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基于粒度聚类的铁矿石烧结过程运行性能评价

杜胜 吴敏 陈略峰 PEDRYCZ Witold

杜胜, 吴敏, 陈略峰, Pedrycz Witold. 基于粒度聚类的铁矿石烧结过程运行性能评价. 自动化学报, 2023, 49(6): 1272−1282 doi: 10.16383/j.aas.c200267
引用本文: 杜胜, 吴敏, 陈略峰, Pedrycz Witold. 基于粒度聚类的铁矿石烧结过程运行性能评价. 自动化学报, 2023, 49(6): 1272−1282 doi: 10.16383/j.aas.c200267
Du Sheng, Wu Min, Chen Lue-Feng, Pedrycz Witold. Operating performance assessment based on granular clustering for iron ore sintering process. Acta Automatica Sinica, 2023, 49(6): 1272−1282 doi: 10.16383/j.aas.c200267
Citation: Du Sheng, Wu Min, Chen Lue-Feng, Pedrycz Witold. Operating performance assessment based on granular clustering for iron ore sintering process. Acta Automatica Sinica, 2023, 49(6): 1272−1282 doi: 10.16383/j.aas.c200267

基于粒度聚类的铁矿石烧结过程运行性能评价

doi: 10.16383/j.aas.c200267
基金项目: 国家自然科学基金(61210011), 湖北省自然科学基金(2015CFA 010), 高等学校学科创新引智计划项目(B17040), 中国地质大学(武汉)中央高校基本科研业务费资助项目, 国家留学基金(2019 06410029)资助
详细信息
    作者简介:

    杜胜:中国地质大学(武汉)自动化学院教授. 主要研究方向为复杂工业过程建模与控制. E-mail: dusheng@cug.edu.cn

    吴敏:中国地质大学(武汉)自动化学院教授. 主要研究方向为过程控制, 鲁棒控制和智能系统. 本文通信作者. E-mail: wumin@cug.edu.cn

    陈略峰:中国地质大学(武汉)自动化学院教授. 主要研究方向为智能系统, 模式识别和计算智能. E-mail: chenluefeng@cug.edu.cn

    PEDRYCZ Witold:加拿大阿尔伯塔大学电子与计算机工程系教授. 主要研究方向为计算智能, 模糊建模, 粒度计算, 知识发现, 数据挖掘, 模糊控制和模式识别. E-mail: wpedrycz@ualberta.ca

Operating Performance Assessment Based on Granular Clustering forIron Ore Sintering Process

Funds: Supported by National Natural Science Foundation of China (61210011), Hubei Provincial Natural Science Foundation (2015 CFA010), 111 Project (B17040), Fundamental Research Funds for National Universities, China University of Geosciences, and Program of China Scholarship Council (201906410029)
More Information
    Author Bio:

    DU Sheng Professor at the School of Automation, China University of Geosciences. His research interest covers modeling and control of complex industrial processes

    WU Min Professor at the School of Automation, China University of Geosciences. His research interest covers process control, robust control, and intelligent systems. Corresponding author of this paper

    CHEN Lue-Feng Professor at the School of Automation, China University of Geosciences. His research interest covers intelligent systems, pattern recognition, and computational intelligence

    PEDRYCZ Witold Professor in the Department of Electronic and Computer Engineering, University of Alberta, Canada. His research interest covers computational intelligence, fuzzy modeling, granular computing, knowledge discovery, data mining, fuzzy control, and pattern recognition

  • 摘要: 烧结过程的运行性能是生产效率和能源利用的综合表现. 运行性能评价是保持烧结过程的运行性能处于最优等级的前提. 考虑到时间序列数据的冗余, 提出一种基于粒度聚类的铁矿石烧结过程运行性能评价方法. 首先, 利用单因素方差分析方法选取影响运行性能等级的检测参数; 然后, 采用多粒度区间信息粒化实现检测参数时间序列数据的降维, 并进行粒度聚类, 得到聚类标签; 最后, 以聚类得到的聚类标签为输入, 利用随机森林算法进行运行性能等级评价. 利用实际钢铁企业的运行数据进行实验, 构建两个对比实验, 分别采用基于时间序列数据聚类(Time series data clustering, TSDC)方法和基于时间序列特征聚类(Time series feature clustering, TSFC)方法. 实验结果表明, 该方法为有效评价烧结过程的运行性能提供了一套可行方案, 为操作人员提升烧结过程运行性能提供了有力的指导.
  • 图  1  风箱废气温度和烧结带分布

