Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
AI, IoT, and Robotics in Wastewater Treatment: Transforming Process Efficiency through Automation
Authors
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Mengdan Lin
Xuzhou College of Industrial and Technology, Xuzhou 221140, China
DOI:
https://doi.org/10.30564/jees.v8i2.13052Abstract
The challenge of wastewater treatment facilities is growing as they strive to enhance compliance strength and minimize energy consumption, chemical usage, downtime, and labor requirements in the face of increasingly variable influent and climate-related disruptions. The use of recent developments in Internet of Things (IoT), artificial intelligence, and robotics enables a transition to a less reactive mode of operation and more closed-loop automation. This review leads to an understanding of the demonstrations of networked sensing and edge data architecture to enhance observability, transform heterogeneous time-series and multimodal data into monitoring, forecasting, and risk intelligent decision knowledge, and extends robotics ability to measure and intervene in hazardous, distributed, or intermittently observed plant environments. We structure the literature on a deployable sense-think-act structure between unit processes, sensing strategies, Artificial Intelligence (AI) tasks, and execution pathways based on supervisory control and robotic operations. The applications of high leverage are evaluated, such as aeration and nutrient removal optimization, chemical dosing and disinfection control, prediction of membrane fouling and cleaning schedules, solids line stabilization, and predictive maintenance of the important assets. In these areas, we highlight aspects of quality of evidence, benchmarking issues, and operational circumstances that will define persistence of reported efficiency improvements after pilots, such as sensor drift and biofouling control, constraint-based control in service of Supervisory Control and Data Acquisition (SCADA)/Programmable Logic Controller (PLC) systems, cybersecurity-by-design, and model life cycle governance. We bring it to the maturity perspective of resilient, interoperable, and conscientiously independent Wastewater Treatment Plants (WWTPs) with a research requirement of standardized datasets, hybrid digital twins, uncertainty intentional optimization, and adaptive sampling and inspection by robotized techniques.
Keywords:
Wastewater Treatment; Industrial IoT; Artificial Intelligence; Robotics; Process OptimizationReferences
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Lin, M. (2026). AI, IoT, and Robotics in Wastewater Treatment: Transforming Process Efficiency through Automation. Journal of Environmental & Earth Sciences, 8(2), 105–134. https://doi.org/10.30564/jees.v8i2.13052
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