Artificial intelligence in wastewater treatment: critical review of predictive performance, explainability and deployment readiness

Abstract

Artificial intelligence (AI) has been increasingly adopted in wastewater treatment to support soft sensing, effluent prediction, nutrient removal assessment, membrane monitoring, anomaly detection, greenhouse-gas emission modeling, and anaerobic digestion optimization. This critical review synthesizes approximately 50 primary studies and benchmark contributions, structured around four analytical dimensions: operational purpose, modeling paradigm, reporting discipline, and deployment readiness. The literature is heavily concentrated in prediction-oriented applications, particularly effluent quality forecasting and membrane fouling assessment, while AI applications in anaerobic digestion, greenhouse-gas modeling, and constructed wetlands remain comparatively limited. Explainable AI is emerging as a mechanism for strengthening model interpretability and engineering credibility, although its application remains selective and methodologically inconsistent. Recent advances in transformer architectures, transfer learning, federated learning, and digital twin frameworks indicate growing methodological maturity, yet practical implementation remains constrained by deficiencies in data quality, sensor reliability, benchmarking practices, governance frameworks, and cybersecurity preparedness. The environmental burden of AI itself, including energy consumption, water demand, and hardware lifecycle emissions, remains insufficiently considered. The principal challenge extends beyond algorithm selection to developing AI systems that are reliable, transparent, accountable, and operationally deployable. Future progress will require deployment-oriented validation, uncertainty-aware modeling, and closer integration with process knowledge and regulatory outcomes.