过程控制计算机的超薄热带板形控制模型

从机械设备配置和机电控制手段阐述超薄带钢生产的板形控制的特点,重点是板形过程控制系统的控制方法及最终的控制效果。板形控制数学模型的核心数学模型控制程序是PC和弯辊设定模型程序,即SAU数学模型程序。该模型通过五次预计算及三次设定PC角和弯辊力,实现控制板带产品的凸度和平直度,使其最终达到目标凸度及目标平直度值。     

带钢智能板形控制的应用

针对板形控制这一技术难题,在分析常规PID控制的基础上,提出以凸度仪和平坦度仪反馈相结合的智能控制。     

热轧带钢板形改善方法的研究与应用

针对莱钢1500mm热轧带钢存在的板形缺陷问题,分析带钢板形的影响因素,提出板形改善方法,并介绍板形自动控制的应用.     

热轧带钢中板形的计算和控制

热轧带钢中保持良好的板形是生产过程中极其重要的问题。首先,介绍了板形的基础知识包括板形的概念和计算,以及目前生产中板形控制方面实际存在的问题。在此基础上,讨论了影响板形的因素和控制策略,并对由带钢温度引起的缺陷,经过层流冷却获得良好板形的策略做了重点的讨论。从预防的角度阐明了板形的控制方法,并且介绍了一种实际生产中热轧带钢中浪板形控制方法,即PC设定控制方法的具体流程和实际应用效果。     

探析热轧平整机组的板形控制

热轧带钢的板形平整度是衡量带钢产品质量的一项关键指标,如果带钢板形存在质量缺陷将给钢铁生产企业造成巨大的经济损失。因此,在热轧带钢平整过程中,应对带钢的板形进行有效控制,以确保符合行业质量标准要求。本文将对热轧带钢板常见的板形缺陷以及采取的控制措施展开论述。     

支持向量机与神经网络相结合的板带凸度预测

为提高热轧生产过程中板带凸度的预测精度,提出了一种将粒子群优化算法(particle swarm optimization, PSO)、支持向量回归(support vector regression, SVR)和BP神经网络(back propagation neural network, BPNN)相结合的板带凸度预测模型。采用PSO算法优化SVR模型的参数,建立了PSO-SVR板带凸度预测模型,提出采用BPNN建立板带凸度偏差模型与PSO-SVR板带凸度模型相结合的方法对板带凸度进行预测。采用现场数据对模型的预测精度进行验证,并采用统计指标评价模型的综合性能。仿真结果表明,与PSO-SVR、SVR、BPNN和GA-SVR模型进行比较,PSO-SVR+BPNN模型具有较高的学习能力和泛化能力,并且比GA-SVR模型运算时间短。     

宽厚板厚度控制模型的优化

宽厚板厚度控制的目的是轧制出板形良好、板凸度小、同板差和异板差尽可能小的产品.莱钢宽厚板生产线自投人生产以来,在板凸度、厚度的均匀性、同板差和异板差等宽厚板厚度控制的瓶颈问题上都难以达到要求.为了实现对轧制厚度的要求,我们在现有的厚度控制模型的基础上,深入理解其控制功能、补偿功能的作用,找到引起厚度难以控制的主要因素,采取轧辊刚度补偿、轧辊偏心补偿、支撑辊油膜厚度补偿、工作辊热膨胀补偿及冲击力补偿等措施来优化厚度控制模型.自优化相应厚度参数和增加相关补偿控制功能以来,宽厚板厚度控制的精度得到很大的提高,具有很高的推广价值.     

基于小波分析和神经网络的板形模式识别方法

板形模式识别是板形高精度控制过程中的技术难点之一.本文提出了一种基于小波分析和神经网络相结合的方法,可以较好地解决这个难题.首先利用非线性小波变换阈值法有效去除板形检测数据中噪声干扰,从而提高模式识别系统的准确率;然后利用神经网络的鲁棒性使目标的识别更加接近实际.该方法不仅能有效地对复合板形进行正确分类,而且能分辨出所属类型的程度,为制定出相应的优化控制策略提供了重要的依据.     

宝钢热轧层流冷却模型的研究

一、引言 带钢卷取温度是影响成品带钢组织性能和机械性能的关键因素,近年来随着生产和控制技术的发展及用户对产品质量要求的提高,卷取温度成为与带钢厚度、宽度、板凸度、板平直度并列的衡量热轧带钢产品质量的重要指标。世界上许多热轧厂相继研制开发了新一代卷取温度控制系统,其卷取温度乃至冷却速率的控制已达到很高     

基于云自适应差分和BP神经网络的板形动态影响矩阵控制方法

为了克服传统板形控制中产品质量差、控制速度慢、生成效率低,以及静态影响矩阵控制信息不足等缺点,将云自适应差分算法(CADE)优化的BP神经网络应用到板形控制中,建立板形预测神经网络,并在离线状态下,根据板形轧制的历史数据和板形调控机构中的关键影响因素建立动态影响矩阵表。在线轧制过程中只需要与板形控制关键影响因素对应的动态影响矩阵表和板形识别变化量,就可以很快得到主要板形控制手段的控制量。该方法避免了神经网络的在线训练,提高了板形的控制速度和轧制精度。仿真实验表明,该方法稳定性好,控制精度高,适合用于板形的在线控制。     

嵌入式DSP冷轧带钢板形信号处理系统研制

针对冷轧带钢在线板形检测问题,基于整辊镶块式板形仪,研制了嵌入式DSP板形信号处理软硬件系统.利用高速DSP技术、信号隔离屏蔽技术和动态信号补偿算法等,提高板形检测信号的稳定性和可靠性,实现了冷轧带钢原始板形信号的实时高速处理,使其准确反映真实的冷轧带钢在线板形状况,为冷轧带钢在线板形闭环控制提供科学依据.该系统应用于...     

