of the Nusselt number

against Reynolds number with respect all of grid

size mesh. It seems that all of the meshing size are

proper but in this study, the mesh size (20x100)

will be consider as an optimum meshing size

IV. RESULTS AND DISCUSSION

A. Verification process

The verification process is very important to check

the results. It can be perceived in Fig. (3a), with an

increase of Reynolds number the friction factor

decreases under turbulent flow condition. The

Blasius (Eq. (12)) results indicated as a solid black

line. It appears that good agreement among the

CFD results and the equations. On the other side,

the results of the heat transfer coefficient show in

Fig. (3b). The Dittus-Boelter (Eq. (13)) indicated

also, as a solid black line. It seems that there is

good agreement among the CFD analysis and the

equation.

B. The effect of nanofluid type

Fig. (4a), shows the comparison of the friction

factor CFD analysis results for (Al2O3 and CuO)

and pure water nanofluids at turbulent regime. It

seems insigneficant affect of the types of

nanofluids on the friction factor under turbulent

flow condition. Likewise, Fig. (4b), indicates the

Nusselt number with Reynolds number of (Al2O3

and CuO) and pure water nanofluids at turbulent

regimeIt can be perceived that highest values of

Nusselt number detected at CuO nanofluid than

others followed by Al2O3.The reason related to be

highest values of Nusselt number for CuO may be

the highest values of thermal conductivity and

lowest viscosity.


Fig. (5), illustrates the local Nusselt number for

CuO and Al2O3 Nanofluid with volume fraction

and nanoparticles diameter of 3% and 20nm

respectively. The result indicates that increasing

Reynolds number cause to increase local Nusselt

number. Due to the fact that higher Reynolds

number provide higher velocity and temperature

gradient at the tube.

C. The effect of nanofluid volume fraction

Heat transfer coefficient for CuO nanofluid and 1%

to 3% volume fraction with Reynolds number

demonstrates in Fig. (6). It seems that the nanofluid

volume concentration effect is significant. As well

as the heat transfer coefficient for pure water

indicated also, as a solid line. The maximum

deviation is 60% when the volume fraction

increased from 1% to 3%.

D. Validation

Fig. (7), shows comparison among the equation

that provided by Gnielinski and the calculated

values of the Nusselt numbers for Al2O3 nanofluid

[19,20]. As observed, an excellent agreement has

been obtained with calculated values from

theoretical equation within a wide range of

Reynolds numbers. It can be seen the Gnielinski

and Pak and Cho correlations indicated as a dot

black and solid black line respectively.

V. CONCLUSIONS

In the present study, Thermal properties of two

types of nanoparticles suspended in water

calculated depending on the equation of [19].

Forced convection heat transfer under turbulent

flow by numerical simulation with uniform heat

flux boundary condition of straight tube studied.

The heat transfer enhancement due to various

parameters such as Reynolds number and

nanoparticle volume concentration reported. The

governing equations has been solved using finite

volume method with specific presumptions and

proper boundary conditions. The Nusselt number

and friction factor obtained through the numerical

simulation. The study concluded that the

enhancement of Nusselt number and friction factor

are (25%) and (2%) for the narrow at all Reynolds

numbers. The 3% volume concentration of

nanofluid has the highest friction factor values,

followed by (2 and 1%). The Nusselt number of

CuO is the highest value followed Al2O3. There is a

good agreement among the CFD analysis of

Nusselt number and friction factor of nanofluid

with experimental data of [11]. With deviation not

more than 5%.

VI. ACKNOWLEDGEMENTS

The authors would like to be obliged to Universiti

Malaysia Pahang for providing laboratory facilities.

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(2013). A review of forced convection heat transfer enhancement and hydrodynamic characteristics of a

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734–743.

[4]. Meibodi M.E. (2010). An estimation for velocity and temperature profiles of nanofluids in fully developed

turbulent flow conditions. Int. Comm. in Heat and Mass

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[5]. Bahiraei M., Hosseinalipour S.M., Zabihi K. & Taheran E. (2012). Using Neural Network for Determination of

Viscosity in Water-TiO2 Nanofluid. Advances in

Mechanical Engineering, pp. 1687-8132.

[6]. Bobbo S., Fedele L., Benetti A., Colla L., Fabrizio M., Pagura C.& Barison S. (2012). Viscosity of water based SWCNH

and TiO2 nanofluids. Experimental Thermal and Fluid

Science, 36, 65-71.

[7]. Luciu R.S., Mateescu T., Cotorobai V. & Mare T. (2009). Nusselt Number and Convection Heat Transfer Coefficient

for a Coaxial Heat Exchanger Using Al2O3–water ph=5

nanofluid, Bul. Inst. Polit. Ias¸i, t. LV (LIX), f., 2.

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AI2O3/Water Nanofluid as Coolant in a Double-Tube Heat

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NOMENCLATURES

C specific heat [W/kg.°C]

D diameter [m]

E energy [W]

f friction factor

htc convection heat transfer coefficient

[W/m2.°C]

k thermal conductivity [W/m.°C]

Nu Nusselt number [htc .D/Knf]

P pressure [N/m2]

Pr Prandtle number [C.μ/Knf]

Re Reynolds number [ρnf Dh u/Knf]

u velocity [m/s]

μ viscosity [N.s/m2]

ρ density [kg/m3]

τ shear stress [N/m2]

ϕ volume concentration

Subscripts

f liquid phases

p solid particle

nf nanofluid

Fig. 1. Geometrical model

Fig. 2. Grid independent test


a. Friction factor

b. Heat transfer coefficient

Fig. 3. Verification process

a. Friction factor

b . Heat transfer coefficient

Fig. 4. Comparison of the computed

values for pure water and nanofluids in

turbulent regime.

Fig. 5. Local Nusselt number with the

length of tube

Fig. 6. The effect of nanofluid concentration on

the heat transfer coefficient at Reynolds number

Fig. 7. Nusselt numbers validation.


Table 1. Physical properties of metal oxide nano materials


Development of Solar Oven Incorporating Thermal Energy Storage

Application

Roziah Binti Zailan

Faculty of Engineering Technology

Universiti Malaysia Pahang,

Lebuhraya Tun Razak, 26300

Kuantan,Pahang, Malaysia Email:[email protected]

Amir Bin Abdul Razak


Universiti Malaysia Pahang,

Lebuhraya Tun Razak, 26300

Kuantan,Pahang, Malaysia Email:[email protected]

Firdaus Bin Mohamad

Faculty of Mechanical Engineering


26600, Pekan, Pahang

Email:[email protected]

Abstract— A functional solar oven integrated with

thermal storage application was fabricated in a way to

optimize the performance of solar cooking. This paper

presents the characteristic, performance, and efficiency

studies of the solar oven by evaluating different

parameters; aluminum panel, aluminum wall inside the

oven and aluminum wall contained with steel bar as test

load with thermal energy storage. Temperature analysis

towards four set of experiments showing that thermal

energy storage application is the steadfast option in

improving the solar oven performance.

Index Terms— Solar oven; sensible heat storage;

thermal energy storage application; Solar oven energy

efficiency.

I. INTRODUCTION

A solar oven or solar cooker is a device

which exploits sunlight as its energy source, uses

no fuel and involves no operation cost. It is a

form of outdoor cooking and often used in

situations where minimal fuel consumption is

important, or the danger of accidental fires is

high. Most low income families using fuelwood

for cooking or difficulties in getting cooking gas

supply may lead the shifting to solar cooker in

much of the developing world [1,2]. Solar

cooking has become a possible solution as a

substitute for fuelwood in food preparation but its

acceptance is limited partially due to some

barriers. Solar cooker cannot cook the food under

low radiation condition and solely dependent on

the sun radiation which is an inconsistent

variable causing the disabilities of oven to

function at optimal performance [3]. To

overcome this problem, solar oven with the

application of thermal energy storage was

developed with a goal that the heat distribution in

the oven occurred even in sudden decrease of

solar radiation. Capable to retain energy and

maintaining well heat distribution inside the

oven, the thermal energy system also can

preventing the oven from losing heat during the

drop of solar radiation due to changing weather

condition [3,4]. Therefore this study attempts to

contemplate the characteristic and performance

of four sets of functional solar oven.

II. MATERIAL AND METHOD.

A. Designing and sketching process

The design of solar oven is in compliance with

required aspects to ensure the parts are all

functioning. The must considered aspects in

designing are: (i) strength (ii) Ergonomic factors:

user friendly, easy and convenient product (iii)

Glass orientation: Appropriate orientation for

greater solar heat gain. (iv) Reflector: multiple

reflectors to gain more solar radiation incident. (v)

Heat storage: additional heat storage application [4-

6].

B. Material selection

Material selection is particularly done for four main

parts of main body, container, reflector and a

holder. The main body of this solar oven used

plywood material. There are two bodies actually

which is an inner and outer body with 3cm gaps for

air spacing. Trapped air will be used as an

insulation medium for the wall of the main body in

term of convection loses reduction.

Double glasses have been used as the top

cover since it can trap air between the glasses. The

air filled space could reduce heat transfer across the


solar oven body part and preventing heat from

escaping to the outside. Container part is made

using aluminum and it is placed in the main body

for food placement and thermal energy storage

application. There is separate column between food

and thermal energy storage. The aluminum

properties are good in thermal conductivity so as

for thermal energy storage. The reflector made

from aluminum as well due to the light weight

criteria and easily to cutting and shaping.

Aluminum sheet also is a good heat reflector from

the sun direct to the oven and able to reflect

additional heat to the oven. The solar oven can be

aligned in horizontal axis and adjusted 90 degrees

to the direct sun radiation. Technically, the

principle of making solar oven stated that more

glass facing perpendicular to sunlight will gain

more solar incident into the solar oven [4].

C. Thermal energy storage application

Two types of energy storage, sensible heat storage

and latent heat storage (phase change material,

PCM) were considered for the thermal energy

storage application. PCMs have a constant

temperature to store heat energy in it, but sensible

heat storage has no transition temperature to absorb

large energy. Thus, sensible heat storage was

chosen in this study with river rocks as a thermal

energy storage application. The high melting point

criteria makes possible for rocks to absorb much

heat during heating process [3,4].

D. Glazing material

A double glazed box cooker and a double glazing

with suitable thickness and gap in between were

found better than a single thick glazing [7]. The

transparent cover (glazing) is used to reduce

convection losses from the food container through

the restraint of the stagnant air layer between the

food container and the glass. It also reduces

radiation losses from the collector as the glass is

transparent to the short wave radiation received

from the sun but it is nearly opaque to long-wave

thermal radiation emitted by the food container

[7,8].

E. Experimental setup

The experiments were conducted in HVAC

laboratory, Faculty of Mechanical UMP. There are

four experiment sets tested in order to achieve

optimum cooking process. The solar radiation was

assumed to be constant at 1000W/m², equal to

actual mean solar radiation in Malaysia during

daytime. To achieve constant radiation, four sets of

spotlights were used as sources of heat in

replacement of sunlight. The spotlight radiation

was measured using pyrometer. Each experiment

took about 3 hours heating and 30 minutes cooling.

