毕业设计外文翻译

本科毕业设计外文翻译 外外文文译译文文题题目目 斯太斯太尔尔摩摩线线冷却冷却监监控系控系统统的开的开发发与与应应用用 学学 院院 信息科学与工程学院 专专 业业 自动化 学学 号号 200809154012 学生姓名学生姓名 彭红 指导教师指导教师 梁开 日日 期期 二 一二年六月 斯太尔摩线冷却监控系统的开发与应用 摘要 斯太尔摩控制冷却系统已经成功应用在斯太尔摩生产线 它通过本地网络将 材料流体管理系统与 PLC 自动控制系统联系起来 在生产过程中以及预测最终属性 这种在线模型采用有限元时域微分法去计算温度的变化和相的转变 因为不同钢材的 连续冷却温度线在这个模型中是耦合的 所以它可以预测不同钢材的热变化和相转变 也可以为新钢材的生产和优化提供直接的指导 这种在线冷却系统已经被安装在三个 斯太尔摩生产线 并取得好的效果 关键词 冷却控制模型 斯太尔摩 线材生产线 质量预估 1 介绍介绍 随着钢铁行业竞争的加剧 如何生产稳定质量新形钢材成为钢铁产业的重点 在冷 却过程中 斯太尔摩生产线中也需要更可靠预测控制技术 由于斯太尔摩生产线高效的生产率以及以及产品良好的机械性能 斯太尔摩成为最流 行的控制冷却系统 如图 1 斯太尔摩冷却控制系统 1000 度的线材快速通过几个冷 水槽 到达吐丝机 以重叠的方式存储在存放容器中 它的冷却速率靠打开一下一系 列的风扇来控制 钢材最终的机械属性主要取决于在相转换前的钢材化学组成和冷却速度 由于在生 产过程中无法直接观察冷却速率和相转变 那就非常需要去发展一个在线模型去预测 最终的机械属性和相转变 尽管有过类似的研究报告 但是为斯太尔摩生产在线预测 的模型尚没有人研究 采用有限元时域分析方法 这种在线控制冷却模型已经安装在 实际生产过程中了 这篇论文介绍它的基本原理和控制方法 这很有利于生产产品的 稳定性 目前 这种在线模型已经应用在三个斯太尔摩生产线 并取得满意效果 2 数学模型 数学模型 2 1 热力学模型热力学模型 在斯太尔摩生产线中 由于无线长钢条高速移动 轴向热传导可以忽略 这种系统 可以根据以下假设公式化 解决一维热传导 1 轴对称 2 横截面处处相同 3 相同的 初始温度 这些和现实非常接近 解决线材热流体的基本方程如下 注意 g T 是线材由于热导性和相转变而引起的体积变化率 是材料密度 cp 热容量 k 导热系数 为了减少计算中丢失材料信息 实际的实验数据 k 和 cp 可以在系统接口中直接输 入 与传统的回归分析方法 我们用 FDTD crank nicolson 方法解上面方程 因为它的快速性和无条件稳定性 这 可以满足在线实时系统的需要 为了保持系统速度和准确性的平衡 通过反复实验 我们选择 20 个数据 通常 大量的数据能够保证系统的准确性 但是会降低系统的速 度 这在在线实时系统中是不实际的 因为实际的生产过程中 一个线材通过吐丝机 的时间不会超过 2 秒 我们采用下面的边界条件 在中心线 在线材表面 这儿初始条件是 这里 t 是时间 单位秒 r0是线材半径 h 是热转换常数 t0是线材表面温度 ta是周 围空气和水的温度 当系统运行时 tin是来自高温计测量的温度 这个系统将斯太尔摩生产线划分为几个部分 每个部分有它的热转换常数值 h 它可以 自适应来自高温计的实际值 一个常量 h 对应每一个风扇控制台 它可以通过以下方 程自适应系统 这里 hold 是原始的热转换常数 tc 是系统预测的温度 tm 是高温计测量的温度 tair 是空气温度 这个计算系统通过 FDTD 系统和 FEF 系统在同样条件下的比较 并得到相同的结果 作为一个实时在线系统 它需要通过本地网络将材料流和 PLC 自动控制系统联系起来 这个系统需要输入两组数据 一组是来自高温计的实际温度 另外一组是线材的基本 信息 八个高温计已经安装在生产线去提供实际温度数据便于系统去计算比较 这些 温度首先被生产线高温计测量 直接传送给特定的 PLC 然后保存在数据库 最后系统 可以以没每 300 毫秒的速度从数据库中读出数据 另外一组数据例如线材组成成分等 也是系统所需要的 当钢板一旦从火炉中出来时 这些数据被传送给特定的数据库 2 2 相转换系统相转换系统 如何为相转换过程建模是至关重要的 因为在相转换时不同的冷却速度决定了钢材 最终的微观结构 这个系统通过联系热传导分析和相转换以及微观结构变化解决了这 个问题 并且都符合现在的微观工程学 相转换过程可以通过下面的阿夫拉米方程描述 这里 x 是分式变换式 b t n t 是随着温度 成分而变化的参数 用户可以依据 他们的实验数据直接在系统中输入这些参数 一个需要决定的重要参数是奥氏体但到珠光体转换时的起始温度 这个系统可以提供 用户接口去输入特定钢材的实验数据 然后系统可以通过下面方程计算温度 8 这里 a 和 m 可以通过回归得到 ta1 是 TTC 实验的保持温度 可以直接在系统中输 入 在连续冷却台上 之前的相转换次数可以被转换与虚拟时间保持一致 7 在第 j 步 转换次数可以用以下方程描述 在这段时间 转换次数可以描述为 相变热 g t 可以按照下面方程计算 这里 h t 体积变化率 对于高碳刚 它可以按照如下公式计算 2 3 属性预测系统属性预测系统 随着极限抗拉强度系统的发展 使预测线材的微观结构成为可能 这种预测的微观 结构可以和机构 属性关系系统结合去计算机械属性 有很多因素决定钢材的强度 例 如钢材的含碳量 按照 mclvor 和他的同事的研究 有两个最主要的因素影响了钢材的 极限抗拉强度 一个是相变前的冷却速度 另一个是钢材化学组成 这些可以通过下 面的方程描述 这涉及到 mclvor 和他同事的研究成果 cr 在 700 度时的冷却速度 这简单的代表 了相变前的冷去速度 因为对于高含量碳的钢材 相变总发生在刚好 700 度以后 在 CSSC 系统中 cr 按照钢材从奥氏体到珠光体转换的起始温度到 700 度的范围修改 这 是由上述方程决定的 通过以上 我们可以看到 在相变前 加快冷却速度可以提高钢材极限抗拉强度 尽管其它一些元素也能提高极限抗拉强度 但是会降低钢材的延展性 由于 cr 可以通 过热力学模型直接计算获得 化学成分也可以很容易获得 因此我们在生产过程可以 预测钢材的机械性能 抗拉屈服强度可以根据以下关系计算 YTS A UTS 这儿 A 是常量 0 7 3 结果讨论结果讨论 3 1 系统稳定性系统稳定性 准确性和稳定性系统的两个基本要求 我们期望系统能够连续不断的运行 同时 输出结果应当正确便于指导生产 第一个问题是如何通过其它条件如高温计来直接获 得所需参数 经过反复实验 我们找到了合适的解决方法正如图 5 所描述 目前 三条生产线已经采用了这种系统去传送数据给 SCCS 并且没有出现任何问 题 SCCS 可以以 300 毫秒为周期正常运行 在每一个周期中 SCCS 系统为预测了钢材 各处的温度 因此任何时间点的温度是知道的并且可以被控制 所有的数据保存在数 据库便于以后核查 3 2 热力学模型的准确性热力学模型的准确性 经过三年的努力工作和反复实验 这种在线系统已经安装在江苏省宝钢的斯太尔摩 生产线上 经过几次版本的修改 这种系统可以正常的运行了 八个线上高温计也已 经安装了去监测实际温度 一个高温计被用来测量起始温度 另一个安装与吐丝机 用来测量水箱对温度的影响 其它六个被安装在风扇冷却台 用来检测温度的变化并 和系统预测值进行比较 在线系统会计算每点处的温度值 然后自动按照真实值和系 统预测值的比较进行改变热传导常数 所有这些都是在 300 毫秒的周期中完成的 这 可以满足在线实时系统的处理能力要求 此系统能很好的学习热传导常数的变化规律 这就可以正确预测相变和机械属性了 图 6 比较了实际测量值和系统预测值的比较 图 6 表明 实际值和预测值的偏差不超过 20 度 相转换的效果在温度变化曲线上 看的很清楚 它在相变时引起了略微的上升 然后就是逐渐的下降 这个系统能够预 测相转变的时机 这可以给技术人员提供指导去控制风扇的开度 通常 我们期望相 变在短时间里以同样的温度进行 这保证了钢材微观结构的一致性 就像索氏体化钢 丝 3 3 机械属性机械属性 机械属性的预测是钢铁公司的重要问题 如果系统足够的准确 公司会减少样本的 核查数量 这样可以节约大量时间和金钱 在宝钢 SWRH82B 被选择去验证系统的准确 性 首先 例子 11 被选择在 sccs 系统中去预测最终的抗拉强度 在检查 400 个样本 后 我们发现系统预测值是比实际值略高的 