Abstract
Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and predicted concentrations of certain important parameters. In the current study, two RBFNNs (one conventional and one based on particle swarm optimization (PSO)) are employed to accurately predict the removal of chemical oxygen demand (COD) from polluted water streams using submerged biofilter media (plastic and gravel) under the influence of different variables such as temperature (18.00-28.50 degrees C), flow rate (272.16-768.96 m(3)/day), and influent COD (55.50-148.90 ppm). The results of the experimental study showed that the COD removal ratio had the highest value (65%) when two plastic biofilter media were used at the minimum flow rate (272.16 m(3)/day). The mathematical model results showed that the closeness between the measured and obtained COD removal ratios using the RBFNN indicates that the neural network model is valid and accurate. Additionally, the proposed RBFNN trained with the PSO method helped to reduce the difference between the measured and network outputs, leading to a very small relative error compared with that using the conventional RBFNN. The deviation error between the measured value and the output of the conventional RBFNN varied between + 0.20 and - 0.31. However, using PSO, the deviation error varied between + 0.058 and - 0.070. Consequently, the performance of the proposed PSO model is better than that of the conventional RBFNN model, and it is able to reduce the number of iterations and reach the optimum solution in a shorter time. Thus, the proposed PSO model performed well in predicting the removal ratio of COD to improve the drain water quality. Improving drain water quality could help in reducing the contamination of groundwater which could help in protecting water resources in countries suffering from water scarcity such as Egypt.