Micro-peat (M-P) was demonstrated in the present study as a potential low cost natural adsorbent for the removal of COD and ammoniacal nitrogen (NH3-N) from landfill leachate. A series of batch experiments were carried out under fixed conditions and the influence of mixture ratio was investigated. The characteristics of leachate were then determined. Results indicated that leachate is non-biodegradable with high concentration of COD (2739.06 mg/L), NH3-N (1765.34 mg/L) and BOD5/COD ratio (0.09). The optimum ratio for activated carbon (AC) and M-P in the removal of COD and NH3-N obtained were at 2.5:1.5 (87%) and 1.0:3.0 (65%) respectively. The low-cost natural adsorbent used in the present investigation is an attractive alternative to the conventional adsorbent (AC). Thus, M-P can be appropriated for use in leachate treatment that could be cost-effective due its local availability and adsorption property

Abdul Halim, Azhar

Abdul Latiff, Ab Aziz

Awang, Halizah

Daud, Zawawi

Ridzuan, Mohd Baharudin

Rosli, Mohd Arif

English

Micro-peat (M-P) was demonstrated in the present study as a potential low cost natural adsorbent for the removal of COD and ammoniacal nitrogen (NH3-N) from landfill leachate. A series of batch experiments were carried out under fixed conditions and the influence of mixture

ratio was investigated. The characteristics of leachate were then determined. Results indicated that leachate is non-biodegradable with high concentration of COD (2739.06 mg/L), NH3-N (1765.34 mg/L) and BOD5/COD ratio (0.09). The optimum ratio for activated carbon (AC) and M-P

in the removal of COD and NH3-N obtained were at 2.5:1.5 (87%) and 1.0:3.0 (65%) respectively. The low-cost natural adsorbent used in the present investigation is an attractive alternative to the conventional adsorbent (AC). Thus, M-P can be appropriated for use in leachate treatment that could be cost-effective due its local availability and adsorption property

