基于BP神经网络的炉内工件温度互补检测
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基于BP神经网络的炉内工件温度互补检测  

Complementary Detection about Workpiece Temperature of Reheating Furnace Based on BP Neural Network

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作  者:李忠飞 

机构地区:内蒙古科技大学

摘  要:加热炉是钢材热轧生产线上的关键设备之一,其主要作用是把炉内钢坯按一定的升温要求加热到后续轧钢工艺所要求的范围内,以保证钢坯的正常轧制。因此,钢坯在炉内的温度分布尤其是在出炉口处钢坯表面和中心的温度对于实现加热炉的闭环最优控制和预测钢坯的轧制效果具有十分重要的意义,是加热炉运行中钢坯加热的主要质量指标。但是,在许多实际的工业生产中,对加热炉内加热状况和钢坯加热质量的判断主要依靠分布于炉体上下部的热电偶测点。为解决加热炉内钢坯加热质量不可直接检测的问题,通常通过建立合理的加热炉内钢坯温度计算的数学模型来在线估计和预测炉内钢坯温度分布。但目前大多数研究者所建立的钢坯温度计算模型,在其关键边界条件的处理上,均采用根据热电偶所测的炉膛温度,通过经验公式来获得钢坯模型的边界条件。显然经验公式所获得的边界条件与实际边界条件有一定误差,并且导致了求解过程的复杂性。 为了解决钢坯温度检测难所带来的上述问题,本文提出了运用CCD热像仪测温技术,直接测出钢坯表面的温度分布,并应用所测的钢坯表面温度建立钢坯温度的预测模型,对钢坯表面温度及钢坯内部温度场分布进行了深入的研究。 本文的主要工作如下: 1.对国内外辐射测温方法和技术进行了较为系统的论述,对其存在的问题进行了分析,并提出利用CCD热像仪测温的意义和方法。 2.钢坯表面温度预测模型的研究。本文选用BP神经网络建立钢坯表面温度的预测模型并对所建模型进行仿真分析。 3.钢坯内部温度场模型的研究。利用有限差分法对钢坯内部温度场分布进行了计算,并对钢坯内部温度场中出现的各种情况进行了仿真分析。 4.分析了钢坯自身的热物理性参数对钢坯温度测量的影响。Steel rolling reheating furnace is one of the most important equipment in the production line, and its main effect is put inside the furnace temperature required by certain billet heating to follow-up rolling process which requires a certain range, so as to ensure the normal slab rolling. Therefore, the billet temperature distribution in the furnace of steel mouth out especially in surface and the center for realizing the furnace temperature closed loop optimal control and predict billet rolling effect has the extremely vital significance, and it is main quality indicators in the running of the billet reheating furnace heating. However, in many of the industrial production of heating furnace, the heating condition and billet heating quality judgment rely mainly on the thermocouples measuring points which are distributing in the furnace up and down. To solve the billet reheating furnace heating quality can not directly within the detection problem, usually we need establish reasonable mathematical model of heating furnace billet temperature to on-line estimate and predict billet temperature distribution in the furnace. But most of the billet heating model, on dealing with of the key boundary conditions is based on the furnace temperature which is measured by thermocouple, through empirical formula getting the billet model boundary conditions. Apparently empirical formula of boundary conditions has a certain error compared with the practical boundary conditions, so that it will bring on the complex process in the solution of equation. In order to solve the difficulty of billet temperature detection, this paper using CCD Thermal Imager temperature measurement technology, directly measure the billet temperature distribution, use the billet surface temperature to establish the billet temperature prediction model, and research the billet surface temperature and billet temperature distribution field. This article mainly as follows: 1 The radiation temperature measurement method and its existing problem

关 键 词:CCD测温 BP网络 温度场 有限差分 MATLAB仿真 

分 类 号:TG307[金属学及工艺—金属压力加工]

 

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