Neural Network Model Development with Soft Computing Techniques for Membrane Filtration Process

• Published in: International journal of electrical and computer engineering (Malacca, Malacca) Vol. 8; no. 4; p. 2614
• Main Authors: Yusuf, Zakariah, Wahab, Norhaliza Abdul, Sahlan, Shafishuhaza
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.08.2018
• Subjects: Bioreactors; Complex variables; Control systems design; Filtration; Genetic algorithms; Neural networks; Particle swarm optimization; Recurrent neural networks; Search algorithms; Soft computing; Wastewater treatment; Weight
• ISSN: 2088-8708; 2088-8708
• DOI: 10.11591/ijece.v8i4.pp2614-2623

Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IW-PSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.