From Playing "Go" to Automating Water Plants: The Application of Reinforcement Learning
Automation in water purification plants (WPPs) can help reduce the problem of expert shortage in the future, as well as optimize operating costs. WPPs usually have two main purposes: to supply sufficient water and to maintain good water quality. Many WPPs in Japan disinfect river water for human consumption as well as industrial use. Further, WPPs need to adapt to the change in water demand (e.g. hot seasons such as summer vs. cold seasons such as winter, morning and night vs. afternoon, etc.) and the turbidity level in raw water that usually comes from a river. Automation in WPPs using artificial intelligence (AI) is a promising solution, not only to address the challenge of declining number of specialists but also to reduce rising costs in water treatment operations. Among all machine-learning algorithms, reinforcement learning has the potential to train the system (an AI agent) to operate a WPP like an expert or better. The agent can learn how to make different types of decisions, such as how much water to take to meet future demands, or how much chemicals to be injected into the raw water for treatment. In our simulated experiments, our agent can outperform experts in making decisions on the intake of water with greater electrical efficiency and achieve a faster and more stable learning process.