UTHM Institutional Repository

Micro-Peat as a Potential Low-Cost Adsorbent Material for COD  
and NH3-N Removal 
Zawawi Daud1,a*, Mohd Arif Rosli1,b, Ab Aziz Abdul Latiff1,c,  
Mohd Baharudin Ridzuan1,d, Halizah Awang2,e, Azhar Abdul Halim3,f 
1Centre of Advanced Research for Integrated Solid Waste Management (CARISMA), Faculty of 
Civil and Environment Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 
2Faculty of Technical and Vocational Education, Universiti Tun Hussein Onn Malaysia,  
Johor, Malaysia 
3School of Environmental and Natural Resources, Faculty of Science and Technology,  
Universiti Kebangsaan Malaysia, Selangor 
E-Mail: *azawawi@uthm.edu.my, Tel: +607-4564460, Fax: +607-4536588,  
bm.arifrosli@gmail.com, caziz@uthm.edu.my, dmdbahar@uthm.edu.my, ehalizah@uthm.edu.my, 
fazharhalim@ukm.edu.my 
Keywords: Micro-peat, Activated carbon, Adsorption, COD, NH3-N, Landfill leachate 
Abstract. Micro-peat (M-P) was demonstrated in the present study as a potential low cost natural 
adsorbent for the removal of COD and ammoniacal nitrogen (NH3-N) from landfill leachate.          
A series of batch experiments were carried out under fixed conditions and the influence of mixture 
ratio was investigated. The characteristics of leachate were then determined. Results indicated that 
leachate is non-biodegradable with high concentration of COD (2739.06 mg/L), NH3-N        
(1765.34 mg/L) and BOD5/COD ratio (0.09). The optimum ratio for activated carbon (AC) and M-P 
in the removal of COD and NH3-N obtained were at 2.5:1.5 (87%) and 1.0:3.0 (65%) respectively. 
The low-cost natural adsorbent used in the present investigation is an attractive alternative to the 
conventional adsorbent (AC). Thus, M-P can be appropriated for use in leachate treatment that 
could be cost-effective due its local availability and adsorption property. 
Introduction 
Leachate is produced when water percolates through the waste in landfill that may contain large 
amounts of organic contaminants which can be measured as biochemical oxygen demand (BOD) 
and chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), halogenated hydrocarbons, 
suspended solids, significant concentration of heavy metals and inorganic salts [1]. Due to its 
toxicity, landfill leachate can be a potential source of surface and ground water contamination, as it 
may percolate through soils and subsoil, causing pollution to receiving waters if not treated and 
safely disposed [2]. Organic contaminants in terms of COD and NH3-N are two major problems in 
the landfill leachate. The excessive presence of ammonium in water streams and effluent causes 
eutrophication and decreases the dissolved oxygen required by aquatic organisms [3,4]. Moreover, 
almost of wastewater is tremendously polluted with several organics that may create hydrogen 
which contributes to greenhouse gases emissions [5,6]. 
Leachate must be treated properly and systematically before released into the environment. 
Leachate treatment is very complicated, high cost operation and generally requires multiple 
processes especially leachate containing high concentrations of contamination [7]. Young leachates 
are generated during early phase of landfill lifecycle are easily processed when compared to the 
stabilized leachate [8]. For young leachates were mainly containing high concentrations of 
biodegradable organic matters, and thus have a high BOD and COD ratio (BOD5/COD > 0.5) [9], is 
suitable to be processed using biological treatment whereas unsuitable for the stabilized leachate, 
due to decrease of biodegradable matter (BOD5/COD < 0.1) [10], increase of refractory components 