30 minutes cooling data is to determine the

functionality of thermal storage application.

Set 1 (Oven only)

The first set (Fig.1) is a basic oven only to measure

the temperature data. There are three measured

parameters; oven temperature, aluminum tray

temperature and steel bar temperature. All

temperature parameters are measured using

thermocouples. Steel bar is a test load to measure

energy efficiency values of the solar oven.

Figure 1. Set 1 (Oven only)

Set 2 (Oven with aluminum panel)

Meanwhile, Set 2 as in Fig. 2 designed with

combination of oven and additional aluminum

panel. The aluminum panel was installed to

increase the heat incident directly to the oven and

simultaneously increasing the oven temperature.

The measured parameters are same as experiment

1.

Figure 2. Set 2 (Oven with aluminum panel)

Set 3 (Oven with aluminum panel and thermal

storage)


Updating system in Fig. 3 is the third experimental

setup that consists of an oven with aluminum panel

and additional of thermal energy storage

application. The thermal storage application was

technically applied to determine the capability of

oven in maintaining the temperature during the

limitation of heat source. The measured parameters

are oven temperature, aluminum tray temperature,

steel bar temperature and river rock temperature.

Figure 3. Set 3, (a) Oven with panel,(b) Inside

the oven

Set 4 (Oven with panel, thermal storage and

aluminum wall).

The combination system for Set 4 in Fig. 4

technically consists of additional oven parameter;

aluminum panel, thermal energy storage

application and aluminum wall. The aluminum wall

is accounted since it is known that aluminum sheet

is good at reflecting heat.

Figure 4. Set 4, (a) Oven with panel, (b) Inside

the oven


A. Temperature analysis

The temperature analysis for all systems is

illustrated in the corresponding graph. The

temperature trend in Fig. 5 for Set 1 clearly shows

that the highest temperature is steel bar which is

88ºC. Aluminum tray reach maximum temperature

at 85ºC and oven only reach 83ºC. Oven

temperature increases almost at 100 minutes then

slowly stable when reach 120 minutes during the

experiment. Instead, the steel bar is continuously

increased to the maximum value at 180 minutes as

it has a higher capability to store energy than

aluminum tray. Aluminum tray is good at handling

and transferring heat from the oven to the steel bar.

Figure 5. Temperature Analysis- Set 1

Meanwhile, temperature analysis for Set 2 that

illustrated in Fig. 6 shows that the maximum

temperature is steel bar which is 108°C. Then it

followed by aluminum tray at 99°C and oven

temperature at 94°C. It is clear that Set 2 absolutely

increased the overall temperature of the parameter

measured as the aluminum panel eventually

supplies more heat incident to the oven. Oven

temperature also increasing perpendicular to the

time as compared to result for Set 1.


With the addition of thermal energy storage,

temperature analysis for Set 3 in Fig. 7 indicated

that the maximum temperature of oven is at 98°C.

Steel bar followed with 95°C, aluminum tray at

89°C and rock at 77°C. It is also shown that oven

temperature is instantaneously increased in

comparison with the previous set due to the thermal

energy storage application. The oven temperature is

kept almost in stable with less fluctuation and

temperature spike. Temperature of steel bar and

aluminum tray dropped a little bit compared to

previous set likely as the heat energy is absorbed

by rock during heating process.

a b

a b



Eventually, temperature analysis for Set 4

presented in Fig. 8 shows that the maximum

temperature in the oven obtained from the steel bar

at 105°C. Oven at second highest with 102°C,

followed by aluminum tray at 99°C and rock at

90°C. It is also shown that the oven temperature

increased drastically within 30 minutes prior of the

experiment likely happened due to the installed

aluminum wall to reduce heat loses from the oven.

The released heat then was directed to the center of

the oven and kept the steel bar to reach the highest

temperature and defeated the previous set.


B. Solar oven energy efficiency

The solar oven instantaneous efficiency is

calculated from the data collected from the steel bar

as the test load. Energy efficiency of an oven can

be defined as the ratio of energy output (steel bar)

to the energy input (the energy of solar radiation).

Thus the instantaneous energy efficiency of the

oven was calculated as follows:

𝝶= 1 + m.cp (Tf-Ti)/∆t (1)

I.A

From the energy efficiency study, the experimental

results are summarized in Fig. 9. The oven

efficiency is calculated for each 20 minutes period

of experiment. The maximum efficiency is at first

20 minutes for Set 4 with 90% of efficiency. The

efficiency then drastically dropped in perpendicular

with time meanwhile the temperature different

slowly reaching the stability condition. Finally, Set

4 reached 35% of efficiency at the end of heating

processes.

Figure 9. Solar Oven Efficiency

IV. CONCLUSION

In overall, the aluminum panel was specified to

increase the oven and steel bar temperature.

Meanwhile, aluminum wall increases heating rate

to boost up oven, aluminum tray and rock

temperature. Both materials are performing well in

the oven efficiency studies. It was proven during

the experimental study for Set 2 and Set 4 where

the steel bar and oven temperature is highest in

both experiments. Thermal storage application rises

the temperature of oven, aluminum and steel bar

steadily when the radiation started to drop proven

that the using of thermal storage application is the

steadfast option to improve the solar oven

performance.

ACKNOWLEDGMENT

The authors would like to thank faculty of

Mechanical Engineering, University Malaysia

Pahang, for providing equipment and facilities for

this project.

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(2011). A thermodynamic review on solar box

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[8] Kumar, N., Agravat, S., Chavda, T., & Mistry,

H. (2008). Design and development of efficient

multipurpose domestic solar cookers/dryers.

Renewable Energy, 33(10), 2207-2211.

.


Examination of selected Synthesis parameters for composite adhesive-type

Urea-Formaldehyde/activated carbon adhesives

Tanveer Ahmed Khana, Arun Gupta*

a

a Faculty of Chemical and Natural Resources

Engineering, Universiti Malaysia Pahang, Lebuhraya

Tun Razak, 26300, Kuantan, Pahang, Malaysia


S. S. Jamaria, Rajan Jose

b

a Faculty of Chemical and Natural Resources

Engineering, Universiti Malaysia Pahang, Lebuhraya

Tun Razak, 26300, Kuantan, Pahang, Malaysia b Faculty of Industrial Science and Technology,

University Malaysia Pahang, Lebuhraya Tun Razak,

26300, Kuantan, Pahang, Malaysia

Abstract— This paper addresses synthesis of

activated carbon particles by pyrolysis of wood fibers

and their dispersion in urea formaldehyde with an aim

to optimize a stable activated carbon particles/urea

formaldehyde adhesive. All the adhesive hybrids were

characterized with X-ray diffractometry (XRD) and

Fourier transform infrared spectroscopy (FTIR), while

the dispersion of activated carbon particles was studied

with Field Emission scanning electron microscopy

(FESEM). Thermo gravimetric analysis (TGA) shows

that activated carbon particles have little high effect in

the thermal stability of the UF adhesive. It is observed

in the TGA graph that the thermal stability of the UF

based activated carbon particles is higher than UF only.

The scanning micrographs provided confirmation of

the smoother surfaces in the UF adhesive made with

activated carbon particles. This was attributed to the

better encapsulation of activated carbon particles by the

polymer matrix.

Index Terms— Wood Fibers, Urea-Formaldehyde,

Activated Carbon Particles

I. INTRODUCTION

Urea - formaldehyde adhesives have been widely

used by the wood composites industry for over 100

years, as well their concert in the manufacture of

composite wood panels, because they have a low

cost and high reactivity. Their weakness is lower

water resistance and high formaldehyde emissions

from wood panel, resulting in lower bond strength

amino - methylene it.

To address this problem, this has made such a

struggle, to change the method of synthesis using

various types of adhesives and additives or

hardener, and others [1-3]. The main goal for

modern adhesives industry is to produce effective

Urea Formaldehyde adhesive with very low

formaldehyde emissions. In addition to the drastic

reduction in the emission of formaldehyde with a

significant increase in resistance Urea

Formaldehyde stability bound composite wood can

extend applications and markets for these products.

To reduce formaldehyde emissions preferably one

effective approach is to reduce the molar ratio F/U

for adhesives synthesized [4-6]. But this shows that

crosslinking is reduced and thus, lower

performance of the adhesive, with respect to water

resistance and mechanical properties [4]. Several

studies have focused on modifying the synthesis

parameters Urea Formaldehyde adhesive than

lower molar ratio F/U. By controlling parameters

such as pH of reaction [7-9], the introduction of a

second addition of urea [10] and the use of

additives [11, 12] .

Additives have an effect on the properties of the

renewal adhesive Urea Formaldehyde [13]. The

addition of a small quantity of melamine has been

used so far in the case of the more challenging

applications. The use of other additives, such as

formaldehyde catcher also had been tested [14].

Addition trimethoxymethylmelamine and

dimethoxymethylmelamine as crosslinking agents

available both for resistance [15]. The carbon fiber

in Urea Formaldehyde has been reported to

improve the mechanical properties of the medium

density fibre board [16] and, therefore, need to be

studied in depth.

This work aims to analyze the effect of using

activated carbon particles in the Urea

Formaldehyde adhesive in order to optimize the

stable of activated carbon particles/urea

formaldehyde adhesives. Therefore, this study

investigates the effect of activated carbon particles

on thermal behavior of UF adhesive, using TGA.

Also, a thorough characterization of the new hybrid

was performed using FTIR, FESEM and XRD. The

novelty of this work is the use of activated carbon

particles in UF adhesive for the first time.

II. EXPERIMENTAL

A. Materials



Urea-formaldehyde (UF) adhesive liquid used for

this study were collected from Dynea Malaysia

Sdn. Bhd. The viscosity UF resin at 30 °C is 170

centipoises, 8:27 pH, density of 1.286 kg/m3 and

gel time at 100 oC is 36s.

B. Activated carbon particles

Activated carbon particles were synthesized by

pyrolysis of wood fibers using a furnace in inert

conditions at 450 °C for 2 h. Carbon particles were

pulverized using a Retsch ZM 200 Chemtical

Grinder at 18000 rpm for 30 seconds. The carbon

content of activated carbon obtained 74.09 % using

the system element analysis (CHNS analyzer).

C. UF adhesive/ activated carbon particles

hybrids synthesis

The final ratio of urea-formaldehyde (UF) adhesive

collected from Dynea Malaysia Sdn. Bhd has an F:

U = 1.07. Activated carbon particles added in

adhesives Urea Formaldehyde in stage 1, 2.5, 3.5

and 5 % (w / w). The UF / activated carbon

particles have a mechanical mixture i.e., stirred for

30 minutes before use. The pure UF resin samples

were named CF -0 while the mixture is named CF -

1, -2.5 CF, CF - 3.5 and CF - 5 each.

