Machine Learning for Wastewater Treatment Optimization

Wastewater treatment is a critical process for environmental sustainability and public health protection. However, traditional treatment systems face challenges such as high operational costs, energy consumption, and variable influent characteristics. Machine Learning (ML) provides a data-driven approach to address these challenges by predicting system behavior, optimizing process parameters, and enabling smart decision-making. This paper explores the role of machine learning in optimizing wastewater treatment operations. Various ML algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SVM),
Random Forest (RF), and Long Short-Term Memory (LSTM) networks are discussed for predicting and controlling key treatment parameters like Biochemical Oxygen Demand (BOD),
Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS). The study also highlights real-world applications, challenges, and future research directions involving the
integration of ML with IoT and digital twin technologies for sustainable wastewater management.