Permeate flux measurement and prediction of submerged membrane bioreactor filtration process using intelligent techniques

Abstract

Recently, membrane technology has become more attractive particularly in solid-liquid separation process. Membrane bioreactor (MBR) has found to be a reliable technology to replace the conventional activated sludge (CAS) process for water and wastewater treatment by adopting membrane filtration technology and bioreactor. However, numerous drawbacks arise when using membrane which includes high maintenance cost and fouling problem. An optimal MBR plant operation is needed to be determined in order to reduce fouling and at the same time reduce the cost of running the MBR. It is crucial to have a reliable MBR filtration prediction that can measure and predict the filtration dynamic performance especially the effect of fouling to the filtration and cleaning operations. With this prediction tool, suitable action can be taken to improve the operation in order to find the optimum setting of the filtration process. This paper presents the permeate flux measurement and prediction development for submerged membrane filtration process. Three input filtration parameters were used to predict the permeate flux in the filtration process. This work employed feed forward artificial neural network (FFNN) and radial basis function neural network (RBFNN) for the prediction purpose. The permeate flux prediction method was developed using operation settings such as aeration airflow, suction pump voltage and transmembrane pressure (TMP) under schedule relaxation condition. The result shows that FFNN method gives better performance compared with RBFNN method in terms of accuracy and reliability.