D. Fourier Transform Infrared spectroscopy

Hybrid adhesive was studied by FTIR as a partially

cured UF/activated carbon particles adhesive in

solid form. The partially cured adhesive was

prepared by drying the liquid adhesive in an oven

at 105 ° C for 2 h. The FTIR transmittance spectra

were obtained with a Perkin Elmer Spectrum 1000

- spectrometer in the spectral region 400-4000 cm -

1, with a resolution of 2 cm- 1

and 50 scans. For

adhesive in solid samples, KBR pellets with a

weight of 1 % from the resulting powder material.

E. Field Emission scanning electron

microscopy (FESEM)

Morphological structure prepared samples were

investigated in JEOL JSM - 7500F. The sample

consists of carbon -coated to provide good

conductivity electron beam. Operating conditions

have been accelerating voltage of 20 kV;

investigate the current 45 nA, and calculate time 60

s.

F. X-ray diffraction (XRD)

X -ray Diffraction (XRD) measurements of soled

Urea Formaldehyde adhesive containing activated

carbon particles and without activated carbon

particles were studied. The X-ray diffraction

(XRD) was carried out in an XRD analyzer. The

samples were scanned in between 3-80 ° 2θ at 1deg

/ min. The spacing between the layers (D002)

carbon particles was calculated according to the

Bragg equation: λ = 2d sinθ.

G. Thermogravimetric analysis (TGA)

Thermo Gravimetric Analysis (TGA) was used to

measure the amount and rate of change in material

weight, are important as a function of temperature

or time in a controlled atmosphere. This technique

can characterize materials that exhibit weight loss

or gain due to decomposition, oxidation, or

dehydration. The Standard Practice for Thermo

gravimetry follows the ASTM 1582 method.

Thermal stability were investigated by non-

isothermal thermo gravimetric analysis (TGA)

using a TA Instruments. Samples (6 ± 0.2 mg)

stored in alumina crucibles. An empty alumina

crucible was used as reference. The samples were

heated 30-600 °C in a stream of 50ml/min nitrogen

with a heating rate of 10 °C/min.


A. Characterization of resins, interactions

with activated carbon particles

Urea Formaldehyde adhesive chemical structure

can be expressed as a poly (methylene

hydroxymethylureas methylene ether which caused

by condensation reaction with an aqueous solution

of urea formaldehyde. Activated carbon particles

have a surface carbon and some hydrogen and

nitrogen that can react with the hydroxyl end

groups of macromolecules mostly through

condensation reactions [17]. In the first step of a

side reaction between urea and formaldehyde 1, 3 -

bishydroxymethyl (dimethylolurea) urea is

produced, which has two hydroxyl groups and can

interact with the carbon and make C-OH attraction

mode. To confirm the formation of UF/activated

carbon particles mixed adhesive, all samples were

studied by FTIR spectroscopy and the spectrum is

recorded is shown in Fig. 1. diverse and broad

peaks in the spectrum CF- 0 resin is caused by

entanglement adhesive polymer structure .

In the spectrum of adhesives Urea Formaldehyde,

broad peak around 3350-3450 cm - 1 can be

attributed to hydrogen bond O- H and N-H. C- OH

attraction mode can be found at 3440 cm-1

and

1508 cm-1

, respectively. Peak occurs at 1161 cm-1

is characteristic C-O is stretching in lactonic,

alcohol groups and carboxylate moieties [18]. The

peak from 1600 to 1650 cm-1

multiples and several

overlapping peaks appear in the spectrum of pure

Urea Formaldehyde adhesive. This peak is assigned

to CO stretching amide I and II, as well as -N-H

scissors of the amide I. In the area of 1500-1600


cm-1

peak overlap is caused by -N-H bending

vibration of the amide II. Multiple peaks at 1460-

1470 cm-1

can be associated with C-H bending

vibration of CH2-N group, while in 1320-1450 cm-1

small peak can be attributed to the strain C - N

vibrations of amide I and II , he was also appointed

for C - H stretching and -O-H bending vibration of

alcohol [19] . Strong and broad peak at 1250 cm-1

assigned to the C-N stretching vibration of amide II

[20]. Peak at 1161 cm-1

is caused by two strains of

asymmetry N-CH2-N and asymmetric stretching -

O-C-C-ether network [21]. The FTIR spectrum and

adhesives Urea Formaldehyde UF / CF are shown

in Table 1.

Fig. 1. FTIR transmittance spectra of CF-0, CF-1, CF-2.5, CF-3.5 and CF-5.

A spectrum UF/activated carbon particles adhesive

shows a strong absorption band between 3446-3448

cm-1

region and 3421 cm-1

for pure samples. It is

characteristic of hydrogen bonded NH-NH2 formed

by reaction methylenization during cross [20].

Strong absorption bands observed in the spectrum,

near 1648, 1639, 1647, 1647 and 1640 cm-1

for

CF-0, CF-1, CF-2.5, CF-3.5 and CF-5, assigned to

strain C=0 ( amide -I ) in the group -CONH2 . Very

strong absorption band around 1508.56, 1509, and

1509.08 to 1509.10 for CF-0, CF-1, CF-2.5, CF-3.5

and C-5, it may be caused by -NH (amide II) is

given. Stretching vibration between 1350-1400 cm-

1 for sample adhesive, represented by CH bending

mode in CH2/CH2OH/N-CH2-N. Absorption band

intermediate between 1115-1162 cm-1

may arise

due to stretching vibration group –N-CH2-N of

ether linkages.

B. X-ray diffraction (XRD)

The X-ray diffraction (XRD) profiles of all studied

adhesive hybrids in powder form are presented in

Fig. 2.

Fig. 2. XRD pattern of UF containing different ratios of activated carbon particles.

All samples XRD patterns confirm that all adhesive

hybrids are mainly amorphous with a small degree

of order, while the presence of activated carbon

particles does not change the appearance of the

pattern. Only in CF-1 and CF-3.5 pattern, a peak at

22o seems to be a little wider than in UF-0, which

can be caused by the presence of activated carbon

particles and a higher degree of amorphisation. In

fact, the narrow sharp diffraction peak shows the

crystal structure, while the peak width is

amorphous structure. XRD pattern of adhesives

Urea Formaldehyde containing different ratios of

activated carbon particles shows that changes in the

network structure that occurs in the amorphous

region of Urea Formaldehyde adhesive.

C. Field Emission Scanning Electron

Microscopy (FESEM) Analysis

Adhesive surface morphology was studied by

FESEM. All the pictures were taken with an 8000 ×

magnification. In the case of adhesives, some

microphotographs of samples CF-0, CF-1, CF-2.5

and CF-5 are presented in Fig. 3.

Fig. 3. FESEM microphotographs of (a) CF-0, (b) CF-1, (c) CF-2.5 and (d) CF-5 samples.

These images show that the activated carbon

particle concentration increases, a larger amount of

light areas appear on the sample. These

micrographs clearly show that in comparison to

morphological differences compared with the

morphology of polymer composites CF-0, CF-1,

CF-2.5 and CF-5 (Figure 3a - 3d). It is seen that

when Urea Formaldehyde adhesive matrix

reinforced with different load activated carbon

particles, some morphological changes occur

depending on the bond between the loading of

different activated carbon particles and adhesives

Urea Formaldehyde. In lower case load of activated

carbon particles (1 or 2.5 %) bonding between

matrix and reinforcement is higher and when the

content of the activated carbon particles are higher

loading of lower bond between matrix and

reinforcement.

D. Effect of activated carbon particles on the

thermal stability of the resins

A carbon material when added to a polymer matrix

increases the thermal stability of the polymer [16].

It was also expected in this study and to evaluate

the TGA has been used. In Fig. 4 TGA curves for

all samples are presented.

Fig.4. TG curves of all samples: (1) CF-0, (2) CF-1, (3) CF-2.5, (4) CF-3.5, and (5) CF-5.

It is revealed that the addition of activated carbon

particles for Urea Formaldehyde adhesives have

little effect on thermal stability. From the TGA

curves it is clear that the study can be divided into

two regions [22]. The first step corresponds to 3-

4.5 % loss in mass and it applies to all the samples

between 50 and 100 oC. This step corresponds to

the evaporation of water from the sample. At a

temperature of 100-200 oC slow formaldehyde

emissions cause massive losses small in each

sample. The main degradation steps initiated above

200 oC, when starting the chain scissions and

encourage the formation of radicals formed cyclic

structure in the polymer chain. This process leads

to solving many of the polymers. Cured adhesive

degradation began with the release of formaldehyde

from the group dimethlene ether [21] and occurs

when the maximum degradation rate of methylene

ether stable relationship collapses [22]. Above 200 oC, which compares the mass loss curves for all

samples adhesive, it is seen that the hybrids affect

the thermal stability of adhesive . The adhesive

with a lower content of activated carbon particles

have a value intermediate between those CF-1 and

CF-2.5, which is slightly higher than UF -0.

However, at high temperatures, Urea

Formaldehyde adhesive with activated carbon

particles have better performance in thermal

stability than without particles of activated carbon.

IV. CONCLUSIONS

The purpose of activated carbon particles is added

to improve the properties of activated carbon

particles/urea formaldehyde adhesives. The degree,

which is done, is very dependent on the

deployment of additional material into the

adhesive. In this work, it is confirmed from FTIR

spectroscopy that activated carbon particles can

make hydrogen bonds with UF adhesive. However,

this is not effective to have a fine dispersion of

particles of activated carbon as individual particles

in the polymer matrix and some aggregates are

formed. Furthermore, the activated carbon particles

as additive in adhesives in UF likely to affect many

properties of the hybrid adhesive. TGA graph of

this was found that the thermal stability of UF

based activated carbon particles is higher than the

pure UF. Scanning micrographs provided evidence

of the smooth surface at UF with activated carbon

particles.

ACKNOWLEDGMENTS

The authors are grateful to University Malaysia

Pahang to provide post-graduate scholarships and

funding for the completion of this study.

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and high-temperature stability of cured urea–formaldehyde

resins. J. Wood Sci. 2000; 46:303–309.

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[11] Dutkiewicz J. Preparation of cured urea–formaldehyde

resins of low formalde-hyde emission. J. Appl. Polym. Sci.

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[12] Pizzi A. Advanced Wood Adhesives Technology. Marcel

Dekker, New York 1994.

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panels and ways to reduce them. Monument Environ. 1994;

2:57–64.

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Resins and Process for Manufacturing Them, Louisville, Ohio. US Patent 4410685: 1983.

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Synthesis and Characterization of Carbon Fibers and its

Application in Wood Composite. BioResources 2013; 8(3): 4171–4184.

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approach to prepare poly(ethylene terephthalate)/silica

nanocomposites with increased molecular weight and fully adjustable branching or crosslinking by SSP, Macromol. Rapid

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.

