Study On Converting Furnace Endpoint Prediction Model In Vanadium Refining Based On RBF Neural Network And Genetic Algorithms

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 hitratio 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.