LF精炼成分预报模型的开发

采用经验计算、统计回归以及基于自学习的参考炉次法,建立了LF精炼成分的预报模型,对精炼过程中及精炼终点的钢水成分进行预报。结果表明,采用经验计算结合统计回归的方法可以预报铌、钛、碳、锰等收得率稳定的合金元素含量,采用基于自学习的参考炉次法可以预报硅、铝等收得率不稳定的合金元素含量,该模型基本满足了现场生产的要求。     

LF炉精炼温度预报模型的发展应用

全面综述了LF炉精炼温度预报模型在国内外的发展应用状况。国内外所开发的LF炉钢水温度预报模型,主要采用机理模型、“黑箱模型”、“灰箱模型”三种方法。简单讨论了这三种模型的特性,并预测了LF炉精炼温度模型的发展趋势。     

LF钢包炉精炼终点钢水温度的预报模型

采用多元回归分析方法建立了宝钢一炼钢厂LF钢包炉精炼终点钢水温度的预报模型，应用该模型对LF精炼终点钢水温度进行预测，对预测结果进行了统计分析，结果表示该模型对LF钢包炉精炼终点温度的预测误差在＋10℃时的命中率达到95％。     

LF精炼终点钢水温度灰箱预报模型

LF精炼工序在炼钢过程起着调节温度的关键作用,准确预报LF精炼终点钢水温度对实际生产有重要意义.传统的LF精炼预报模型包括机理模型与黑箱模型.机理预报模型能够体现各工艺因素对终点钢水温度的影响,但由于LF精炼传热机理研究尚不完善,依靠机理模型预报终点钢水温度,难以达到预期效果;黑箱预报模型能够准确预报终点钢水温度,但不能反映精炼过程各工艺因素对钢水温度的影响,尤其当生产工艺条件发生改变时,黑箱模型在应用上会受到限制.本文以方大特钢LF精炼炉为研究对象,建立一种机理预报模型与黑箱预报模型(BP神经网络预报模型)相结合的LF精炼终点钢水温度灰箱预报模型.该模型既能反映各工艺因素对终点钢水温度的影响,又能准确预测终点钢水温度,其终点钢水温度预测误差在±5℃以内的命中率可以达到95%以上.      

LF炉双路底吹控制系统设计

LF炉是钢铁生产中炉外精炼的主要方法之一,是电弧炉的一种特殊形式。作为钢水精炼的重要环节,LF炉起到了钢水含量精调、钢水温度调节、改善钢水纯净度、造渣等作用。邯钢新区LF炉采用双路底吹的方式,通过钢包底部的透气砖底吹氩气来均匀钢水成分,底吹阀门站采用西门子200smart控制系统,通过OPC通讯完成底吹阀门站和精炼主系统的通讯,实现远程自动控制。     

基于PCA-BP神经网络的LF精炼终点温度预测

为提高LF精炼钢水终点温度控制水平，提出了基于主成分分析（PCA）和BP神经网络的联合方法预测LF钢包炉精炼钢水终点温度。基于冶金理论和实际生产实践，选取了42CrMo钢生产过程的10个对终点温度有显著影响的因素作为预测模型的指标体系，然后借助主成分分析法对样本数据进行处理，得到了7个主成分变量，累计方差贡献率为87.24%，消除了数据之间的关联性，以此为基础，建立了基于PCA-BP神经网络的LF炉终点温度预测模型，该模型预测误差在±25℃时，模型的命中率为98.71%，模型有较好的识别能力，能够达到LF炉生产过程预测终点温度的目的。     

LF炉外精炼工艺研究与优化

针对唐钢一钢轧厂150 t LF炉外精炼冶炼工艺进行系统性研究并进行优化,通过优化LF炉外精炼脱氧工艺、推行LF炉快速冶炼工艺、提升LF终点成分控制能力、改进创新钙处理工艺等一系列改进关键工艺技术措施的实施与集成,唐钢一钢轧厂LF炉外精炼工艺得到全面优化与改进,最大程度的发掘了LF炉外精炼的潜力,提高了钢水质量,为以后生产高品质钢打下扎实的基础。     

涟钢LF精炼温度变化研究

采用多元回归分析方法建立了涟钢210转炉厂LF钢包炉精炼终点钢水温度的变化模型,应用该模型对LF精炼终点钢水温度进行预测,对预测结果进行了统计分析,结果表明该模型对LF钢包炉精炼终点温度的预测误差较小,能对现场产生指导意义。     

西西伯利亚钢铁公司转炉厂350 t LF炉的应用

经350 t LF炉精炼,除有利于均匀钢水成分与温度、脱硫、去夹杂外,还可降低转炉出钢温度,提高转炉炉龄.LF精炼钢水有增氮现象,其中08Al钢板坯增氮量平均0.000 3%.控制LF精炼钢水后,可减少浸入式水口堵塞现象,有利于提高连浇炉数.     

氩站精炼技术的开发运用

玉钢无LF精炼设备,限制了新产品的研发。通过对氩站设备作适当的改进,研究并实施精炼处理工艺,在氩站实现温度微调、成分调整,夹杂物变性和去除等精炼功能,钢水洁净度接近LF炉精炼工艺水平,生产成本低于LF炉工艺。     

宣钢LF炉冶炼SAE10B06A钢的工艺控制

宣钢在生产SAE10B06A低碳低硅高铝钢的过程中,对钢水的可浇性和对LF炉进行了保铝处理,制定了LF炉钢水渣系的控制措施,成功地生产了第一单SAE10B06A钢,铸坯的化学成分和表面质量全部合格,为宣钢开发研究高铝钢提供了参考.     

