Advanced Process Controls – Autonomous Control and Optimization of Water Treatment Plants

Tzahi Cath

tcath@mines.edu

Today’s operations in water treatment and production facilities are highly focused on continuously meeting the facility’s performance standards regardless of disturbances caused by (a) variability in water source, demand, and quality and (b) degradation of critical components, such as process equipment and sensors. So far, the need for reliability has been addressed through redundancy (volumes, equipment), thus leading to inefficient use of resources. Today, machine learning and adaptive control strategies offer a novel promise: the same reliability at unit- and plant-wide scale without redundancy, while spending minimal energy and resources. To evaluate this promise, we are developing and test broadly applicable control methods for adaptive optimization. The unique challenge is to design a single control framework that is applicable across a variety of water treatment systems, thus making efficient, low-cost operation available in rural, urban, coastal, and inland settings. We are developing and testing technologies and economic methods to address these challenges in coordination with multiple industrial partners. The project will use a demonstration scale water treatment system that was recently constructed and now operated by Mines in the field (starting this spring in the city of Aurora).

Grand Challenge: Provide access to clean water.

Data-driven performance analyses of wastewater treatment plants: A review Life-cycle assessment of two potable water reuse technologies: MF/RO/UV-AOP treatment and hybrid osmotic membrane bioreactors World Water Day 2022: Innovative Mobile Direct Potable Reuse Trailer Receives WateReuse Award for Education and Outreach    

Tzahi Cath, tcath@mines.edu | Mason Manross, Mason Manross, mmanross@mines.edu |