Smart Industrial Water Treatment: Integrating AI and IoT for Real-Time Monitoring and Optimization
Smart industrial water treatment is progressively sought after to achieve stricter discharge and product-water requirements, decreased energy and chemical use, and enhanced stability at fluctuating influent and working regimes. This article provides a review of how the Internet of Things (IoT-based) sensing and artificial intelligence analytics may be combined in order to facilitate real-time monitoring and optimization in industrial treatment trains, including pretreatment and biological treatment systems, membranes, and zero-liquid-discharge (ZLD) systems. The initial contextual framework (smart treatment) is placed on industrial performance goals and limitations, focusing on partial observability, sensor pollutions/fouling, measurement delay, and multi-objective optimization amongst compliance, cost, particle recovery objectives, and asset health. We next consider IoT and architectures of sensing used to ensure trustworthy monitoring, such as time-based pipelines of data, edge cloud pattern of installation and engineering of data quality, which is used to validate, redundancy, and fault detection. It is based on these backgrounds that we consider artificial intelligence (AI) techniques in anomaly detection, fault diagnosis, soft sensing, and probabilistic forecasting, and point out how regime awareness, explainability, and uncertainty quantification must be applied to risk-sensitive operations. Prescriptive capabilities are considered in a control maturity perspective, between decision support and constrained supervisory and closed-loop control. We contrast classical methods, e.g., model predictive control, with data-based optimal control, e.g., Bayesian optimization, safe/offline reinforcement learning, and explain why digital twins can be used to enable validation and operator training. Last but not least, we discuss deployment facts-OT/IT (operational technology/information technology) integration, cybersecurity, lifecycle management, and human factors, and offer a vision of the future based on interoperable data models, strong cross-site transfer, and optimization that is proven to be safe.