    Fig.  1  Temperature of exhaust gas in bellows andsintering zone distribution

    图  2  运行性能等级评价方案

    Fig.  2  Scheme of operating performance grade assessment

    图  3  部分检测参数的数据箱图

    Fig.  3  Data box diagram of some detection parameters

    图  4  多粒度区间信息粒化

    Fig.  4  Multi-granular interval information granulation

    图  5  时间序列信息粒化结果

    Fig.  5  Result of the information granulation of time series

    图  6  TSDC得到的CH系数

    Fig.  6  The CH coefficient obtained by TSDC

    图  7  TSFC得到的CH系数

    Fig.  7  The CH coefficient obtained by TSFC

    图  8  TSGC得到的CH系数

    Fig.  8  The CH coefficient obtained by TSGC

    $T_{BTP} $ 烧结终点(Burn-through point, BTP)温度(℃)
    $L_{BTP} $ 烧结终点位置
    $T_{i} $ i个风箱废气温度(℃)
    $P_{N} $ 主风箱负压(kPa)
    $H_{M} $ 料层厚度(mm)
    $C_{pm} $ 田口过程能力指数
    ${V_T} $ 台车速度 (m/min)
    $P_i $ i个检测参数
    $L_i $ i个聚类标签
    $G_i $ i个运行性能等级
    $F_T $ 检验统计量
    $\rho $ 检验概率
    $X $ 时间序列
    $s_k $ k个时间序列片段
    $\Omega_k $ k个信息粒
    ${\rm{rep}}(s_k) $ $\Omega_k$的数值代表
    ${\rm{rep}}(X) $ 粒时间序列
    $c $ 聚类数目
    $C_i $ i个簇的聚类中心
    $u_{ij} $ 属于第i个簇的隶属度
    $C_H(c) $ Calinski-Harabasz系数
    下载: 导出CSV

    表  1  运行性能等级划分

    Table  1  Operating performance grade division

    运行性能等级 描述
    优 (Perfect, Pe) $C_{pm}\geq$ 1.67
    良 (Good, Go) 1.67 $>C_{pm}\geq \;$1.33
    一般 (General, Ge) 1.33 $>C_{pm}\geq$1.00
    差 (Poor, Po) 1.00 $> C_{pm}\geq$ 0.67
    不可接受 (Unacceptable, Un) 0.67 $>C_{pm}$
    下载: 导出CSV

    表  2  单因素方差分析结果

    Table  2  Results of one-way analysis of variance

    参数 $\rho$
    $T_{1}$ 6.76 × 10–8
    $T_{2}$ 1.56 × 10–5
    $T_{3}$ 8.26 × 10–5
    $T_{5}$ 6.40 × 10–2
    $T_{7}$ 1.90 × 10–2
    $T_{9}$ 4.26 × 10–3
    $T_{11}$ 2.47 × 10–25
    $T_{13}$ 5.85 × 10–20
    $T_{15}$ 6.43 × 10–20
    $T_{17}$ 4.17 × 10–15
    $~~T_{18}$ 9.39 × 10–25
    $T_{19}$ 2.89 × 10–18
    $T_{20}$ 1.84 × 10–21
    $~~T_{21}$ 6.53 × 10–18
    $T_{22}$ 3.59 × 10–20
    $T_{23}$ 1.24 × 10–16
    $T_{24}$ 2.35 × 10–35
    $P_N$ 2.46 × 10–26
    $H_M$ 1.46 × 10–13
    $V_T$ 6.25 × 10–2
    下载: 导出CSV

    表  3  运行性能评价结果 (%)

    Table  3  Results of operating performance assessment (%)

    评估等级 实际等级 精度
    Pe Go Ge Po Un
    TSDC Pe 89.08 7.96 1.20 0.70 1.06 79.70
    Go 8.97 75.41 9.21 3.38 3.03
    Ge 4.58 8.50 66.45 13.73 6.75
    Po 2.29 4.30 14.61 67.34 11.46
    Un 1.53 4.81 5.03 8.10 80.53
    TSFC Pe 90.08 7.08 1.20 0.64 0.99 80.28
    Go 8.84 75.55 8.96 3.90 2.76
    Ge 4.43 9.09 67.63 11.31 7.54
    Po 1.37 5.75 13.97 66.85 12.05
    Un 1.22 4.22 5.22 8.10 81.24
    TSGC Pe 94.24 5.04 0.14 0.36 0.22 83.40
    Go 8.35 79.52 10.41 1.37 0.34
    Ge 0.44 12.66 67.03 12.45 7.42
    Po 0 1.15 11.17 74.50 13.18
    Un 0 0.54 5.59 11.60 82.28
    下载: 导出CSV
  • [1] Chen X, Lan T, Shi X, Tong C. A semi-supervised linear–nonlinear least-square learning network for prediction of carbon efficiency in iron ore sintering process. Control Engineering Practice, 2020, DOI: 10.1016/j.conengprac.2020.104454.
    [2] Huang X, Fan X, Chen X, Gan M, Zhao X. Soft-measuring models of thermal state in iron ore sintering process. Measurement, 2018, 130: 145-150. doi: 10.1016/j.measurement.2018.07.095
    [3] Wang S, Li H, Zhang Y, Zou Z. A hybrid ensemble model based on ELM and improved AdaBoost.RT algorithm for predicting the iron ore sintering characters. Computational Intelligence and Neuroscience, 2019, DOI: 10.1155/2019/4164296.
    [4] Du S, Wu M, Chen X, Lai X, Cao W. Intelligent coordinating control between burn-through point and mixture bunker level in an iron ore sintering process. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2017, 21(1): 139-147. doi: 10.20965/jaciii.2017.p0139
    [5] Chen X, Shi X, Tong C. Multi-time-scale TFe prediction for iron ore sintering process with complex time delay. Control Engineering Practice, 2019, 89: 84-93. doi: 10.1016/j.conengprac.2019.05.012
    [6] Liu Y, Wang F, Chang Y, Ma R. Comprehensive economic index prediction based operating optimality assessment and nonoptimal cause identification for multimode processes. Chemical Engineering Research and Design, 2015, 97: 77-90. doi: 10.1016/j.cherd.2015.03.008
    [7] Liu Y, Chang Y, Wang F. Online process operating performance assessment and nonoptimal cause identification for industrial processes. Journal of Process Control, 2014, 24(10): 1548-1555. doi: 10.1016/j.jprocont.2014.08.001
    [8] 邹筱瑜, 王福利, 常玉清, 王敏, 蔡庆宏. 基于分层分块结构的流程工业过程运行状态评价及非优原因追溯. 自动化学报, 2019, 45(2): 315-324.