基于GA的模糊控制器在弯辊控制中的应用

UC轧机中间辊弯辊控制回路的数学模型具有很强的时变性和不确定性,为实现其精确控制,设计了一种基于遗传算法的模糊控制器并将其应用于该控制回路中。系统利用遗传算法来优化模糊控制器的隶属函数及量化因子和比例因子的初值,并且根据模糊控制查询表的输出来在线调整量化因子和比例因子。仿真结果表明,用该方法设计的模糊控制器具有一定的自适应能力,将该控制器应用于UC轧机中间辊弯辊控制回路可以使二次型板形缺陷得到快速有效的控制,具有良好的控制性能。     

基于工况识别的辊式淬火过程板形预报方法

板形是衡量淬火后钢板质量的重要指标之一,板形的预报对高质量钢板的持续稳定生产具有重要的指导意义.本文提出一种基于工况识别的辊式淬火过程板形预报方法,为淬火生产控制决策提供参考依据.首先对淬火过程进行特性分析;然后采用模糊C均值聚类算法对淬火过程进行工况识别,使用支持向量机建立各工况的板形预报模型,并运用改进的粒子群优化算法提高模型的精度;最后利用工业生产数据进行实验,结果验证了本文所提方法的可行性与有效性.     

A new convexity measure for polygons

Abstract-Convexity estimators are commonly used in the analysis of shape. In this paper, we define and evaluate a new convexity measure for planar regions bounded by polygons. The new convexity measure can be understood as a "boundary-based" measure and in accordance with this it is more sensitive to measured boundary defects than the so called "area-based" convexity measures. When compared with the convexity measure defined as the ratio between the Euclidean perimeter of the convex hull of the measured shape and the Euclidean perimeter of the measured shape then the new convexity measure also shows some advantages-particularly for shapes with holes. The new convexity measure has the following desirable properties: 1) the estimated convexity is always a number from (0, 1], 2) the estimated convexity is 1 if and only if the measured shape is convex, 3) there are shapes whose estimated convexity is arbitrarily close to 0, 4) the new convexity measure is invariant under similarity transformations, and 5) there is a simple and fast procedure for computing the new convexity measure.     

辊型在线超声检测可行性研究􀀁

随着工业技术的发展，人们对所需要钢的质量和性能要求越来越高，作为轧制在生产线上的一个重要工具－轧辊，其性能对轧制钢的影响是不容忽视的，轧辊在轧制过程中始终处于高温、高负荷和高速的状态下，因此随着轧制的进行，轧辊表面逐渐磨损，变得的粗糙，此时就会引起板厚精度，板形及带钢等的恶化，因此，必须对轧辊进行监测和磨新     

双参数四点细分法及其性质

在经典4点插值细分法的基础上，提出一类既能造型光滑插值曲线，又能造型光滑逼近曲线的双参数4点细分法．采用生成多项式等方法对细分法的一致收敛性、C^k连续性及保凸性进行了分析，给出并证明了极限曲线存在、C^k连续及均匀控制顶点情形下保凸的充分条件．在给定初始数据的条件下，可通过对形状参数的适当选择来实现对极限曲线的形状调整和控制．     

Tandem mill scheduling as a two point boundary value problem

We consider the problem of a tandem cold rolling mill to achieve correct output gauge, and satisfactory shape, and surface finish. The essential process variables are roll force, shape and thermal roll crown. The formulation of the equations for these key variables is described. The scheduling problem is formulated as a constrained two point boundary value problem. This is solved using conjugate gradient and projection techniques developed for optimal control problems. Results showing the improvements which can be made in strip shape and general mill operating conditions, by optimising the schedules, are included and discussed.     

Design and implementation issues of a control system for rotary saw cutting

Improvements over the previous years have expanded the range of applications of rotary saw cutting. In the present work, design and implementation issues of a control system for a steel tube cutting rotary saw are presented. During the cutting stage, tracking of the linear velocity of the tube and preservation of flatness of the edges of the tube compel the angular velocity of the saw to vary cosinusoidally. The tubes are cut in specified lengths that the operator defines. The motion is calculated based on feedback data of angular position via an encoder connected to the secondary axis of the motor. Feedback data regarding the length and the velocity of the tube are provided also via the encoder connected to the axis of the sensor roll, -adjacent to the tube at the saw entry.     

Data mining-based flatness pattern prediction for cold rolling process with varying operating condition

Data-rich environments in modern rolling processes provide a great opportunity for more effective process control and more total quality improvement. Flatness is a key geometrical feature of strip products in a cold rolling process. In order to achieve good flatness, it is necessary to reveal the factors that often influence the flatness quality, to develop a general flatness pattern prediction model that can handle the varying operating condition during the rolling of products with different specifications and to realize an effective flatness feedback control strategy. This paper develops a practical data mining-based flatness pattern prediction method for cold rolling process with varying operating condition. Firstly, the high-dimensional process measurements are projected onto a low-dimensional space (i.e., the latent variable space) using locality preserving projection method; at the same time, the Legendre orthogonal polynomials are used to extract the basic flatness patterns by projecting the high-dimensional flatness measurements into several flatness characteristic coefficients. Secondly, a mixture probabilistic linear regression model is adopted to describe the relationships between the latent variables and the flatness characteristic coefficients. Case study is conducted on a real steel rolling process. Results show that the developed method has not only the satisfactory prediction performance, but good potentials to improve process understanding and strip flatness quality.