Experimental investigation, modeling, and optimization of combined electro-(fenton/coagulation/flotation) process: design of experiments and artificial intelligence systems

Abstract

In this study, a combined electro-(Fenton/coagulation/flotation) (EF/EC/El) process was studied via degradation of Disperse Orange 25 (DO25) organic dye as a case study. Influences of seven operational parameters on the dye removal efficiency (DR%) were measured: initial pH of the solution (pH0), applied voltage between the anode and cathode (V), initial ferrous ion concentration (CFe), initial hydrogen peroxide concentration (CH2O2), initial DO25 concentration (C0), applied aeration flow rate (FAir), and process time (tP). Combined design of experiments (DOE) was applied, and experiments were conducted in accordance with the design. The experimental data were collected in a hand-made laboratory-scaleglass cylindrical batch reactor equipped with four graphite barcathodes, an aluminum sheet anode, an aeration pump equipped with an air filter and air distributer, a 150-rpm mixer, and a DC power supply. A DR% of 98 was achieved with a pH0 of 4, V of 10, CFe of 7.5, CH2O2 of 0, C0 of 140, and FAir of 0. The data were used for modeling using normal and reduced multiple regression models (MLR & r-MLR) and artificial neural networks (ANN & r-ANN). Further statistical tests were applied to determine the models’ goodness and to compare the models. Based on statistical comparison, ANN models clearly outperformed the stepwise multiple linear regression (SMLR) models. Finally, an optimization process was carried out using a genetic algorithm (GA) over the outperformed ANN model. The optimization procedure was used to determine the optimal operating conditions of the combined process.

Keywords

• fenton reagents concentration
• artificial neural network
• genetic algorithm
• dye removal efficiency
• electro-(fenton/coagulation/flotation)