Coagulant Dosage Determination in a Water Treatment Plant Using Dynamic Neural Network Models

A common step in most of water treatment plants is the chemical coagulation. The chemical coagulation is the process of destabilizing the colloidal particles suspended in raw water by the addition of coagulants. Generally, the determination of the quantity of coagulant to be added to water is made manually by jar tests. However, the manual control has slow response to changes of raw water and it requires intensive laboratory analysis. To reduce the manual effort and to improve the response to change in raw water quality, this work proposes the determination of the coagulant dosage using dynamic neural network modeling using the available sensors as input of the model. The case of study is a large scale water treatment plant in Ceará, Brazil, where the kinds of coagulants added to water are the aluminum sulphate (AS) and poly aluminum chloride (PAC). Several dynamic neural network models with different combinations of the input variables have been evaluated. The best solution found is composed by a non-linear autoregressive with exogenous input (NARX) model having three input variables, the pH in raw and coagulated water, and the turbidity in the coagulated water, with coefficient of determination of R2 = 0.95 and R2 = 0.91 for the AS and PAC dosage prediction, respectively.