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.
Popescu et al. (Thu,) studied this question.