Optimizing the treatment of drinking water using reinforcement learning

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The goal of the project is to develop machine learning techniques for the automation of membrane ultra- filtration in drinking water treatment. We will investigate the feasibility and efficacy of these approaches on a pilot-scale experimental platform, towards eventual full-scale deployment. Currently, this complex process is managed by human operators. Thus, the cost-effectiveness of water- treatment process control in modern plants relies heavily on operator skill, judgment and intuition, which can only be gained over many seasons of experience. As a result, the effectiveness of water treatment will be limited by the availability of experienced human operators, the bounded frequency with which human operators are able to adjust process parameters due to their other job requirements, and the limited amount of information available to them on which to base their decisions. As the complexity of operating large plants increases, recruiting and training human operators is getting ever more challenging, especially for small communities. We will investigate the use of reinforcement learning for automating parts of the water treatment process. These approaches uses a constant stream of sensor information, including information about water quality, flow, demand and electricity prices, to minimize costs without impacting water quality or availability. For example, these learning algorithms could determine how to optimize operation of pumps to reduce electricity costs. As another example, cleaning the filters is a significant cost; these algorithms could provide more fine-grained control, that makes decisions over seconds or minutes rather than hours or days, reducing cleaning costs and increasing the life-span of the filters. With even small optimizations, this project has the potential to produce significant savings for water treatment.