Abstract
In this study, adaptive neuro-fuzzy inference system (ANFIS) was applied to estimate the parameters of a coagulation chemical dosing unit for water treatment plants. The dosing unit has three input variables (sudfloc 3835, ferric chloride and hydrated lime flow rates) and two output variables (surface charge and pH values). The ANFIS model is compared with multilayer backpropagation network (MBPN) with four different training algorithms for performance evaluation purpose. The results of evaluation tests using the average percentage error (APE), root mean squared error (RMSE), correlation coefficient (R) and average relative variance (ARV) criteria show that ANFIS is the most efficient and reliable estimator when the models were presented with noiseless and noisy input datasets.
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