Mass transfer behaviour of Mg in low carbon aluminium killed steel during LF refining

Inclusions containing Mg existed in low carbon aluminium killed steel even though Mg is not added during LF treatment. To investigate the mass transfer mechanism of Mg in low carbon aluminium killed steel, both industrial practice and kinetic calculations were carried out in the present work. The results from industrial practice showed that Mg concentration in molten steel and inclusions increased with refining time during ladle furnace treatment. The inclusion size tended to become smaller with the increase of Mg concentration in the inclusions. The erosion rate of refractory with different composition was tallied. A refractory-slag-metal-inclusion multiphase reaction model was developed to investigate mass transfer mechanism underlying the variation of Mg among the steel, the slag, inclusions and the refractory. The calculated results exhibited a good predictability of the content of Mg in the molten steel, slag and inclusions. The results showed that Mg dissolved into molten steel in two ways: the first is in the way of slag/steel reaction, the second is in the way of refractory erosion which is the main way.     

400系易切削不锈钢硫铁加入方式优化

邢钢一步法(脱磷站+60t AOD+60t LF)生产400系易切削不锈钢过程中,前期采用硫铁全部在AOD出钢时加入配[S],AOD出钢至上机浇铸过程中钢渣碱度始终处于低碱度范围(R=1.40～1.95),硫铁消耗较大,钢液氧含量偏高,随着冶炼炉数的增加,炉衬侵蚀严重,影响AOD炉龄和钢坯质量,且钢渣较长时间处于低碱度状态,极易造成钢中[C]含量的上升(尤其是430F、430FR低碳类钢种),很难实现多炉连浇。后期通过优化硫铁加入方式,在LF后期加硫铁,AOD炉渣碱度2.0～2.3,LF炉渣碱度1.6～2.0,缩短低碱度渣处理时间,降低[S]损耗和钢液氧含量及对炉衬侵蚀。使易切削不锈钢[S]的收得率由62%提高到75%,吨钢硫铁消耗下降2.12 kg,铸坯皮下气泡等缺陷得到控制。     

基于AdaBoost.RS算法的LF炉钢水温度预报分析

LF炉钢水温度的精准控制有利于缩短钢的冶炼时间,从而节约其生产成本。而获得准确的LF炉钢水温度预报是钢水温度控制的先决条件。通过分析LF炉冶炼过程对钢水温度的影响因素,提出一种适用于LF炉钢水温度预报同时具有增量学习功能的AdaBoost.RS集成建模算法。该算法引入松弛变量和遗忘因子2个参数,在提高预测精度的同时,可以克服大噪声数据带来的干扰,同时增量学习可以降低早期生产数据对模型的影响。以福建三钢有限责任公司100tLF炉为研究对象,采用5个测试函数验证算法的抗噪性能,分别用静态数据和动态数据对钢水出站的终点温度进行预报。实验结果表明,预测的绝对误差小于10℃的样本数量超过了样本总数的90%,算法精度较高,有利于实际生产应用。     

低碳低硅-铝镇静钢C4C-Q工艺优化实践

对 120t BOF-LF-RH-CC 流程生产的低碳低硅-铝镇静钢 C4C-Q(/％:0.02～0.06C,≤0.06Si,0.02～0.05Al)冶炼硅含量高,可浇性差的原因进行了分析.通过工艺优化,转炉采用出钢两次挡渣模式和出钢后脱碳工艺,降低出钢氧含量,减少后期脱氧过程中生成Al2O3夹杂;通过优化LF脱氧模式...     

End Temperature Prediction of Molten Steel in LF Based on CBR

In order to improve the temperature control level of molten steel in ladle furnace (LF), a case‐based reasoning (CBR) method has been proposed for predicting end temperature of molten steel in LF. To predict the temperature accurately and efficiently, this paper develops two‐step retrieval approach and the correlation based feature weighting (CFW) method for CBR. And, the study evaluates the prediction effect of CBR method by the experiment of comparison with back propagation neural network (BPNN) model and CBR model. Experimental results show that CBR model achieves better accuracy than BPNN model and the CBR method is effective to predict end temperature of molten steel in LF.     

SPHC钢的冶炼实践

介绍了迁安轧一钢铁集团炼钢厂生产低碳低硅铝镇静钢SPHC的生产实践。通过优化生产工艺,控制转炉出钢过程中下渣量,保护浇铸,LF炉精炼等措施,使钢水成分得到精确控制,钢中夹杂物大量减少,钢水的可浇性提高,铸坯表面及内部质量均达到了标准要求,满足了用户需求。     

150 t LF冷轧料用钢SPHD生产实践

唐钢150 t LF(ladle furnace))精炼炉通过对冷轧用钢成分、温度、可浇性的合理控制,精炼合格率由原来的70%提高到98%以上.     

Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF

 Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM (extreme learning machine) for predicting the molten steel temperature in LF. Secondly, the self adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.     

Mass Balance Modeling for Electric Arc Furnace and Ladle Furnace System in Steelmaking Facility in Turkey

  In the electric arc furnace (EAF) steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace (LF). This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 9593%. These efficiencies are calculated for all input elements as 845% for C, 3031% for Si, 4636% for Mn, 3064% for P, 4196% for S, and 6979% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.