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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
Topics that may be treated from the perspective of Engineering Technology include: Advance Machining, Material
& System, Advance Manufacturing, Composite Engineering, Manufacturing System & Optimization, Biopharmaceutical Production, Pharmaceutical Production, Pharmaceutical Technology, Pharmaceutical Management, Sustainable Infrastructure, Asset & Facilities Management, Traffic & Economic Infrastructure, Geometric & Spatial Application, Green Technology in Infrastructure, Renewable Energy & Material,
Environmental, Biofuel/Green Material, Renewable Energy/Thermo fluid, Energy Management, Industrial Control & Electronics, Industrial Control, Industrial Instrumentation, Industrial Electronics
Topics that may be treated from the perspective of Safety and Health Sciences include: Occupational Safety, Health & Environmental Science & Technology, Safety Science & Engineering, Occupational Health Science,
Environmental Health, Science and Occupational Safety & Health Management This journal invites research and intellectual discussions on issues of Engineering Technology and Safety and Health Sciences. The paper should be written in English.
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
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,
Universiti Malaysia Pahang
Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang,
Malaysia
N. Harun Faculty of Chemical and Natural Resources Engineering,
Universiti Malaysia Pahang
Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang,
Malaysia
noorlisa@ump.edu.my
F. Adam Faculty of Chemical and Natural Resources Engineering,
Universiti Malaysia Pahang
Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang,
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
Fig. 3: Prediction of diffusion coefficient graph for MEA, H2O and CO2 at 40
oC
Fig. 4: Prediction of diffusion coefficient graph for MEA, H2O and CO2 at 25
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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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INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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]
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
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INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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 zaidisidek@ump.edu.my,
Mohd Syahidan Kamarudin
Faculty of Manufacturing Engineering,
Universiti Malaysia Pahang, 26600 Pekan,
Pahang
syahidankamarudin@gmail.com,
Mohamad Nafis Jamaluddin
Faculty of Manufacturing Engineering,
Universiti Malaysia Pahang, 26600 Pekan,
Pahang mohdnafis89@gmail.com
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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).
References
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INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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 mrkhancep@yahoo.com
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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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INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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]
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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)
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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:
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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)
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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)
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
Fig 2: Shows the relationship between noise
variance and the spike frequency.
Figure 3, shows the membrane size for potassium is
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
variance and the spike frequency.
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.
Figure 6, shows the membrane size for potassium is
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
variance and the spike frequency.
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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INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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: engmadjid@gmail.com
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
III. RESULTS AND DISCUSSION
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
and behavioral Sciences1 2009; 1500-1506.
3. Bistong, J. Language choice and cultural
imperialism. ELT Journal, 1995; 49(2): 129-132.
4. Cheng, C.C., Communication apprehension,
willingness to communicate, and EFL college
students' oral performance. Proceedings of the
eighteenth symposium On English teaching, 2009;
203-211.Taipei, Taiwan: Crane.
5. Crystal, D., An Encyclopaedic Dictionary of
Language and Languages. Oxford Blackwell
Publishers. 1992
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
(Eds.), learning strategies in language learning.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
Englewood Cliffs. NJ: Prentice Hall. 1987; 119-
129
9. Krashen, S., Principles and Practice in Second
Language Acquisition. (1985). Retrieved Dec.28,
2011 from http://www.sdkrashen com/ Principales
_ and _ Practice/index.html.
10. Sung, C.C.M., English as a lingua franca and
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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: abdolbaqi.mk@gmail.com
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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)
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
REFERENCES
[1]. Hussein A.M., Sharma K.V., Bakar R.A., Kadirgama K.
(2014). Study of forced convection nanofluid heat transfer in the automotive cooling system. Case Studies in Thermal
Engineering, 2, 50-61.
[2]. Hussein A.M., Bakar R.A., Kadirgama K., & Sharma K.V.
