Modeling of electrolysis process in wastewater treatment using different types of neural networks

Abstract

Indirect electrolysis has been used for the removal of chlorophyll a (as indicator of algae) from the final effluent of aerated lagoons in the wastewater treatment plant of Bu-Ali Industrial Estate. The efficiency of the process was studied experimentally and by simulation using neural networks. The process analysis was done in different conditions of retention time (5–50 min) and using two types of electrodes based on aluminum and stainless steel, with different distances between electrodes (from 1.0 to 3.5 cm). The electrical current and the average voltage applied were between 5 and 90 A (0.74–12 A dm −3) and 50 V, respectively. The influence of the main parameters of the electrolysis process on the final values for chlorophyll a, TSS and COD is evaluated experimentally. On the other hand, predictions of the main system outputs of a treated waste as a function of initial characteristics (initial values of chlorophyll a, TSS, COD) and operation conditions (temperature, electric power, time, electrode distance, and electrode type) were performed using artificial neural networks. The modeling methodologies elaborated in this paper are based on different types of neural networks, used individually or aggregated in stacks. They were developed gradually in the sense of improving the model performance. The neural network results represent accurate predictions, useful for experimental practice.