基于神经网络的转炉炼钢终点控制的研究

【作者】 郑良峰；

【导师】 杨国亮； 杜承阳；

【作者基本信息】 江西理工大学 ， 控制工程， 2011， 硕士

【摘要】 转炉炼钢是生产钢铁的一道关键工序,它所要完成的是冶炼出温度和各种成分(关键是含碳量)都达到预期所需要的钢水。因为钢水的温度及碳含量无法连续检测,并且冶炼过程的边界条件变化复杂,这就导致冶炼过程的终点控制有很大困难,在实际生产中,很难精准掌控钢水碳、温从而导致重复吹炼,因此提高转炉炼钢的终点命中率是非常有必要的。因为转炉炼钢过程是一个相当复杂的多元多相高温物理化学过程,他的机理的解析还不是很透彻,输入输出之间的非线性化非常突出,常规建模方法总是无法满足生产要求。因此,研究基于神经网络的转炉炼钢控制问题是必要的。本文,首先简单介绍了转炉炼钢原理和设备状况和转炉冶炼终点控制技术的概况和现状。其次,通过对几种BP算法之间特性归纳和比较,得出Levenberg ?Marquardt(LM)算法拥有收敛速度快,并且学习性能好的特性。再次,简要介绍了神经网络的方法及几种神经网络转炉炼钢终点预报模型。最后,提出转炉炼钢基于LM算法的神经网络智能预报模型和控制模型。本文研究的方法有充实的理论基础,通过了理论和试验的双重验证。在研究过程中选用某转炉炼钢厂60炉实际生产数据为样本,用对转炉冶炼终点温度和碳有影响因素作为输入参数,提出了有三层网络结构的BP神经网络预报和控制模型,也就是先对终点温度和含碳量进行预报,然后以此为前提决定补吹时期所需的吹氧量和辅助原料量。通过仿真研究表明了此方法有效,能够在实际转炉炼钢使用。更多还原

【Abstract】 BOF is a key process to produce steel, whose task is smelting steel that meets the desired requirements about the temperature and various components. In the actual production process, because the temperature of molten steel can be measured continuouly, and the boundary condition of smelting process is very complex, the terminal control of smelting process is achieved difficultly. Because the precise control of carbon and temperature of steel is very difficult, it is very necessary to improve the terminal control efficiency of BOF. Meantime, because the process of BOF is a very complex physical and chemical processes with multi-component multi-phase and high temperature, whose mechanism about the input and output is nonlinear and not very clear , the conventional modeling methods can not always meet the production requirements.In this paper, firstly, the principle and equipment and the overview and status about terminal control technology of BOF are introduced briefly. Secondly, the neural network and several predicting methods of terminal model of BOF by using the neural network are introduced briefly. Thirdly, the result that the Levenberg-Marquardt(LM) method has fast convergence and good learning performance is got by summarizing and comparing features between several BP algorithm. Finaly, the intelligent forecast and control model about BOF besed on LM neural network algorithm is proposed.In this paper the researched method has a substantial theoretic base and has been validated by the theory and experiment. The actual data of continuous 60 batches from a converter steel mill are chosen as example, input parameter that influence and endpoint steel temperatures in converter are determined. In this paper establish three-layers BP prediction neural networks, endpoint various and steeltemperature content. The model of endpoint various and steel temperature prediction have established. On the basis of this, the method based on BP neural network is proposed so as to determine the blown oxygen and auxiliary raw materials. Simulationed showed the method is effective and can be used to practical process to produce steel.更多还原