ADVANCEMENTS IN MONITORING, CONTROL, AND AUTOMATION TECHNOLOGIES FOR INTEGRATED SYSTEMS IN INDUSTRIAL EFFLUENT TREATMENT: A REVIEW

Abstract

Effluent treatment in industries is of utmost importance in maintaining water quality, ecosystems, and public health. However, there exist challenges with respect to the response of traditional effluent treatment systems to effluent variations as well as high energy and chemical requirements. This paper presents recent developments in the field of monitoring, control, and automation techniques in integrated industrial effluent treatment systems with special consideration of IoT-based monitoring, artificial intelligence (AI), machine learning (ML), model predictive control (MPC), adaptive control, PLC/SCADA, and robotics. A systematic literature review was conducted based on PRISMA criteria for papers published between 2018 and 2026.  This synthesis shows that IoT sensors provide continuous monitoring of the quality of water, while AI/ML algorithms can be applied for predicting, detecting anomalies, and optimizing processes. MPC and adaptive controls allow increasing responsiveness to changes in process conditions, while PLC/SCADA serves as the basis for the operational infrastructure for automated control and monitoring. Robotics is used in certain applications such as sampling, cleaning, inspection, sludge handling, and dosing chemicals. Integrating all these technologies will allow increasing the number of monitoring times, responsiveness of control, data consolidation, resource management, and efficiency of operations. Nevertheless, the evidence base is still heterogeneous with many studies relying on single location or study under controlled conditions, thus making generalizations difficult. Important obstacles are related to the implementation cost, sensor reliability, cybersecurity, lack of specialists, adjustment to regulations, and interoperability. The evidence supports progressive, human-supervised automation and increasingly data-driven integrated treatment systems rather than immediate full autonomy.