Optimal controlling of boiler combustion and denitration process based on DDPG. Issue 11 (19th August 2022)

• Record Type: Journal Article
• Authors: Jiang, Wenchao; Xiong, Guangsi; Lin, Kangwei; Liang, Tiancai; Xiao, Hong
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 11(2022)
• Page Start: 9357
• Page End: 9372
• Publication Date: 2022-08-19
• Subjects: combustion process -- cost optimization control -- denitration Process -- NOx emission; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22996
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Aiming at the problems of secondary pollution and resource waste caused by inaccurate input of coal and ammonia in coal‐fired power plant, an optimal controlling method of combustion and denitration coordinated operation based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper. First, the environmental model is constructed by the Stacking algorithm to predict the NO x emission concentration of the combustion and denitration system, which provides environmental state feedback for the optimal controlling model. Second, the optimization controlling model is constructed based on the DDPG algorithm within the standard limitation of denitration efficiency and NO x emission concentration. This model takes the minimization of comprehensive cost as its optimization objective to realize the optimal control of controllable variables in the cooperative operation process of combustion and denitration. The experimental results of real operational data from 1000 MW boiler unit in a power plant locating in south China show that the optimization results of coordinated operation for the combustion and denitration system are better than single‐stage optimization results. In addition, the total cost is reduced by 1%–3% on average compared with before optimization.