Maximum system loadability based on optimal multi facts location

Maher A. Kadim

Department of operation management, Al-farabi

Institute For High Studies, Baghdad, Iraq

Yahya N. Abdalla

Department of operation management, Al-farabi

Institute For High Studies, Baghdad, Iraq

Abstract— Parallel computation algorithm show a great

challenging in order to improve the performance of power system

field by optimizing the location of multi Flexible AC

Transmission System (FACTS) in. In order to identify the

optimal location for these two devices, Backtracking Search

algorithm (BSA) will be used to maximize power system

loadability. Two types of FACTS devices which are Thyristor

Controlled Series Compensator (TCSC) and Unified Power Flow

Controller (UPFC). To find the optimal location, both buses

voltage limit and lines thermal limit are taken as constraints.

The optimizations are performed on 14 IEEE bus system and

compared with other two optimization techniques based on multi

FACTS devices location, and their setting for maximum system

loadability.

Index Terms— FACTS, TCSC, UPFC, Backtracking Search

I. INTRODUCTION

The industrialization increasing the urbanization of life

style has led to increasing dependency on the electrical

energy. This has resulted to rapid growth of power system

stabilizer which also resulted into few uncertainties. For

example, power disruption is one of the major problems

which can affect the country economy. When there are

rapid change in technologies and demand, transmission

system being pushed to operate near stability limits which

mean at the same time reaching the thermal limits. These

constraints affect the quality of power delivered. However,

these constraints can be suppressed by enhancing the power

system control. One of the best methods for reducing these

constraints is using FACTS devices. Unfortunately,

sometimes the generation pattern are resulting in heavy

flow, provide greater losses as well threatening stability of

the system. Transmission line need to transfer the power in

efficiency and it is difficult to maximize the system

loadability. All this problems had limited the amount of

electric power which can be transmitted between two

locations through a transmission network [5].

For overall, FACTS devices are capable to maximize the

system loadability to the transmission line capacity limits

[1]. By utilizing FACTS devices, it can increase the

transmission system reliability and availability besides raise

the dynamic and transient grid stability [2]. Donsion [1]

study regarding Power Quality, Benefits of Utilizing

FACTS Devices In Electrical Power Systems state that

FACTS devices can be divided in three groups depending

on their switching technology which is mechanically

switched (such as phase shifting transformers), using

IGBT’s, and thyristor switched. However, in the study of

Power System Operation and Control Using FACTS

Devices, [2,3] found that the FACTS devices can be divided

into four categories which are series controllers, shunt

controllers, combined series – series controllers, and

combined series - shunt controllers. Even though both of

this study has some different in order to categorised FACTS

devices, but still their share same point of view regarding

the basic model of FACTS devices. Theoretically, series

controllers inject voltage in series with the line. Meanwhile,

the shunt controllers inject current into the system at the

point of connection. The combined series–shunt controllers

inject current into the system with the shunt part of the

controllers and voltage in series in the line with the series

part of the controllers [2,4]. Both series controller and shunt

controller only supplies or consumes reactive power. On

the other hand, series-shunt controller can have an exchange

of active power between them through their link when the

series and shunt controllers are unified. In order to resolve

this optimization problem, provider or engineers will

identify the optimal location to install the devices. Mostly,

they will use application of Artificial Intelligence (AI)

techniques. Some of AI techniques are Genetic Algorithm

(GA), Particle Swarm Optimization (PSO), and many more

ways. For this project, two types of FACTS devices will be

used which are Thyristor Controlled Series Compensator

(TCSC) and Unified Power Flow Controller (UPFC). There

are several aspects implanted to optimization the problem

which include finding of optimal location, it optimal size

and its controller parameters.

In this paper, the maximum benefit can be obtained.

Iimplementing the Backtracking Search Optimization (BS)

Technique using MATLAB software to find the parameter

(types and optimal location) of FACTS device. Using the

application of BSA to locate the optimal placement of

FACTS Device by reducing the total power losses and

improve voltage profile in IEEE 14-bus system.

II. RELATED WORK

A. Flexible Alternating Current Transmission

System (FACTS) Devices

FACTS devices can be fully utilized to control power flow

and enhance system stability. Particularly with the current

situation, there is an increasing interest in using FACTS

devices in the operation and control of power system

stabilizer. A better utilization of the existing power system

stabilizer to increase their capacities and controllability by

installing FACTS devices becomes imperative. FACTS

devices are effective alternatives to new transmission line

construction. Judge by this situation, flexible power system

operation according to power flow control capability of

FACTS devices should be consider.

According to IEEE, FACTS which is the abbreviation of

Flexible AC Transmission Systems, is defined as an

alternating current transmission systems incorporating

power electronics based and other static controllers to

enhance controllability and available power transfer

capacity.

The FACTS controllers are classified as follows:

Thyristor controlled based FACTS controllers such as

TSC, TCR, SVC, TCSC etc.

VSI based FACTS controllers such as SSSC,

STATCOM, UPFC etc.

The main drawback of thyristor controlled based FACTS

controllers is the resonance phenomena occurs but VSI

based FACTS controllers are free from this phenomena. So

that the overall performance of VSI based FACTS

controllers are better than of that the thyristor controlled

based FACTS controllers. For this project, two types of

FACTS devices will be used which is Unified Power Flow

Controller (UPFC) and Thyristor Controlled Series

Compensator (TCSC).

Unified Power Flow Controller (UPFC): The UPFC allows

a secondary but important function such as stability control

to suppress power system oscillations improving the

transient stability of power system. Basically, UPFC is a

combination of shunt and series controller. It has three

controllable parameters namely, the magnitude of the

boosting injected voltage (VT), phase of this voltage (ØT)

and the exciting transformer reactive current (Iq).

Therefore, UPFC is a device which can control

simultaneously all three parameters which are line

impedance, line voltage and phase angle of line power flow

[6,7]. Since UPFC is combination between series and shunt

controllers, it can be employed to release power flow

congestion and control voltage bus simultaneously. Also,

by combination of a STATCOM and an SSSC, UPFC

actually able to control both the active and reactive power

flow in the line. In fact, UPFC is the most multipurpose one

among the available FACTS devices that can be used to

enhance steady state stability, dynamic stability and

transient stability [8]. By referring to [9] study on Study

and Effects of UPFC and its Control System for Power Flow

Control and Voltage Injection in a Power System (2010), he

says that basic components of UPFC are two voltage source

inverters (VSIs) sharing a common dc storage capacitor, and

connected to the power system through coupling

transformers. One VSI is connected to in shunt to the

transformers via shunt transformers, while the other one is

connected in series through a series transformer as shown in

Figure 1 below [9]. This is same as researched done by [8],

which in his research, he say that by the same condition as

[9] Vibhor statement above, the DC side of the two

converters is connected through a common capacitor,

provides DC voltage for the converter operation. Hence, the

power balance between the series and shunt converters is a

prerequisite to maintain a constant voltage across the DC

capacitor [8]. The voltage source at the sending bus is

connected in shunt and it is called as shunt voltage source.

While, the second source known as series voltage source, is

placed between the sending and the receiving busses. The

UPFC is placed on high-voltage transmission lines. In order

to allow the use of power electronics devices for the UPFC,

the arrangement needs step-down transformers.

Fig 1. Structure of Unified Power Flow Controller

(UPFC)

The components of equivalent power injections at buses i

and j, 𝑃𝑖 , 𝑄𝑖 , 𝑃𝑗 and 𝑄𝑗 are formulated as follows:

𝑃𝑖 = 0.02 𝑟𝑏𝑠𝑒𝑉𝑖2 sin 𝛾 − 1.02𝑟𝑏𝑠𝑒𝑉𝑖𝑉𝑗 sin(𝜃𝑖 − 𝜃𝑗 + 𝛾) (1)

𝑄𝑖 = −𝑟𝑏𝑠𝑒𝑉𝑖2 cos 𝛾 (2)

𝑃𝑗 = 𝑟𝑏𝑠𝑒𝑉𝑖𝑉𝑗 sin(𝜃𝑖 − 𝜃𝑗 + 𝛾) (3)

𝑄𝑗 = 𝑟𝑏𝑠𝑒𝑉𝑖𝑉𝑗 cos(𝜃𝑖 − 𝜃𝑗 + 𝛾)

(4)

Where 𝑃𝑖 , 𝑄𝑖 and 𝑃𝑗 , 𝑄𝑗 : respectively the equivalent omplex

power injected into the two bus bars, buses i and j, which

are practically the resultant power injections contributed by

both the series and shunt branches of UPFC.

𝑉𝑖, 𝑉𝑗 : respectively the phase angle components of the

voltages on buses i and j.

𝑏𝑠𝑒 : the leakage susceptance of the series coupling

transformer.

r and 𝛾 : respectively, magnitude and phase and angle of

series voltage source, UPFC parameters.

Thyristor Controlled Series Compensator (TCSC):

Thyristor Controlled Series Capacitor (TCSC) is one of the

important members of FACTS devices, used for many years

to increase the power transfer as well as to enhance system

stability. It can have various roles in the operation and

control of power systems. These includes scheduling power

flow, decreasing unsymmetrical components, reducing net

loss, providing voltage support, limiting short-circuit

currents, damping the power oscillation, and enhancing

transient stability [8]. Based on the researched, TCSC

configurations comprise controlled reactors in parallel with

sections of capacitor bank. This combination allows smooth

control of fundamental frequency capacitive reactance over

a wide range. The capacitor bank of each phase is mounted

on a platform to enable full insulation to ground. The

thyristor valve contains a string of series connected high

power thyristor (surge inductor). This was supported by

Murali, [8] mentioned that TCSC consists of three main

components which are capacitor bank C, bypass inductor L

and bidirectional thyristors SCR1 and SCR2 as the main

circuit shown in Figure 2. The adjustment of the TCSC

reactance in accordance with a system control algorithm is

controlled by the firing angles of the thyristors, normally in

response to some system parameter variations. According to

the variation of the thyristor firing angle or conduction

angle, this process can be modelled as a fast switch between

corresponding reactances offered to the power system [8].

