Modélisation et optimisation du procédé de la coagulation

Abstract

In this study, the Sidi Khelifa drinking water treatment system (Oued El Athmania) was selected as the application site. The data used were collected over a representative period and included: raw water turbidity, pH, temperature, and conductivity. A deep neural network (DNN) model was developed and trained in MATLAB using the Levenberg-Marquardt learning algorithm. Several architectures were tested to identify the optimal network structure. The activation functions used included the sigmoid hyperbolic tangent for the hidden layers and a linear function for the output. The results obtained showed that the DNN model is capable of accurately predicting the optimal dosage of coagulant (12 mg/L), with a correlation coefficient greater than 0.95 between the predicted and measured values during the training, validation, and testing phases. The model thus makes it possible to anticipate reagent needs, adjust dosages in real time, and ensure that the treated water complies with drinking water standards. This intelligent modeling approach can prove particularly useful in the field of drinking water treatment, where raw water quality is subject to significant seasonal fluctuations. It helps reduce overdosing, improve treatment efficiency, and control costs while ensuring better protection of public health.