Application of artificial neural network to predict water flux from pre-treated palm oil mill effluent using direct contact membrane distillation

Abstract

Using computational models, engineers and researchers are able to observe the results of their research beyond the range of their tested experimental parameters. Process modelling resembles the approximation behaviour. This paper reports the development of a mathematical model in predicting the water flux from Pre-Treated Palm Oil Mill Effluent (POME) using Direct Contact Membrane Distillation (DCMD). This model allows the identification for the range of feed temperature and feed velocity, in which optimal operating conditions (maximum production of water flux) can be obtained. An artificial neural network (ANN) was selected to predict the production of flux at various temperatures due to its capability to predict any nonlinear system. A multilayer Feedforward Artificial Neural Network (FANN) model consisting of 1 hidden layer with 13 hidden neurons, was developed with three input variables (feed temperature, feed velocity and membrane) and one output (permeate water flux) with 18 experimental data points. The lowest mean square error (MSE) obtained was 0.0034, while the regression coefficient (R) values for training, validation, and testing were 0.9859, 0.9986, and 0.9984, respectively. The most sensitive parameter was the feed temperature compared to feed velocity. The usage of this model may lead to the development of efficient and economical designs for separation processes.