如图 7 数据分析表明 强度平均值在 30Mpa 与标准值相差 37Mpa 所有的样本显示出类似的 趋势 这表明系统的准确性还可以进一步提高 为了进一步提高系统的准确性 我们采用多元回归的方法去修改化学成分 冷却速度 等方面的一些系数 新的方程如下 通过比较例子 11 和下面的 13 我们可以发现 二者几乎相同 唯一的不同是各种 参数的系数 这些数据我们可以从实际生产获得 根据 13 系统的稳定性已经提高 统计分析表明 平均值差都是 0 39Mpa 标准差 16 8Mpa 我们可以从图 7 发现基本一 致 这提供了系统的可靠性的保障 至于其它组成不同的高碳钢 实验与 SWRH82B 类似 首先 例子 11 被用来预测 UTS 并与真实值比较 然后按照多元回归的结果修改它的相对系数 3 4 生产中在线系统的应用生产中在线系统的应用 需要指出的是 由于各种因素的影响 在大量生产中可能导致 UTS 差异 合理的在 线预测能够提醒工人把注意力集中在一些关键因素上 例如每个点的温度和冷却速度 目前 在线 Sccs 系统已经安装在斯太尔摩生产线超过一年 并显示出它强大的优势 在安装之前 工人不能直接观察出每一点的相变和温度 这往往会导致严重的产品质 量问题 安装此系统后 工人可以直接得到生产进度通过友好的界面 并据此调节风 扇开度 控制各处温度在合理范围 并且使相变发生在设定的区域 根据来自生产线 的数据 安装此系统后的生产效率大大提高 产品质量也大大改善 4 总结总结 一个特殊的在线预测系统在斯太尔摩生产线成功实现 下面的总结包含了基本的工 作经验 1 系统模型分三个部分 通过 FDTD 实现的热力学模型 通过根据实验数据解 Avrami 方程实现相变模型 通过生产数据的回归计算实现的物理机械属性模型 三个模型有 着深刻的内在关系 2 这个在线系统是开放的 以便得到生产过程的生产信息 这样就可以将材料流体和 PLC 自动控制通过本地网络相结合 3 没有任何系统是完美无缺和绝对准确的 因此 我们设计了自适应功能去根据系统 运行时的参数自动调整各种系数 目前的结果显示 系统是能够在误差允许范围正确 预测最终钢材的机械性能 Development and application of online Stelmor Controlled Cooling System Abstract An online Stelmor Controlled Cooling System SCCS has been developed successfully for the Stelmor production line which can communicate with the material fl ow management system and Program Logic Control System PLCs automatically through local network This online model adopts Implicit Finite Difference Time Domain FDTD method to calculate temperature evolution and phase transformation during the production process and predicts fi nal properties As Continuous Cooling Temperature CCT curves of various steels can be coupled in the model it can predict the latent heat rise and range of phase transformation for various steels which can provide direct guidance for new steel development and optimization of present Stelmor cooling process This unique online system has been installed in three Stelmor production lines at present with good results 1 Introduction With increasing tough competition in the steel industry how to develop new steel products and stabilize quality of present products becomes the major concern for steel producers Pushing the mechanical properties of rod wire closer to its technical limits the demand on more reliable predictive control technique for the cooling process in Stelmor production line increases continuously Stelmor is the most popular controlled cooling process to produce the steel wire due to its fast production speed and homogeneous mechanical properties along the length of wire coil In Stelmor process as shown in Fig 1 a rod wire with temperature above 1000 C coming from the fi nishing mill quickly passes through several water tanks to the laying head at a specifi c temperature to form into loops depositing on to a conveyor in an overlapping pattern the specifi c cooling rate is achieved by opening of a series of fans below The fi nal mechanical properties depend mainly on the chemical composition and the cooling rate before the phase transformation for high carbon steel 1 2 6 As the cooling rate and phase transformation cannot be observed directly during production it is urgently required to develop an online model to predict the fi nal mechanical properties and phase transformation Although there are several research reports in this fi eld 3 6 the online quality prediction model for Stelmor process has not found reported yet Adopting Implicit Finite Difference Time Domain FDTD method an online controlled cooling model was developed and installedto monitor the real production process This paper focuses on introduction of its basic theory and control method of the online system which is helpful for stabilization of the product