Utilization of Response Surface Methodology in Optimization and Modelling of a Microbial Electrolysis Cell for Wastewater Treatment Using Box–Behnken Design Method
Nhlanganiso Ivan Madondo, Sudesh Rathilal, Babatunde Femi Bakare
Abstract
A vast quantity of untreated wastewater is discharged into the environment, resulting in contamination of receiving waters. A microbial electrolysis cell (MEC) is a promising bioelectrochemical system (BES) for wastewater treatment and energy production. However, poor design and control of MEC variables may lead to inhibition in the system. This study explored the utilization of Response Surface Methodology (RSM) on the synergistic aspects of MEC and magnetite nanoparticles for wastewater treatment. Influences of temperature (25–35 °C), voltage supply (0.3–1.3 V) and magnetite nanoparticle dosage (0.1–1.0 g) on the biochemical methane potentials (BMPs) were investigated with the aim of optimizing biogas yield, chemical oxygen demand removal and current density. The analysis of variance (ANOVA) technique verified that the quadratic models obtained were substantial, with p-values below 0.05 and high regression coefficients (R2). The optimum biogas yield of 563.02 mL/g VSfed, chemical oxygen demand (COD) removal of 97.52%, and current density of 26.05 mA/m2 were obtained at 32.2 °C, 0.77 V and 0.53 g. The RSM revealed a good comparison between the predicted and actual responses. This study revealed the effective utilization of statistical modeling and optimization to improve the performance of the MEC to achieve a sustainable and eco-friendly situation.