共查询到18条相似文献,搜索用时 156 毫秒
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研究回执炉优化控制问题,带钢张力是影响带钢质量和产量的重要因素.针对带钢张力的波动主要受带钢规格、辊子速度等各种因素的影响,为了获得良好的带钢张力控制性能,保证退火过程中具有稳定的动特性,以连续退火机组中加热炉区域的带钢张力为研究对象,建立了带钢的张力模型.首先建立了加热炉带钢张力的动态机理模型和状态空间表达式,然后根据现场的张力控制系统,利用Matlab软件,开发了由模型仿真程序和控制系统仿真程序组成的带钢张力闭环控制仿真模型.仿真结果表明,所建立的带钢张力模型和仿真软件能够及时、准确的反映带钢张力的动态和静态特性,为科研人员和现场工程师对连续退火机组张力控制系统设计的改进提供了依据. 相似文献
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在冷轧带钢连续退火过程中加热温度的控制是确保产品性能合格的重要保证,带钢连续退火炉具有非线性、大滞后性、多干扰等特性,采用传统的PID控制已经不能满足现代控制要求,为使得控制系统具有更高的稳态精度和更快的动态响应,本文采用PID控制和模糊控制相结合的控制方法.通过仿真,得到了不错的控制效果,验证的本文提出方法的优点.同时,本文采用InTouch组态软件设计了对应的监控系统,并采用VB.net设计开发了数据查询和分析系统,实现了良好的人机交互. 相似文献
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宝钢连续退火机组加热炉带温控制技术 总被引:4,自引:3,他引:4
在宝钢No 1(2030)连续退火机组改造项目中,用DCS系统成功地实现了加热段带钢温度的自动控制功能。通过在现场调试过程中采集的大量数据,分析了加热炉对象的特性,比较了对象在不同扰动下的响应特点,并且介绍了宝钢No 1,No 2和No 3连续退火机组加热段对带钢温度的不同控制方案,提出了采用带钢温度调节器通过一定的比例分配,直接控制各区煤气流量的方案(带温-煤气二级串级控制)是较好的控制方案,该方案在实际生产中得到了较好的控制效果。 相似文献
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宝钢冷轧1550连续退火机组的带钢温度控制 总被引:2,自引:5,他引:2
为适应当前市场对汽车板材优质高产的需求,引进了日本钢管公司NKK的最新退火工艺,自行设计、完成了宝钢三期冷轧1550连续退火机组带钢连续退火DCS系统的控制软件。特别在带温控制过程中,通过对大量现场调试数据的提取、分析、总结,建立了具有自适应功能的炉温校正模块,解决了带钢温度控制难以长时间稳定在高精神范围内的难题,克服了传统生产流程中耗费大量过渡钢卷的缺点。自投产以来,控制性能稳定,生产出的成品钢材,无论是表面质量还是内部材质性能,都达到了世界一流的水平。 相似文献
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以连续退火机组跳动辊区域的带钢张力为研究对象,建立了带钢张力的机理模型和状态空间表达式,克服了现有张力模型中的2个缺点.根据现场的控制系统,开发了控制仿真程序,并建立了由张力模型仿真程序和控制仿真程序所组成的带钢张力闭环控制仿真软件.仿真实验表明,所开发的仿真软件能够及时、准确地反映带钢张力的稳态和动态特性. 相似文献
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针对连退生产过程中带钢质量波动大和生产能耗过大的问题,基于数据解析方法构建带钢质量的预测模型,进而建立连退生产过程多因子操作优化模型.该模型的任务是求得一个最优工艺参数设定方案,使得模型中所包含的两个相互影响但并不冲突的目标能够实现同时最优化.针对该问题,提出一种改进的自适应多因子进化算法(AdaMFEA),将不同优化目标作为不同类别因子,通过父代解在不同因子上的性能评价指标决定子代解的搜索方向.为了改进算法的鲁棒性和搜索效率,算法使用多种交叉算子,并基于各算子的搜索性能分析提出多种交叉算子的自适应选择机制;同时提出基于回溯直线搜索和拟牛顿法的个体学习策略,对个体进行局部搜索.基于Benchmark问题的实验结果表明,AdaMFEA能够有效提升传统多因子进化算法(MFEA)的求解效率;基于实际工业问题的实验结果表明,AdaMFEA可有效求解连退生产过程多因子操作优化问题,实现多个非冲突目标在一个种群的进化过程中同时达到最优. 相似文献
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The twin-roll strip casting process is a steel-strip production method that combines continuous casting and hot rolling process
in a single operation. The quality of strip casting process depends on many process parameters, such as molten steel level
in the tundish, solidification position and roll gap. Their relationships are complex and the strip casting process has the
properties of nonlinear uncertainty and time-varying characteristics. Hence, it is difficult to establish an accurate process
model for designing a model-based controller to monitor the strip quality. In this paper, a model-free control strategy is
employed to overcome this problem. The self-organizing fuzzy control (SOFC) strategy is developed to control the molten steel
level of a strip casting process. It has on-line learning ability and the rule tables can be modified automatically and continuously
for responding to the system’s nonlinear and time-varying behaviors. Since this model-free controller has simple control structure
and small number of control parameters, it is easy to implement. Simulation results based on semi-experimental system dynamic
model and parameters are executed to show the control performance of the proposed intelligent controller. 相似文献
