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基于静-动态特性协同感知的复杂工业过程运行状态评价

褚菲 许杨 尚超 王福利 高福荣 马小平

褚菲, 许杨, 尚超, 王福利, 高福荣, 马小平. 基于静-动态特性协同感知的复杂工业过程运行状态评价. 自动化学报, 2021, 47(x): 1−14 doi: 10.16383/j.aas.c201035
引用本文: 褚菲, 许杨, 尚超, 王福利, 高福荣, 马小平. 基于静-动态特性协同感知的复杂工业过程运行状态评价. 自动化学报, 2021, 47(x): 1−14 doi: 10.16383/j.aas.c201035
Chu Fei, Xu Yang, Shang Chao, Wang Fu-Li, Gao Fu-Rong, Ma Xiao-Ping. Evaluation of complex industrial process operating state based on static and dynamic cooperative perception. Acta Automatica Sinica, 2021, 47(x): 1−14 doi: 10.16383/j.aas.c201035
Citation: Chu Fei, Xu Yang, Shang Chao, Wang Fu-Li, Gao Fu-Rong, Ma Xiao-Ping. Evaluation of complex industrial process operating state based on static and dynamic cooperative perception. Acta Automatica Sinica, 2021, 47(x): 1−14 doi: 10.16383/j.aas.c201035

基于静-动态特性协同感知的复杂工业过程运行状态评价

doi: 10.16383/j.aas.c201035
基金项目: 国家自然科学基金(61973304, 62003187, 62073060, 61873049), 江苏省科技计划项目(BK20191339), 江苏省六大人才高峰项目(DZXX-045), 徐州市科技创新计划项目(KC19055), 矿冶过程自动控制技术国家重点实验室开放课题(BGRIMM-KZSKL-2019-10)资助
详细信息
    作者简介:

    褚菲:中国矿业大学信息与控制工程学院副教授. 2014年获中国东北大学控制理论与控制工程博士学位. 主要研究方向包括复杂工业过程建模、控制与优化、机器学习及运行状态评价等. E-mail: chufeizhufei@sina.com

    许杨:中国矿业大学信息与控制工程学院硕士研究生. 2019年获中国矿业大学信息与控制工程学院学士学位. 主要研究方向为复杂工业过程建模及运行状态评价. E-mail: xuyang_668@sina.com

    尚超:清华大学自动化系助理教授. 2016年获清华大学自动化系博士学位. 主要研究方向为大数据解析及工业应用, 过程监控与故障诊断, 工业过程建模等. 本文通讯作者. E-mail: c-shang@tsinghua.edu.cn

    王福利:东北大学教授. 1988年获东北大学自动化系博士学位. 主要研究方向为复杂工业系统的建模、控制与优化, 过程监测和故障诊断等. E-mail: wangfuli@ise.neu.edu.cn

    高福荣:香港科技大学化工系教授. 主要研究方向为过程建模、控制和监测. E-mail: kefgao@ust.hk

    马小平:中国矿业大学信息与控制工程学院教授. 2001年获中国矿业大学信息与电气工程博士学位. 主要研究方向为过程控制, 网络化控制系统及故障检测. E-mail: xpma@cumt.edu.cn

Evaluation of Complex Industrial Process Operating State Based on Static and Dynamic Cooperative Perception

Funds: Supported by the National Natural Science Foundation of China (61973304, 62003187, 62073060, 61873049), Jiangsu Science and Technology Plan Project (BK20191339), Six Talent Peak Projects of Jiangsu Province (DZXX-045), and Xuzhou City Science and Technology Innovation Plan Project (KC19055), Open Foundation of State Key Laboratory of Process Automation in Mining and Metallurgy (BGRIMM-KZSKL-2019-10)
More Information
    Author Bio:

    CHU Fei Associate professor at the College of Information and Control Engineering, China University of Mining and Technology. He received his Ph. D. degree from Northeastern University in 2014. His research interest covers modeling, control and optimization of complex industrial process, statistical process monitoring and operating performance assessment, etc

    XU Yang M.Sc. candidate at the College of Information and Control Engineering, China University of Mining and Technology. He received his bachelor degree from the College of Information and Control Engineering, China University of Mining and Technology in 2019. His research interest covers optimization of complex industrial process and operating performance assessment

    SHANG Chao Associate professor at the Department of Automation, Qinghua University. He received his Ph. D. degree from Qinghua University in 2016. His research interest covers big data analysis and industrial applications, process monitoring and fault diagnosis, industrial process modeling, etc. Corresponding author of this paper

    WANG Fu-Li Professor at Northeastern University. He received his Ph. D. degree from Northeastern University in 1988. His research interest covers modeling, control andoptimization of complex industrial process, process monitoring and fault diagnosis, etc

    GAO Fu-Rong Professor in the Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology. His research interest covers process modeling, control and monitoring

    MA Xiao-Ping Professor at the College of Information and Control Engineering, China University of Mining and Technology. He received his Ph. D. degree from China University of Mining and Technology in 2001. His research interest covers process control, networked control system, and fault detection

