基于数据驱动的高炉煤气复合预测模型
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摘要
对钢铁企业高炉煤气系统科学准确的预测,可以为煤气的合理调度提供依据,对企业提高能源利用效率、减少煤气放散和环境污染有着非常重要的意义。针对钢铁企业高炉煤气系统设备工况复杂、煤气量波动频繁、难以准确预测的问题,依据小波分析方法、BP神经网络、最小二乘支持向量机的性质建立了基于数据驱动的高炉煤气的复合预测模型。该模型综合考虑高炉煤气系统生产计划和检修计划,对高炉煤气系统的产耗用户在不同工况下分别建立训练数据集,利用多组模型参数预测高炉煤气产生量、消耗量和缓冲量。利用某大型钢铁企业实际数据进行测试,该模型能够结合设备的实际生产工况变化,实现煤气的准确预测。结果表明,该模型平均绝对百分比误差小于4.95%,对变工况煤气系统有较好的预测效果。
The scientific and accurate prediction of the blast furnace gas system in iron and steel company can provide a basis for the reasonable dispatching of the coal gas,which was very important for the enterprise to improve the energy efficiency,reduced the gas discharge and the environmental pollution.The equipment conditions of blast furnace gas system in iron and steel works were complex,and the fluctuation of gas quantity was frequent,so it was difficult to predict accurately.In terms of these issues,a data-driven compound prediction model of blast furnace gas was built on the basis of wavelet analysis,BP neural network and least squares support vector machines.The model taked the production plan and maintenance schedule of the blast furnace gas system into account,sets up training data sets for the production and consumption users of blast furnace gas system under different working conditions and uses multi group model parameters to predict the production,consumption and buffering capacity of blast furnace gas.Tested by the actual data of a large iron and steel enterprise,the model combined with the actual production equipment condition and the accurate prediction of gas can accurately predict the gas.The results showed that the average absolute percentage error of the model was less than 4.95%,which had a good prediction effect on the variable working gas system.
The scientific and accurate prediction of the blast furnace gas system in iron and steel company can provide a basis for the reasonable dispatching of the coal gas,which was very important for the enterprise to improve the energy efficiency,reduced the gas discharge and the environmental pollution.The equipment conditions of blast furnace gas system in iron and steel works were complex,and the fluctuation of gas quantity was frequent,so it was difficult to predict accurately.In terms of these issues,a data-driven compound prediction model of blast furnace gas was built on the basis of wavelet analysis,BP neural network and least squares support vector machines.The model taked the production plan and maintenance schedule of the blast furnace gas system into account,sets up training data sets for the production and consumption users of blast furnace gas system under different working conditions and uses multi group model parameters to predict the production,consumption and buffering capacity of blast furnace gas.Tested by the actual data of a large iron and steel enterprise,the model combined with the actual production equipment condition and the accurate prediction of gas can accurately predict the gas.The results showed that the average absolute percentage error of the model was less than 4.95%,which had a good prediction effect on the variable working gas system.
引文
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[2]郝聚显,赵贤聪,韩玉召,等.热风炉煤气消耗量中期预测模型[J].中国冶金,2018,28(2):17.
[3]孙敏敏,宁晓钧,张建良,等.炼铁系统节能减排技术的现状和发展[J].中国冶金,2018,28(3):1.
[4]张琦,张薇,王玉洁,等.中国钢铁工业节能减排潜力及能效提升途径[J].钢铁,2019,54(2):7.
[5]张薇,王玉洁,刘帅,等.基于CSC方法的钢铁行业节能减排技术潜力分析[J].中国冶金,2019,29(1):70.
[6]卞和营,李宏伟.基于神经网络的煤气回收系统预测控制[J].工矿自动化,2009,35(8):69.
[7]邱东,陈爽,仝彩霞,等.钢铁企业高炉煤气平衡与综合优化[J].计算机技术与发展,2009,19(3):196.
[8]王军,袁大明.基于神经网络的高炉煤气消耗预测模型研究[J].科技与企业,2013(9):108.
[9]ZHANG X P,ZHAO J,WANG W,et al.An optimal method for prediction and adjustment on byproduct gas holder in steel industry[J].Expert Systems with Applications,2010,38(4):4588.
[10]HAN Z Y,LIU Y,ZHAO J,et al.Real time prediction for converter gas tank levels based on multi-output least square support vector regressor[J].Control Engineering Practice,2012,20(12):1400.
[11]李红娟,王建军,王华,等.基于PNN-HP(2)-ENN模型的钢铁企业转炉煤气柜柜位预测[J].信息与控制,2014,43(2):229.
[12]王延明.Grey能源预测模型的研究及应用[J].冶金动力,2015(2):57.
[13]Gomes F S V,Salles J L F,Wasem L A.A new prediction model for liquid level in blast furnaces based on time series analysis[C]//Proceedings of International Conference on Control and Automation.Santiago:IEEE,2011:772.
[14]LI H J,WANG J J,WANG H,et al.Application of Elman neural network with HP filter in the trend supply of self-provided power plant forecasting in the iron and steel industry[J].Advanced Materials Research,2013,712/713/714/715:3211.
[15]张琦,李鸿亮,赵晓宇,等.高炉煤气产生量与消耗量动态预测模型及应用[J].哈尔滨工业大学学报,2016,48(1):101.