A comprehensive review of new technologies, challenges and trends in wastewater treatment plants modeling and control

ABSTRACT A comprehensive analysis of new technologies, challenges and trends in wastewater treatment plant modeling and control Wastewater treatment is a critical process for protecting water resources and ensuring environmental sustainability. The modeling of key parameters such as dissolved oxygen (DO), nitrogen (in various forms like ammonia, nitrite, and nitrate), plays a fundamental role in understanding and optimizing the performance of wastewater treatment plants (WWTPs). The present document offers a thorough review of modeling strategies for oxygen and nitrogen compounds, emphasizing the importance of predictive accuracy and process control. A comprehensive review of the extant literature reveals the current state of the field, while also analyzing recent research findings to identify gaps and limitations in existing models. This review employed a systematic approach to analyze 64 peer-reviewed studies (2021–2025) using PRISMA criteria to identify emerging trends in AI-driven and hybrid modeling. The paper's conclusions offer a series of recommendations for the refinement of the model and its subsequent implementation in real-time monitoring systems. The review underscores recent advancements in data-driven, classical, and hybrid modeling strategies, with a particular emphasis on the increasing integration of artificial intelligence and machine learning techniques into conventional process models. Key trends, limitations, and research gaps are identified.