【作者】 钟志强；

【导师】 曹长修；

【作者基本信息】 重庆大学 ， 控制理论与控制工程， 2002， 硕士

【摘要】 钒在钢铁冶金、电子及国防工业等方面有着重要的应用价值，是一种具有战略价值的金属材料。我国目前的转炉提钒为人工操作模式，由操作人员根据经验和感觉进行操作，自动化水平低，存在着钒渣质量和半钢质量不稳定的问题；在国外，俄罗斯等一些国家已经在使用静态模型对提钒过程进行控制，取得了较好的效果，但是这些模型大都是根据复杂的物理化学规律研制的机理模型，要求有非常稳定的生产流程和工艺条件，对于铁水成分、生产设备等变化的适应性差，不但移植困难，而且模型价格异常昂贵。研制具有高性价比的转炉提钒模型是我国钒生产企业的迫切需要，对如提钒这样的复杂冶金工业过程建模也是目前国内外的研究热点之一。随着计算智能研究的兴起，一些新的建模方法如RBF神经网络和遗传算法等为复杂的冶金工业过程建模提供了新的思路和方法。本文使用RBF神经网络和遗传算法建立了转炉提钒终点预报模型，并取得了较好的效果，为计算智能方法在复杂工业过程建模中的应用作出了有益的尝试。本文首先根据冶金学原理，找出影响转炉提钒终点状态的主要因素，并从某大型钢厂的原始数据中提取合符要求的数据进行建模。从降低模型的复杂度的角度，本文将整个终点预报模型分成三个独立的模型，即终点温度模型、终点碳模型、终点钒模型。在深入研究神经网络理论的基础上，本文使用RBF神经网络对终点的三个指标分别建模。由于RBF神经网络本身对其神经元个数、Spread等参数比较敏感，所以这些参数的选取对RBF神经网络的逼近效果影响很大，靠经验方法和手工测试方法选取的神经元个数、Spread等网络参数得到的网络模型对测试集误差较大，也就是说模型的泛化能力不好。本文通过对泛化理论的研究，分析了影响泛化能力的主要因素，提出了使用遗传算法对RBF神经网络的神经元个数、Spread等参数进行优化，求取具有较好泛化能力的神经网络的方法，试验证明通过这种方法求得的一组网络模型具有较好泛化能力，并成功的对测试集进行了准确的预报。最后得到的转炉提钒终点状态预报模型对终点温度的预报命中率达到86.4%，对终点碳的预报命中率达到83.7%，终点钒的预报命中率达到59.4%，碳温同时命中率达到76.3%，三者同时命中率达到43.2%。更多还原

【Abstract】 Vanadium is important and valuable in steel-making, electronic production and national defence industry etc. Now, the operation pattern of refining vanadium in our nation is based on human experiences, and the automation degree is still at a low level. Operation mode based on human experiences is one of the main reasons make the quality of semi-steel and vanadium product unstable. In developed contury such as Russian, static model was used to control the process of vanadium refining. But most of these models are based on complex physical and chemical reactions. It’s difficult to transplant these models from equipment to another, and these models are very expensive. Cheep and good models were strong desired. Building model for a complex metallurgic process such as vanadium refining is one of the main focal point in Control Theory Researchs. This paper discusses the construction procedure of Converting Furnace Endpoint Prediction Model in Refining vanadium based on RBF neural networks and Genetic Algorithms.At first this paper find out the main factor that affects endpoint status according to metallurgy, then build the Endpoint Prediction Model. From the aspect of diminishing the complex degree of the model, this paper separates the Endpoint Prediction Model to three detached models, Endpoint Temperature Model, Endpoint Carbon Model and Endpoint Vanadium model. Based on the penetrating research of neural networks, the author use RBF neural networks to construct models of the three endpoint criterions. Because RBF is very sensitive to some parameters such as the number of the neuron and the spread, choosing those parameters contributes a lot to the approach effect of the RBF neural networks. The valid data collecting from the scene is not enough and inevitably has some noise because of the restriction of measurement methods, so when use the network model obtained by choosing parameters such as the number of neuron and spread based on experiences and handmade test, there will be a large deviation, that is, the generalization ability of the model is not good. Based on the research of the Generalization Theory, this paper analyses main factors effecting the generalization ability, and present a method that uses the genetic algorithm optimize parameters such as the number of neuron and spread and get a method with a better generalization ability. It is proved by experiments that the group of network models obtained by this method have better generalization abilities, and those experiments forecast the independent check set accurately and successfully. The hit<WP=6>ratio of the Converting Furnace Endpoint Prediction Model in Refining Vanadium is 86.4% to the endpoint temperature, 83.7% to the endpoint carbon, 59.4% to the endpoint vanadium, 76.3% to both the temperature and the carbon, and 43.2% to all of the three.更多还原