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城市固废焚烧过程数据驱动建模与自组织控制

丁海旭 汤健 乔俊飞

丁海旭, 汤健, 乔俊飞. 城市固废焚烧过程数据驱动建模与自组织控制. 自动化学报, 2022, 45(x): 1−17 doi: 10.16383/j.aas.c220570
引用本文: 丁海旭, 汤健, 乔俊飞. 城市固废焚烧过程数据驱动建模与自组织控制. 自动化学报, 2022, 45(x): 1−17 doi: 10.16383/j.aas.c220570
Ding Hai-Xu, Tang Jian, Qiao Jun-Fei. Data-driven modeling and self-organizing control of municipal solid waste incineration process. Acta Automatica Sinica, 2022, 45(x): 1−17 doi: 10.16383/j.aas.c220570
Citation: Ding Hai-Xu, Tang Jian, Qiao Jun-Fei. Data-driven modeling and self-organizing control of municipal solid waste incineration process. Acta Automatica Sinica, 2022, 45(x): 1−17 doi: 10.16383/j.aas.c220570

城市固废焚烧过程数据驱动建模与自组织控制

doi: 10.16383/j.aas.c220570
基金项目: 科技创新2030——“新一代人工智能”重大项目 (2021ZD0112300), 国家自然科学基金创新群体项目 (62021003), 国家科技重大专项 (61890930), 国家自然科学基金(62073006), 北京市自然科学基金(4212032, 4192009)资助
详细信息
    作者简介:

    丁海旭:北京工业大学信息学部博士研究生.主要研究方向为城市固废焚烧过程特征建模与智能控制. E-mail: dinghaixu@emails.bjut.edu.cn

    汤健:北京工业大学信息学部教授. 主要研究方向为小样本数据建模, 城市固废处理过程智能控制. E-mail: freeflytang@bjut.edu.cn

    乔俊飞:北京工业大学信息学部教授.主要研究方向为智能控制与智能信息处理, 环境感知和保护自动化技术. 本文通信作者. E-mail: adqiao@bjut.edu.cn

Data-driven Modeling and Self-organizing Control of Municipal Solid Waste Incineration Process

Funds: Supported by Science and Technology Innovation 2030-“New Generation Artificial Intelligence” Major Project of China (2021ZD0112300), Innovative Research Group Project of the National Natural Science Foundation of China (62021003), National Science and Technology Major Project of China (61890930), National Natural Science Foundation of China (62073006), Natural Science Foundation of Beijing (4212032, 4192009)
More Information
    Author Bio:

    DING Hai-Xu Ph. D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers feature modeling and intelligent control of municipal solid waste incineration process

    TANG Jian Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control and intelligent information processing, environmental awareness, and protection automation technology. Corresponding author of this paper

  • 摘要: 城市固废焚烧(Municipal solid waste incineration, MSWI)是处置城市固废(Municipal solid waste, MSW) 的主要手段之一. 中国MSW来源范围广、组分复杂、热值波动大, 其焚烧过程通常依靠人工干预, 这导致MSWI过程智能化水平较低且难以满足日益提升的控制需求. MSWI具有多变量耦合、工况漂移等诸多不确定性特征, 因而难以建立其被控对象模型并设计在线控制器. 针对以上问题, 提出了一种面向MSWI过程的数据驱动建模与自组织控制方法. 首先, 构建了基于多输入多输出Takagi Sugeno 模糊神经网络(Multi-input multi-output Takagi Sugeno fuzzy neural network, MIMO-TSFNN) 的被控对象模型; 然后, 设计了基于多任务学习的自组织模糊神经网络控制器(Multi-task learning fuzzy neural network controller, MTL-SOFNNC)用于同步控制炉膛温度与烟气含氧量, 其通过计算神经元的相似度与多任务学习(Multi-task learning, MTL)能力对控制器结构进行自组织调整; 接着, 通过Lyapunov定理对MTL-SOFNNC稳定性进行了证明; 最后, 通过北京市某MSWI厂的过程数据验证了模型与控制器的有效性.
  • 图  1  MSWI的工艺流程

    Fig.  1  MSWI process flow

    图  2  MIMO-TSFNN模型

    Fig.  2  MIMO-TSFNN model

    图  3  基于MTL-SOFNNC的MSWI控制系统

    Fig.  3  MSWI control system based on MTL-SOFNNC

    图  4  基于MTL-SOFNNC的控制框架

    Fig.  4  Control structure based on MTL-SOFNNC

    图  5  MSWI执行机构和检测设备

    Fig.  5  MSWI actuators and detection equipment

    图  6  模型训练过程的RMSE

    Fig.  6  RMSE of the model training process

    图  7  测试样本拟合效果

    Fig.  7  Testing samples fitting effect

    图  8  MTL-SOFNNC的结构自组织过程

    Fig.  8  Self-organizing process of MTL-SOFNNC structure

    图  9  操作变量的自校正过程

    Fig.  9  Self-tuning process of manipulated variables

    图  10  多变量跟踪控制结果

    Fig.  10  Multi-variables control tracking results

    图  11  多变量跟踪控制误差

    Fig.  11  Multi-variables control tracking errors

    表  1  实验对象的运行范围

    Table  1  Operating range of experimental subjects

    变量名 运行范围 单位
    一次风总流量 40~100 km3N/h
    干燥炉排速度百分比 0~100 %
    二次风流量 0~30 km3N/h
    炉膛温度 850~1050
    烟气含氧量 2~14 %
    主蒸汽流量 65~85 t/h
    下载: 导出CSV

    表  2  被控对象建模效果评价

    Table  2  AEvaluation of modeling effect of controlled object model

    被控模型 评价指标 炉膛温度
    模型
    烟气含氧
    量模型
    主蒸汽流
    量模型
    MIMO-TSFNN 训练 RMSE 3.88 ℃ 0.30% 0.43t/h
    APE 0.27% 3.16% 0.45%
    测试 RMSE 4.18 ℃ 0.58% 0.49t/h
    APE 0.31% 6.97% 0.59%
    下载: 导出CSV

    表  3  MSWI过程多变量控制器性能比较

    Table  3  Performance comparison of multi-variable controllers for MSWI process

    控制器 神经元个数 炉膛温度 烟气含氧量
    IAE ISE$ \bar{\sigma} $%$ \bar{t}_r $/s IAE ISE $ \bar{\sigma} $% $ \bar{t}_r $/s
    MTL-SOFNNC 10 1.883 28.828 0.39% 23.93 0.151 0.124 3.12% 21.47
    M-DSNNC 21 2.379 29.374 0.58% 25.68 0.188 0.150 3.14% 29.47
    SOFC 20 2.464 30.229 0.46% 30.43 0.194 0.152 3.92% 29.37
    SOTSFNNC 17 2.872 30.414 0.72% 23.38 0.214 0.136 4.13% 27.72
    TSFNNC 20 2.854 30.728 0.75% 24.14 0.217 0.151 5.45% 34.77
    下载: 导出CSV
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  • 收稿日期:  2022-07-12
  • 录用日期:  2022-09-01
  • 网络出版日期:  2022-10-26

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