Total organic carbon (TOC) in raw water is considered one of the major precursors to formation of disinfection by-products. Many drinking water treatment plants in the United States depend on coagulation, flocculation, and precipitation processes to bring treated water TOC levels to within acceptable limits. However, predicting TOC removal efficiency for a known coagulant dosage is not a trivial task and requires popular but time-consuming jar tests. This article presents successful implementation of artificial neural network (ANN) technology in predicting the TOC removal efficiency based on routinely measured physical and chemical raw-water characteristics and coagulant dosage. ANN predictions were better than multiple linear regression models. A new promising approach to develop a dose-response curve from the trained ANN is proposed and demonstrated in this study. This approach will facilitate instantaneous prediction of TOC at the outlet. It can also help in estimating optimal coagulant dosage in a drinking water treatment plant.
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