Modeling and Optimization of COD Removal from Cold Meat Industry Wastewater by Electrocoagulation Using Computational Techniques
In this paper, electrocoagulation (EC) treatment for the removal of chemical oxygen demand (COD) from cold meat industry wastewater is modeled and optimized using computational techniques. Methods such as artificial neural networks (ANNs) and response surface methodology (RSM), based on the Box–Behnken design using three levels, were employed to calculate the best control parameters for pH (5–9), current density (2–6 mA/cm2) and EC time (20–60 min). Analysis of variance (ANOVA) and 3D graphs revealed that pH and current density are the main parameters used for depicting the EC process. The developed models successfully describe the process with a correlation coefficient of R2 = 0.96 for RSM and R2 = 0.99 for ANN. The models obtained were optimized applying the moth-flame optimization (MFO) algorithm to find the best operating conditions for COD removal. ANN-MFO was used and showed superior COD removal (92.91%) under conditions of pH = 8.9, current density = 6.6 mA/cm2 and an EC time of 38.62 min. The energy consumption with these optimal conditions was 6.92 kWh/m3, with an operational cost of $3.14 (USD)/m3. These results suggest that the proposed computational model can be used to obtain more effective and economical treatments for this type of effluent.