(e.g. humic and fulvic acids), and inhibitory compounds (e.g. ammonium) [11]. 
Defect and Diffusion Forum Submitted: 2017-09-15
ISSN: 1662-9507, Vol. 382, pp 297-301 Accepted: 2017-09-25
doi:10.4028/www.scientific.net/DDF.382.297 Online: 2018-01-10
© 2018 Trans Tech Publications, Switzerland
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans
Tech Publications, www.scientific.net. (#106710459, University of British Columbia, Kelowna, Canada-06/02/18,08:23:05)
In recent years, the greater interest has been focused on physico-chemical treatment for treating 
wastewater and landfill leachate [10-12]. One of the effective ways to remove the contaminants 
from stabilized landfill leachate is adsorption by using AC or other adsorbents, such as zeolite, 
activated alumina, or low-cost adsorbents (e.g., micro-peat, limestone and rice husk ash). The 
advantages of adsorption method are the low energy and maintenance costs, simple design and the 
reliability [13]. Normally the conventional adsorbent using as the main medium for adsorption is 
usually AC is very expensive. ACs are highly effective adsorbents for removing organic pollutants 
in the aqueous or gaseous phase, thus widely applied in the purification of water and air [14]. 
A low-cost adsorbent generally can be defined as one which requires little processing and is 
abundant in nature [15,16]. The use of low-cost natural adsorbent such as micro-peat as a replaces 
conventional adsorbent consumptions for leachate treatment was the focus of this study. Micro-peat 
is relatively inexpensive material had been extensively used in the removal of organic contaminants. 
Various studies had indicated the capabilities of micro-peat as an adsorbent and filter material in 
leachate treatment processes [17,18]. Micro-peat is a highly organic substance derived primarily 
from plant materials and a representative material of soft soils [19]. It is formed when organic 
(usually plant) matter accumulates more quickly than it humidifies (decays). Other crops such as 
pineapples, corn, cassava is often typically growing in micro-peat [20]. Micro-peats are those soils 
that have an organic matter more than 65% or less than 35% of the mineral content and highly 
porous with a surface area of more than 200 m2/g [21,22].  
In this study, the potential of micro-peat as a low-cost natural and easily adsorbent to replace 
conventional adsorbent (AC) partially of removal efficiency for COD and NH3-N which are among 
the significant contaminants present in the Simpang Renggam landfill site (SRLS) was investigated. 
The purpose of this study is to determine the optimum mixture ratio for the adsorption material 
above based on removal of COD and NH3-N from landfill leachate. The results will be used as a 
basis for the partially replacement of an inexpensive and effective low-cost adsorbent for this type 
of landfill sites. 
Materials and Methods 
Adsorbents Preparation. Activated coconut shell carbon was purchased locally at about 
RM4500 per ton. Micro-peat was obtained from Kampung Parit Nipah, coordinate at 1°88’ N / 
103°20’E which is located about 10 kilometers from Universiti Tun Hussein Onn Malaysia 
(UTHM). The M-P was dried at a temperature of 105 ºC for 24 hours. Prior to the experiment, the 
composition of AC and M-P used in the experiment were determined by X-Ray Fluorescence 
spectrometry (XRF) (Model Bruker S4 Pioneer). The density of both AC and M-P were determined 
conventionally, i.e., weight/volume of media. Using ceramic ball mill, all media were ground to 
obtain particle size of less than 150 μm. The general properties of media were shown in Table 1. 
