Developing an Enhanced Soft Sensor for Wastewater Treatment Plants: A Comparative Study of Multiple Machine Learning Approaches

Abstract

Wastewater treatment plants (WWTPs) require continuous monitoring of critical water quality parameters to ensure operational efficiency and regulatory compliance. Traditional physical sensors are accurate but expensive and maintenance-intensive, creating a need for cost-effective alternatives. This research investigates the development of enhanced soft sensors using advanced machine learning techniques to estimate key wastewater parameters including Chemical Oxygen Demand (COD) and Total Phosphorus (TP) concentrations at both influent and effluent points. The study addresses fundamental limitations of existing soft sensor implementations particularly their inability to capture complex non-linear relationships which is suspected to have sensor drift and degradation due to seasonal variations and equipment aging. Through comprehensive evaluation of multiple machine learning approaches including Neural Networks and Decision Tree-based methods with the aim to develop robust, adaptable soft sensor models that maintain accuracy over extended periods with reduced recalibration requirements. The methodology involves systematic data collection from a Norwegian WWTP, comprehensive preprocessing to handle data quality issues, feature engineering and rigorous comparative evaluation based on prediction accuracy, computational efficiency and adaptability. Expected outcomes include deployable soft sensor models offering reliable real-time monitoring capabilities, significant cost savings, and improved operational efficiency for WWTPs. The research contributes both theoretical insights into soft sensor design and practical solutions for the wastewater treatment industry.