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Zakariah Yusuf
Universiti Teknologi Malaysia, 81310 Skudai Johor Bahru, Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration Zakariah Yusuf; Norhaliza Abdul Wahab; Abdallah Abusam
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.769 KB) | DOI: 10.11591/ijece.v7i3.pp1538-1545

Abstract

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.
Co-Authors Abdallah Abusam Norhaliza Abdul Wahab