PREDICTION OF EFFLUENT QUALITY OF SEWAGE TREATMENT PLANT BY MACHINE LEARNING BASED MODELS

Abstract

The use of machine learning (ML) models is on the rise in the field of wastewater treatment plant (WWTP) modeling and optimization due to their ability to effectively capture nonlinearities, manage uncertainty, adapt to changing conditions, and provide fast results. WWTPs are complex systems that involve various biological, chemical, and physical processes and are highly subject to uncertainty. Traditional models may struggle to capture the nonlinearities in these systems, but ML models can model these interactions effectively. ML models can help operators manage uncertainty by providing accurate and reliable predictions, enabling them to make real-time adjustments to operations. The data-driven nature of ML models allows them to learn and adapt to changing conditions based on historical data, making them well-suited to modeling complex systems such as WWTPs where the interactions between variables are not always fully understood. Traditional models for WWTPs can be time-consuming and computationally expensive, especially when optimizing complex systems. ML models, on the other hand, can provide faster and more cost-effective results. This can ultimately improve the efficiency and performance of these systems, leading to better wastewater treatment, lower costs, and reduced environmental impact.