Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

Zakariah Yusuf, Norhaliza Abdul Wahab, Abdallah Abusam


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.


ANN modeling, CPSOGSA, model predictive control, real time optimization, SMBR

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ISSN 2088-8708, e-ISSN 2722-2578