A Novel Artificial Neural Network Approach for Troubleshooting of Sewage Treatment Plant Process

Abstract

Efficient operation and proactive fault diagnosis in Sewage Treatment Plants (STPs) are essential for maintaining water quality and ensuring regulatory compliance. This paper presents a detailed data analysis of two STP plants, focusing on key operational parameters, including influent and effluent characteristics, aeration rates, and environmental conditions. The target features of the study—Effluent BOD (mg/L), Effluent COD (mg/L), Effluent TSS (mg/L), and Operational Issue Detected—are critical indicators of the plant's treatment efficiency and potential operational issues. Using synthetic datasets, we conducted comprehensive exploratory data analysis (EDA) to identify correlations between influent parameters and effluent quality, revealing important patterns that influence plant performance. Visualizations, including correlation heat maps and scatter plots, provided insights into key factors affecting effluent quality and operational issues. Furthermore, the study demonstrates the potential of Artificial Neural Networks (ANNs) in predicting these target features, offering a predictive framework for fault detection and diagnosis in STPs. By integrating ANN-based models, this research contributes to improving predictive maintenance strategies, ensuring optimal performance, and enhancing decision making in sewage treatment operations. The findings underscore the value of data-driven approaches in optimizing the management and sustainability of wastewater treatment systems.