Harnessing AI for Operational Improvements in the Water/Wastewater Industry

Overview

The water and wastewater industry faces increasing pressure to deliver high water treatment quality while minimizing energy consumption and chemical use. Traditional PID controllers, while reliable, often struggle to adapt to dynamic process conditions and exogenous disturbances leading to suboptimal performance.

This presentation introduces an innovative approach leveraging artificial intelligence (AI) to apply to new treatment applications or augment existing PID control systems, enabling holistic optimization of treatment processes. By integrating machine learning models with real-time sensor data, our AI framework enhances control quality, dynamically adjusting operational parameters to maintain effluent quality under varying effluent conditions.

We will discuss how improved control quality can pave the way for systematic optimization of the treatment process where energy efficiency and chemical usage are simultaneously optimized to reduce the environmental footprint without compromising performance.

Simulation results against a widely adopted benchmark problem demonstrate the efficacy of the AI-driven control approach. This work showcases how AI can transform conventional control strategies into smarter, more sustainable water treatment system optimization.