    Zou X, Wang F, Chang Y, Wang M, Cai Q. Plant-wide process operating performance assessment and non-optimal cause identification based on hierarchical multi-block structure. Acta Automatica Sinica, 2019, 45(2): 315-324.
    [9] 邹筱瑜, 王福利, 常玉清, 郑伟. 基于两层分块GMM-PRS的流程工业过程运行状态评价. 自动化学报, 2019, 45(11): 2071-2081.

    Zou X, Wang F, Chang Y, Zheng W. Plant-wide process operating performance assessment based on two-level multiblock GMM-PRS. Acta Automatica Sinica, 2019, 45(11): 2071-2081.
    [10] Zou X, Zhao C. Concurrent assessment of process operating performance with joint static and dynamic analysis. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2776- 2786. doi: 10.1109/TII.2019.2934757
    [11] Zou X, Wang F, Chang Y. Assessment of operating performance using cross-domain feature transfer learning. Control Engineering Practice, 2019, 89: 143-153. doi: 10.1016/j.conengprac.2019.05.007
    [12] Du S, Wu M, Chen L, Cao W, Pedrycz W. Operating mode recognition of iron ore sintering process based on the clustering of time series data. Control Engineering Practice, 2020, DOI: 10.1016/j.conengprac.2020.104297.
    [13] Cho H, Choi N, Lee B. Oscillation recognition using a geometric feature extraction process based on periodic timeseries approximation. IEEE Access, 2020, 8: 34375-34386. doi: 10.1109/ACCESS.2020.2974259
    [14] Guo H, Wang L, Liu X, Pedrycz W. Information granulation-based fuzzy clustering of time series. IEEE Transactions on Cybernetics, 2020, DOI: 10.1109/TCYB.2020.2970455.
    [15] Pedrycz W, Bargiela A. Granular clustering: a granular signature of data. IEEE Transactions on Systems, Man, and Cybernetics−Part B: Cybernetics, 2002, 32(2): 212-224. doi: 10.1109/3477.990878
    [16] Lu W, Chen X, Pedrycz W, Liu X, Yang J. Using interval information granules to improve forecasting in fuzzy time series. International Journal of Approximate Reasoning, 2015, 57: 1-18. doi: 10.1016/j.ijar.2014.11.002
    [17] Boyles R A. The Taguchi capability index. Journal of Quality Technology. 1991, 23(1): 17-26. doi: 10.1080/00224065.1991.11979279
    [18] Du S, Wu M, Chen L, Zhou K, Hu J, Cao W, Pedrycz W. A fuzzy control strategy of burn-through point based on the feature extraction of time-series trend for iron ore sintering process. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2357-2368. doi: 10.1109/TII.2019.2935030
    [19] Wu C W. An efficient inspection scheme for variables based on Taguchi capability index. European Journal of Operational Research, 2012, 223(1): 116-122. doi: 10.1016/j.ejor.2012.06.023
    [20] Booker J M, Raines M, Swift K G. Designing Capable and Reliable Products. Oxford: Butterworth-Heinemann, 2001.
    [21] 卢伟. 基于粒计算的时间序列分析与建模方法研究 [博士论文], 大连理工大学, 中国, 2015

    Lu Wei. Time Series Analysis and Modeling Method Research Based on Granular Computing [Ph.D. dissertation], Dalian University of Technology, China, 2015
    [22] Wang W, Pedrycz W, Liu X. Time series long-term forecasting model based on information granules and fuzzy clustering. Engineering Applications of Artificial Intelligence. 2015, 41: 17-24. doi: 10.1016/j.engappai.2015.01.006
    [23] Bezdek J C, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 1984, 10(2- 3): 191-203.
    [24] Caliński T, Harabasz J. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 1974, 3(1): 1-27. doi: 10.1080/03610927408827109
    [25] Chai Z, Zhao C. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification. IEEE Transactions on Industrial Informatics, 2020, 16(1): 54-66. doi: 10.1109/TII.2019.2915559
    [26] Räsänen T, Kolehmainen M. Feature-based clustering for electricity use time series data. In: Proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms. Kuopio, Finland: 2009. 401−412
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出版历程
  • 收稿日期:  2020-04-30
  • 网络出版日期:  2023-01-07
  • 刊出日期:  2023-06-20

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