Real-Time Pollutant Forecasting using Edge AI Fusion in Wastewater Treatment Facilities

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analysing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analysing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge computing-based smart framework that captures data from sensors including ultraviolet, electrochemical, microfluidic and other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyses the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments and other treatment plans. A logic layer is designed at the server level to process the realtime data from the sensor clusters and identifies the discharge of nutrients, metals and emerging contaminants in the water that affects the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonise sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad-hoc analyses even during critical conditions. An user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability.