Operating Performance Assessment Based on Granular Clustering forIron Ore Sintering Process
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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.Abstract: The operating performance of the sintering process is about the comprehensive representation of production efficiency and energy utilization. The operating performance assessment is a prerequisite to maintain the operating performance of the sintering process at the optimal grade. By considering the redundancy of time series data, an operating performance assessment method based on granular clustering for the iron ore sintering process is presented in this paper. First, the one-way analysis of variance method is used to select the detection parameters that affect the operating performance grade. Then, the multi-granularity interval information granulation is used to achieve dimensionality reduction of the time series data for the detection parameters, and the granules are clustered to form the clustering labels. Finally, with labels obtained by clustering being the input, the random forest algorithm is used to assess the operating performance grade. Experiments are performed using the actual running data of iron and steel enterprises, and two comparative experiments are constructed using a method based on time series data clustering (TSDC) and a method based on time series feature clustering (TSFC), respectively. Results show that the proposed method provides a feasible scheme to assess the operating performance of the sintering process effectively, and provides powerful guidance for operators to improve the operating performance of the sintering process.
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Key words:
- Granular clustering /
- sintering process /
- time series /
- operating performance
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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
$T_{BTP} $ 烧结终点(Burn-through point, BTP)温度(℃) $L_{BTP} $ 烧结终点位置 $T_{i} $ 第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 $ 第i个检测参数 $L_i $ 第i个聚类标签 $G_i $ 第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 $ 第k个时间序列片段 $\Omega_k $ 第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 $ 第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 
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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 $>C_{pm}$
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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
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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
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