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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Nur Sakinah Ahmad Yasmin
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Estimation of pH and MLSS using Neural Network Nur Sakinah Ahmad Yasmin; Muhammad Sani Gaya; Norhaliza Abdul Wahab; Yahaya Md Sam
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6144

Abstract

The main challenges to achieving a reliable model which can predict well the process are the nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent approaches revolved as better alternative in predicting the system. Typical measured variables for effluent quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN) modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feed- forward neural network techniques as nonlinear function approximators have demonstrated the capability of predicting nonlinear behaviour of the system. The data for the period of two years and nine months sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The ANFIS model may serves as a valuable prediction tool for the plant.
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.
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.
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.
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.
Co-Authors Aznah Nor Anuar Muhammad Sani Gaya Mustafa Bob Norhaliza Abdul Wahab Yahaya Md Sam