Artificial Neural Network (ANN) Modelling of Palm Oil Mill Effluent (POME) Treatment with Natural Bio-coagulants

Abstract

Raw palm oil mill effluent (POME) is classified as a highly polluting effluent, which needs to be treated to an acceptable level before being discharged into water bodies. Currently, chemical coagulants are widely used in treating POME, but their hazardous nature has caused several health and environmental problems. Therefore, this research presents the use of natural materials such fenugreek and okra as bio-coagulants and bio-flocculants, respectively, for the treatment of POME. Artificial neural network (ANN) modelling technique was used for the estimation of predicted results of the coagulation-flocculation process. The responses of the process were the percentage removal of total suspended solid (TSS), turbidity (TUR) and chemical oxygen demand (COD), while the inputs were fenugreek dosages, okra dosages, pH and mixing speed. The ANN model was developed using 12 different training algorithms. Scaled conjugate algorithm (SCG) proved to be the best training algorithm with lowest mean-squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) values for all the three outputs. The MSE, MAE and MAPE values were: 16.64, 2.10, 0.03 for the TSS response; 5.05, 1.03, 0.01 for the TUR response; and 54.59, 3.82, 0.07 for the COD response, respectively. The ANN model with a regression R of 0.8629 proved to be the best for the prediction of all the responses in this study. The results proved that ANN can be applied to predict TSS, TUR and COD of POME.