In the prediction of the end-point molten steel temperature of the ladle furnace, the influence of some factors is nonlinear. The prediction accuracy will be affected by directly inputting these nonlinear factors into...In the prediction of the end-point molten steel temperature of the ladle furnace, the influence of some factors is nonlinear. The prediction accuracy will be affected by directly inputting these nonlinear factors into the data-driven model. To solve this problem, an improved case-based reasoning model based on heat transfer calculation(CBR-HTC) was established through the nonlinear processing of these factors with software Ansys. The results showed that the CBR-HTC model improves the prediction accuracy of end-point molten steel temperature by5.33% and 7.00% compared with the original CBR model and 6.66% and 5.33% compared with the back propagation neural network(BPNN)model in the ranges of [-3, 3] and [-7, 7], respectively. It was found that the mean absolute error(MAE) and root-mean-square error(RMSE)values of the CBR-HTC model are also lower. It was verified that the prediction accuracy of the data-driven model can be improved by combining the mechanism model with the data-driven model.展开更多
With the increasing demand for energy conservation and emission reduction,more attentions have been paid to the intelligentization,greenization and low carbonization during the transformation and upgrading of steelmak...With the increasing demand for energy conservation and emission reduction,more attentions have been paid to the intelligentization,greenization and low carbonization during the transformation and upgrading of steelmaking plants.Ladle furnace(LF)refining is one of the key procedures in steelmaking process and has been widely used in steelmaking plants for its high equipment matching degree,low equipment investment and outstanding refining performance.According to the main tasks of LF refining process,the modeling methods of temperature prediction model,slag-making model,alloying model,argon blowing model and model of inclusions behavior were systematically reviewed,and the advantages and disadvantages of each modeling method were summarized.In addition,the technical framework for the future has also been proposed based on existing works,including classification of raw materials,graphic representation of knowledge,introduction,upgradation and management of device/equipment,customization of steelmaking,modeling of refining process,synergy of models,intelligentization of decision-making,automation of control,and digitization of processes and operations,aiming to provide a reference for the modeling and intelligent development of LF refining process.展开更多
针对LF精炼炉钢液温度控制过度依赖人工经验的问题,马钢长材事业部以120 t LF精炼炉为研究对象,基于能量平衡原理,计算分析LF精炼过程中输入电能、合金化、炉渣热效应、钢包内衬散热、渣面辐射、吹氩搅拌和烟气热损失等热量对钢液温度...针对LF精炼炉钢液温度控制过度依赖人工经验的问题,马钢长材事业部以120 t LF精炼炉为研究对象,基于能量平衡原理,计算分析LF精炼过程中输入电能、合金化、炉渣热效应、钢包内衬散热、渣面辐射、吹氩搅拌和烟气热损失等热量对钢液温度的影响,建立LF精炼钢液温度的预测模型。经过跟踪实际生产试验、测温校正并优化模型,使模型取得了良好的应用效果。模型预测温度与实际测量值偏差绝对值≤5℃的比例为97.73%,偏差绝对值≤6℃的比例为100%。展开更多
以钢厂120 t LF精炼过程钢水、炉渣和合金为研究体系,以能量平衡机理模型为基础,建立精炼钢水温度预报模型。根据钢种、钢水质量和温度、目标出钢温度及处理时间、渣料和合金加入量及各种热损失所需投入的电能,确定精炼过程合理的供电...以钢厂120 t LF精炼过程钢水、炉渣和合金为研究体系,以能量平衡机理模型为基础,建立精炼钢水温度预报模型。根据钢种、钢水质量和温度、目标出钢温度及处理时间、渣料和合金加入量及各种热损失所需投入的电能,确定精炼过程合理的供电曲线。并根据现场供电和工艺参数,预报钢水温度。20炉50RH1钢(%:0.48～0.50C、0.22～0.30Si、0.60～0.70Mn)测试结果表明,模型预报与实测钢水温度误差为±5℃。展开更多
以LF精炼炉的智能测温需求为背景,运用高温红外图像技术和西门子PLC设计一套智能测温装置,并对装置控制系统进行软硬件设计开发。采用模块化设计方法,利用Visual Studio 2017开发环境下的MFC框架开发了基于视觉的智能测温软件系统,优化...以LF精炼炉的智能测温需求为背景,运用高温红外图像技术和西门子PLC设计一套智能测温装置,并对装置控制系统进行软硬件设计开发。采用模块化设计方法,利用Visual Studio 2017开发环境下的MFC框架开发了基于视觉的智能测温软件系统,优化了精炼过程中的测温工序,同时系统的远程控制与图像可视化大大提高了作业效率,降低了测温成本。测试结果表明,该系统能够满足LF精炼炉智能测温的功能需求,系统运行稳定,人机交互效果良好,可视化程度高,具有很高的实用性,对钢铁冶金行业在LF精炼炉测温环节的自动化和智能化提供了参考。展开更多
从LF炉精炼脱硫热力学和动力学原理出发分析了影响脱硫的相关因素。对200 t LF炉超低硫钢生产实例的分析表明:当钢液初始硫含量(质量分数,下同)低于0.012%时,精炼终点硫含量可脱除至0.002%以下;1550~1600℃的钢液温度为LF炉精炼超低硫...从LF炉精炼脱硫热力学和动力学原理出发分析了影响脱硫的相关因素。对200 t LF炉超低硫钢生产实例的分析表明:当钢液初始硫含量(质量分数,下同)低于0.012%时,精炼终点硫含量可脱除至0.002%以下;1550~1600℃的钢液温度为LF炉精炼超低硫钢经济合理的冶炼温度;LF炉进站钢水酸溶铝含量为0.02%~0.05%,满足LF炉深脱硫要求;当精炼渣碱度为4.0左右时,LF炉脱硫效率最高;要获得大于80%的脱硫率,渣中(FeO)+(MnO)的含量需尽量控制在0.8%以下;从冶炼成本和钢水质量的角度考虑,最经济合理的LF炉精炼渣量为15~20 kg/t;LF炉底吹氩气量控制在0.10~0.18 Nm^(3)/t时可获得较好的脱硫效果。展开更多
基金financially supported by the National Natural Science Foundation of China (No.51674030)the Fundamental Research Funds for the Central Universities (Nos.FRF-TP-18-097A1 and FRF-BD-19-022A)。
文摘In the prediction of the end-point molten steel temperature of the ladle furnace, the influence of some factors is nonlinear. The prediction accuracy will be affected by directly inputting these nonlinear factors into the data-driven model. To solve this problem, an improved case-based reasoning model based on heat transfer calculation(CBR-HTC) was established through the nonlinear processing of these factors with software Ansys. The results showed that the CBR-HTC model improves the prediction accuracy of end-point molten steel temperature by5.33% and 7.00% compared with the original CBR model and 6.66% and 5.33% compared with the back propagation neural network(BPNN)model in the ranges of [-3, 3] and [-7, 7], respectively. It was found that the mean absolute error(MAE) and root-mean-square error(RMSE)values of the CBR-HTC model are also lower. It was verified that the prediction accuracy of the data-driven model can be improved by combining the mechanism model with the data-driven model.