  • 摘要: 针对当前过程监测和运行状态评价方法等对工况信息感知不全面, 漏报和误报现象严重等问题, 本文在深入研究工业现场数据静-动态特性协同感知方法的基础上, 提出综合经济指标驱动的慢特征分析算法. 将综合经济指标信息融入至慢特征分析中, 协同感知复杂工业过程的静-动态特性变化, 并进一步通过计算潜变量之间的相似度及其一阶差分之间的相似度实现对过程稳态和过渡的评价, 在此基础上建立了基于静-动态特性协同感知的过程运行状态评价统一框架. 针对非优状态, 提出了基于稀疏学习的非优因素识别方法, 实现对非优因素变量的准确识别. 最后, 通过重介质选煤过程实际生产数据和田纳西·伊斯曼过程数据验证了所提方法的有效性.
    1)  收稿日期 2020-12-14 录用日期 2021-06-06 Manuscript received December 14, 2020; accepted June 6, 2021 国家自然科学基金 (61973304, 62003187, 62073060, 61873049), 江苏省科技计划项目 (BK20191339), 江苏省六大人才高峰项目 (DZXX-045), 徐州市科技创新计划项目 (KC19055), 矿冶过程自动控制技术国家重点实验室开放课题 (BGRIMM-KZSKL-2019-10)资助 Supported by the National Natural Science Foundation of China (61973304, 62003187, 62073060, 61873049), Jiangsu Science and Technology Plan Project (BK20191339), Six Talent Peak Projects of Jiangsu Province (DZXX-045), and Xuzhou City Science and Technology Innovation Plan Project (KC19055), Open Foundation of State Key Laboratory of Process Automation in Mining and Metallurgy (BGRIMM-KZSKL-2019-10)
    2)  本文责任编委 谢永芳 Recommended by Associate Editor XIE Yong-Fang 1. 中国矿业大学信息与控制工程学院 徐州 221116 2. 中国矿业大学地下空间智能控制教育部工程研究中心 徐州 221116 3. 清华大学自动化系 北京 100084 4. 东北大学信息科学与工程学院 沈阳 110819 5. 香港科技大学化工系 香港 中国 1. China University of Mining and Technology, School of Information and Control Engineering, Underground Space Intelligent Control Engineering Research Center of the Ministry of Education, Xuzhou 221116, China 2. Department of Automation, Tsinghua University, Beijing 100084, China 3. College of Information Science and Engineering, Northeastern University, Shenyang 110819 4. Department of Chemical Engineering,Hong Kong University of Science and Technology, Hong Kong, China 5
  • 图  1  基于静-动态特性协同感知的复杂工业过程运行状态在线评价流程图

    Fig.  1  Flow chart of online evaluation of the operating status of complex industrial processes Based on Static and Dynamic Cooperative Perception

    图  2  基于稀疏学习的非优因素识别流程图

    Fig.  2  Traceability flowchart of non-optimal factors based on sparse learning

    图  3  重介质选煤工艺流程图

    Fig.  3  Process flow chart of heavy medium coal preparation

    图  4  基于PLS的复杂工业过程运行状态评价

    Fig.  4  Evaluation of the operation status of complex industrial processes based on PLS

    图  5  基于静-动态特性协同感知的复杂工业过程运行状态评价

    Fig.  5  Evaluation of the operating state of complex industrial processes based on the cooperative perception of static-dynamic characteristics

    图  7  基于PLS的TE过程运行状态评价

    Fig.  7  Evaluation of the operation status of TE process based on PLS

    图  6  非优因素识别结果: (1)良 (2)中

    Fig.  6  Non-optimal factors retrospective results: (1) Good (2) Medium

    Figure a: Traceability results of non-optimal factors based on variable contribution rate Figure b: Traceability results of non-optimal factors based on sparse learning

    图  8  基于静-动态特性协同感知的TE过程运行状态评价

    Fig.  8  Evaluation of the operating state of TE process based on the cooperative perception of static-dynamic characteristics

    图  9  非优因素识别结果: 图a: 基于变量贡献率的非优因素识别结果图; b: 基于稀疏学习的非优因素识别结果(TE过程)

    Fig.  9  Non-optimal factors retrospective results: Figure a: Traceability results of non-optimal factors based on variable contrib ution rate;Figure b: Traceability results of non-optimal factors based on sparse learning (TE process)

    表  1  溢流灰分和对应的状态等级

    Table  1  Overflow ash content and corresponding state level

    灰分 (%)状态等级c
    6.0 ~ 6.51 (优)
    6.5 ~ 6.71 ~ 2 过渡
    6.7 ~ 7.22 (良)
    7.2 ~ 7.52 ~ 3 过渡
    7.5 ~ 8.03 (中)
    下载: 导出CSV

    表  2  在线评价误识别率

    Table  2  Misrecognition rate of online evaluation

    评价指标阈值误识别样本数误识别率
    0.85121.113%
    0.8403.711%
    0.7746.864%
    下载: 导出CSV

    表  3  反应器温度和对应的状态等级

    Table  3  Reactor temperature and corresponding state level

    反应器温度状态等级运行成本
    121.641.99−96.09
    111.6非优
    下载: 导出CSV

    表  4  过程变量(间隔采样0.02 s)

    Table  4  Process variables (sampling at intervals of 0.02 s)

    变量描述单位
    1A进料(流1)${\text{k}}{{\text{m}}^3}/{\text{h}}$
    2D进料(流2)${\text{kg}}/{\text{h}}$
    3E进料(流3)${\text{kg}}/{\text{h}}$
    4总进料(流4)${\text{k}}{{\text{m}}^3}/{\text{h}}$
    5再循环流量${\text{k}}{{\text{m}}^3}/{\text{h}}$
    6反应器进料速度(流6)${\text{k}}{{\text{m}}^3}/{\text{h}}$
    7反应器温度$^ \circ {\text{C}}$
    8排放速度(流9)${\text{k}}{{\text{m}}^3}/{\text{h}}$
    9产品分离器温度$^ \circ {\text{C}}$
    10产品分离器压力${\text{kPa}}$
    11分离器塔底低流量(流10)${{\text{m}}^3}/{\text{h}}$
    12汽提塔压力${\text{kPa}}$
    13汽提塔温度$^ \circ {\text{C}}$
    14反应器冷却水出口温度$^ \circ {\text{C}}$
    15分离器冷却水出口温度$^ \circ {\text{C}}$
    下载: 导出CSV
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  • 收稿日期:  2020-12-14
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