Table 1. Composition of activated carbon (AC) and micro-peat (M-P) in percent. 
Formula C CaO SiO2 Al2O3 MgO K2O Fe2O3 TiO2 SO3 Na2O Others 
AC 
M-P 
93 
23.42 
2.3 
2.96 
2.3 
48.18 
0.31 
9.55 
0.35 
2.44 
0.2 
0.26 
0.2 
4.81 
- 
0.21 
0.6 
7.67 
- 
0.32 
0.74 
- 
Adsorbate. The raw leachate used in this study was collected aseptically from Simpang 
Renggam landfill site in Kluang, Johor, Malaysia. The samples were collected in 30-L plastic 
containers, transported, and stored in the laboratory according with the Standard Methods for the 
preservation and storage of wastewater [23]. The characterization of the raw leachate was achieved 
before preservation and storage. All chemicals used were of analytical grade. 
Optimum Ratio. The determinations of optimum ratio between AC and M-P adsorbent were 
determined based on the adsorption properties towards COD and NH3-N, the major contaminants in 
leachate. Batch experiment study was performed in accordance to the procedure outline by [11,12] 
at pH7, 200 rpm, 120 minutes at room temperature to identify the adsorption properties that produce 
298 Engineering and Innovative Materials VI
the optimum ratio (i.e. the ratio that reveal in terms of achievable maximum removal of both 
contaminants). The percentage removal of COD and NH3-N in the solution was evaluated by using 
Eq. (1) [2] 
 Removal (%) = [(Ci – Cf) / Ci] x 100                  (1) 
where, Ci and Cf are the initial and final of COD and NH3-N concentrations of leachate in mg/L, 
respectively. 
Leachate Analysis. COD and NH3-N was determined by reactor digestion closed reflux 
colorimetric method and Nesslerization method respectively [23]. 
Results and Discussion 
Leachate Characterization. SRLS leachates have considerable amounts of COD              
(2739.06 mg/L), low amount of BOD5 (258.06 mg/L), and a very low BOD5/COD average ratio 
(0.09). Due to its low BOD5/COD ratio and the high strength of NH3-N (1765.34 mg/L), SRLS 
leachate can be classified as a stabilized leachate. The leachate is considered as a stable due to its 
pH (>7.5) and the BOD5/COD ratio is very small (<0.1) [9]. Therefore, adsorption via AC or other 
adsorbents, becomes one of the appropriate options for stabilized leachate treatment. The 
characteristics of landfill leachate from the SRLS are illustrated in Table 2. 
Table 2. Characteristics of landfill leachate sample from Simpang Renggam landfill site. 
Parameter Values Std. Dev. 
 Min. Max. Ave.  
pH 8.05 8.32 8.19 0.11 
SS (mg/L) 143 213 177.22 22.63 
NH3-N (mg/L) 1555 2010 1765.34 190.54 
COD (mg/L) 2440 2990 2739.06 225.68 
BOD5 at 20°C (mg/L) 156 379 258.06 76.43 
BOD5/COD  0.06 0.13 0.09 0.03 
Fe (mg/L) 6.45 8.94 7.19 0.93 
Color (mg.Pt-Co/L) 4061 4748 4548.72 263.38 
Optimum Mix Ratio. See Figure 1 and Figure 2. The figures show the removal percentage of 
COD and NH3-N respectively in the sample after mixing different ratio of M-P with activated 
carbon (AC) in 4.0 g at fixed conditions to obtain the optimum mix ratio. As can be seen, the 
optimum percentage for AC at 62.5% while M-P at 37.5%, where the removal percentage of COD 
declined after the percentage of AC increased. In other words, about 37.5% of AC could be 
replaced by M-P at maximum COD removal. The AC and M-P mixture attained the maximum 
removal rate, whereas the percentage of M-P increased to 75.0%; thus, only 25.0% of AC could be 
replaced by M-P for maximum NH3-N removal. This is believed to occur due to considerably when 
the ratio achieves a significant substitution of the conventional adsorbent without detriment to 
contaminant removal. The results obtained agreed with previous work carried by [2,11].  
 