基金funded by the National Natural Science Foundation of China(Grant Nos.50874014,51974023,52374321)the Program for New Century Excellent Talents in University(Grant No.NCET 07-0067)+1 种基金the Fundamental Research Funds for Central Universities(Grant No.FRF-BR-17-029A)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China(Grant Nos.41620007 and 41621005).
文摘With the increasing demand for energy conservation and emission reduction,more attentions have been paid to the intelligentization,greenization and low carbonization during the transformation and upgrading of steelmaking plants.Ladle furnace(LF)refining is one of the key procedures in steelmaking process and has been widely used in steelmaking plants for its high equipment matching degree,low equipment investment and outstanding refining performance.According to the main tasks of LF refining process,the modeling methods of temperature prediction model,slag-making model,alloying model,argon blowing model and model of inclusions behavior were systematically reviewed,and the advantages and disadvantages of each modeling method were summarized.In addition,the technical framework for the future has also been proposed based on existing works,including classification of raw materials,graphic representation of knowledge,introduction,upgradation and management of device/equipment,customization of steelmaking,modeling of refining process,synergy of models,intelligentization of decision-making,automation of control,and digitization of processes and operations,aiming to provide a reference for the modeling and intelligent development of LF refining process.
文摘针对LF精炼炉钢液温度控制过度依赖人工经验的问题,马钢长材事业部以120 t LF精炼炉为研究对象,基于能量平衡原理,计算分析LF精炼过程中输入电能、合金化、炉渣热效应、钢包内衬散热、渣面辐射、吹氩搅拌和烟气热损失等热量对钢液温度的影响,建立LF精炼钢液温度的预测模型。经过跟踪实际生产试验、测温校正并优化模型,使模型取得了良好的应用效果。模型预测温度与实际测量值偏差绝对值≤5℃的比例为97.73%,偏差绝对值≤6℃的比例为100%。
文摘以钢厂120 t LF精炼过程钢水、炉渣和合金为研究体系,以能量平衡机理模型为基础,建立精炼钢水温度预报模型。根据钢种、钢水质量和温度、目标出钢温度及处理时间、渣料和合金加入量及各种热损失所需投入的电能,确定精炼过程合理的供电曲线。并根据现场供电和工艺参数,预报钢水温度。20炉50RH1钢(%:0.48～0.50C、0.22～0.30Si、0.60～0.70Mn)测试结果表明,模型预报与实测钢水温度误差为±5℃。
文摘以LF精炼炉的智能测温需求为背景,运用高温红外图像技术和西门子PLC设计一套智能测温装置,并对装置控制系统进行软硬件设计开发。采用模块化设计方法,利用Visual Studio 2017开发环境下的MFC框架开发了基于视觉的智能测温软件系统,优化了精炼过程中的测温工序,同时系统的远程控制与图像可视化大大提高了作业效率,降低了测温成本。测试结果表明,该系统能够满足LF精炼炉智能测温的功能需求,系统运行稳定,人机交互效果良好,可视化程度高,具有很高的实用性,对钢铁冶金行业在LF精炼炉测温环节的自动化和智能化提供了参考。
文摘从LF炉精炼脱硫热力学和动力学原理出发分析了影响脱硫的相关因素。对200 t LF炉超低硫钢生产实例的分析表明:当钢液初始硫含量(质量分数,下同)低于0.012%时,精炼终点硫含量可脱除至0.002%以下;1550~1600℃的钢液温度为LF炉精炼超低硫钢经济合理的冶炼温度;LF炉进站钢水酸溶铝含量为0.02%~0.05%,满足LF炉深脱硫要求;当精炼渣碱度为4.0左右时,LF炉脱硫效率最高;要获得大于80%的脱硫率,渣中(FeO)+(MnO)的含量需尽量控制在0.8%以下;从冶炼成本和钢水质量的角度考虑,最经济合理的LF炉精炼渣量为15~20 kg/t;LF炉底吹氩气量控制在0.10~0.18 Nm^(3)/t时可获得较好的脱硫效果。
