Optimization of Swine Farm Wastewater Treatment Using Mixed Microalgae: Statistical Modeling and Performance Evaluation via Response Surface Methodology

Abstract

Swine farm wastewater contains high concentrations of organic matter and nutrients, requiring treatment approaches that are both effective and economically feasible. This study evaluated the performance of mixed indigenous microalgae for the removal of chemical oxygen demand (COD) and total phosphorus (TP) from swine farm wastewater and optimized the effects of initial pH and algal concentration using Response Surface Methodology (RSM) with a Central Composite Design (CCD). Thirteen experimental runs were conducted under outdoor conditions to reflect field applicability. COD and TP removal efficiencies ranged from 74.30–83.85% and 75.17–83.58%, respectively. Statistical analysis showed that pH significantly influenced both COD and TP removal, whereas algal concentration exerted a stronger effect on COD removal but a comparatively weak influence on TP, in agreement with ANOVA results. The quadratic model demonstrated strong predictive performance for COD (R² = 0.9879; predicted R² = 0.9245), while the TP model displayed limited predictive capability, suggesting that additional unmeasured factors may govern phosphorus reduction. Numerical optimization identified pH 7.69 and algal concentration (A600) 2.072 as the optimal conditions, yielding predicted removals of 78.21% COD and 82.00% TP. Although TP levels approached regulatory thresholds, COD remained above discharge limits, highlighting the need for a polishing step prior to release. Overall, the results demonstrate that mixed indigenous microalgae offer a robust, low-cost treatment strategy for swine wastewater and provide optimized operational conditions to support practical implementation.