ThermalNet: A deep reinforcement learning-based combustion optimization system for coal-fired boiler. (September 2018)

• Record Type: Journal Article
• Authors: Cheng, Yin; Huang, Yuexin; Pang, Bo; Zhang, Weidong
• Is Part Of: Engineering applications of artificial intelligence. Volume 74(2018)
• Page Start: 303
• Page End: 311
• Publication Date: 2018-09
• Subjects: Combustion optimization -- DQN -- LSTM -- Reinforcement learning -- DCS; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.07.003
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a combustion optimization system for coal-fired boilers that includes a trade-off between emissions control and boiler efficiency. Designing an optimizer for this nonlinear, multiple-input multiple-output problem is challenging. This paper describes the development of an integrated combustion optimization system called ThermalNet, which is based on a deep Q-network (DQN) and a long short-term memory (LSTM) module. ThermalNet is a highly automated system consisting of an LSTM–ConvNet predictor and a DQN optimizer. The LSTM–ConvNet extracts the features of boiler behavior from the distributed control system (DCS) operational data of a supercritical thermal plant. The DQN reinforcement learning optimizer contributes to the online development of policies based on static and dynamic states. ThermalNet establishes a sequence of control actions that both reduce emissions and simultaneously enhance fuel utilization. The internal structure of the DQN optimizer demonstrates a greater representation capacity than does the shallow multilayer optimizer. The presented experiments indicate the effectiveness of the proposed optimization system.