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基于分层粒度对比网络的钢铁燃气调度知识获取与建模

王天宇 赵珺 王伟 王天鑫

王天宇, 赵珺, 王伟, 王天鑫. 基于分层粒度对比网络的钢铁燃气调度知识获取与建模. 自动化学报, 2022, 48(9): 2212−2222 doi: 10.16383/j.aas.c211198
引用本文: 王天宇, 赵珺, 王伟, 王天鑫. 基于分层粒度对比网络的钢铁燃气调度知识获取与建模. 自动化学报, 2022, 48(9): 2212−2222 doi: 10.16383/j.aas.c211198
Wang Tian-Yu, Zhao Jun, Wang Wei, Wang Tian-Xin. Hierarchical granular contrastive network-based knowledge acquisition and modeling for gas scheduling of steel industry. Acta Automatica Sinica, 2022, 48(9): 2212−2222 doi: 10.16383/j.aas.c211198
Citation: Wang Tian-Yu, Zhao Jun, Wang Wei, Wang Tian-Xin. Hierarchical granular contrastive network-based knowledge acquisition and modeling for gas scheduling of steel industry. Acta Automatica Sinica, 2022, 48(9): 2212−2222 doi: 10.16383/j.aas.c211198

基于分层粒度对比网络的钢铁燃气调度知识获取与建模

doi: 10.16383/j.aas.c211198
基金项目: 国家重点研发计划(2017YFA0700300), 国家自然科学基金(62125302, 62076182, 61833003, U1908218), 大连市杰出青年科技人才计划(2018RJ01), 中国博士后科学基金面上项目(2021M700667)资助
详细信息
    作者简介:

    王天宇:大连理工大学控制科学与工程学院博士后. 2020年获得大连理工大学工学博士学位. 主要研究方向为工业系统建模与优化, 能源调度和机器学习. E-mail: wangty@dlut.edu.cn

    赵珺:大连理工大学控制科学与工程学院教授. 2008年获得大连理工大学工学博士学位. 主要研究方向为工业生产调度, 计算机集成制造, 智能优化和机器学习. 本文通信作者. E-mail: zhaoj@dlut.edu.cn

    王伟:大连理工大学控制科学与工程学院教授. 1988年获得东北大学工业自动化博士学位. 主要研究方向为自适应控制, 计算机集成制造和工业过程计算机控制. E-mail: wangwei@dlut.edu.cn

    王天鑫:大连理工大学电子信息专业硕士研究生. 主要研究方向为基于数据的预测和调度, 数据挖掘和深度强化学习. E-mail: wtx@mail.dlut.edu.cn

Hierarchical Granular Contrastive Network-based Knowledge Acquisition and Modeling for Gas Scheduling of Steel Industry

Funds: Supported by the National Key Research & Development Program of China (2017YFA0700300), National Natural Science Foundation of China (62125302, 62076182, 61833003, U1908218), the Outstanding Youth Science-Technology Talent Program of Dalian (2018RJ01), and the China Postdoctoral Science Foundation (2021M700667)
More Information
    Author Bio:

    WANG Tian-Yu Postdoctor at Faculty of Control Science and Engineering, Dalian University of Technology. He received his Ph.D. degree in engineering from Dalian University of Technology in 2020. His research interest covers industrial system modeling and optimization, energy scheduling, and machine learning

    ZHAO Jun Professor at Faculty of Control Science and Engineering, Dalian University of Technology. He received his Ph.D. degree in engineering from Dalian University of Technology in 2008. His research interest covers industrial production scheduling, computer integrated manufacturing, intelligent optimization, and machine learning. Corresponding author of this paper

    WANG Wei Professor at Faculty of Control Science and Engineering, Dalian University of Technology. He received his Ph.D. degree in industrial automation from the Northeastern University in 1988. His research interest covers adaptive controls, computer integrated manufacturing, and computer controls of industrial processes

    WANG Tian-Xin Master student in electronic information, at Dalian University of Technology. His research interest covers data-based forecasting and scheduling, data mining, and deep reinforcement learning

  • 摘要: 对于钢铁燃气系统的实时有效调度是实现企业节能降耗的关键. 考虑燃气产消过程所包含的多工况特征, 提出了一种基于分层粒度对比网络的调度知识获取与建模方法. 鉴于深度对比学习对于语义信息的处理能力, 定义和描述了一系列信息粒度, 以建立能源数据的语义表示. 为初步提取多工况调度知识, 采用长短时记忆(Long and short-term memory, LSTM)网络学习具有时变特性的粒度变量特征. 在此基础上, 利用专家经验知识定性地划分对比学习样本, 建立基于粒度对比学习的知识表征网络. 为挖掘调度数据中所包含的深层次知识, 进一步提出了基于反馈机制的分层对比网络模型, 并通过网络输出层实现调度建模任务. 实验部分采用了国内某钢铁厂高炉煤气系统的实际数据进行了多组对比实验, 结果表明所提方法获得的知识表示能够有效提高燃气系统的建模精度, 帮助实现专家级别的调度表现.
  • 图  1  典型钢铁工业燃气系统结构图

    Fig.  1  A typical structure of the gas system in steel industry

    图  2  燃气产消多工况特征及调度过程

    Fig.  2  The multi-condition characteristics and their scheduling process of gas generation and consumption

    图  3  所提方法整体框架图

    Fig.  3  The overall framework of the proposed method

    图  4  粒度对比网络结构图

    Fig.  4  The structure of the granular contrastive network

    图  5  样本数据知识表征的计算过程描述

    Fig.  5  The calculation process of the knowledge representation of sample data

    图  6  分层粒度对比网络结构

    Fig.  6  The structure of hierarchical granular contrastive network

    图  7  分层粒度对比网络实施过程

    Fig.  7  The implement process of the hierarchical granular contrastive network

    图  8  调度量结果对比

    Fig.  8  Comparative results of scheduling amount

    图  9  各阶段对比学习模型的误差比较

    Fig.  9  Absolute error of the scheduling amount during different contrastive learning phases

    图  10  不同方法获得的调度量绝对误差对比

    Fig.  10  Absolute error of the scheduling amount obtained by the comparative algorithms

    图  11  通过知识表示构建多工况组合模型与单一预测建模的结果对比

    Fig.  11  Prediction modeling results with and without using the granular contrastive learning model for extracting multiple working-condition knowledge

    表  1  各对比学习阶段的误差统计

    Table  1  Error statistical results of different contrastive learning phases

    模型MAERMSEMAPE
    二分类知识学习25.553835.903847.8148
    多分类知识学习25.280537.314648.4387
    分层次对比网络 (本文方法)14.262118.012025.9330
    下载: 导出CSV

    表  2  各对比方法的误差及耗时统计

    Table  2  Statistical results of error and time consumption of the comparative algorithms

    模型MAERMSEMAPETC (s)
    LSSVM30.811240.587458.34361.8990
    Actor-critic52.654262.478699.1213195.1375
    LSTM25.486738.186048.689511.7582
    本文方法14.262118.012025.933039.0892
    下载: 导出CSV

    表  3  不同场景下的煤气柜位预测误差统计

    Table  3  The error statistics of the gas tank level prediction under different situations

    统计误差
    MAERMSEMAPE
    接近柜位存储上限单工况1.82482.81280.9093
    多工况0.95761.16460.4794
    接近柜位存储下限单工况2.01872.68311.3711
    多工况1.17721.50330.7981
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-15
  • 录用日期:  2022-06-06
  • 网络出版日期:  2022-07-11
  • 刊出日期:  2022-09-16

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