quality At present this online model SCCS has been installed in three Stelmor production lines with satisfactory performance 2 Mathematical model 2 1 Thermal model In the Stelmor production line axial heat conduction can be ignored because of an infi nitely long steel rod moving at high speed the model can be formulated to solve 1D heat conduction based on following assumptions 1 radial symmetry 2 uniform circular cross section and 3 uniform initial temperature which is briefl y close to reality Basic equation to solve the heat fl ow within the rod is following Note that g T is the volumetric rate of heat generation within the rod due to phase transformation q material density Cp the heat capacity and k the thermal conductivity In order to reduce loss of material message during calculation real experimental data q k and Cp can be input directly in the interface of model as shown in Figs 2 and 3 compared with traditional regression method The FDTD Crank Nicolson method has been adopted to solve above equation as it is fast and unconditionally stable which can meet the requirement of online modeling In order to maintain balance between speed and accuracy of the model 20 nodes has been selected along radial direction after trial and error In general large number of nodes is helpful to guarantee accuracy of the model but it will cause the slow speed of the model which is unacceptable for the online model as in real production one rod wire can pass the laying head in no more than 2 s The following boundary conditions have been applied Where t is time in second r0 the radius of rod wire h the heat transfer coeffi cient T r0 rod surface temperature Ta air or water temperature surround When the online model is running Tin is the measured temperature coming from the pyrometer installed after the fi nishing mill The model divides the Stelmor line into several stages eachwith its constant heat transfer coeffi cient h value which can be self adapted to match real measurements from several pyrometers installed in the production line In one Stelmor production line in Sha Steel company there are 14 fan machines put below in sequence to control the cooling rate of steel rod by opening its fan volume A constant h is assigned to each fan machine controlled stage which can be self adapted according to the following self developed equation where hold is the original heat transfer coeffi cient Tc the model predicted temperature at one pyrometer position on the fan cooling production TM the measured temperature from the specifi ed pyrometer and Tair the air temperature The computer model was checked by comparison between the FDTD model and commercial FEM model FEMLAB under the exact same conditions Fig 4 shows the exact same results from both models at different condition and proves the accuracy of the FDTD model As an online model the SCCS model needs to communicate with the material fl ow management system and PLC automatically through local network continuously The model needs to input two groups of data one is the real temperature from online pyrometers another is the basic wire rod information Eight pyrometers have been installed in the production line to provide real temperature data for the model to calculate and compare These temperature data is fi rst detected by online pyrometers directly passed to specifi c PLC then saved to Oracle database in the database sever by the model designed by Intouch software fi nally the model can read these data from the database at every 300 ms another group of data that the model also needs to know is basic information