Chemical coagulation of greywater: modelling using artificial neural networks

In the present study, chemical coagulation with alum and polyaluminium chloride (PACl) was utilized for greywater treatment. More than 140 jar tests on greywater with varying characteristics were conducted in order to determine the optimum coagulant dosage and treated greywater characteristics. The average removal efficiencies of turbidity, chemical oxygen demand (COD) and total suspended solids were obtained as 91, 73 and 83% using alum and 93, 74 and 89% using PACl, respectively. For similar initial turbidity levels, optimum PACl dosages required were significantly less compared to optimum alum dosages. Further, PACl produced treated greywater with lower levels of turbidity compared to alum. Results of the coagulation tests were used to design artificial neural network (ANN) models for the prediction of the optimum coagulant dosage and treated greywater quality parameters. ANN models with initial turbidity, pH, conductivity and alkalinity as the input parameters could predict the optimum coagulant dose and treated greywater quality. The performance of the models was found to be good, with correlation coefficient values greater than 0.80. Empirical formulas for the prediction of alum and PACl dosages were also derived using the algorithm weights and bias values from the networks eliminating the need for running the ANN software.