Research On The Endpoint-controlling Model For BOF Steelmaking

Basic Oxygen Furnace (BOF) Steelmaking is one of the most important parts of the iron & steel producing industry, the primary task of which is to provide the steel bath of which both the temperature and element (mainly the endpoint carbon content) hit the tipping aim slot at the steelmaking endpoint simultaneously. In actual BOF steelmaking process, steel bath element and temperature can't be measured continuously and the operation conditions are very frequently, which makes it difficult to control the BOF endpoint precisely, and it often happens that operators have to re-smelt the steel bath due to the low control precision. So improving the control precision of BOF steelmaking process is very important. The most astringent fact is that the process of steelmaking is a synthesis of multi-substance, multi-phase, high temperature and attended by many physical chemistry reactions. The research on the mechanism of reactions is not very clear. And there are severe nonlinear and coupling relation between the inputs and outputs. So the effects of normal models are always not ideal.This paper has analyzed the development and actuality of control technology about the modem converter endpoint, introduced some artificial neural network models that are used to predict the endpoint of BOF steelmaking. In this paper, we combine the static control method based on the sublance information on BOF steelmaking process. And these methods are improved by introducing neural network. According to the process and data from spot, the paper has analyzed the factors for influence C and endpoint steel temperature in converter. The control variables for [%C], endpoint steel temperature prediction and control model were determined. We improve the BP algorithm and establish two prediction neural networks by the advantage of Levenberg-Marquardt (LM) algorithm that the convergence speed is the quickest and its performance is the most excellent for neural network. The .characteristic and performance of fast BP algorithms are generalized and contrasted; the results show that the model based on LM algorithm has higher precision.The proposed control model has a rigorous theoretic base and has been validated by the theory and experiment. We improve the BP algorithm and establish two prediction neural networks. The actual data of continuous 60 batches from a converter are chosen as example, 9 input variables that influence C and endpoint steel temperatures in converter are determined. We establish two three-layers BP prediction neural networks, and predict C and endpoint steel temperature content. The model of [%C] and endpoint steel temperature prediction have been established. On the basis of this, the method based on BP neural network was proposed so as to determine the blown oxygen and the added coolant during the re-blowing. Simulation results showed that the method is effective and can be used to practical BOF process.