of slab such as its composition and steel serial number and rod diameter These data are delivered to the specifi c database once slab comes out from the reheating furnace 2 2 Phase transformation model How to model the phase transformation process is critical to the success of the model as the various cooling rates during phase transformation can determine the fi nal microstructure The model solved this problem by coupling of heat transfer analysis with phase transformation and microstructure changes all unifi ed in what is already called Microstructural Engineering Phase transformation process is described by following Avrami equation 7 Where X is the transformation fraction b T n T are parameters that vary with temperature steel composition and austenite grain size User can input these parameters in the model directly based on their experimental data One of the important parameters that need to determine is starting temperature of austenite to pearlite transformation The model can provide user the interface to input CCT experimental data of specifi c steels then model can internally calculate the temperature according to the following relation 8 Where a and m can be regressively obtained from CCT fi gure TA1 is the holding temperature during CCT experiment which can be input in the model directly The additivity principle was adopted to calculate the heat and amount of phase transformation During continuous cooling stage at every time temperature stage previous phase change amount can be transferred to the corresponding virtual time 7 At step j amount of transformation can by described as During this time period amount of transformation can be described as Phase change heat g T can be calculated based on following equation 6 Where H T is the volumetric rate of heat generation within the rod due to phase transformation For high carbon steel it can calculated based on following formulas 2 3 Property prediction model With the development of microstructural evolution models it is now becoming possible to predict fi nal rolled product microstructures with increasing confi dence 7 11 These predicted micro structures can then be combined with the structure property relationship models to calculate mechanical properties There are a large number of factors that contribute to the strength of a steel such as high carbon steel according to the equation by Mclvor and coworker 1 there are two major factors that affect the fi nal ultimate tensile strength UTS value fi rst is the cooling rate CR before phase transformation and second is the chemical composition which can be described as It needs to mention that at the paper put forward by Mclvor and co worker 1 CR means the cooling rate at 700 C which briefl y refer to the cooling rate before phase transformation as for high carbon steel phase transformation occurs just after 700 C In the CSSC model CR is modifi ed to refer to the cooling rate in the region from 700 C to start temperature from austenite to pearlite transformation which can be determined from the above equation 4 It can be seen that high cooling rate before the transformation can increase UTS correspondingly and strong infl uence of nitrogen and phosphorus on UTS Although the presence of elements like phosphorus manganese chromium and silicon are quite useful to increase UTS they also bring about low ductility of steel more over increase the risk of precipitation of metastable phases like bainite and or martensite 1 As CR can be calculated directly by the thermal model chemical composition of specifi c slab can be known immediately