Optimizing the treatment of drinking water using reinforcement learning

使用强化学习优化饮用水处理

• 批准号: 520966-2017
• 负责人: White, Martha
• 金额: $ 13.61万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668467
• 关键词: Optimizing; treatment; drinking; water; using

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.**

该项目的目标是开发机器学习技术，用于饮用水处理中膜超滤的自动化。我们将在一个试点规模的实验平台上研究这些方法的可行性和有效性，最终实现全面部署。目前，这个复杂的过程由人类操作员管理。因此，现代工厂中水处理过程控制的成本效益在很大程度上依赖于操作员的技能、判断力和直觉，而这些只能通过多年的经验才能获得。因此，水处理的有效性将受到以下因素的限制：有经验的人类操作员的可用性、人类操作员由于其其他工作要求而能够调整过程参数的有限频率、以及他们可用于其决策的有限信息量。随着运营大型工厂的复杂性增加，招聘和培训人类操作员变得越来越具有挑战性，特别是对于小型社区。* 我们将研究使用强化学习来自动化水处理过程的各个部分。这些方法使用恒定的传感器信息流，包括有关水质、流量、需求和电价的信息，以在不影响水质或可用性的情况下最大限度地降低成本。例如，这些学习算法可以确定如何优化泵的操作以降低电力成本。作为另一个例子，清洁过滤器是一个显着的成本;这些算法可以提供更细粒度的控制，在几秒钟或几分钟内而不是几小时或几天内做出决定，降低清洁成本并增加过滤器的寿命。即使是很小的优化，该项目也有可能为水处理节省大量资金。