Artificial neural network modelling of faecal coliform removal in an intermittent cycle extended aeration system-sequential batch reactor based wastewater treatment plant

Abstract

Artificial neural network (ANN) models have been designed to predict faecal coliform and total coliform removal for an intermittent cycle extended aeration-sequential batch reactor (ICEAS-SBR). Wastewater influent pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), oil & grease (O&G), total kjeldahl nitrogen (TKN), ammonical nitrogen (AN), total phosphorus (TP), faecal coliform, and total coliform were used to develop the network. The data used to train and test the network were obtained from the Jamnagar municipal corporation-waste water treatment plant (JMC-WWTP). Feedforward backpropagation algorithm with learngdm learning function was used to develop ANN models. The number of neurons in hidden layer were varied between 2–10 to find the most reliable network. The optimum ANN models were selected for faecal coliform and total coliform on trial-and-error method. The performance of designed models was tested by computing correlation coefficient, mean absolute deviation (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE). The network with 6 hidden neurons was optimum for faecal coliform, and 8 hidden neurons was optimum for total coliform. The produced simulation results were within 5 % of MAPE for both faecal and total coliform. The ANN models allow faecal coliform and total coliform levels in the treated wastewater effluent to be regulated that reduces the public and in particular the oyster consumer’s health risks.