from the local material management system it is possible to predict the fi nal mechanical properties during production Yield tensile strength YTS is calculated based on following relation where A is constant and selected to be 0 7 in the model 3 Results and discussion 3 1 Stability of the online model It is required to make the model online one which is a challenge for us as no mature experience can be learnt There are two basic requirements for the online model stability and accuracy It is expected that the online model can run continuously without stop at the same time the output result is accurate enough to provide production guidance First question is how to get the related parameters directly from other sources such as several online pyrometers and related slab parameters After trial and error a suitable solu tion has been found as shown in Fig 5 At present three production lines have adopted this method to pass data to SCCS model without any problem SCCS model can run in cycles of 300 ms In each cycle the SCCS model calculates again the temperature model including the phase transformation and mechanical properties for every rod point along the length of the cooling section so that at any time the temporal temperature course of each rod is known and can be controlled All predicted data is saved in Oracle database and can be checked later 3 2 Accuracy of the thermal model After 3 years of hard work and repeated trials the online model has been installed in Stelmor production lines in Sha Steel company at Jiang Su Province After several version modifi cation the online model can run normally Eight online pyrometers have been installed to feed the model the real temperature One is installed after the fi nishing mill which provides the starting temperature for the online model Next one is installed at the laying head which checks the effect of several water boxes Other six are installed at the fan cooling stage with each section interval which monitor temperature evolution in the production line and compare with modeling results The online model will calculate the temperatures at every point then automatically adapted heat transfer coeffi cient HTC at every section according to comparison between the real measurement and modeling result all these can be fi nished in cycles of 300 ms which can meet speed requirement for the online model It is observed that calculated values of HTC and actual ones measured in operating rod mills can be considerably different the online model can easily study its difference and modify its HTC value correspondingly which is helpful for accurate prediction of phase transformation and fi nal mechanical property Fig 6 shows the comparison between the real measurement and modeling result Fig 6 shows the close match between the real measurement and the modeling result with no more than 20 C difference The effect of phase transformation on the temperature evolution can be seen clearly which causes slight rise during phase change stage then drops gradually The model can predict duration of phase transformation which provides guidance for technicians to adjust the opening of fans In general it is expected that phase transformation can be carried out at the same temperature within the short time such as no more than 10 s for 8 mm steel wire which can guarantee the uniform microstructure just like patenting treatment 3 3 Mechanical property Mechanical property prediction is a major concern for the company if the model is accurate enough the company will reduce the sample checking number which can save time and mone