EXPERIMENTAL INVESTIGATION, MODELING, AND OPTIMIZATION OF COMBINED ELECTRO-(FENTON/COAGULATION/FLOTATION) PROCESS BY MEANS OF DESIGN OF EXPERIMENTS AND ARTIFICIAL INTELLIGENCE SYSTEMS

摘要

In the present study, combined Electro-(Fenton/Coagulation/Flotation) (EF/EC/EI) process was studied via degradation of Disperse Orange 25 (DO25), an organic dye, as a case study. Influences of seven operational parameters including initial pH of solution (pH_0), applied voltage between anode and cathode (V), initial ferrous ion concentration (C_(Fe)), initial Hydrogen peroxide concentration (C_(H202)), initial D025 concentration (C_0), applied aeration flow rate (F_(Air)), and process time (t_p) were measured on the dye removal efficiency (DR%). Combined design of experiments (DOE) was designed and experiments were conducted in accordance with the design. The experimental data collected in a laboratory scaled, handmade glass cylindrical batch reactor equipped with four graphite bar electrode as cathode, an Aluminum sheet (Al) electrode as an anode, an aeration pump equipped with air filter and air distributer, 150 rpm mixer, and DC power supply. The DR% of 98 was achieved under experimental conditions at pH_0 4, V 10, C_(Fe) 7.5, C_(H202)O, Co 140, and F_(air) 0. The data was used for model building by employing two more popular models in this kind of studies: normal and reduced multiple regression model (MLR & r-MLR) and artificial neural network (ANN & r-ANN). Further statistical tests were applied to exhibit models goodness and to compare models. Based on statistical comparison, ANN models obviously outperformed SMLR models. Finally, optimization process was carried out using genetic algorithm (GA) over the outperformed ANN model. The optimization procedure causes optimal point which gave an insight of optimal operating conditions of the combined process.