Plant-Wide AI Control for Water and Wastewater Treatment

Fri, 07 Aug 2026 00:00:00 +0000

Project Overview

This project establishes a plant-wide closed-loop AI control system spanning sensing, forecasting, decision-making, control, and performance evaluation for drinking-water and wastewater treatment plants. It unifies time-series foundation models, soft sensors, multivariable model predictive control (MPC), real-time optimization (RTO), and prognostics and health management (PHM) within one industrial control architecture.

The platform integrates with existing PLC, SCADA, DCS, OPC UA, and industrial data systems. Existing safety interlocks and operator authority remain in place, while AI control strategies run online with continuous performance evaluation and model adaptation.

Plant-wide closed-loop AI architecture for water and wastewater treatment

Core Technical Architecture

Multi-source industrial data integration

The platform combines flow, water-quality, pressure, level, energy, vibration, temperature, current, and operating-condition data in a unified plant-wide time-series foundation. It handles practical issues including asynchronous sampling, missing measurements, sensor drift, abnormal data, and operating-mode transitions.

Time-series foundation models and process soft sensors

Industrial time-series foundation models learn multivariable dynamics under strong coupling, long delays, and non-stationary operating conditions. Core capabilities include:

Multi-horizon forecasting of influent flow, water quality, and pollutant load
Soft sensing and trend forecasting for turbidity, residual chlorine, ammonia nitrogen, total nitrogen, and other key indicators
Water-demand, network-pressure, pumping-load, and plant-wide energy forecasting
Abnormal-condition detection, operating-mode transfer, and online model updates

Coordinated control of complex multi-stage processes

Local control loops are coordinated at plant level to address interactions and constraints across treatment stages:

Drinking water: intake and pump scheduling, coagulation dosing, sedimentation and filtration, disinfection, clear-water-tank level, and distribution-network pressure
Wastewater: influent-load forecasting, aeration and dissolved-oxygen control, recycle and sludge discharge, carbon-source and chemical dosing, advanced treatment, and energy optimization
MPC and RTO jointly manage water-quality constraints, equipment capacity, production load, energy consumption, and chemical consumption
A closed loop connects forecasting, optimization, execution, feedback, and continuous learning

Predictive maintenance of critical equipment

Operating and condition-monitoring data are combined for pumps, blowers, mixers, dosing systems, valves, and other critical electromechanical assets. The PHM layer provides:

Detection of performance degradation and efficiency deviation
Early warning of vibration, temperature, current, pressure, and operating anomalies
Fault-risk forecasting and maintenance-window recommendations
Coordination between equipment operating strategy and process-control strategy

Engineering Implementation and Software Interface

The system combines modular engineering software with edge controllers. Model configuration, control simulation, constraint management, and performance evaluation are completed in the engineering environment, while real-time control strategies are connected to the plant through industrial controllers. Each water project is configured for its actual process stages, instrumentation, access control, and industrial communication protocols.

Predictive-control engineering software and embedded controller

The image shows the laboratory’s existing predictive-control software and controller engineering foundation; it is not a screenshot from any specific water-treatment plant.

Main Application Modules

Module	Controlled objects	Main capabilities
Time-series foundation model	Flow, water quality, load, pressure, and energy	Forecasting, soft sensing, anomaly detection, and online learning
Intelligent dosing control	Coagulants, disinfectants, carbon sources, and other chemicals	Feedforward-feedback control, chemical optimization, and quality constraints
Aeration and biological-process optimization	Blowers, valves, dissolved oxygen, and biological indicators	Multi-basin coordination, load tracking, and water-quality/energy balancing
Pump-group control and scheduling	Intake, lift, delivery, and booster pumps	Best-efficiency-region operation, pressure/level coordination, and peak-load optimization
BD-PHM equipment health management	Pumps, blowers, valves, dosing, and mixing equipment	Condition monitoring, fault prediction, and maintenance decisions
Plant-wide multi-objective optimization	Water quality, production, chemicals, energy, and equipment life	Constrained optimization, process coordination, and performance evaluation

Industry Collaboration

The team has established long-term cooperation with leading industry partners including Nanfang Pump Industry and Beijing Enterprises Water Group (BEWG), covering algorithm validation, engineering implementation, platform integration, pump-system energy optimization, critical-equipment health management, and scaled deployment.