Figure 1. Mix ratio between AC and M-P in COD removal. 
Defect and Diffusion Forum Vol. 382 299
 
Figure 2. Mix ratio between AC and M-P in NH3-N removal. 
Conclusions 
The use of commercially AC in this study could therefore partially substituted be in place of 
expensive adsorbent such as micro-peat due to its availability, removal efficiency and low-cost. The 
results obtained showed that combination of AC and M-P can be used for COD and NH3-N removal 
from leachate. In respectively, the optimum COD removal percentage of 87% was obtained by 
AC:M-P (2.5:1.5), whereas NH3-N removal percentage of 65% was obtained by AC:M-P (1.0:3.0). 
These results showed greater effectiveness for organic and inorganic matters from leachate at a 
considerably lower cost. This would be of benefit not only to the manufacturing industry in terms of 
minimizing cost of COD and NH3-N treatment, but also to improve profit and furthermore to 
minimize the impact on the environment. 
Acknowledgements 
The authors would like to acknowledge the Ministry of Higher Education, Malaysia and Universiti 
Tun Hussein Onn Malaysia for financial support under Fundamental Research Grant (FRGS) No. 
1457 and 1571 and MyBrain15 (MyPhD). 
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Defect and Diffusion Forum Vol. 382 301


Micro-peat as a potential low-cost adsorbent material for COD and NH3-N removal

 Available online at www.sciencedirect.com  Procedia Technology 8C (2013) 138 – 143   www.elsevier.com/locate/procedia  2212-0173 © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia.  The 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013)   Speed Tracking of Indirect Field Oriented Control Induction Motor using Neural Network Azuwien Aida Boharia,*, Wahyu Mulyo Utomoa, Zainal Alam Harona,  Nooradzianie Muhd. Zina, Sy Yi Sima, Roslina Mat Ariffa   aFaculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia Abstract Development of a neural network (NN) based indirect field oriented control (IFOC) of induction motor (IM) drive is presented in this paper. The neural network indirect field oriented control (NNIFOC) is designed to generate signal of quadrature current, hence the motor speed will follow the reference speed value. The simulation model using Matlab/Simulink was developed to validate the effectiveness of the proposed controller. The simulation results show that the proposed NNIFOC schemes of IM drive is more effective and found to be a proper replacement of the proportional integral derivative (PID) controller.  © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia. Keywords: Induction  Motor; Indirect Field Oriented Control; Neural Network Controller. 1. Introduction A three phase IM is designed to operate from a three phase source of alternating voltage and it is a one of asynchronous AC motor. The advantages of the IM includes high reliability, relatively simple, has rugged structure,     * Corresponding author. Tel.: +60107604886; fax: +6074276876. E-mail address: he110247@siswa.uthm.edu.my  Azuwien Aida Bohari et.al. / Procedia Technology 8C (2013) 138 – 143 139 low cost, robustness and high efficiencies. These advantages make the IM advance in all aspects like speed change, speed reversal, starting and braking. The overall system performance depends on the IM dynamic operation. Due to the swift development in microprocessor and power electronic, the advanced control methods have IM possible for high performance applications. A lot of researcher has been attracted to the field of electric drives by IM control over time [1-14].  IM with variable speed ac drives and employed the field oriented control method expands in recent years to achieve better performances set by DC drives. In order to provide good steady-state performance in fast dynamic response, decoupling between the torque and flux is highly recommends. High dynamic performance in IM can be achieved by means of field-oriented control where it provides a suitable mathematical description of three phase IM. The translation of coordinate from the fixed references stator frame to the frame of rotating synchronous is implied by the vector control [1]. In early 1970s, the decoupling technique makes the possibility of separated control for torque and flux in the complex dynamic for IM [2]. By applying the control techniques such as adaptive control [3] a good performance can be achieve with parameter sensitive property. A great attention has been made by NN due to its natural parallelism in the field of power electronic, thus allow and permit the high speed processing. The NN have capability for tolerance, to miss data, to fault, and to carry out in noise environment [4].  To identify and control nonlinear dynamic systems and nonlinear parameter estimation, the use of NN has been proposed [5][6]. An estimation of the stator flux and trained to map the nonlinear behavior of a rotor flux is performed on the proposed NN [7]. High performance control has been the objectives of [8], which presents robust neural controller and against variation of parameter. Without knowing the exact mathematical model of the system, the desired mapping between the input and outputs signals of the system can be learned by the NN. The NN are excellent estimators in non-linear system because it does not use the mathematical model of the system [9]. This paper will present a NN based IFOC of IM drive. The performance of three phase IM drive system with NN controlled applied in this paper. The theoretical principle, numerical simulation procedures and the results of these methods are discussed and compared with conventional PID controller. 