环境卫生工程 ›› 2026, Vol. 34 ›› Issue (5): 30-29.doi: 10.19841/j.cnki.hjwsgc.2026.05.004

• 环境卫生系统自动化、智能化、智慧化管理 • 上一篇    下一篇

基于 ACC+AI 的垃圾焚烧炉助燃风控制系统研究

何术东,袁宏伟   

  1. 1.上海康恒环境股份有限公司;2.上海康恒环境科技有限公司
  • 出版日期:2026-10-29 发布日期:2026-10-29

Research on Combustion Auxiliary Air Control System of Waste Incinerators Based on ACC+AI

He Shudong, Yuan Hongwei   

  1. 1. Shanghai SUS Environment Co. Ltd.; 2. Shanghai SUS Environment Technology Co. Ltd.
  • Online:2026-10-29 Published:2026-10-29

摘要: 垃圾焚烧已成为高效处理生活垃圾的关键技术。为提升焚烧效率并减少污染,精准控制焚烧炉内的助燃风成为重点研究方向。本研究提出一种结合自动燃烧控制系统(ACC)和人工智能(AI)的垃圾焚烧炉助燃风控制系统,通过模型预测控制(MPC)技术,实现了对助燃风的动态调节,解决了传统比例-积分-微分(PID)控制中存在的滞后问题。MPC模型形式多样,能够直接解决控制纯滞后问题,具有良好的跟踪性能、较强的抗扰动能力和较强的鲁棒性。系统在实时获取垃圾焚烧炉蒸发量和焚烧参数后,利用MPC模型预测未来风机频率,并通过ACC精确调节各区域风量实现高效燃烧控制。运行结果表明,该系统显著提升了燃烧过程的稳定性,投运后主蒸汽流量、锅炉出口氧含量及燃烬区域上部温度的离散系数分别降至5.33%、16.33%和6.10%,风系统设备操作频次降低87.06%。

关键词: 预测控制, 垃圾焚烧炉, 助燃风, 自动燃烧控制, 人工智能

Abstract: Waste incineration has become a key technology for the efficient treatment of domestic waste. To enhance incineration efficiency and reduce pollution, precise control of the combustion-supporting air in the incinerator has become a key research direction. The study proposed a combustion-supporting air control system for waste incinerators that integrated an automatic combustion control (ACC) system with artificial intelligence (AI). Through utilizing model predictive control (MPC) technology, the system achieved dynamic regulation of the combustion-supporting air, addressed the lag issues inherent in traditional proportional-integral-derivative (PID) control. MPC models are versatile and can directly handle the control of processes with pure lag. It has excellent tracking performance, strong disturbance resistance capability and high robustness. After the system acquired the evaporation volume and combustion parameters of the waste incinerator in real time, it used MPC model to predict the future frequency of the fans, and precisely regulated the air volume in each area through ACC to achieve efficient combustion control. Results showed that this system significantly improved the stability of the combustion process. After implementation, the discrete coefficients for main steam flow, boiler outlet oxygen content, and the temperature in the upper part of the burnout zone were reduced to 5.33%, 16.33%, and 6.10%, respectively, while the operational frequency of the air system equipment decreased by 87.06%.

Key words: predictive control, waste incinerator, auxiliary air, automatic combustion control, artificial intelligence

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