Fig 2. Structure of Thyristor Controlled Series

Compensator (TCSC)

The corresponding power injection model of TCSC,

incorporated in the transmission line, is shown in Figure 2

[10-11]. The real (𝑃𝑖𝐹) and reactive (𝑄𝑖

𝐹) power injections,

which is inserted with TCSC at buses i and j are given by

the following equations:

𝑃𝑖𝐹 = 𝑉𝑖

2∆𝐺𝑖𝑗 − 𝑉𝑖𝑉𝑗[∆𝐺𝑖𝑗 cos(𝛿𝑖 − 𝛿𝑗) + ∆𝐵𝑖𝑗 sin(𝛿𝑖 − 𝛿𝑗)]

(5)

𝑄𝑖𝐹 = −𝑉𝑖

2∆𝐵𝑖𝑗 − 𝑉𝑖𝑉𝑗[∆𝐺𝑖𝑗 sin(𝛿𝑖 − 𝛿𝑗) − ∆𝐵𝑖𝑗 cos(𝛿𝑖 −

𝛿𝑗)] (6)

𝑃𝑗𝐹 = 𝑉𝑗

2∆𝐺𝑖𝑗 − 𝑉𝑖𝑉𝑗[∆𝐺𝑖𝑗 cos(𝛿𝑖 − 𝛿𝑗) − ∆𝐵𝑖𝑗 sin(𝛿𝑖 − 𝛿𝑗)]

(7)

𝑄𝑗𝐹 = −𝑉𝑗

2∆𝐵𝑖𝑗 + 𝑉𝑖𝑉𝑗[∆𝐺𝑖𝑗 sin(𝛿𝑖 − 𝛿𝑗) + ∆𝐵𝑖𝑗 cos(𝛿𝑖 −

𝛿𝑗)] (8)

Where,

∆𝐺𝑖𝑗 =𝑥𝑐𝑟𝑖𝑗(𝑥𝑐 − 𝑥𝑖𝑗)

(𝑟𝑖𝑗2 + 𝑥𝑖𝑗

2) {𝑟𝑖𝑗2 + (𝑥𝑖𝑗 − 𝑥𝑐)

2}

(9)

∆𝐵𝑖𝑗 =𝑥𝑐(𝑟𝑖𝑗

2 − 𝑥𝑖𝑗2 + 𝑥𝑐𝑥𝑖𝑗)

(𝑟𝑖𝑗2 + 𝑥𝑖𝑗

2) {𝑟𝑖𝑗2 + (𝑥𝑖𝑗 − 𝑥𝑐)

2}

(10)

Where,𝑉𝑖, 𝑉𝑗 and 𝛿𝑖 , 𝛿𝑗 are voltage and angle at buses i and j,

respectively. 𝐺𝑖𝑗 and 𝐵𝑖𝑗 are the conductance and

susceptance of the line-ij.

B. Backtracking Search Algorithm (BSA)

BSA is initialized with a group of random particles

(solution) which searched for optima by updating

generations [12-13]. In the every iteration, each particle is

updated by following two “best” values. The first one is the

best solution which has achieved do far. This value called

pbest another one called gbest which is global best, this

value is tracked by the particle optimizer that is the best

value, obtained so far by any particle in the population. The

basic algorithm of the canonical BSA can be described as

follow:

Step 1: Initialize n particles x1∈ 𝑅𝑁𝑑𝑖𝑚and velocities

v1∈ 𝑅𝑁𝑑𝑖𝑚

Step 2: Compute fitness function f (i) for each particle;

Step 3: Find current best position for each pbest and gbest

Step 4: For each particle, update the particle velocities and

positions:

Step 5: If the stop criterion is satisfied, gbest is the final

optimal solution with fitness f (g); Otherwise, return to Step

2.

III. PROPOSED METHOD

A. System operation condition

Voltage Limit: Both utility and customer equipment are

designed to operate at a certain rated or nominal supply

voltage. A large, prolonged deviation from this nominal

voltage can adversely affect the performance of, as well as

cause serious damage to system equipment. Current flowing

through the transmission lines may produce an unacceptable

large voltage drop at the receiving end of system. This

voltage drop is primarily due to the large reactive power

loss, which occurs ad the current flows through the system.

If the reactive power produced by generators and other

sources are not sufficient to supply the system’s demand,

voltage will fall, outside the acceptable limit that is typically

±6% around the nominal value. System often requires

reactive support to help prevent low voltage problems. The

amount of available reactive support often determines

power transfer limits. A system may be restricted to a lower

level of active power transfer than desired because the

system does not possess the required reactive power

reserves to sufficiently support voltage.

Thermal Limit: Thermal limits are due to thermal capability

of power system equipment. As power transfer increases,

current magnitude increases a key to thermal damage. For

examples in a power plant, sustained operation of units

beyond their maximum operation limits will result in

thermal damage. The damage may be to the stator windings

or to rotor windings of unit. Both active and reactive

powers play a role to current magnitude. Out in the system,

transmission lines and associated equipment must also

operate within the thermal limits. Sustained excessive

current flow on an overhead line causes the conductors to

sag thus decreasing the ground clearance and reducing

safety margins, extreme levels of current flow eventually

damage the metallic structure of the conductors producing

permanent sag. Unlike overhead lines, underground cables

and transformers must depend on insulation other than air to

dissipate the generated heat. These types of equipment are

tightly restricted in the amount of current they can safely

carry. For the equipment, sustained overloading will result

in a reduction in services life due to damage to the

insulation. Most power system equipment can be safely

overloaded. The important aspect is how much is the

overload and how long it does [14].

B. Optimized Algorithm

The overall optimization algorithm shown in Figure 3.

Fig 3. Over all proposed Algorithm

IV. RESULT AND DISCUSSION

In order to analyse the optimal location of multi FACTS

devices, the IEEE 14-bus system simulation programming

are done. From the analysis, we can found the optimal

location and their behaviour towards power system network

based on the result get. The difference between without

installed FACTS device and with installation of FACTS

devices are being observed by take account the type, setting

and number of devices. All of this is to get the better result.

Actually, this project is about to evaluate, analysis and

develop the best performance, effects and behaviour of

installing the multi FACTS devices for optimal location in

power system. In this case, TCSC and UPFC have been

choosing as a multi FACTS device. Therefore, we must

first generate the IEEE 14 – bus system. With the line and

bas data collected, we can do power flow analysis using

Newton-Raphson method. Firstly, a power flow analysis

without the installation of any FACTS devices (UPFC and

TCSC) been done. All the data will be interpreted and

collected. After that, the FACTS device either in single or

combination of both devices will be installed in power

system transmission line. This is to find the optimal

location and types of multi FACTS devices by using load

flow analysis. This will mainly focus at bus system of

transmission line where optimal location of FACTS as the

priority issue. Finally, the best location of two UPFC, two

TCSC, or both combination UPFC and TCSC devices is

investigated by using the application of BSA.

In this project assessment, standard IEEE 14-bus system is

applied by using MATLAB program in order to test the

algorithm of the system. For IEEE 14-bus system shown in

Figure 4, there are 14 buses, out of which 5 are generator

buses. Bus 2 is the slack bus while bus 1, 3, 6 and 8 are

taken as PV generator buses and the rest are PQ load buses.

The network has 20 branches, 17 of which are transmission

lines and 3 are tap changing transformers. It is assumed that

capacitor compensation is available at buses 9 and 14.

Totally, there are nine control variables which consist of

four PV generator voltages, three tap changing transformers

with 20 discrete steps of 0.01 p.u. each and two shunt

compensation capacitor banks with three discrete steps of

0.06 p.u. each.

Fig 4. IEEE 14-Bus System

Table 1, shows the power at line flow and line loss for each

transmission line. By using two UPFC, the value of line

losses for each transmission line can be reduced. This is

important as to increase the system loadability of power

system network.

Table 1. Comparison power losses at line when using two UPFC

Line Power at line flow Line loss Power at line flow Line loss

From To MW Mvar MVA MW Mvar MW Mvar MVA MW Mvar

1 2 47.2 8.947 48.04 0.409 -4.601 41.61 10.646 42.95 0.331 -4.84

1 5 72.336 21.423 75.442 2.797 6.411 69.021 20.619 72.035 2.554 5.393

2 3 110.438 21.802 112.569 5.5 18.72 109.793 16.682 111.053 5.344 18.018

2 4 92.251 17.55 93.906 4.729 10.887 89.754 16.145 91.194 4.459 10.058

2 5 76.102 15.128 77.591 3.171 6.124 73.732 14.185 75.084 2.97 5.501

3 4 -26.942 9.798 28.669 0.594 0.309 -27.43 13.965 30.781 0.673 0.495

4 5 -68.139 -2.063 68.17 0.657 0.849 -67.484 -0.572 67.487 0.64 0.788

4 7 39.023 0.954 39.035 0 3.227 38.091 1.8 38.134 0 3.06

4 9 22.182 7.26 23.339 0 3.012 21.664 7.629 22.968 0 2.898

5 6 63.033 18.865 65.796 0 9.822 60.466 19.61 63.566 0 9.123

6 11 10.718 11.109 15.436 0.218 0.456 9.924 11.159 14.934 0.204 0.426

6 12 11.226 4.421 12.066 0.172 0.358 11.265 4.367 12.082 0.172 0.359

6 13 25.409 13.622 28.83 0.528 1.041 25.597 13.439 28.91 0.531 1.047

7 8 0 -25.384 25.384 0 1.146 0 -24.666 24.666 0 1.079

7 9 39.023 23.111 45.354 0 2.285 38.091 23.407 44.708 0 2.215

9 10 7.099 0.224 7.102 0.017 0.045 5.878 0.841 5.937 0.012 0.032

9 14 12.806 1.61 12.907 0.225 0.478 12.578 1.842 12.712 0.218 0.463

10 11 -5.518 -7.941 9.671 0.082 0.192 -6.734 -7.31 9.94 0.086 0.202

12 13 2.514 1.823 3.106 0.021 0.019 2.552 1.768 3.105 0.021 0.019

13 14 8.474 6.266 10.538 0.195 0.397 8.696 6.021 10.577 0.196 0.4

No FACTS Devices

With 2 UPFC


transmission line when two TCSC are used. By using two

TCSC, the line loss value still can be reduced although from

the data above it looks like same, but it maybe consume

more number of TCSC used in order to get value as we

installed two UPFC.

Table 2. Comparison power losses at line when using two TCSC



1 2 47.2 8.947 48.04 0.409 -4.601 47.2 8.947 48.04 0.409 -4.601

1 5 72.336 21.423 75.442 2.797 6.411 72.336 21.423 75.442 2.797 6.411

2 3 110.438 21.802 112.569 5.5 18.72 110.438 21.802 112.569 5.5 18.72

2 4 92.251 17.55 93.906 4.729 10.887 92.251 17.55 93.906 4.729 10.887

2 5 76.102 15.128 77.591 3.171 6.124 76.102 15.128 77.591 3.171 6.124

3 4 -26.942 9.798 28.669 0.594 0.309 -26.942 9.798 28.669 0.594 0.309

4 5 -68.139 -2.063 68.17 0.657 0.849 -68.139 -2.063 68.17 0.657 0.849

4 7 39.023 0.954 39.035 0 3.227 39.023 0.954 39.035 0 3.227

4 9 22.182 7.26 23.339 0 3.012 22.182 7.26 23.339 0 3.012

5 6 63.033 18.865 65.796 0 9.822 63.033 18.865 65.796 0 9.822

6 11 10.718 11.109 15.436 0.218 0.456 10.718 11.109 15.436 0.218 0.456

6 12 11.226 4.421 12.066 0.172 0.358 11.226 4.421 12.066 0.172 0.358

6 13 25.409 13.622 28.83 0.528 1.041 25.409 13.622 28.83 0.528 1.041

7 8 0 -25.384 25.384 0 1.146 0 -25.384 25.384 0 1.146

7 9 39.023 23.111 45.354 0 2.285 39.023 23.111 45.354 0 2.285

9 10 7.099 0.224 7.102 0.017 0.045 7.099 0.224 7.102 0.017 0.045

9 14 12.806 1.61 12.907 0.225 0.478 12.806 1.61 12.907 0.225 0.478

10 11 -5.518 -7.941 9.671 0.082 0.192 -5.518 -7.941 9.671 0.082 0.192

12 13 2.514 1.823 3.106 0.021 0.019 2.514 1.823 3.106 0.021 0.019

13 14 8.474 6.266 10.538 0.195 0.397 8.474 6.266 10.538 0.195 0.397


transmission line. By using this combination, value of line

loss for each line is reduced. So, this combination also is

acceptable configuration for installation of multi FACTS

devices as it fulfils the main objective as to increase the

loadability of power system network.

