Converter Steelmaking Oxygen Consumption Prediction Based on Granularity Clustering
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摘要: 转炉炼钢是钢铁企业的主要耗氧工序, 预测转炉炼钢的氧气消耗量对氧气系统合理调度、保证生产安全具有重要意义. 考虑到转炉冶炼工况多、钢种数据粒度不统一, 提出一种基于粒度聚类的转炉炼钢氧气消耗量预测方法. 首先, 利用孤立森林异常检测法剔除历史数据库中的异常数据; 接着, 采用皮尔逊相关性分析和互信息相关系数选取相关影响因子, 对不同钢种数据进行信息粒化, 实现数据特征提取和维度统一, 使用模糊C均值(Fuzzy C-means, FCM) 划分工况并建立不同工况下的氧气消耗量预测子模型; 最后, 利用企业的实际生产数据进行实验, 验证所提方法的准确性和有效性.Abstract: Oxygen consumption prediction in converter steelmaking is of great significance for the rational scheduling of the oxygen system and ensuring production safety in steel enterprise. Considering the diverse operating conditions of converter smelting and the inconsistent granularity of steel grade data, this paper proposes a prediction method for oxygen consumption in converter steelmaking based on granularity clustering. Firstly, the isolation forest anomaly detection method is used to remove abnormal data from the historical database. Then, Pearson correlation analysis and mutual information correlation coefficient are employed to select relevant influencing factors and achieve information granulation for different steel grade data, thereby extracting data features and unifying dimensions. Fuzzy C-means (FCM) clustering is utilized to divide the operating conditions and establish oxygen consumption prediction sub-models for different conditions. Finally, the accuracy and effectiveness of the proposed method are validated through experiments using actual production data from the steel enterprise.
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图 2 基于粒度聚类的氧气消耗量预测方案
Fig. 2 Oxygen consumption prediction scheme based on granularity clustering
图 4 基于信息粒化的钢种模糊聚类
Fig. 4 Fuzzy clustering of steel types based on information granulation
图 7 基于单一模型的氧气消耗量预测结果
Fig. 7 Oxygen consumption prediction results based on a single model
图 8 基于不同组合模型的氧气消耗量预测结果
Fig. 8 Oxygen consumption prediction results based on different combination models
表 1 符号说明
Table 1 Symbol description
符号 含义 符号 含义 ${C_1}$ 铁水重量 ${C_2}$ 废钢重量 ${C_3}$ 石灰重量 ${C_{\rm{Si}}}$ 铁水中化学元素Si的含量 ${C_{\rm{Mn}}}$ 铁水中化学元素Mn的含量 ${C_{\rm{P}}}$ 铁水中化学元素P的含量 ${C_{\rm{S}}}$ 铁水中化学元素S的含量 ${C_{\rm{Cu}}}$ 铁水中化学元素Cu的含量 ${C_{\rm{As}}}$ 铁水中化学元素As的含量 ${C_{\rm{Sn}}}$ 铁水中化学元素Sn的含量 ${C_{\rm{Ti}}}$ 铁水中化学元素Ti的含量 ${C_{\rm{V}}}$ 铁水中化学元素V的含量 ${C_{\rm{Pb}}}$ 铁水中化学元素Pb的含量 ${C_{\rm{Zn}}}$ 铁水中化学元素Zn的含量 ${C_{\rm{Cr}}}$ 铁水中化学元素Cr的含量 ${C_{\rm{Ni}}}$ 铁水中化学元素Ni的含量 ${C_{\rm{Nb}}}$ 铁水中化学元素Nb的含量 ${C_{\rm{Mo}}}$ 铁水中化学元素Mo的含量 ${C_{\rm{Sb}}}$ 铁水中化学元素Sb的含量 ${C_{\rm{W}}}$ 铁水中化学元素W的含量 ${Y}$ 氧气消耗量 $S({X})$ 样本${X}$的异常得分 ${\rho _{\rm{pcc}}}$ 皮尔逊相关系数值 ${\rho _{\rm{NMI}}}$ 归一化互信息相关系数值 ${X_A}$ A钢种 ${\tilde X_A}$ A钢种信息粒 ${r_1}$ 均方根误差 (Root mean square error, RMSE) ${r_2}$ 平均绝对误差 (Mean absolute error, MAE) ${r_3}$ 平均绝对百分比误差 (Mean absolute percentage error, MAPE) ${r_4}$ 最大绝对百分比误差 (Maximum absolute percentage error, MaxAPE) ${D_b}$ Davies-Bouldin指数 
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表 2 相关参数与吹氧量的${\rho _{\rm{pcc}}}$和${\rho _{\rm{NMI}}}$ 值
Table 2 ${\rho _{\rm{pcc}}}$ and ${\rho _{\rm{NMI}}}$ values between relevant parameters and oxygen blowing amount
过程参数 $C_1$ $C_2$ $C_3$ $C_{\rm{Si}}$ $C_{\rm{Mn}}$ ${\rho _{\rm{pcc}}}$ 0.281 0.510 0.340 0.054 0.180 ${\rho_{\rm{NMI}}}$ 0.776 0.820 0.831 0.864 0.856 过程参数 $C_{\rm{P}}$ $C_{\rm{S}}$ $C_{\rm{Cu}}$ $C_{\rm{As}}$ $C_{\rm{Sn}}$ ${\rho _{\rm{pcc}}}$ 0.016 0.117 0.120 0.102 0.012 ${\rho_{\rm{NMI}}}$ 0.758 0.812 0.766 0.757 0.710 过程参数 $C_{\rm{Ti}}$ $C_{\rm{V}}$ $C_{\rm{Pb}}$ $C_{\rm{Zn}}$ $C_{\rm{Cr}}$ ${\rho _{\rm{pcc}}}$ 0.025 0.102 0.053 0.015 0.022 ${\rho_{\rm{NMI}}}$ 0.851 0.841 0.542 0.554 0.770 过程参数 $C_{\rm{Ni}}$ $C_{\rm{Nb}}$ $C_{\rm{Mo}}$ $C_{\rm{Sb}}$ $C_{\rm{W}}$ ${\rho _{\rm{pcc}}}$ 0.019 0.019 0.046 0.036 0.030 ${\rho_{\rm{NMI}}}$ 0.718 0.535 0.472 0.314 0.609
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表 3 不同模型预测误差
Table 3 Prediction errors of different models
预测模型 ${r_1}$ ${r_2}$ ${r_3}$ ${r_4}$ Elman 575.60 465.41 4.85 0.19 IG-FCM-Elman 565.11 415.35 4.35 0.24 ELM 480.12 359.38 3.74 0.17 IG-FCM-ELM 471.48 352.71 3.68 0.24 RF 417.17 324.69 3.36 0.13 IG-FCM-RF 400.04 313.11 3.24 0.12 SVR 415.40 321.54 3.33 0.16 IG-FCM-SVR 407.64 304.74 3.16 0.14
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