Cleanliness of Ti-bearing Al-killed ultra-low-carbon steel during different heating processes

During the production of Ti-bearing Al-killed ultra-low-carbon (ULC) steel, two different heating processes were used when the converter tapping temperature or the molten steel temperature in the Ruhrstahl-Heraeus (RH) process was low:heating by Al addition during the RH decarburization process and final deoxidation at the end of the RH decarburization process (process-I), and increasing the oxygen content at the end of RH decarburization, heating and final deoxidation by one-time Al addition (process-Ⅱ). Temperature increases of 10℃ by different processes were studied; the results showed that the two heating processes could achieve the same heating effect. The T.[O] content in the slab and the refining process was better controlled by process-I than by process-Ⅱ. Statistical analysis of inclusions showed that the numbers of inclusions in the slab obtained by process-I were substantially less than those in the slab obtained by process-Ⅱ. For process-I, the Al₂O₃ inclusions produced by Al added to induce heating were substantially removed at the end of decarburization. The amounts of inclusions were substantially greater for process-Ⅱ than for process-I at different refining stages because of the higher dissolved oxygen concentration in process-Ⅱ. Industrial test results showed that process-I was more beneficial for improving the cleanliness of molten steel.     

基于案例推理的电弧炉终点预报

在电弧炉(EAF)冶炼生产过程中,出钢温度、碳含量和磷含量等终点参数直接关系到后续生产工艺,甚至影响产品质量.基于增量预报模型的思想,提出采用案例推理方法进行电弧炉炼钢的终点预报,该方法由于建立在已有的炼钢案例基础上,具有方法简单、不需要复杂数学推理的优点,并具有较强的鲁棒性.考虑到电弧炉炼钢影响因素较多、具有较强的非线性,采用支持向量机对案例推理的结果进行增量补偿修整.实践证明,经补偿后的案例推理方法预报精度较高,预报结果接近实际值,该方法是切实可行并有效的,可以用于电弧炉炼钢终点钢水温度、碳含量和磷含量的预报.     

一种基于改进CBR模型的LF钢水温度预测方法

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 by 5.33% 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.     

Coordinated control of carbon and oxygen for ultra-low-carbon interstitial-free steel in a smelting process

Low residual-free-oxygen before final de-oxidation was beneficial to improving the cleanness of ultra-low-carbon steel. For ul-tra-low-carbon steel production, the coordinated control of carbon and oxygen is a precondition for achieving low residual oxygen during the Ruhrstahl Heraeus (RH) decarburization process. In this work, we studied the coordinated control of carbon and oxygen for ultra-low-carbon steel during the basic oxygen furnace (BOF) endpoint and RH process using data statistics, multiple linear regressions, and thermodynamics computations. The results showed that the aluminum yield decreased linearly with increasing residual oxygen in liquid steel. When the mass ratio of free oxygen and carbon ([O]/[C]) in liquid steel before RH decarburization was maintained between 1.5 and 2.0 and the carbon range was from 0.030wt%to 0.040wt%, the residual oxygen after RH natural decarburization was low and easily controlled. To satisfy the re-quirement for RH decarburization, the carbon and free oxygen at the BOF endpoint should be controlled to be between 297 × 10?6 and 400 × 10?6 and between 574 × 10?6 and 775 × 10?6, respectively, with a temperature of 1695 to 1715°C and a furnace campaign of 1000 to 5000 heats.     

Development of an improved CBR model for predicting steel temperature in ladle furnace refining

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.     

基于钢包运行稳定性的炼钢厂生产计划优化

针对炼钢厂在不满负荷生产的情况下,缺少相应指导原则编排生产计划的问题,提出一种基于钢包运行稳定性的生产计划优化方法.首先,通过对炼钢厂钢包周转过程的分析,提出在线运行钢包个数的计算方法.然后,分别针对单台铸机和两台铸机顺序开浇的两种情况,分析生产计划对钢包运行状态的影响,建立计算模型.以某炼钢厂生产计划为例进行解析,并以钢包运行稳定性为评价参数,利用遗传算法对生产计划进行优化.结果表明,优化的生产计划可以减少钢包使用个数及其温度波动,减少烘烤煤气消耗量和降低转炉出钢温度,提高钢水温度的控制水平.     

