FeO Content Prediction in Sintering Process Based on Fusion of Data-Knowledge and AW-ESN
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摘要: 氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标, 烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义. 然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战, 为此, 提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法. 首先, 针对烧结过程热状态参数缺失的问题, 建立烧结料层最高温度分布模型, 实现基于料层温度分布特征的FeO含量等级划分; 其次, 针对烧结过程参数波动频繁的问题, 提出基于核函数高维映射的多尺度数据配准方法, 有效抑制离群点的影响, 提升建模数据的质量; 最后, 针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题, 将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合, 建立DK-AWESN模型, 有效提升复杂工况下FeO含量的预测精度. 现场工业数据试验表明, 所提方法能实时准确地预测烧结过程FeO含量, 为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.Abstract: FeO content is an important index to characterize the strength and reducibility of sinter. Real-time and accurate prediction of FeO content in sintering process is of great significance for improving sintering quality and optimizing sintering process. However, the lack of thermal state parameters in sintering process and the frequent fluctuation of process parameters bring great challenges to the high-precision prediction of FeO content. In order to alleviate these problems, a method of FeO content prediction in sintering process by fusing data-knowledge and adaptive weight echo state network (DK-AWESN) is proposed in this paper. Firstly, aiming at the problem of lacking thermal state parameters in sintering process, the temperature distribution model of sinter bed is established, and the state of FeO content can be obtained based on the temperature distribution characteristics of sinter bed; Secondly, aiming at the frequent fluctuation of sintering process parameters, a multi-scale data registration method based on kernel function high-dimensional mapping is proposed, which effectively suppresses the influence of outliers and improves the quality of modeling data; Finally, to alleviate the problem of low prediction accuracy of data-driven model due to the lack of mechanism knowledge, the expert knowledge extracted from the process data is fused with adaptive weight echo state network (AW-ESN) to establish DK-AWESN, which improves the FeO content prediction performance of the model under complex working conditions. Industrial verification shows that the proposed method can accurately predict the FeO content in real time and provide effective FeO content information for the intelligent control of the sintering process.
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图 2 料层全时空最高温度分布
Fig. 2 Maximum temperature distribution of sinter bed in whole time and space
图 5 基于DK-AWESN的FeO含量预测方法框图
Fig. 5 Schematic of FeO content prediction method based on DK-AWESN
图 6 ESN和AW-ESN预测值与实际值对比
Fig. 6 Comparison between predicted values and actual values of ESN and AW-ESN
图 7 AW-ESN和DK-AWESN预测值与实际值对比
Fig. 7 Comparison between predicted values and actual values of AW-ESN and DK-AWESN
图 8 ESN和DK-ESN预测值与实际值对比
Fig. 8 Comparison between predicted values and actual values of ESN and DK-ESN
图 10 不同方法预测值和实测值的散点图
Fig. 10 Scatter plot of predicted values and measured values by different methods
表 1 反应速率计算参数表
Table 1 Reaction rate parameters
参数名称 符号 值 指前因子 $ k $ $6.89 \times {10^5} \sim 8.3 \times {10^5}\;{{\rm{s}}^{ - 1} }$ 反应活化能 $ E $ ${ {125.61 \sim 137.16\;{\rm{kJ} } } \mathord{\left/ {\vphantom { {125.61 \sim 137.16\;{\rm{kJ} } } { {\rm{mol} } } } } \right. } {{\rm{mol}}} }$ 比例系数 $ R $ $8.314\;{\rm{kJ}}/({\rm{mol}} \cdot {\rm{K}})$ 抽风负压 $ P $ ${\text{1} }{\text{.2} } \times {\text{1} }{ {\text{0} }^4}\;{\rm{Pa} }$ 料前氧分压 ${P_{ {{\rm{O}}_2}1} }$ ${\text{0} }{\text{.21} }\;{\rm{P} }$ 料后氧分压 ${P_{ {{\rm{O}}_2}2} }$ $0.09 \sim 0.11\;{\rm{P}}$ 总氧气扩散系数 ${D_{ {{\rm{O}}^2} } }$ $ {\text{2}}.03 \times {10^{ - 5}}{T^{1.87}} $ 雷诺数 $ Re $ $ {\text{2}} \times {\text{1}}{{\text{0}}^3} \sim 3.5 \times {10^4} $ 燃料孔隙率 $ {\varepsilon _c} $ $ {\text{0}}{\text{.39}} $ 有效孔隙率 $ B $ $ 0.15 $ 施密特数 $Sc$ $ {\text{0}}{{.6 \sim 2}}{\text{.5}} $ 氧气浓度 ${C_{ {{\rm{O}}_2} } }$ 9.735% 
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表 2 FeO含量等级推理结果与实际值对比
Table 2 Comparison of the inference results with measured values of FeO content
序号 料层最高温度 (℃) 料层高度 (mm) 燃料比 (%) 全铁 (%) 推理结果 化验数据 1 1 278.55 749.129 4.187 61.874 正常 正常 (8.96) 2 1 127.32 736.383 4.496 60.899 正常 正常 (9.27) 3 1 158.76 761.918 4.492 61.854 正常 正常 (9.06) 4 1 211.36 718.536 4.331 61.715 正常 正常 (9.47) 5 1 274.22 717.253 4.162 60.706 偏小 偏小 (7.37) $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ 558 1 160.56 649.579 4.561 63.755 正常 正常 (9.47) 559 1 176.76 650.864 4.305 60.802 偏大 偏大 (10.60) 600 1 308.99 710.711 4.286 61.067 正常 正常 (8.16)
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表 3 各过程参数的灰色关联度
Table 3 The grey relational degree of process parameters
序号 变量名称 关联度 序号 变量名称 关联度 1 风箱废气温度 0.803 11 空支流量 0.559 2 烧结机机速 0.798 12 CMgO 0.557 3 料层高度 0.778 13 透气性 0.549 4 烧结终点 0.737 14 返矿比 0.539 5 $ {\rm{C}}_{{\rm{SiO}}_2}$ 0.733 15 风箱负压 0.527 6 碱度 0.703 16 CCaO 0.459 7 燃料配比 0.669 17 烟道压力 0.337 8 环冷机速度 0.641 18 风机入口温度 0.327 9 点火温度 0.618 19 混一温度 0.271 10 煤支流量 0.574 20 圆辊速度 0.249
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表 4 模型输入变量
Table 4 The input variables of the model
序号 变量名称 序号 变量名称 1 风箱废气温度 9 空支流量 2 烧结机机速 10 CMgO 3 料层高度 11 透气性 4 烧结终点 12 返矿比 5 ${\rm{C}}_{{\rm{SiO}}_2} $ 13 风箱负压 6 碱度 14 燃料配比 7 环冷机速度 15 点火温度 8 煤支流量
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表 5 储备池规模对DK-AWESN性能的影响
Table 5 Influence of reservoir size on the performance of DK-AWESN
储备池规模 训练时间 (s) 测试 NRMSE 平均值 标准差 50 21.821 0.425 0.0332 100 21.832 0.371 0.0258 150 21.840 0.332 0.0254 200 21.841 0.301 0.0218 250 21.850 0.343 0.0262 300 21.864 0.399 0.0246 350 21.866 0.435 0.0321 400 21.891 0.482 0.0326
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表 6 各模型的预测性能指标比较
Table 6 Comparison of prediction performance indicators for different algorithms
性能指标 ESN DK-ESN AW-ESN DK-AWESN MAE 0.351 0.254 0.298 0.251 RMSE 0.420 0.316 0.345 0.301 HR (%) 70.00 83.33 78.33 86.67
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