Research On Prediction Of Molten Steel End-point Temperature In EAF

Electric arc furnace (EAF) steelmaking is one of the modern large-scale steelmaking methods and also the main way to produce special steel and high alloy steel. EAF steelmaking with the scrap steel as its main material, gets a substantial development all over the world. EAF steelmaking is carried out at a high temperature. The real-time measurement of molted steel becomes hard for the limits of the measuring means and costs, so prediction of molted steel end-point temperature based on soft-sensing technique is very important.In this paper, the craft process of EAF steelmaking is firstly introduced, and then the energy budget during the process is analyzed in detail. Based on the analysis, the main factors influencing the end-point temperature of molted steel are gained. And then, the prediction model of end-point temperature of molted steel in EAF based on case-based reasoning (CBR) is built for the advantages of CBR, such as easiness to acquire knowledge, simple reasoning and the ability of self-learning. But, the method to ascertain the weight coefficients of the cases matched in a linear way and the reuse without any compensation of them in CBR cause the hit rate and precision of the prediction model lower.In CBR, the way to ascertain the weight coefficients of the cases matched has a direct influence on the solution of the current case. In the original model, the way to ascertain the weight coefficients would weaken the reference value of the cases with higher similarities. So a modified way to ascertain the weight coefficients is proposed to raise the weights of the cases with higher similarities. The simulation result indicates the modified model has an advantage on the hit rate and precision over the original model.The reuse without any revision of the cases matched would cause a large error for the differences between the current case and them. To solve the problem, an incremental model with a linear expression is proposed to compensate the solutions of the cases matched. Then the cases compensated are reused. But the process of EAF steelmaking is one with high nonlinearity. A nonlinear compensation model based on back-propagation neural network (BPNN) is proposed to replace the original incremental model. Further more, an algorithm of particle swarm optimization (PSO) is used to overcome the shortcomings of BPNN, such as a low rate of convergence and easiness to fall into local minimums.In the final part of this paper, the prediction model of end-point temperature of molted steel in EAF based on CBR compensated by PSO-BPNN is simulated and the result indicated the model has a higher hit rate and better precision.