2. Dynamic Modeling of IM The IM model has been presented in a various reference frames. This makes it easier to adjust the model accordingly and to fix the reference frame to a particular motor quantity. Normally, a rotary type of induction is used with a rotating rotor and stationary stator. In order to derive the IM dynamic model of three phase coordinate into two phases direct and quadrature coordinate is required. The numeric model in stationary reference frame is given [10].  0 00 0ds dss s mqs qss sm r m r r r rdr drr m m r r r rqr qrV iR L p L pV iR L p LpL p L R L p LV iL L p L R L pV i                                                                                                                             (1) The reference frame of fixed d-q stator in IM model is described as follow,  ( )s s s sdV R idt                                                                                                  0 ( )r r r r r rdV R i jdt                                                                                                           s s s mL i L                                                                                                                                                                         r m s r rL i L i                                                                                                                          140 Azuwien Aida Bohari et.al. / Procedia Technology 8C (2013) 138 – 143 Whereas the mechanical equation is given as below:  ( )ds ds s dsV R i dt                                                                                                                                 ( )qs qs s qsV R i dt                                                                                                                 1tan ( )qssds                                                                           The electromagnetic torque of the machine can be presented as follow:     3( )4e ds qs qs dspT i i                                                                                                     3. IFOC  System Description The IFOC consists of controlling the stator currents represented by a vector. This control is based on projections which transform three phase time and speed dependent system into two co-ordinate time invariant system. This technique makes IM control scheme similar to a DC motor control scheme. In this IFOC technique, input and torque reference constant is required which aligned with the d-q axis by using this method. The control promise good quality of IM for both dynamic and steady-state operation. 4. Neural Network Generally a NN controller was developing based on features of the human brains to handle complex problem and many task in one time. Basically the human brains system has ability to process the input information and the output data simultaneously and generate response instantaneously in handle multi job. By this reason many researcher believe that development of the NN control systems suitable for the nonlinear system. Development of the structure and learning algorithm of the NN is explained as follows [11].  4.1. Proposed NN speed controller  This paper proposed a NN control method of IFOC based on hysteresis to reduce the overshoot and torque ripple. The NN control is added to the speed controller to produce the torque reference. The block diagram of the proposed NNIFOC of IM drive is shown in Fig. 1.   Fig. 1. The proposed NNIFOC diagram HYSTERISIS CURRENT CONTROL  Pb  IM d-q       αbc 1/Lm  Ψ* (1/Tr)(1/Ids) (2/3Pb)*(Lr/Lm)*(1/Ψ*) ω* NN d-q       αbc Iq ωm ωsl ∫ Id  Azuwien Aida Bohari et.al. / Procedia Technology 8C (2013) 138 – 143 141 4.2.  Structure of NNIFOC  Generally, input and output neuron number of the NN control system will be based on the input signal and output signal numbers. In order to develop the NNIFOC a multilayer perception with certain structure was proposed as shown in Fig. 2.  Fig. 2. Structure of NNIFOC  The controller consists of input layer, hidden layer and output layer. Based on number of the neuron in the layers, the NNIFOC is defined as a 1-3-1 network structure. The first neuron of the output layer is used as a torque reference signal (a21=mf). The connections weight parameter between jth and ith neuron at mth layer is given by wmij, while bias parameter of this layer at ith neuron is given by bmi. Transfer function of the network at ith neuron in mth layer is defined by: 111mSm m m mi ij j ijn w a b  The output function of neuron at mth layer is given by:  ( )m m mi ia f n Where f  is activation function of the neuron. In this design the activation function of the output layer is unity and for the hidden layer is a tangent hyperbolic function given by: 22( ) 11mim minf ne Updating of the connection weight and bias parameters are given by: ( )( 1) ( )m mij ij mijF kw k w kw   ( )( 1) ( )m mi i miF kb k b kb   where k is sampling time, α is learning rate, and F performance index function of the network.  142 Azuwien Aida Bohari et.al. / Procedia Technology 8C (2013) 138 – 143 5. Simulation Results A simulation was constructed for the entire system in Fig 1 with the implementation of NN based IFOC scheme for the three-phase IM using Borland C++, and then embedded as S-function in Matlab/Simulink software. In this paper, the pattern of the reference speed that used to verify the effectiveness of the proposed controller is shown in Fig 3 the parameters for the motor are 240V, 50Hz, Poles=4, Rs=0.3Ω, Rr=0.25Ω, Ls=0.0415H, Lr=0.0412H, Lm=0.0403H, J=0.1kg-m2. