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A synergetic control framework for pulverizing systems of coal-fired power plants: Performance optimization based on DRL and DMC

  • Zheng Jing
  • , Xiaoyang Hu
  • , Shuai Wang
  • , Binbin Qiu
  • , Zhu Wang
  • , Weixiong Chen
  • , Jinshi Wang
  • Xi'an Jiaotong University
  • Yulin University

Research output: Contribution to journalArticlepeer-review

Abstract

Enhancing operational flexibility has emerged as a pivotal technical requirement in the transformation and modernization of coal-fired power plants. Among the various subsystems, the pulverizing system constitutes a primary factor constraining overall unit flexibility. Under transient load fluctuations, conventional PID control strategies frequently encounter limitations. Such systems often lack the responsiveness required to adapt effectively to rapid variations in outlet parameters. To address these challenges, a data-mechanism hybrid model of the pulverizing system was established. Based on the model, this study attempted to employ the advanced control algorithms to improve pulverizing system performance. Specifically, the effectiveness of a deep reinforcement learning (DRL)-compensated fuel control framework was evaluated, and the utilization of the deep deterministic policy gradient (DDPG) algorithm with additive compensation was proposed. The results showed significant improvements in control accuracy due to the capability of data representation and intelligent decision-making. Compared to the PID control system, the mean absolute error (MAE) of pulverized coal output using DDPG algorithm was reduced by 18%, while the MAE of unit power output decreased by 22%. The performance of the dynamic matrix control (DMC) algorithm was evaluated for the coupled regulation of mill hot and cold air valves. Relative to the PID control system, the multi-parameter DMC strategy significantly reduced the overshoot of the mill outlet temperature by 24% and the response time by 38%. Furthermore, the response time of the primary air flow rate was reduced by 18%, and the response time for suspended coal storage within the suspension zone decreased by 22%. In conclusion, the control strategy significantly strengthens the control performance of the pulverizing system and attains a notable enhancement in the operational flexibility of the coal-fired power plant.

Original languageEnglish
Article number132052
JournalApplied Thermal Engineering
Volume302
DOIs
StatePublished - Aug 2026

Keywords

  • Control strategy
  • Deep reinforcement learning (DRL)
  • Dynamic matrix control (DMC)
  • Operational flexibility
  • Pulverizing system

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