Viral particle prediction in wastewater treatment plants using nonlinear lifelong learning models

Abstract

Predicting new unseen data using only wastewater process inputs remains an open challenge. This paper proposes lifelong learning approaches that integrate long short-term memory (LSTM), gated recurrent unit (GRU) and tree-based machine learning models with knowledge-based dictionaries for real-time viral prediction across various wastewater treatment plants (WWTPs) in Saudi Arabia. Limited data prompted the use of a Wasserstein generative adversarial network to generate synthetic data from physicochemical parameters (e.g., pH, chemical oxygen demand, total dissolved solids, total suspended solids, turbidity, conductivity, NO₂-N, NO₃-N, NH₄-N), virometry, and PCR-based methods. The input features and predictors are combined into a coupled dictionary learning framework, enabling knowledge transfer for new WWTP batches. We tested the framework for predicting total virus, adenovirus, and pepper mild mottle virus from WWTP stages, including conventional activated sludge, sand filter, and ultrafiltration effluents. The LSTM and GRU models adapted well to new data, maintaining robust performance. Tests on total viral prediction across four municipal WWTPs in Saudi Arabia showed the lifelong learning model’s value for adaptive viral particle prediction and performance enhancement.