Abstract
For predicting the key technology index of electroslag remelting (ESR) process (the melting rate and cone purification coefficient of the consumable electrode), a radial basis function (RBF) neural network soft-sensor model optimized by the artificial fish swarm algorithm (AFSA) is proposed. Based on the technique characteristics of ESR production process, the auxiliary variables of soft-sensor model are selected. Then the AFSA is adopted to train the RBF neural network prediction model in order to realize the nonlinear mapping between input and output variables. Simulation results show that the model has better generalization and prediction accuracy, which can meet the online soft sensing requirement of ESR process real-time control.
Highlights
Electroslag remelting (ESR) process is an advanced smelting method to make purified steels based on rudiment steel in order to reduce impurity and get the high-quality steel which is uniformity, density, and crystal in vertical [1]
The current goes through the consumable electrodes and the slag resistance heat appears in the slag pool to melt the metallic electrode to produce metallic droplets
The algorithm procedure of the mutation operator based artificial fish swarm algorithm (AFSA) for optimizing the Radial basis function neural network (RBFNN) soft-sensor model is described as follows
Summary
Electroslag remelting (ESR) process is an advanced smelting method to make purified steels based on rudiment steel in order to reduce impurity and get the high-quality steel which is uniformity, density, and crystal in vertical [1]. The main purpose of ESR process is to purify metal and get the ingot with uniform density crystallization. The steel undergoing the ESR process has many advantages, such as high-purity, lower sulfur and inclusion of nonmetal, smooth surface of the ingot, the uniform density crystallization, and the uniform metal structure and chemical composition. The metallic droplets undergo the physical and chemical reaction by the way of dripping in the slag pool and are cooled and recrystallized in the compendium. The quality of the electricity slag ingots depends on the proper ESR technique and the effective control methods
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