MODELING AND OPTIMIZATION OF COAGULATION-FLOCCULATION FOR LEACHATE TREATMENT

Abstract

Coagulation and flocculation are one of the most important steps in leachate treatment. The main difficulty is to determine the optimal dose of coagulant to be injected according to the characteristics of the extract. Poor control of this process can lead to a significant increase in operating costs and failure to meet quality objectives at the outlet of the treatment plant. Aluminium sulphate is the most commonly used coagulant reagent. The determination of the coagulant dose is made using the test called the "Jar Test" conducted in the laboratory. This type of approach has the disadvantage of having a relatively long delay time and therefore does not allow automatic control of the coagulation process. The present work describes a Takagi Sugeno (TK) neuro-fuzzy model, developed for the prediction of the coagulant and flocculant dose used during the clarification phase in the Moroccan leachate treatment plant. The ANFIS model (fuzzy inference system based on adaptive neural networks), which combines fuzzy and neural techniques by forming a supervised learning network, was applied during the calibration phase and tested during the validation period. The results obtained by the ANFIS model were compared with those obtained with a multilayer perceptron neuron network (MLP) and a third model based on multiple linear regression (MLR). A coefficient of determination (R2) of the order of 0.92 during the validation period was obtained with the ANFIS model, whereas for the MLP, it is of the order of 0.65, and for the MLR model it does not exceed 0.4. The results obtained are of great importance for the management of the installation.