Table 3. Comparison power losses at line when using TCSC and UPFC



1 2 47.2 8.947 48.04 0.409 -4.601 44.028 9.908 45.13 0.363 -4.741

1 5 72.336 21.423 75.442 2.797 6.411 71.274 21.482 74.441 2.725 6.113

2 3 110.438 21.802 112.569 5.5 18.72 110.162 21.81 112.3 5.474 18.61

2 4 92.251 17.55 93.906 4.729 10.887 91.676 17.607 93.351 4.674 10.719

2 5 76.102 15.128 77.591 3.171 6.124 75.828 15.128 77.322 3.15 6.058

3 4 -26.942 9.798 28.669 0.594 0.309 -27.192 9.871 28.928 0.605 0.337

4 5 -68.139 -2.063 68.17 0.657 0.849 -66.993 -2.522 67.041 0.635 0.78

4 7 39.023 0.954 39.035 0 3.227 39.07 0.975 38.134 39.082 3.234

4 9 22.182 7.26 23.339 0 3.012 22.208 7.269 23.368 0 3.019

5 6 63.033 18.865 65.796 0 9.822 62.958 18.897 65.733 0 9.802

6 11 10.718 11.109 15.436 0.218 0.456 10.672 11.12 15.413 0.217 0.454

6 12 11.226 4.421 12.066 0.172 0.358 11.221 4.423 12.061 0.172 0.358

6 13 25.409 13.622 28.83 0.528 1.041 25.386 13.626 28.812 0.528 1.039

7 8 0 -25.384 25.384 0 1.146 0 -25.365 25.365 0 1.145

7 9 39.023 23.111 45.354 0 2.285 39.07 23.106 45.391 0 2.289

9 10 7.099 0.224 7.102 0.017 0.045 7.144 0.224 7.147 0.017 0.046

9 14 12.806 1.61 12.907 0.225 0.478 12.835 1.603 12.935 0.226 0.48

10 11 -5.518 -7.941 9.671 0.082 0.192 -5.474 -7.955 9.656 0.082 0.191

12 13 2.514 1.823 3.106 0.021 0.019 2.509 1.826 3.103 0.021 0.019

13 14 8.474 6.266 10.538 0.195 0.397 8.445 6.273 10.52 0.194 0.396

No FACTS

Devices

TCSC & UPFC

V. CONCULSION

Based on the results, installation of two UPFC devices gives

the best solution rather than other cases. This can be seen

by the lowest total power losses and a better voltage profile

in the power system when compare to other cases. As we

know, UPFC is largely being used in today application as is

a combination of series-shunt compensation controller.

Therefore, by using UPFC it can provide user capability to

control real and reactive power in order to achieve

maximum power transfer between transmission lines,

improve power quality and reliability by increase voltage

profile and also give system more stability. When TCSC is

installed in the transmission line, the reactive power have a

slightly changes but for real power is remain same. This is

because TCSC is a devices that can only control the value of

reactive power as it consists of a series capacitor bank

which shunted by a thyristor-controlled reactor. This is to

provide a smoothly variable series capacitive reactance.

Nonetheless, overall of the results proved that by getting the

optimal location for installing FACTS devices, the voltage

profile is improved while the total power losses can be

reduced.

REFERENCES

1. Donsion, M.P., Guemes, J.A., & Rodriguez, J.M. (2007). Power

Quality. Benefits of Utilizing Facts Devices in Electrical Power

Systems, 26-29.

2. Chennapragada Venkata Krishna, Kotamarti. S. B. Sankar,

Pindiprolu. V. Haranath. (2003). Power System Operation and

Control Using Fact Devices. 17th International Conference on

Electricity Distribution, 5(19): 1-6.

3. Song, S.H., Lim J.U., & Moon, S.II. (2004). Installation and

Operation of FACTS Devices for Enhancing Steady-state

Security. Electric Power Systems Research, 70: 7–15.

4. Hadi Saadat. (1999). Power System Analysis. Second Edition.

5. Ramasubramanian, P., Prasana, G. U., & Sumathi, K. (2011).

Optimal Location of FACTS Devices by Evolutionary

Programming Based OPF in Deregulated Power Systems.

SCIENCEDOMAIN international. British Journal of

Mathematics & Computer Science, 2(1): 21-30.

6. Nakul Pandit. (2007). Simulation and Control of Thyristor

Controlled Series Capacitors. Faculty of the Graduate School,

State University of New York at Buffalo.

7. Haddad, S., Haddouche, A., & Bouyeda, H. (2009). The use of

Facts devices in disturbed Power Systems-Modeling, Interface,

and Case Study. International Journal of Computer and

Electrical Engineering, 1(1): 1793-8198.

8. Murali, D., Rajaram, Dr. M., & Reka, N. (2010). Comparison of

FACTS Devices for Power System Stability Enhancement.

International Journal of Computer Applications, 8(4): 30-35.

9. Vibhor, G. (2010). Study and Effects of UPFC and its Control

System for Power Flow Control and Voltage Injection in a

Power System. International Journal of Engineering Science and

Technology. Panjab University, Chandigarh, 2(7): 2558-2566.

10. Samimi, A., & Naderi, P. (2012). A New Method for Optimal

Placement of TCSC Based on Sensitivity Analysis for

Congestion Management. Scientific Researched. Smart Grid

and Renewable Energy, 3: 10-16.

11. Jigar S.Sarda, Vibha N.Parmar, Dhaval G.Patel, Lalit K.Patel.

(2012). Genetic Algorithm Approach for Optimal Location of

FACTS Devices to Improve System loadability and

Minimization of Losses. International Journal of Advanced

Research in Electrical, Electronics and Instrumentation

Engineering, 1(3):114-125.

12. Mostafa Modiri-Delshad , Nasrudin Abd Rahima, (2014),

Solving non-convex economic dispatch problem via

backtracking search algorithm. Energy, 77: Pages 372–381

13. Xianhai Song, Xueqiang Zhang, Sutao Zhao, Lei Li, (2015).

Backtracking search algorithm for effective and efficient surface

wave analysis. Journal of Applied Geophysics, 114:19–31

14. Nadarajah Mithulananthan, Artit Sode-yome, Naresh Acharya.

(2003). Application of FACTS Controllers in Thailand Power

Systems. RTG Budget-Joint Research Project, Fiscal. 545-60




http://www.sciencedirect.com/science/journal/03605442

http://www.sciencedirect.com/science/journal/03605442/77/supp/C





http://www.sciencedirect.com/science/journal/09269851

The effect of filler ER4043 and ER5356 on weld metal structure of 6061

aluminium alloy by Metal Inert Gas (MIG)

Mahadzir Ishak

a,b, Nur Fakhriah Mohd Noordin

a,*

a Faculty of Mechanical Engineering, Universiti

Malaysia Pahang, 26600 Pekan, Pahang, Malaysia bAutomotive Engineering Centre Universiti Malaysia

Pahang, 26600 Pekan, Pahang, Malaysia


Ahmad Syazwan Kamil Razali a, Luqman Hakim

Ahmad Shah a

a Faculty of Mechanical Engineering, Universiti

Malaysia Pahang, 26600 Pekan, Pahang, Malaysia

Abstract— Weldability can be defines as the ability of

a material to be welded under imposed conditions. Good

weldability of aluminums alloys leads to have wide

applications in marines, aircraft construction,

aerospace and automobile industries. In this study,

6061 aluminum alloys will be joined by using automatic

metal inert gas (MIG) welding machine. The filler

metal used is ER5356 and ER4043. ER5356 filler

contains 5.5% of magnesium and ER4043 filler

contains 6% of silicon. The objectives of this research

are to study the consequence of filler metal on

mechanical properties of 6061 aluminum alloys. Based

on the results, ER5356 showed highest tensile strength.

The maximum tensile strength fabricated using

ER5356 obtained at 204.27 MPa and ER4043 obtained

at 200.66 MPa. The hardness value of ER5356 and

ER4043 at welded zone using MIG is 63.4 HV and 40.9

HV.

Index Terms— Aluminium Alloy,

Metal Inert Gas, AA6061, ER5356, ER4043

I. INTRODUCTION

Metal inert gas (MIG) welding process is an vital

element in various industrial processes nowadays

due to its simplicity, versatility, rapidness and

easiness of the training [1-4]. AA6061 is heat

treatable aluminium alloy with main alloying

elements of magnesium and silicon which displays

great strength, excellent extrudability and good

corrosion resistance [5]. Because of the following

characteristic, it is make AA6061 the most widely

used especially in automotive industry [4, 6].

However, nearly all the heat treatable aluminium

alloys are unfortunately disposed to hot cracking.

The susceptibility to solidification cracking is

significantly influenced by the composition of the

weld metal and therefore the appropriate selection

of filler material is an imperative characteristic in

monitoring solidification cracking [7, 8].

In present work, the effects of two different filler

wire ER5356 and ER4043 with various parameters

on the weldabilty of similar AA6061 welded by

MIG process were carried out.

II. EXPERIMENTAL PROCEDURES

A. Materials and mix design

AA6061 with thickness of 2 mm were cut by using

shear machine to dimension of 150 mm × 150 mm

then being welded by single pass welding with

square butt joint configuration. The chemical

compositions of material and filler wire as shown

in Table 1. The operations were performed by

using automated table and MIG welding type Dr

Well DM-500 as shown in Fig. 1. The parameter

used in this operation were welding current,

welding voltage and welding speed.

Fig. 1. Automated table and MIG welding type

Dr Well DM-500

B. Experimental test

For tensile test, the welded AA6061 were cut by

using Electron discharge machine (EDM) model

Sodick AQ535L followed American Standard


Testing Material (ASTM) E8M-04. The schematic

diagram of tensile specimens was shown in Fig. 2.

Fig. 2. Schematic diagrams for dimensions of

specimen for tensile test

For hardness test, the specimens were cut into 10

mm. The specimens were then hot mounted,

grinded and polished. The specimens were etched

by Keller Reagent for microstructural observation.

The hardness of weld was measured by Matsuzawa

MMT-X7 Vickers hardness test.


Total of 28 experimentations with combination of

different parameter of welding current, arc voltage

and welding speed both filler wire were performed.