电磁搅拌对钢坯凝固过程中热工参数的影响

采用自行设计的单侧冷却凝固实验装置进行了有无电磁搅拌作用下钢坯的凝固实验,并通过计算机数据采集系统对钢坯凝固过程中的各种热工参数进行了实时测试和分析·研究结果表明:施加电磁搅拌以后,钢坯凝固初期的铸型热流量明显增加,促进了铸型内钢液过热的耗散,使得钢坯内的温度分布趋于均匀,降低了凝固前沿的温度梯度·这不仅抑制了柱状晶的发展,同时易于在钢液内部同时形核,有利于等轴晶凝固组织的形成,使得钢坯的等轴晶比率由无电磁搅拌作用下的47%提高到76%·这也说明了电磁搅拌促进钢液中的过热耗散是提高铸坯凝固组织中等轴晶比率的重要原因之一·     

基于粗糙集的转炉炼钢知识发现模型

针对转炉炼钢知识发现的特点,采用粗糙集理论进行分析,应用数据清洗、标准化及离散等方式对转炉炼钢生产数据进行预处理,以炼钢生产的主要影响因素作为知识发现的条件属性,以转炉冶炼终点控制目标作为知识发现的决策属性,建立了基于粗糙集方法的转炉炼钢知识发现模型,实现转炉炼钢生产知识的自动发现、获取和规则提取。以转炉冶炼终点钢水温度的变化规律做为知识发现的决策属性,采用210 t转炉炼钢实际生产数据进行模型的应用测试,结果表明提取出的铁水硅含量、铁矿石质量、氧气消耗量等影响因素对转炉冶炼钢水终点温度存在重要影响,且模型提取出的转炉炼钢终点钢水温度知识规则与现行转炉炼钢现场的变化规律一致,证明基于粗糙集方法的转炉炼钢知识发现模型的有效性。     

冷却速率和奥氏体化温度对40 Cr钢球化退火的影响

为探究奥氏体化温度和冷却速率对40Cr钢球化过程的影响,采用双相区球化退火研究了热轧态40Cr钢的球化退火行为和力学性能。奥氏体化温度从760℃提高到800℃,冷却速率从10℃·h-1上升到30℃·h-1,组织硬度随冷却速度呈V形变化,碳化物球化率随冷却速度变化正好与前者相反。奥氏体化温度为760℃,冷却速率为20℃·h-1所得到的球化组织球化率高,且碳化物细小,具有良好的冷成形性能,可大幅度缩短球化退火时间,显著提高生产效率。提出了球化退火过程中离异共析转变机制,控制好球化过程中奥氏体化温度、冷却速率及保温时间有利于离异共析转变的发生。     

钢液精炼和连铸过程中物理去除夹杂物现状及展望

夹杂物去除是钢液精炼的重要任务之一。目前与去除夹杂物相关的工艺方法主要有:钢包-电磁搅拌和钢包底吹氩;RH及RH侧底复吹;中间包-控流装置、气幕挡墙和通道电磁感应加热;结晶器电磁搅拌与电磁制动和水口吹氩。本文总结了钢水精炼中各个反应器去除夹杂物的方法和机理,并分析了影响夹杂物去除效率的主要因素,为钢水二次精炼的夹杂物去除工艺优化提供理论依据和参考。     

旋转磁场作用下真空精炼装置内脱碳过程的模拟研究

为了了解旋转磁场下真空精炼装置内的脱碳反应,对RH装置内钢液流场与脱碳过程进行了耦合计算;采用修正的均相流模型计算流场,避免了先前的一些不具推广性的方法,如简化计算区域或者预先规定含气率分布等.分别对有、无旋转磁场作用时的RH系统进行了模拟计算.模拟结果表明:流场和脱碳过程与现有文献中实验结果相符合;当旋转磁场作用后,磁感应强度为0.04 T时,初期脱碳速度比无旋流作用时增加了约15%,这是由于旋流增加了系统的搅拌能,提高了脱碳反应速率.脱碳反应进行到最后阶段,不同磁感应强度下的碳含量趋于同一稳定值.     

RH处理超低碳铝镇静钢的总氧预测模型及应用

根据300t钢包RH真空处理超低碳铝镇静钢的实验数据,建立了RH处理过程钢中总氧含量的预测模型,得到了钢中氧含量的预测公式.模型综合考虑了处理时间、真空室吹氩流量、钢水环流量、浸渍管直径和钢包渣中(FeO+MnO)含量等因素对总氧含量的影响,并对改进RH处理工艺进行了讨论.模型分析表明,促进夹杂物上浮的手段有增大吹氩流量、增加浸渍管直径,但都有一个合适的范围.     

基于案例推理预测精炼开始钢水温度

针对BP神经网络训练时间长的问题,采用基于案例推理的方法预测精炼开始钢水温度.首先,应用层次分析法确定影响精炼开始钢水温度的各个因素的权值,并使用灰色关联度来计算案例的相似度,克服了传统相似度计算方法在案例信息不完整的情况下无法获取准确结果的缺点.然后,提出一个包含类选、粗选、精选和择优的四步检索方法,大大缩短了检索时间.最后,实验比较了人工神经网络和基于案例推理两种方法,结果表明基于案例推理比人工神经网络具有更高的命中率.     

