Evaluation of the optimal operation conditions using response surface methodology for the treatment of DMP by electrocoagulation

Phthalic acid esters, commonly called as phthalates, are of a common use in the industrial activities and are known with their hazardous impact on the environment and on humans such as endocrine disrupting agents, carcinogenic and toxic effects. These adverse effects have led to increasing interest and research on the treatment and control of phthalates. Over the past two decades, there has been growing interest in the use of electrochemical techniques such as electrocoagulation (EC) for the treatment of organic pollutants particularly toxic organics. During the course of EC, where iron or stainless steel electrodes are used as electrodes, different mechanisms are operative for the elimination of organic matter, namely (a) adsorption (b) direct anodic oxidation, and (c) indirect oxidation of pollutants in the bulk solution. A statistical-based technique named as response surface methodology (RSM) is a powerful tool for modeling the complex systems (such as EC), evaluating the simultaneous effects of several factors (independent variables), and thus searching optimum conditions for desirable responses (dependent variables). Until now, RSM has not been used as a modeling and optimization tool for the EC treatment of phthalates. In this study, EC treatment, using stainless steel anode, of dimethyl phthalate (DMP), was investigated and optimized via RSM, central composite design (CCD). Initial DMP concentration (DMP_o; 20-100 mg L^-1), current density (J_c; 4.5-22.5 mA cm^-2), electrolyte concentration (NaCl, 750-1750 mg L^-1), treatment time (t_r; 60-180 min) were selected as critical process parameters while DMP, total organic carbon (TOC) removals and electrical energy consumption (EEC, in kWh m^-3) values were selected as the responses. The EC process was optimized to improve the abatements of DMP and TOC and to reduce the EEC values. Separate validation experiments were conducted for each initial DMP concentrations at optimum EC conditions established by the software module to check the goodness of fit and quality of the regression models. According to the established second-order polynomial regression models, DMP, TOC removal efficiencies and EEC values were affected by the process variables in the following decreasing order; J_c>t_r>DMP_o>NaCl (negative impact), t_r>DMP_o (negative impact)>J_c>NaCl (negative impact) and t_r>J_c>NaCl (negative impact)>DMP_o (negative impact), respectively. Analysis of variance indicated that the experimental design models obtained for the EC treatment of aqueous DMP solutions in terms of the model pollutant and mineralization were statistically significant. The response surfaces of DMP established between initial DMP concentration and current density showed that DMP removal efficiencies can be enhanced by increasing the current density to a certain value indicating that an optimum value of current density exists for maximum DMP removal.