Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Syahira Ibrahim
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Optimization of artificial neural network topology for membrane bioreactor filtration using response surface methodology Syahira Ibrahim; Norhaliza Abdul Wahab; Fatimah Sham Ismail; Yahaya Md Sam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (925.565 KB) | DOI: 10.11591/ijai.v9.i1.pp117-125

Abstract

The optimization of artificial neural networks (ANN) topology for predicting permeate flux of palm oil mill effluent (POME) in membrane bioreactor (MBR) filtration has been investigated using response surface methodology (RSM). A radial basis function neural network (RBFNN) model, trained by gradient descent with momentum (GDM) algorithms was developed to correlate output (permeate flux) to the four exogenous input variables (airflow rate, transmembrane pressure, permeate pump and aeration pump). A second-order polynomial model was developed from training results for natural log mean square error of 50 developed ANNs to generate 3D response surfaces. The optimum ANN topology had minimum ln MSE when the number of hidden neurons, spread, momentum coefficient, learning rate and number of epochs were 16, 1.4, 0.28, 0.3 and 1852, respectively. The MSE and regression coeffcient of the ANN model were determined as 0.0022 and 0.9906 for training, 0.0052 and 0.9839 for testing and 0.0217 and 0.9707 for validation data sets. These results confirmed that combining RSM and ANN was precise for predicting permeates flux of POME on MBR system. This development may have significant potential to improve model accuracy and reduce computational time.
Modeling of submerged membrane filtration processes using recurrent artificial neural networks Zakariah Yusof; Norhaliza Abdul Wahab; Syahira Ibrahim; Shafishuhaza Sahlan; Mashitah Che Razali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.443 KB) | DOI: 10.11591/ijai.v9.i1.pp155-163

Abstract

The modeling of membrane filtration processes is a challenging task because it involves many interactions from both biological and physical operational behavior. Membrane fouling behaviour in filtration processes is complex and hard to understand, and to derive a robust model is almost not possible. Therefore, it is the aim of this paper to study the potential of time series neural network based dynamic model for a submerged membrane filtration process. The developed model that represent the dynamic behavior of filtration process is later used in control design of the membrane filtration processes. In order to obtain the dynamic behaviour of permeate flux and transmembrane pressure (TMP), a random step was applied to the suction pump. A recurrent neural network (RNN) structure was employed to perform as the dynamic models of a filtration process, based on nonlinear auto-regressive with exogenous input (NARX) model structure. These models are compared with the linear auto-regressive with exogenous input (ARX) model. The performance of the models were evaluated in terms of %R2, mean square error (MSE,) and a mean absolute deviation (MAD). For filtration control performance, a proportional integral derivative (PID) controller was implemented. The results showed that the RNN-NARX structure able to model the dynamic behavior of the filtration process under normal conditions in short range of the filtration process. The developed model can also be a reliable assistant for two different control strategies development in filtration processes.
Data-driven adaptive predictive control for an activated sludge process Mashitah C. Razali; Norhaliza Abdul Wahab; Syahira Ibrahim; Azavitra Zainal; M. F. Rahmat; Ramon Vilanova
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i5.2257

Abstract

Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Co-Authors Azavitra Zainal Fatimah Sham Ismail M. F. Rahmat Mashitah C. Razali Mashitah Che Razali Norhaliza Abdul Wahab Ramon Vilanova Shafishuhaza Sahlan Yahaya Md Sam Zakariah Yusof