A Novel RNN-LSTM Based Framework for Predictive Modeling and Quality Assessment in Wastewater Treatment Plants

The increasing complexity of wastewater treatment processes demands advanced modeling techniques to ensure efficient operation and compliance with environmental standards. This study introduces a hybrid approach integrating statistical methods with artificial intelligence, specifically leveraging Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units, to infer knowledge and predict effluent quality in dynamic wastewater treatment systems. Unlike conventional fuzzy logic and clustering methods, the proposed model captures temporal dependencies and non-linear patterns in time-series data, enhancing prediction accuracy and interpretability. The framework processes key parameters such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS) to model treatment efficiency. Experimental results demonstrate the model’s superiority in handling sequential data and providing reliable forecasts, thereby supporting decision-making in plant automation and semi-automation. This research signifies a substantial advancement in intelligent environmental support systems, offering a scalable solution for real-time quality assessment and operational optimization.