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带钢连续热镀锌退火过程的模型化 总被引:6,自引:1,他引:5
针对大型冷轧带钢连续热镀锌退火过程的模型化和控制难题,本文以目前国内最先进的宝钢热镀锌生产线为对象,建立了用于实进控制的两类新疑数学模型,带温分布模型和带温跟踪模型,仿真和工业运行效果良好。 相似文献
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在带钢的生产过程中可能会因为生产工艺的问题导致带钢表面出现缺陷,传统的带钢表面检测方法存在检测速度慢、检测精度低等问题。在计算机深度学习快速发展的今天,为实现带钢表面缺陷快速有效的检测,提出改进的掩码区域卷积神经网络(Mask R-CNN)算法,使用[k]-means II聚类算法改进区域建议网络(RPN)锚框生成方法;同时调整Mask R-CNN模型的网络结构,去掉掩码分支,提高了模型的缺陷检测速度。实验在NEU-DET数据集的5种缺陷检测中将原算法的均值平均精度(mAP)从0.810?2提升到0.960?2,检测速度达到5.9?frame/s。并且能够实现对缺陷目标的检测和实例分割,以便研究人员观测缺陷的大小和形状,从而改进工艺。相比于目前其他深度学习的缺陷检测算法,更能满足带钢的生产检测要求。 相似文献
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Guishan Xing Jinliang Ding Tianyou Chai Puya Afshar Hong Wang 《Engineering Applications of Artificial Intelligence》2012,25(2):418-429
In this paper, a hybrid intelligent parameter estimation algorithm is proposed for predicting the strip temperature during laminar cooling process. The algorithm combines a hybrid genetic algorithm (HGA) with grey case-based reasoning (GCBR) in order to improve the precision of the strip temperature prediction. In this context, the hybrid genetic algorithm is formed by combining the genetic algorithm with an annealing and a local multidimensional search algorithm based on deterministic inverse parabolic interpolation. Firstly, the weight vectors of retrieval features in case-based reasoning are optimised using hybrid genetic algorithm in offline mode, and then they are used in grey case-based reasoning to accurately estimate the model parameters online. The hybrid intelligent parameter estimation algorithm is validated using a set of operational data gathered from a hot-rolled strip laminar cooling process in a steel plant. Experiment results show the effectiveness of the proposed method in improving the precision of the strip temperature prediction. The proposed method can be used in real-time temperature control of hot-rolled strip and has potential for parameter estimation of different types of cooling process. 相似文献
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《Journal of Process Control》2014,24(6):916-923
In the steel strip pickling process, the control of the acid concentration is an important part for ensuring the strip surface quality. Now only X-ray method is used to detect the acid concentration online, but the price is very high and the maintenance is very hard. The acid concentration is not measured in most of the steel strip pickling lines online. In this paper, a soft measurement of acid concentration is developed. The pressure differential, conductivity and temperature are used to calculate the acid concentration including ferrous chloride (FeCl2) and hydrogen chloride (HCl) concentration. The real pickling process is under a multi-mode condition. First, the spectral clustering based on geodesic distance is used to cluster the data into some groups. There are clearly linear relationship between the condition variables and the acid concentration. Then, orthogonal signal correction-iteratively reweighted least squares (OSC-IRLS) models based on the cluster data set are built to predict the acid concentration. The real field data set from cold-rolled strip steel pickling process is used to validate the model. The results demonstrate that the clustering method can improve the prediction result. 相似文献