A. Macrostructure

The MIG welded specimens were exposed to

metallographic with magnification of 10X

investigation prior to macrostructure survey. The

resulted photographs were illustrated in Table 2 for

welded using filler ER5356 and in Table 3 for

welded using filler ER4043. From the result

obtained, by using filler ER5356 and ER4043 the

highest strength recorded is 204.27 MPa and

200.66 MPa, respectively.

B. Microstructure

Fig. 3 (a), in the HAZ region, the average grain size

measured was 65.86 μm. Meanwhile, the FZ region

shows the average measured grain size was

36.02 μm. Fig. 3 (b), represent a fine grain size at

the fusion zone, therefore the tensile strength value

obtained was 204.27 MPa.

Fig. 4 (a), in the HAZ region, the average grain size

measured was 91.65 μm. Meanwhile, the FZ region

shows the average measured grain size was

51.4 μm. Fig. 4 (b) represent a dendritic grain size

at the fusion zone, therefore the tensile strength

value obtained was 200.66 MPa.

C. Hardness Test

The effect of filler ER5356 and filler ER4043 on

the distribution of hardness value in welded cross

section were illustrated in Fig. 5 and Fig. 6,

respectively. In the fusion zone (FZ) region, value

recorded for welding using filler ER5356 were

62.5 HV, higher compared by using filler ER4043,

with value of 46.87 HV. This is due to, ER4043

with high Si content not as strong as ER5356 with

Mg is the main alloying element which is make the

strength much more than using ER4043 [9] [10].

D. Tensile Test

Tensile test were conducted by using Universal

Testing Machine Instron with 3369.50 kN load

applied to the tensile specimens. The crosshead

speed to pull the specimen at 1 min/mm was used.

The tensile stresses of these three specimens were

recorded and then the averages of the value were

calculated in order to obtain the results.

Fig. 7 represents the comparison tensile stress of

different filler. From the graph, it is described that,

higher tensile stress but lower stress elongation

were obtained.

Fig. 3. Cross-sectional microstructure of MIG

welding by using ER5356

a) 5x heat affected zone b) 20x fusion zone

Fig. 4. Cross-sectional microstructure of MIG

welding by using ER4043

a) 5x heat affected zone b) 20x fusion zone

Table. 1. Chemical composition of materials and filler wire

Material Al Si Fe Cu Mn Mg Zn

AA6061 Bal 0.890 0.33 0.29 0.025 0.86 0.007

ER5356 Bal 0.25 04 - - 5.5 0.10

ER4043 Bal 6.0 0.8 - - 0.05 0.10

Table. 2. The variable parameters with ranges, heat inputs and quality of welding appearances by using

filler ER5356

Specimens

No

Macrostructure Current

(A)

Voltage

(V)

Speed

(mm/s)

Heat Input

(J)

Tensile Test

(MPa)

1

105 19 4 498.75 97.77

2

105 17 4 446.25 136.83

3

110 17 5 374.00 192.65

4

110 19 3 696.67 189.54

5

110 19 5 418.00 88.57

6

115 18 5 414.00 151.91

7

105 18 5 378.00 178.37

8

105 18 3 630.00 196.78

9

115 19 4 546.25 147.24

10

110 18 4 495.00 201.37

11

110 17 3 623.33 204.27

12

115 18 3 690.00 202.35

13

110 18 4 495.00 109.77

14

115 17 4 488.75 147.96

Table. 3. The variable parameters with ranges, heat inputs and quality of welding appearances by using

filler ER4043

Specimens

No

Macrostructure Current

(A)

Voltage

(V)

Speed

(mm/s)

Heat Input

(J)

Tensile Test

(MPa)

1

105 19 4 498.75 198.30

2

105 17 4 446.25 71.48

3

110 17 5 374.00 75.78

4

110 19 3 696.67 200.66

5

110 19 5 418.00 130.73

6

115 18 5 414.00 74.16

7

105 18 5 378.00 84.83

8

105 18 3 630.00 141.99

9

115 19 4 546.25 198.46

10

110

18 4 495.00 111.98

11

110 17 3 623.33 57.62

12

115 18 3 690.00 180.43

13

110 18 4 495.00 123.88

14

115 17 4 488.75 75.47

Fig. 5. Hardness value using ER5356.

Fig. 6. Hardnes value using ER4043.

Fig. 7. Tensile strength of welded specimens by using

different filler wire

IV. CONCLUSIONS

The following conclusions can be drawn from this research:

i. The weld joint fabricated by ER5356 show highest

strength which is 204.27 MPa compared to ER4043 at

200.66 MPa by using MIG welding.

ii. MIG specimen joined using ER5356 obtained highest

microhardness which is 63.4 HV at welded area and

followed by ER4043 at 40.9 HV.

iii. Welding with ER4043 produced defects such as

distortion and cracks. However, fewer defects occurred to

weld using filler ER5356.

iv. In this research, its shows that, weldability of AA6061 is

better by using filler ER5356.

V. ACKNOWLEDGMENTS

The author would like to thank the technical staff of

Universiti Malaysia Pahang for all of the work within which

the experiments were conducted. Also, financial support by

the Ministry of Education Malaysia through Universiti

Malaysia Pahang for Fundamental Research Grant Scheme

(FRGS), project no. FRGS/1/2013/TKOI/UMP/02/2 is

gratefully acknowledged.

REFERENCES

1. I. Sevim, F. Hayat, Y. Kaya, N. Kahraman, and S.

Şahin, "The study of MIG weldability of heat-

treated aluminum alloys," The International

Journal of Advanced Manufacturing Technology

2012; 66: 1825-1834.

2. S. S. Kulkarni, S. R. Joshi, and J. P. Ganjigatti, "A

review on Effect of welding parameters on

mechanical properties for Aluminum alloys using

MIG welding," International Journal of Latest

Trends in Engineering and Technology (IJLTET),

2014.

3. E. Mahdi, E. O. Eltai, and A. Rauf, "The Impact of

Metal Inert Gas Welding on the Corrosion and

Mechanical behavior of AA 6061 T6,"

International Journal of Electrochemical Science,

2014.

4. K. A. Zakaria, S. Abdullah, and M. J. Ghazali,

"Comparative study of fatigue life behaviour of

AA6061 and AA7075 alloys under spectrum

loadings," Materials & Design 2013;49: 48-57.

5. K. Mutombo and M. d. Toit, "Corrosion fatigue

behaviour of aluminium alloy 6061-T651 welded

using fully automatic gas metal arc welding and

ER5183 filler alloy," International Journal of

Fatigue 2011; 33: 1539-1547.

6. Luijendijk, "Welding of dissimilar aluminium

alloys," Journal of Materials Processing

Technology, 2000.

7. V. Balasubramanian, V. Ravisankar, and G.

Madhusudhan Reddy, "Influences of pulsed current

welding and post weld aging treatment on fatigue

crack growth behaviour of AA7075 aluminium

alloy joints," International Journal of Fatigue

2008; 30: 405-416,.

8. A. K. Lakshminarayanan, V. Balasubramanian, and

K. Elangovan, "Effect of welding processes on

tensile properties of AA6061 aluminium alloy

joints," The International Journal of Advanced

Manufacturing Technology 2007; 40: 286-296,.

9. R. A. Woods, "Metal Transfer in Aluminum

Alloys," Welding Research Supplement, 1980.

10. R. P. Verma, K. Pandey, and Y. Sharma, "Effect of

ER4043 and ER5356 filler wire on mechanical

properties and microstructure of dissimilar

aluminium alloys, 5083-O and 6061-T6 joint,

welded by the metal inert gas welding,"

Proceedings of the Institution of Mechanical

Engineers, Part B: Journal of Engineering

Manufacture, 2014.

Effect of strong base during co-digestion of petrochemical waste water and cow

dung

Md. Nurul Islam SIddique


University Malaysia Pahang

Kuantan, Malaysia

[email protected]

A.W. Zularisam*


University Malaysia Pahang

Kuantan, Malaysia

[email protected]

Abstract—Inadequacy of nitrogenous resource

and buffering ability were detected as reverting

failure in previous work treating petrochemical

wastewater (PWW) in anaerobic continuous

stirred tank reactors. The aim of this study is to

explore the use of ammonium bicarbonate

(NH4HCO3) as supplementation assuring

nitrogenous supply and buffering requirement. To

observe the effect of strong base such as

NH4HCO3 on the anaerobic process, a set of

dosing up to 40 mg L-1

was examined. The

outcomes were assessed in terms of biogas yield. It

was observed that10 mg L-1

dosing was the

optimal dosing without affecting methanogenesis.

Furthermore, mathematical calculation explained

that this optimum dosing can enhance biogas

yield up to 27.77% compared to control PWW

digestion. Results showed an obvious financial

advantage to make the industrial application

feasible.

Index Terms— ammonium bicarbonate,

Anaerobic digestion, Petrochemical wastewater,

CSTR, Methane

I. INTRODUCTION

Anaerobic digestion among other treatment

methods has been accepted as the key system of an

advanced technology for environmental protection

[1]. Earlier studies suggested that PWW craves

supplementary substrates to sustain its critical

operational parameters such as alkalinity, pH and

biomass. Investigation during the operation flashed

that those parameters had gone below

recommended levels. Consequently, after

increasing the organic loading rate (OLR) to the

level of 6 kg COD m-3

day-1

, the whole

experimental operation had failed. The failure was

countervailed by a sudden drop of pH and

increasing concentration of volatile fatty acid

(VFA). It is well known that these two factors are

limiting to the anaerobic digestion process,

especially to the sensitive methanogen group of

bacteria. However, the amount of organic loading

at 6 kg COD m-3

day-1

was found to be too low to

cause failure in anaerobic upflow fixed film reactor

(UFFR) [2,3]. Thereupon, insufficient buffering

control and disruption of microbial population

balance between non-methanogen and methanogen

to convert carbonaceous organic to CH4, were

identified to be the main reason of operational

failure. To control the level of volatile fatty acid in

the system, alkalinity has to be maintained by

recirculation of treated effluent [4,5] to the digester

or addition of lime

and bicarbonate salt [6]. As this process has been

shown to be a proficient alternative both to

pollution control and to produce CH4 as the

bioenergy, hindrances while operation should be

mend.

This study was undertaken to propose the use of

ammonium bicarbonate (NH4HCO3), due to its

buffering requirement against acidity throughout

the treatment operation and also to maintain the

microbial population balance. So, significant roles

will be performed by NH4+ as the recommended

bacterial nutrient for nitrogen and buffering

capacity in an anaerobic digester [6]. However,

excessive NH4HCO3 concentrations create free

ammonia toxicity especially to the methanogen [7].

Hence, the optimal dosage for NH4HCO3 applied

as supplement in anaerobic co-digestion process

should be determined.

