Optimization of Wastewater Treatment Processes Using AI-Driven Machine Learning Algorithms for Enhanced Biological Degradation Efficiency

Abstract

Wastewater treatment is essential for environmental protection, yet traditional biological methods often struggle with efficiency, particularly under varying influent conditions. This study addresses the limitations of conventional biological treatment by integrating machine learning (ML) and genetic optimization to enhance degradation efficiency. The objectives were to develop an AI-driven model that optimizes key parameters, such as temperature and dissolved oxygen, to improve Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) removal. Data collection included influent and effluent quality parameters, which were preprocessed through normalization and outlier handling. The methodology involved testing several ML algorithms, with Gradient Boosting emerging as the most accurate, achieving Root Mean Square Error (RMSE) values of 7.1 for COD and 6.8 for BOD. Genetic algorithms then optimized parameter settings, achieving COD and BOD reductions of 58% and 55%, respectively, compared to traditional methods’ 42% and 38%. Sensitivity analysis identified temperature and dissolved oxygen as critical factors, confirming the effectiveness of real-time, AI-driven adjustments in maintaining pollutant removal efficiency. These findings establish AI-driven optimization as a promising, scalable solution for enhancing wastewater treatment processes, offering significant improvements over conventional approaches.