基于机器视觉的薄带铸轧熔池液位识别方法

为解决薄带铸轧熔池液位难检测的问题,研发机器视觉识别熔池钢水液位的方法.利用线阵CCD下熔池钢水与铸轧辊灰度值有明显差别的图像特征,建立熔池液位的机器视觉检测系统.利用所选线阵相机采集图像速度快的特性,提出基于时间建立熔池液位图像原型的方法,建立了以滤波处理、二值化处理和边界识别为核心的熔池液位视觉识别模型.使用建立的系统和模型在实验室铸轧机和感应加热炉上进行了测试实验,结果表明:所建的熔池液位视觉检测系统和识别方法具备可行性,具有较高的检测精度和较快的检测速度,能够满足工业生产需要.     

基于遗传算法的电炉载能值综合优化

引入载能体的方法,统筹电炉炉料结构、电炉供氧、配碳和供电等多种因素,建立了电炉载能值综合优化模型.模型约束复杂,采用线性规划难以求解,本文采用遗传算法进行求解.对BH1H、BHDDQ和SUS304钢种进行能值计算.结果表明:在保证电炉出钢钢水化学成分、温度和渣的碱度等指标符合要求的前提下,每炉钢水能值分别降低了22.8%、21.4%和23.6%.     

基于微分进化的钢水温度预报混合模型

针对LF冶炼过程钢水温度预报问题对机理模型进行了分析.将基于微分进化的BP网络引入混合模型,以确定模型中难以准确获得的参数,建立了基于微分进化的钢水温度混合预报模型.模型通过适当时刻变异因子的随机选取和重复进行种群个体初始化的方法,对微分进化算法进行了改进,有效解决了BP网络训练的困难,避免了算法早熟.实验结果表明,混合模型具有较好的预测结果,基本满足了实际生产的需要.     

真空脱气过程中钢水温降的理论分析

为了研究真空脱气过程中影响钢水温降的主要因素,从而找到对应的措施控制钢水温降,对VD过程的传热模式和传热机理进行了分析,根据国内某厂的实际情况,结合数值模拟方法分别得到了烘烤过程和LF加热过程中钢包温度场的变化,以LF结束时的温度值作为初始值对VD过程的温度场进行了模拟。根据模拟结果分别计算了渣面和钢水裸露面的热辐射、钢包衬蓄热、包壁和包底的传导散热和氩气吸热等因素造成的钢水散热量,对其影响程度进行了评估。通过计算发现,渣面、钢水裸露面的热辐射和包衬蓄热是造成VD过程钢水温降的主要因素,分别根据各个因素的影响特点提出了控制钢水温降的具体措施。     

领域CBR本体及其在范例表示和范例修改中应用研究

范例表示和范例修改是基于实例推理领域中的两个研究热点.针对此,提出一种基于领域CBR本体的范例表示和范例修改方法,该方法引入本体表示范例,添加范例特征意图项指导范例修正,提高了范例库的重用性和范例修正的成功率.首先,以经典本体概念和CBR理论为基础,提炼出领域CBR本体的定义和主要组成部分.其次,与传统范例表示方法对比,给出了领域CBR本体范例表示的优势,并对领域CBR本体中的关键类——范例特征意图类及其作用进行了重点介绍.而后,对领域CBR本体的构建过程和基于领域CBR本体的范例修改过程进行了详细阐述.最后,应用OWL语言对斗轮CBR本体进行了描述,并通过斗轮CBR本体在机械设计重用中的应用分析验证了此方法的有效性.     

RH精炼过程循环流量及夹杂去除的水模型研究

以某钢厂120tRH真空精炼炉为原型建立水模型,研究不同工艺参数对RH精炼过程钢液循环流量和夹杂去除率的影响。结果表明,钢液循环流量随着驱动气体流量、浸入深度、真空度、气孔数的增大而增大,随处理量的增大而减小,实验室循环流量最佳工艺参数为:气体流量2.8m3/h,浸入深度150mm,真空度3614Pa,气孔数12个;夹杂去除率随驱动气体流量、浸入深度、真空度和气孔数的变化均不是单调的,而是存在一个最佳值使夹杂去除率最高,实验室去除夹杂的合理工艺条件为:气体流量2.2m3/h,浸入深度125mm,真空度为3500Pa,气孔数8个。     

复吹转炉锰矿直接合金化热力学模型研究

基于某钢厂铁水、废钢和造渣料等的成分得出转炉炼钢的渣量计算模型,根据热力学原理得出锰的收得率和终点锰含量的预报模型。对模型研究结果表明,提高终点出钢温度、渣碱度、锰矿品位和铁水Mn含量,降低终渣含量及渣中FeO含量,可显著提高Mn的收得率和终点Mn含量。