Modeling of submerged membrane filtration processes using recurrent artificial neural networks

Abstract

The modeling of membrane filtration processes is a challenging task because it involves many interactions from both biological and physical operational behavior. Membrane fouling behaviour in filtration processes is complex and hard to understand, and to derive a robust model is almost not possible. Therefore, it is the aim of this paper to study the potential of time series neural network based dynamic model for a submerged membrane filtration process. The developed model that represent the dynamic behavior of filtration process is later used in control design of the membrane filtration processes. In order to obtain the dynamic behaviour of permeate flux and transmembrane pressure (TMP), a random step was applied to the suction pump. A recurrent neural network (RNN) structure was employed to perform as the dynamic models of a filtration process, based on nonlinear auto-regressive with exogenous input (NARX) model structure. These models are compared with the linear auto-regressive with exogenous input (ARX) model. The performance of the models were evaluated in terms of %R², mean square error (MSE,) and a mean absolute deviation (MAD). For filtration control performance, a proportional integral derivative (PID) controller was implemented. The results showed that the RNN-NARX structure able to model the dynamic behavior of the filtration process under normal conditions in short range of the filtration process. The developed model can also be a reliable assistant for two different control strategies development in filtration processes.