Optimizing wastewater treatment plants with advanced feature selection and sensor technologies

Abstract

Abstract This research establishes a foundational framework for the development of virtual sensors and provides significant preliminary results. Our study specifically focuses on identifying the key factors essential for accurately predicting total nitrogen in the effluent of wastewater treatment plants. This contribution enhances the predictive capabilities and operational efficiency of these plants, demonstrating the practical benefits of integrating advanced feature selection methods and innovative sensor technologies. These findings provide crucial insights and pave the way for future advancements in the field. In this study, four different feature selection methods are employed to comprehensively explore the variables influencing total nitrogen predictions. The effectiveness of these methods is then evaluated by applying three regression techniques. The findings indicate acceptable levels of accuracy in all applied cases, with one method demonstrating particularly promising results, applicable to several wastewater treatment plants. This validation of the selected variables not only underlines their effectiveness, but also lays the foundation for future virtual sensor applications. The integration of such sensors promises to improve the accuracy and reliability of predictions, marking a significant advance in wastewater treatment plant instrumentation.