(2013). Experimental Measurements of Nanofluids Thermal Properties, International Journal of Automotive
& Mechanical Engineering, 7, 850-864.
[3]. Hussein A.M., Sharma K.V., Bakar R.A. & Kadirgama K.
(2013). A review of forced convection heat transfer enhancement and hydrodynamic characteristics of a
nanofluid. Renewable and Sustainable Energy Reviews, 29,
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
Transfer, 37,895-900.
[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.
[8]. Prajapati O.S. (2012). Effect of Al2O3-Water Nanofluids in Convective Heat Transfer. Int. J. of Nano science, 1, 1-4.
[9]. Bozorgan N. & Mafi M. (2012). Performance Evaluation of
AI2O3/Water Nanofluid as Coolant in a Double-Tube Heat
Exchanger Flowing under a Turbulent Flow Regime, Hindawi Publishing Corporation Advances in Mechanical
Engineering, 891382, 1-8.
[10]. Hussein A.M., Bakar R.A., Kadirgama K. & Sharma K.V.
(2013). The effect of nanofluid volume concentration on heat transfer and friction factor inside a horizontal tube.
Journal of Nanomaterials, Article ID, 859563, 1-12.
[11]. Rostamani. M, Hosseinizadeh. S.F, Gorji. M& Khodadadi.
J.M. (2010). Numerical Study Of Turbulent Forced
Convection Flow Of Nanofluids In A Long Horizontal Duct
Considering Variable Properties. International Communications in Heat and Mass Transfer, 37, 1426–
1431.
[12]. Sharma, K. V., et al. "Correlations to predict friction and
forced convection heat transfer coefficients of water based nanofluids for turbulent flow in a tube."International
Journal of Microscale and Nanoscale Thermal and Fluid
Transport Phenomena 3 (2010): 283-308.
[13]. Sundar L.S.& Sharma K.V. (2010). Turbulent Heat Transfer and Friction Factor of Al2O3 Nanofluid in Circular Tube
with Twisted Tape Inserts, Inter. J. Heat and Mass Transfer,
53, 1409 – 1416.
[14].Dehghandokht M., Khan M.G., Fartaj A., Sanaye S. (2011).
Flow and heat transfer characteristics of water and ethylene
glycol-water in a multi-port serpentine meso-channel heat
exchanger. Int. J. of Thermal Sciences.50, 1615-1627.
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[15]. Oliet C., Oliva A., Castro J., Segarra S.D. (2007). Parametric
studies on automotive radiators. Applied Thermal Engineering, 27, 2033-2043.
[16]. Leong K.Y., Saidur R., Kazi S.N.& Mamun A.M. (2010).
Performance investigation of an automotive car radiator
operated with nanofluid-based coolants (nanofluid as a coolant in a radiator). Applied Thermal Engineering, 30,
2685-2692.
[17][ Gunnasegaran P., Shuaib N.H., Abdul Jalal M.F.& Sandhita
E. (2012). Numerical Study of Fluid Dynamic and Heat Transfer in a Compact Heat Exchanger Using Nanofluids,
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[18].Durmus, M. Esen. (2002). Investigation of heat transfer and
pressure drop in a concentric heat exchanger with snail
entrance. Applied Thermal Engineering, 22, 321–332.
[19]. Bejan A. (2004). Convection Heat transfer. New York: John Wiley & Sons Inc.
[20].Pak B.C. & Cho Y.I.(1998). Hydrodynamic and heat transfer
study of dispersed fluids with submicron metallic oxide
particles. Exp. Heat Transfer, 11, 70-151.
[21]. Duangthongsuk W., Wongwises S. (2010). An Experimental Study on The Heat Transfer Performance and Pressure Drop
of TiO2-water nanofluids flowing under a Turbulent Flow
Regime, Int. J. of Heat and Mass Transfer. 53,334–344.
