Article Per Year (5 Year)
p-Index From 2020 - 2025
0.961
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Norhaliza Abdul Wahab
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Performance comparison of SVM and ANN for aerobic granular sludge Nur Sakinah Ahmad Yasmin; Norhaliza Abdul Wahab; Aznah Nor Anuar; Mustafa Bob
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (775.903 KB) | DOI: 10.11591/eei.v8i4.1605

Abstract

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
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.
Improved support vector machine using optimization techniques for an aerobic granular sludge Nur Sakinah Ahmad Yasmin; Norhaliza Abdul Wahab; Aznah Nor Anuar
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 | Full PDF (850.668 KB) | DOI: 10.11591/eei.v9i5.2264

Abstract

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.
Co-Authors Abubakar Ibrahim Tukur Aliyu Muhammad Abdu Azavitra Zainal Aznah Nor Anuar Fatimah Sham Ismail Irma Wani Jamaludin Wani Jamaludin M. F. Rahmat Mashitah C. Razali Mashitah Che Razali Mubarak Auwal Saleh Muhammad Sani Gaya Muhammad Sani Gaya Muhammad Sani Gaya Mustafa Bob Nur Sakinah Ahmad Yasmin Nurazizah Mahmod Parvaneh Esmaili Ramon Vilanova Sani Isah Abba Shafishuhaza Sahlan Syahira Ibrahim Yahaya Md Sam Zakariah Yusof Zakariah Yusuf