A Stage-Wise Literature Review Toward Smart Digital Twin Developments of Wastewater Treatment Plants

TEchMA 2025 – new frontiers in mechanical engineering International Conference on Technologies for the Wellbeing and Sustainable Manufacturing Solutions A Stage-Wise Literature Review Toward Smart Digital Twin Developments of Wastewater Treatment Plants Sara Mota (a), António Andrade-Campos (b) (a),(b) TEMA - Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro; LASI - Intelligent Systems Associate Laboratory; (a),(b) Aveiro, Portugal (a) [email protected], (b) [email protected] Abstract — Wastewater treatment plants (WWTPs) are essential for safeguarding public and environmental health. However, they are among the most energy-intensive infrastructures in urban systems, consuming over 1–3% of global electricity and more than 233 GWh annually in Europe [1]. These challenges are compounded by increased inflow variability due to climate change and more restrictive environmental regulations. To address these issues, data-driven modelling and smart digital tools have become pivotal in enhancing WWTP operational efficiency and sustainability. This systematic review explores recent advances in Machine Learning (ML), the Internet of Things (IoT), and Digital Twin (DT) technologies as applied to WWTPs. Using a stage-wise approach covering preliminary, primary, secondary, tertiary, and sludge treatment stages, this review maps out how data-driven techniques are being used to optimise specific processes. While predictive maintenance dominates applications in the preliminary phase, primary and secondary treatments, especially aeration as the most energy-intensive stage, have seen the extensive deployment of ML algorithms for real-time flow prediction, effluent quality modelling, and energy optimisation. Similarly, tertiary treatment and sludge processing increasingly use AI for fault detection, disinfection control, and biogas yield forecasting [2]. Recent studies have revealed two primary modelling categories: (1) system simulation and (2) energy optimisation. Hybrid models that combine physics-based simulations with ML techniques, such as neural networks, reinforcement learning, and AI-CFD, show improved prediction accuracy and generalisability. Several studies have reported significant gains, including up to 60% energy savings, improved effluent compliance, and enhanced fault detection through soft sensing and online learning [3]. Notably, the integration of IoT technologies supports the realtime monitoring of influent characteristics and operational states, thereby enabling adaptive process control. While these technological advances show promising results, the practical, widespread implementation of full-scale digital twins encounters significant obstacles. Most implementations focus on academic simulations, with limited real-world deployment or scalability owing to challenges such as poor data quality, limited data science expertise in utilities, and high computational costs. Furthermore, commercial tools provide robust platforms for simulation and control strategy development but are often underutilised in dynamic, real-time contexts. This review also identifies a significant research gap: the lack of integrated, crossstage optimisation strategies that bridge localised model insights and global plant performance. The novelty of this work lies in its stage-wise analysis of the literature, providing a structured perspective that helps identify specific WWTP stages where optimisation is most effective. It also serves as a foundational assessment for the design of Smart Predictive Digital Twins (SPDTs) that combine real-time data, MLdriven modelling, and modular architectures to enable adaptive, efficient, and resilient WWTP operations. Keywords — Water Systems; Wastewater Operation; Machine Learning; Digital Twin; Cost and Energy Reduction; Climate Change Impact; TOPIC 2) b.: Technologies for the Wellbeing – Innovative Technologies for Smart Cities. ACKNOWLEDGMENTS This work is supported by the project UID/00481 Centre for Mechanical Technology and Automation (TEMA), and through the FEDER and Regional Operational Program of the Center Region (CENTRO2030) within project I-ReTiS-LeaksD&Op nº 17304 (CENTRO2030-FEDER-01177300) and through the Portuguese Foundation for Science and Technology (FCT) within project IReTiS-Leaks (2024.07270.IACDC). REFERENCES [1] I. C. for Resource Recovery from Water, “Circular economy: Tapping the power of wastewater.” [2] G. Sabia et al., “Energy saving in wastewater treatment plants: A methodology based on common key performance indicators for the evaluation of plant energy performance, classification and benchmarking”, Energy Conversion and Management, vol. 220, 2020 [3] J. F. de Canete et al., “Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach”, Computers & Chemical Engineering, vol. 144, 2021.