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5020406080100120140Time (s)Speed (rad/s)REFERENCE SPEED Fig. 3. Pattern of the reference speed  The output response of the system based on the speed reference patern is shown in Fig. 4. and Fig. 5.  0 0.5 1 1.5 2 2.5 3020406080100120140160Time (s)Speed (rad/s)PID CONTROLLERNN CONTROLLER0.5 0.505 0.51 0.515 0.52 0.525 0.53 0.535 0.54 0.545 0.5520253035404550Time (s)Speed (rad/s)PID CONTROLLERNN CONTROLLER Fig. 4. Acceleration ramp response of NN and PID speed controller  2 2.5 3 3.5 4 4.5 5020406080100120140160Time (s)Speed (rad/s)PID CONTROLLERNN CONTROLLER3.7 3.705 3.71 3.715 3.72 3.725 3.73 3.735 3.74 3.745 3.7590919293949596979899100Time (s)Speed (rad/s)PID CONTROLLERNN CONTROLLER Fig.5. Deceleration ramp response of NN and PID speed controller   Azuwien Aida Bohari et.al. / Procedia Technology 8C (2013) 138 – 143 143 Referring to the Fig 4, the acceleration ramp of the system by using both controllers in between time 0 to 2 seconds, shows that NN speed controller follow the shape of speed reference pattern and have a response improved from the conventional PID controller. While, in Fig 5 between time 2 to 3 seconds, the speed continue at 130 rad/s. Then, NN speed controller follow the shape of deceleration ramp at the time of 3 to 5 seconds.  The results for the both figure shows the great reduce in ripple of the transient respose and improved the performance at the same time rather than PID controller.   6. Conclusion  The NN based IFOC of IM drive system has been presented in this paper. The purpose of this paper is to investigate the performance of NN compared to the PID controller. The comparative results show that NN based IFOC schemes of IM drive is more effective and, hence, found to be a suitable replacement of the conventional PID controller. It is concluded that IFOC by using NN are useful and able to improve the performance. The result shows that the performance of the system is improved by reducing the ripple.  Acknowledgement   The author would like to gratitude University Tun Hussein Onn Malaysia for any valuable supports during conducting this research and in preparing this manuscript. References [1] Goyat, S. and Ahuja,  R. K., “Speed Control of IM using Vector or Field Oriented Control”, Internationl Journal of Advances in Engineering and Technology. 2012. [2] Blaschke, F., “The principle of field orientation as applied to the new transvector closed-loop control system for rotating-filed machines,” Siemens. 1972. Vol.34.p. 217-220. [3] Von Zuben, F.J., Netto, M.L.A., Bim, E., Szajner, J., "Adaptive vector control of a three-phase IM using NNs," NNs, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on , 1994. Vol 6.p.3750-3755  [4] Ba-Razzouk, A., Cheriti, A., Olivier, G.,1997."A NNs based field oriented control scheme for IMs," Industry Applications Conference, 1997. Thirty-Second IAS Annual Meeting, IAS '97., Conference Record of the 1997 IEEE , vol.2, no., pp.804-811. [5] Wishart, M.T., Harley, R.G.,.  "Identification and control of induction machines using artificial NNs," Industry Applications, IEEE Transactions on , 1995;31(3):612-619.  [6] Narendra, K.S., Parthasarathy, K., "Identification and control of dynamical systems using NNs," NNs, IEEE Transactions on , 1990. 1(1).p.4-27. [7] Ba-Razzouk, A., Cheriti, A., Olivier, G., Sicard, P., "Field oriented control of IMs using NNs decouplers," Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on , 1995.Vol. 2.p.1428-1433. [8] Miloudi, A., Draou, A., 2001. "Neural controller design for speed control of an indirect field oriented induction machine drive," Industrial Electronics Society, IECON '01. The 27th Annual Conference of the IEEE.  2001. p.1225-1229 . [9] Sivasubramaniam, A., “Application of neural network structure in voltage vector selection of direct torque control induction  motor” International Journal of Applied Engineering Research, 2009. 4(6),p.903. [10] Kumar,  Rajesh, Gupta, R.A.,  Bhangale, S.V., Gothwal, Himanshu; 2007. "Artificial NN based direct Torque Control of IM drives," Information and Communication Technology in Electrical Sciences (ICTES 2007), ICTES. IET-UK International Conference on , 2007. p.361-367. [11] Yatim A.H.M., and Utomo W.M.,. “On-Line Optimal Control of Variable Speed Compressor  Motor Drive Using Neural Control Model.” PECon Proceedings Conference, 2004. p. 83-87.  

Adaptive vector control of a three-phase IM using NNs,&quot; NNs,

Application of neural network structure in voltage vector selection of direct torque control induction motor”

Artificial NN based direct Torque Control of IM drives,&quot;

Field oriented control of IMs using NNs decouplers,&quot; Industrial Electronics, Control, and Instrumentation,

G.,1997.&quot;A NNs based field oriented control scheme for IMs,&quot; Industry Applications Conference,

Identification and control of dynamical systems using NNs,&quot; NNs,

Identification and control of induction machines using artificial NNs,&quot; Industry Applications,

Neural controller design for speed control of an indirect field oriented induction machine drive,&quot;

On-Line Optimal Control of Variable Speed Compressor Motor Drive Using Neural Control

Speed Control of IM using Vector or Field Oriented Control”,

The principle of field orientation as applied to the new transvector closed-loop control system for rotating-filed machines,” Siemens.

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