Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Digital transformation of industry has gained emphasis in recent years in academia and industry. Organizations need to be more competitive and efficient and improve their processes and performance to cope with changes in environmental legislation, efficient management of resources and energy, and the trend toward zero waste. These factors have led to the emergence of a new concept. This paper studies data-driven fuzzy-based models for process monitoring focused on Wastewater Treatment Plants (WWTPs). This work aims to study interpretable industrial process monitoring models, which must be easily interpretable by expert process operators. For this purpose, different fuzzy-based models were studied. Exhaustive validations are performed. The studied models employ 16 key variables at 14 different points throughout the waterline of a treatment plant. The learning and testing of each model for every key variable at each involved point use distinct sets of input variables and varied learning model parameters. The impact of the selected input variables and the learning parameters on the model accuracy, and the accuracy versus interpretability tradeoff are analyzed. The best model for each key variable is developed based on the accuracy versus interpretability tradeoff.

Wastewater treatment plants (WWTPs) are increasingly evolving from conventional treatment facilities into resource recovery hubs aligned with circular economy (CE) and sustainability goals. However, practical frameworks integrating real-time optimization with multi-dimensional sustainability and circularity assessment remain limited. This study proposes an integrated framework based on the expanded Multi-Sectoral Water Circularity Assessment, grounded in the Water-Energy-Food-Ecosystems nexus, combined with Reinforcement Learning and Model Predictive Control strategies for real-time WWTP optimization. The framework links circular operational actions, including dynamic dissolved oxygen setpoint adjustment and biogas-based energy recovery, with sustainability, circularity, and effluent quality indicators. To enable computationally efficient real-time optimization, an Artificial Neural Network surrogate model was developed to approximate the plant’s dynamic behavior. The proposed methodology integrates sustainability, circularity, and effluent quality metrics to directly relate plant performance to CE principles and Sustainable Development Goals. Results showed a nitrogen removal efficiency of 76.6%, aeration energy consumption of 4095.2 kWh/d, and a total nitrogen concentration of 9.4 mg/L, below the representative operational target of 10 mg/L associated with the revised Urban Wastewater Treatment Directive (EU) 2024/3019. The framework also demonstrated improved resilience under hydraulic shock conditions. The results indicate that real-time optimization combined with circular operational strategies can support more sustainable, energy-efficient, and resilient WWTP operations.

This paper introduces the Evolving Multi-layer Stream Mapping (E-MSM) framework, an evolving fuzzy approach for dynamic efficiency monitoring of industrial processes under varying operational regimes. The proposed method is applicable to a wide range of industrial systems and is demonstrated in this work through its application to Wastewater Treatment Plants (WWTPs). The framework extends the Multi-layer Stream Mapping (MSM) methodology through a data-driven approach that dynamically identifies operational regimes using an evolving fuzzy system. E-MSM autonomously generates, updates, and merges fuzzy rules from process data, enabling the adaptive estimation of context-aware reference values for more reliable efficiency and waste assessment under changing operating conditions. The approach is evaluated on a WWTP operating under diverse scenarios, including droughts, rainfall events, and storm disturbances. Results demonstrate that E-MSM significantly improves efficiency assessment across key water-line units, including the primary clarifier, biological reactor (anoxic and aerated sections), and secondary clarifier. By enabling adaptive and context-aware performance monitoring, the proposed framework supports more informed decision-making, facilitates the identification of operational bottlenecks, and promotes continuous improvement in dynamically evolving industrial systems.