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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 维托尔德·佩德里茨

杜胜, 吴敏, 陈略峰, 维托尔德·佩德里茨. 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, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200267
引用本文: 杜胜, 吴敏, 陈略峰, 维托尔德·佩德里茨. 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, 2020, 46(x): 1−11 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, 2020, 46(x): 1−11 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, 2020, 46(x): 1−11 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: 国家自然科学基金重点国际(地区)合作研究项目(61210011), 湖北省自然科学基金创新群体项目(2015CFA010), 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(201906410029)资助
详细信息
    作者简介:

    杜胜:中国地质大学(武汉)自动化学院博士研究生. 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)自动化学院副教授, 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

    维托尔德·佩德里茨 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, 模糊建模和粒度计算, 知识发现和数据挖掘, 模糊控制和模式识别. E-mail: wpedrycz@ualberta.ca

Operating Performance Assessment Based on Granular Clustering for Iron Ore Sintering Process

Funds: Supported by the National Natural Science Foundation of China under Grant 61210011, the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010, the 111 Project under Grant B17040, the Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan), and the Program of China Scholarship Council under Grant 201906410029
  • 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, 本文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, 利用随机森林算法对聚类得到的标签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了实验, 构建两个对比实验, 分别采用基于时间序列数据聚类的方法和基于时间序列特征聚类的方法. 实验表明所提出的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 and 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  7  不同聚类数目的Calinski-Harabasz系数(TSFC)

    Fig.  7  Calinski-Harabasz coefficients for different number of clusters (TSFC).

    图  5  时间序列信息粒化结果. (a) 原始时间序列. (b) 信息粒化后的时间序列.

    Fig.  5  Result of the information granulation of time series. (a) Original time series. (b) Time series after information granulation.

    图  6  不同聚类数目的Calinski-Harabasz系数(TSDC)

    Fig.  6  Calinski-Harabasz coefficients for different number of clusters (TSDC).

    图  8  不同聚类数目的Calinski-Harabasz系数(TSGC)

    Fig.  8  Calinski-Harabasz coefficients for different number of clusters (TSGC).

    表  1  运行性能等级划分

    Table  1  Operating performance grade divination

    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> $C_{pm}\geq$ 1.0
    差(Poor, Po) 1.0> $C_{pm}\geq$ 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 $T_{1}$ $T_{2}$ $T_{3}$ $T_{5}$
    $\rho$ 6.76×10–8 1.56×10–5 8.26×10–5 6.40×10–2
    参数 $T_{7}$ $T_{9}$ $T_{11}$ $T_{13}$
    $\rho$ 1.90×10–2 4.26×10–3 2.47×10–25 5.85×10–20
    参数 $T_{15}$ $T_{17}$ $~~T_{18}$ $T_{19}$
    $\rho$ 6.43×10–20 4.17×10–15 9.39×10–25 2.89×10–18
    参数 $T_{20}$ $~~T_{21}$ $T_{22}$ $T_{23}$
    $\rho$ 1.84×10–21 6.53×10–18 3.59×10–20 1.24×10–16
    参数 $T_{24}$ $P_N$ $H_M$ $V_T$
    $\rho$ 2.35×10–35 2.46×10–26 1.46×10–13 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 0.00% 1.15% 11.17% 74.50% 13.18%
    Un 0.00% 0.54% 5.59% 11.60% 82.28%
    下载: 导出CSV
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