Water and wastewater treatment facilities face constant pressure; fluctuating inputs, diverse pollutants, and strict regulatory demands, around the clock. Enviro Control’s AI and Real-Time Monitoring platform deploys online sensors across treatment plants, desalination facilities, reuse systems, and waste-to-energy infrastructure to measure key process parameters continuously and in real time.
All data is monitored through a PLC-based SCADA system, feeding an AI platform that applies Machine Learning and IoT technologies to transform how facilities operate; predicting problems before they occur, optimizing resources automatically, and delivering cost savings of up to 30–50%.
Enviro has deployed AI-integrated monitoring across municipal sewage treatment plants, large-scale water treatment facilities, UF and RO membrane systems, and biogas-based waste-to-energy plants, bringing intelligent automation to every stage of the water cycle.
IoT-enabled online field devices measure key process parameters in real time across treatment plants, pipelines, pumping stations, and membrane systems to capture a continuous, detailed picture of plant performance.
Gathered data is transmitted to local servers or the cloud, where it is securely stored and made accessible to the AI platform for ongoing analysis, reporting, and MIS generation.
The AI platform analyses equipment and process performance across the entire facility, identifying inefficiencies, deviations, and emerging issues before they escalate.
Advanced algorithms detect unusual patterns or parameter deviations in real time, triggering immediate alerts so operators can respond before performance is compromised.
Interactive SCADA dashboards provide a clear, configurable human-machine interface, enabling live data monitoring, automated reporting, and MIS generation for operators and management.
The system generates proactive maintenance alerts based on equipment history and real-time performance data, allowing repairs to be scheduled before failures occur, reducing downtime and repair costs.
The AI platform self-learns from its expanding database, continuously improving the accuracy of its predictions and the efficiency of its recommendations over time.
PLC-SCADA
AI & Machine Learning
IoT Sensor Networks
Digital Twin Modelling
Predictive Analytics
Remote Monitoring
Analyses historical and real-time data to forecast system behaviour, enabling proactive maintenance and operational decisions before issues arise.
Online real-time measurement devices feed data directly into SCADA-integrated automation systems, creating a connected network accessible to authorised personnel via the cloud.
Configurable SCADA dashboards provide an intuitive human-machine interface for live monitoring, data reporting, and MIS generation across all facility types.
Creates a virtual replica of water infrastructure systems, enabling operators to simulate, monitor, and optimise processes in real time, reducing downtime and supporting proactive management across treatment and distribution networks.
Draws on comprehensive equipment history and live performance data to identify and flag faults faster, reducing the risk of severe or unexpected failures.
IP-based web browsing functionality allows authorised personnel to access, monitor, and control treatment facilities remotely via secure login, from anywhere in the world.
Programmed from historical equipment data, these algorithms operate systems at optimal efficiency based on operational trends, continuously minimising energy consumption across the facility.
Matrix-based AI programs draw on real-time database arrays to generate proactive alerts well in advance of equipment breakdown, reducing abrupt shutdowns and extending the life of critical systems.
Regulatory Compliance: 100% compliance with project specified discharge norms for treatment, disposal, and reuse.
Industry Certifications: ISO Framework – Systems integrate ISO 9001 for quality and environmental frameworks.
