Response surface methodology optimization of the effect of pH, contact time, and microbial concentration on chemical oxygen removal potential of vegetable oil industrial effluents.

The vegetable oil refinery industry generates highly polluted effluents during oil production, necessitating proper treatment before discharge to prevent environmental hazards. Treating such wastewater has become a major environmental concern in developing countries. Chemical oxygen demand (COD) is a key parameter in assessing the wastewater's organic pollutant load. High COD levels can lead to reduced dissolved oxygen in water bodies, negatively affecting aquatic life. Various technologies have been employed to treat oily wastewater, but microbial degradation has gained attention due to its potential to remove organic pollutants efficiently. This study aims to optimize the biodegradation treatment process for vegetable oil industrial effluent using response surface methodology (RSM). The wastewater's physicochemical properties were characterized to achieve this, and COD removal was analyzed. Furthermore, RSM was used to investigate the combined effects of pH, contact duration, and microbial concentration on COD removal efficiency. The result showed that the microbial strain used recorded a maximum COD removal of 92%. Furthermore, a quadratic model was developed to predict COD removal based on the experimental variables. From the analysis of variance (ANOVA) analysis, the model was found to be significant at p < 0.0004 and accurately predicted COD removal rates within the experimental region, with an R2 value of 90.99% and adjusted R2 value of 82.89%. Contour plots and statistical analysis revealed the importance of contact duration and microbial concentration on COD removal. Practitioner Points: Response surface methodology (RSM) optimization achieved a significant chemical oxygen demand (COD) removal efficiency of 92% in vegetable oil industrial effluents.The study's success in optimizing COD removal using RSM highlights the potential for efficient and environmentally friendly wastewater treatment.Practitioners can benefit from the identified factors (pH, contact time, and microbial concentration) to enhance the operation of treatment systems.The developed predictive model offers a practical tool for plant operators and engineers to tailor wastewater treatment processes.This research underscores the importance of sustainable practices in wastewater treatment, emphasizing the role of microbial degradation in addressing organic pollutant loads. [ABSTRACT FROM AUTHOR]