Abstract：
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.
摘要：
植物油炼油行业在石油生产过程中产生高度污染的废水，需要在排放前进行适当的处理，以防止环境危害。在发展中国家，处理此类废水已成为主要的环境问题。化学需氧量(COD)是评价废水有机污染物负荷的关键参数。高COD水平会导致水体中溶解氧减少，对水生生物产生负面影响。已经采用了各种技术来处理含油废水，但是微生物降解由于其有效去除有机污染物的潜力而受到关注。本研究旨在使用响应面法（RSM）优化植物油工业废水的生物降解处理工艺。废水的物理化学性质进行了表征，以实现这一目标，并对COD的去除进行了分析。此外，RSM用于研究pH值的综合影响，接触持续时间,微生物浓度对COD去除效率的影响。结果表明，所使用的微生物菌株的最大COD去除率为92％。此外，建立了基于实验变量的二次模型来预测COD去除率。从方差分析(ANOVA)分析，该模型被发现是显著的p<0.0004和准确预测的COD去除率在实验区域内，R2值为90.99%，调整后的R2值为82.89%。等高线图和统计分析显示了接触时间和微生物浓度对COD去除的重要性。实践要点：响应面法（RSM）优化在植物油工业废水中实现了92％的显着化学需氧量（COD）去除效率。该研究在使用RSM优化COD去除方面的成功突出了有效和环保废水处理的潜力。从业者可以从确定的因素(pH，接触时间,和微生物浓度）以增强处理系统的运行。开发的预测模型为工厂操作员和工程师提供了定制废水处理过程的实用工具。这项研究强调了可持续实践在废水处理中的重要性，强调微生物降解在解决有机污染物负荷中的作用。