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
Table 1. Physical properties of metal oxide nano materials
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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:roziahz@ump.edu.my
Amir Bin Abdul Razak
Faculty of Engineering Technology
Universiti Malaysia Pahang,
Lebuhraya Tun Razak, 26300
Kuantan,Pahang, Malaysia Email:amirrazak@ump.edu.my
Firdaus Bin Mohamad
Faculty of Mechanical Engineering
Universiti Malaysia Pahang
26600, Pekan, Pahang
Email:redhill.ent@gmail.com
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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)
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
III. RESULTS AND DISCUSSION
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.
Figure 6. Temperature Analysis- Set 2
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
Figure 7. Temperature Analysis- Set 3
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.
Figure 8. Temperature Analysis- Set 4
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.
REFERENCES
[1] Abdullah, K., & Sayigh, A. (1998). The Solar
Oven : Development And Field-Testing Of
User-Made Designs In Indonesia. Solar
Energy, 64, 121-132
[2] Ibrahim, M. A. (1995). The performance of a
solar cooker in Egypt. Renewable Eneryy,
Vol. 6, No. 8,1041-1050.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
[3] Sharma, A., Chen, C. R., Murty, V. V. S., &
Shukla, A. (2009). Solar cooker with latent
heat storage systems: A review. Renewable
and Sustainable Energy Reviews, 13(6-7),
1599-1605.
[4] Fernandez, a. I., Martínez, M., Segarra, M.,
Martorell, I., & Cabeza, L. F. (2010). Selection
of materials with potential in sensible thermal
energy storage. Solar Energy Materials and
Solar Cells, 94(10), 1723-1729.
[5] Nandwani, S. S. (1996). Solar cookers cheap
technology with high ecological benefits.
Ecological Economics 17, 73-81.
[6] G. Hernández-Luna, G.. (2008). Solar oven for
intertropical zones: Optogeometrical design.
Energy Conversion and Management, 49,
3622–3626.
[7] Saxena, A., Pandey, S. P., & Srivastav, G.
(2011). A thermodynamic review on solar box
type cookers. Renewable and Sustainable
Energy Reviews, 15(6), 3301-3318.
[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.
.
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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
E-mail: arun@ump.edu.my
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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.
III. RESULTS AND DISCUSSION
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
INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY AND SCIENCES (IJETS) Vol.3 (1) June 2015
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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properties and performance of urea–formaldehyde resins. Holzforschung 1994; 48:527–532.
[8] Gu JY, Higuchi M, Morita M, Hse CY. Synthetic conditions
and chemical struc-tures of urea–formaldehyde resins. I.
Properties of the resins synthesized by three different procedures. Mokuzai Gakkaishi 1995; 41:1115–1121.
[9] Tohmura S, Hse CY, Higuchi M. Formaldehyde emission
and high-temperature stability of cured urea–formaldehyde
resins. J. Wood Sci. 2000; 46:303–309.
[10] Tomita B, Hatono S. Urea–formaldehyde resins III. Constitutional characterizations by carbon-13 Fourier transform
NMR spectroscopy. J. Appl. Polym. Sci. 1978; 16:2509–2525.
[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.
[13] Du GB. Application of mineral filler of urea formaldehyde resin as plywood adhesive. China Adhes. 1995; 4:39–42.
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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
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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
Table 2, shows the power at line flow and line loss for each
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
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 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
Table 3, shows the power at line flow and line loss for each
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
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 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.
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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
E-mail: fakhriahnidroon@yahoo.com
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.
III. RESULTS AND DISCUSSION
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.
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Şahin, "The study of MIG weldability of heat-
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tensile properties of AA6061 aluminium alloy
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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
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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
Faculty of Engineering Technology
University Malaysia Pahang
Kuantan, Malaysia
rupu_kuet@yahoo.com
A.W. Zularisam*
Faculty of Engineering Technology
University Malaysia Pahang
Kuantan, Malaysia
zularisam@ump.edu.my
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.
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