II. MATERIALS AND METHODS

A. Preparation of substrate

A 100 L of PWW sample was collected in plastic

containers at the point of discharge in to the main

stream and from the receiving stream. The

petrochemical wastewater is a complex mixture of

organic pollutants can be fermented to methane,

which has been analyzed to be lacking in alkalinity

and nitrogenous resources. Preparation of PWW

was accomplished according to a previous study by

diluting the stock liquor [5]. Table 1 explains the

characterization of PWW. The dilution of

concentrated petroleum resulted in a consistent

concentration of wastewater up to 3000 mg L-1 of

COD, which is in the range of medium strength

wastewater [8]. With a view to remove the debris

the prepared sludge was initially passed through a

screen. The microbial activity of the seed sludge

was examined according to M.N.I. Siddique’s

method 2014 [5].

B. Batch test of toxicity

Immersing a set of air sealed digesters (1L) in a

water bath; the effect of NH4HCO3 on the

anaerobic digestion of PWW was investigated. The

operating temperature was maintained at 37˚ C. In

order to monitor biogas generation, the digesters

were linked to biogas measuring device. All

digesters were seeded with 300 mL of stabilized

sludge and 150 mL of PWW with COD of 3000 mg

L-1

, before testing by batch operation. The reason

behind it was to pretend non-critical COD loading

at 0.5 kg m-3

so that the shock loading to seed

substrate could be avoided. An incremental set of

concentrations up to 40 mg L-1

were prepared in

duplicates of five containers via dosing of

NH4HCO3.

Table 1 Composition and Characteristics of

PWW

Parameters PWW

pH 6.5-8.5

BOD 8-32

COD 15-60

TOC 6-9

Total solids 0.02-0.30

Acetic acid 46.60

Phenol 0.36

Total Nitrogen 0.05-0.212

Total Phosphate 0.102-0.227

Volatile fatty acids 93-95

*Except pH and Acetic acid, all parameters in gL-1

The supplementation dosing up to 40 mg L-1

was

preferred to find optimal one. To ensure sufficient

mixing and to assist the yield of biogas, all

digesters were mildly stunned per 10 min. The

optimum dosing for NH4HCO3 was calculated

depending on the cumulative biogas yield. An

assumption might be made that accelerated biogas

yield would generate within 3 h of batch process

for similar substrate [9]. Nonetheless, the toxicity

of NH4HCO3 especially to the methanogen in the

system could be indicated in contrast of the

maximum biogas yield [7]. In the previous study

Configuration of CH4:CO2 was at the ratio of

25:75. Even so, the biogas generation in this work

was assumed to be too little for analysis by gas

analyzer. Liquid displacement method was applied

to measure gas generation [10].

B. Batch test of toxicity

Former operational breakdown that was provoked

by VFA agglomeration might have happened

through supplementary confines like as

micronutrients (Fe, Mg, Ni, Cu, Co and P).

Nonetheless, theoretically the scarcity of

micronutrients might be abolished on the basis of

mineral percentage. As seed sludge was collected

from partially digested sewage, the content of

phosphorus must be sufficient. Consequently, the

lack of phosphorus was also not being addressed.

For the time being, the existence of ammoniacal

nitrogen as the resource macronutrient in a

stabilized digested sludge is acknowledged to be at

an outstanding concentration after de-nitrification

process is accomplished [8]. Sodium nitrate is an

alternative supplementation to meet up the want of

nitrogenous resource. Still, in case of its

application, the discharge of NO3-1 would enhance

the oxidation–reduction potential (ORP) of the

reactor. The ORP potential of the reactor supposed

to maintain above -300 mV. It was due to the cause

that methanogenesis is deteriorated at lesser ORP

[6]. In order to adjust the buffering capacity of the

substrate solution, chemical selection is a rate

limiting factor. Unwanted solids are created due to

Precipitation of CaCO3.

C. Biogas production

Fig. 1 illustrates the effect of NH4HCO3

supplementation to anaerobic co-digestion process.

However, the digestion performance has been

evaluated in terms of cumulative biogas generation

vs. time graph. It slows through termination of raw

resources. While NH4HCO3 dosing, total

cumulative biogas generation was detected to

increase. More specifically, at 10 mg/L dosing and

contact time ranging from 15 to 180 min,

cumulative biogas generation was enhanced.

Subsequently, the cumulative biogas generation

was detected to drop in case of 20, 30, 40 mgL-1

dosing applied to the process. The C: N ratio was

maintained fixed at within the range of 25/1 to

30/1.

However, the detailed data revealed that the

maximum biogas generation took place while 10

mg L-1 of NH4HCO3 was applied. It can be

studied from previous work, the CH4 yield form

the petroleum wastewater COD added ranged

between 0.37–0.43 [2]. During the current work,

CH4 yield was calculated assuming similar

substrate digestion. The maximum CH4 yield from

this study could be equal to 60 mL, as listed in

Table 2. From Fig. 1 it is obvious that the data

collected during digester operation is consistent

enough having regression co-efficient value of

0.9925, 0.9868, 0.9825, 0.9872 and 0.9935.

Table 2: Results of NH4HCO3 dosing to anaerobic

digestion system in terms of Cumulative biogas

generation

Contact time (min)

Mean cumulative biogas generation (mL)

NH4HCO3 dosing (mg/L)

0 10 20 30 40 16 15 19 18 15 15 30 18 23 24 19 19 45 23 27 27 22 23 60 27 31 30 26 27 75 31 36 34 29 30 90 36 39 38 32 33 120 40 42 41 35 37 105 43 46 43 40 41 135 46 48 46 42 43 150 49 51 49 45 46 165 51 55 51 47 48 180 54 62 53 49 49

Figure 1: Evaluation of digestion performance in

terms of cumulative biogas generation vs. time

graph

Figure 2: Effluent microbial cell concentration vs.

solid retention time for co-digestion process.

For the calculation of % increase in biogas yield

the following formula was employed:

% increase in biogas yield = (A – B) / B * 100

Where, A = Biogas yield at dosing 10 mg/L

B = Biogas yield at dosing 0 mg/L

(control)

For example, at contact time of 16 min, % increase

in biogas yield = (19 -15) / 15 * 100 = 26.67 %.

Fig. 2 is actually a comparison of effectiveness of

NH4HCO3 dosing with control PWW digestion.

The obvious effect of 10 mg/L NH4HCO3 dosing

has been demonstrated along with contact time

ranging between 15 to 180 min. From contact time

vs. % increase in biogas yield curve it can be stated

that the maximum enhancement in biogas yield is

27.77% at contact time of 30 min. It might be due

to the fast reaction took place at that specific

environmental condition. It has been studied, for

the transformation of carbonaceous materials in to

CH4 during the anaerobic digestion system,

sustaining methanogenesis was the key operational

process. H2 and CO2 will be used by

Hydrogenotropic methanogens while acetic acid

and CO2 will be used by acetoclastic methanogens

to give CH4 as eventual outcome [11]. The volatile

fatty acid (VFA) accumulation is suggested to be

avoided employing supplementation of strong

bases and co-digestion with other wastes [12]. This

strategy provides appropriate C/N ratio and strong

buffering capacity to pH change. As a result

methanogenesis occurs with great stability leading

to enhanced CH4 generation. It can be concluded

from Fig. 2 that 10 mg/L NH4HCO3 dosing can

provide up to 27.77% enhanced biogas yield

compared to control PWW digestion.

III. CONCLUSION

This study reveals that the NH4HCO3 might be

accelerate the anaerobic digestion of PWW.

Ultimately, 10 mg L-1

of NH4HCO3 dosing was

found to be the optimal dose for the substrate in

comparison to the enhanced doses up to 40 mg L-1

.

Moreover, the effectiveness of NH4HCO3

supplementation was also explained by formula.

According to the calculation, 10 mg/L NH4HCO3

dosing can provide up to 27.77% enhanced biogas

yield compared to control PWW digestion. This

achievement can obviously add some financial

advantage to make the treatment policy more

feasible for industrial application.

IV. ACKNOWLEDGMENT

The authors would like to thank faculty of

engineering technology, university Malaysia

Pahang, for providing continuous laboratory

facility. The present research was made possible

availing facility provided by RDU-0903113.

REFERENCES

[9] M.N.I. Siddique, M.S.A. Muniam, and A. W. Zularisam,

“Mesophilic and thermophilic biomethane production by co-digesting pretreated petrochemical wastewater with

beef and dairy cattle manure”, J. Ind. Eng. chem, vol. 20,

pp. 331-337, 2014. [10] M.N.I. Siddique, M.S.A. Muniam, and A. W. Zularisam,

“Feasibility analysis of anaerobic co-digestion of activated

manure and petrochemical wastewater in Kuantan (Malaysia)”, J. Clean. Prod,

doi:10.1016/j.jclepro.2014.08.003, 2014.

[11] M.N.I. Siddique, M.S.A. Muniam, and A. W. Zularisam, “Role of biogas recirculation in enhancing petrochemical

wastewater treatment efficiency of continuous stirred tank

reactor”, J. Clean. Prod, doi:10.1016/j.jclepro.2014.12.036, 2014.

[12] M.N.I. Siddique, M.S.A. Muniam, and A. W. Zularisam,

“Influence of flow rate variation on bio-energy generation

during anaerobic co-digestion”, J. Ind. Eng. chem,

doi:10.1016/j.jiec.2014.12.017, 2014.

[13] M.N.I. Siddique, M.S.A. Muniam, and A. W. Zularisam, “Role of Bio-filtration in Petrochemical Wastewater

treatment using CSTR”, Int. J. Eng. Tech. Sci, vol.2 (1),

2014. [14] M.N.I. Siddique, and A. W. Zularisam, “Sustainable Bio-

methane generation from Petrochemical Wastewater using

CSTR”, Int. J. Eng. Tech. Sci, vol.1 (1), 2014. [15] R. Kumar, L. Singh, and A. W. Zularisam, “A

comparative study for electricity generation and

wastewater treatment from palm oil mill effluent by microbial fuel cell using different sizes of electrodes”, Int.

J. Eng. Tech. Sci, vol.1 (1), 2014.

[16] P. Mishra, L. Singh and A. W. Zularisam, “Rnase purification from isolated Rns”, Int. J. Eng. Tech. Sci,

vol.1 (1), 2014.

[17] R. Kumar, L. Singh, and A. W. Zularisam, “An overview

on biological concept of microbial fuel cells”, Int. J. Eng.

Tech. Sci, vol.2 (1), 2014.

[18] APHA. Standard methods for the examination of water and wastewater. 19 th ed. Washington, DC: American

Public Health Associayion, 2005. [19] H. Movahedyan, A. Assadi, and A. Parvaresh,

“Performance evaluation of an anaerobic baffled reactor

treating wheat flour starch industry wastewater” Iran J. Environ. Health. Sci. Eng., vol. 4, pp. 77–84, 2007.

[20] S. Michaud, N. Bernet, P. Buffiere, M. Roustan, and R.

Moletta, “Methane yield as a monitoring parameter for the start-up of anaerobic fixed film reactors” Wat. Rese., vol.

36, pp.1385–1391, 2002.

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