Parametric analysis of lignocellulosic ultrafiltration in lab scale cross flow module using pore blocking and artificial neural network model.

In this study, fouling mechanism and modelling analysis of synthetic lignocellulose biomass and agricultural palm oil effluent was studied using polyethersulfone (PES) ultrafiltration (UF) 10 kDa membrane. The impact of process variables (transmembrane pressure (TMP), pH and concentration of feed solution) on lignocellulosic flux was analysed using pore blocking model. The feasible approaches on utilising deep learning artificial neural network (ANN) to predict smaller flux datasets are studied.