Neural Network Model Identification and Advanced Control of a Membrane Biological Reactor

Abstract

System identification with different input-output structures, for a membrane biological reactor (MBR), was performed using artificial neural networks (ANN) black-box modeling. The ANN models were able to capture the dynamic flux experimental literature data. Sensitivity analyses were applied on the ANN models to quantify the effects of variation in the process inputs (backwash pressure, vacuum pressure, backwash and vacuum time) on the process output (flux rate. Sensitivity analysis was applied on the developed NN in order to find the optimum backwash scheduling. The maximum flux was attained at around 165 (L/m2·day) that corresponded to an optimum vacuum-to-backwash time ratio of 10 minutes vacuum to 2 minutes backwash. Advanced control strategy using neuro-model predictive control (NN-MPC) methodology was applied to control the MBR system. The NN-MPC parameters were tuned to attain an optimum performance. The NN-MPC was efficient in tracking the flux set-point changes by adjusting vacuum-to-backwash time ratio within the operation constraints.