关键词: 钢包精炼炉, 配料称重, 微粒群优化算法, 神经网络, PID 控制

Abstract:

In accordance with the control features of material proportioning process of the ladle refining furnace，e. g. ，inertia，time
lag，non-linearity，a kind of compound control algorithm is proposed based on particle swarm optimization algorithm( PSO) ， error back
propagation( BP) neural network and proportion integration differentiation( PID) algorithm． The PSO-BP-PID compound algorithm is applied
in a 150t ladle refining furnace burden weighing control system． The particle swarm optimization algorithm with global optimization
characteristics improves the convergence of the BP neural network which the initial weights of BP neural network is optimized． The optimized
BP neural network is then used to adjust PID parameters on-line． The PID controller based on PSO and the BP neural network
controls real-time the proportioning process of the ladle refining furnace． The simulation and operation experimental results show that the
control effect of the PSO-BP-PID algorithm is better than the control effect of the traditional PID algorithm． The control system of the ladle
furnace ingredients based on PSO-BP-PID algorithm can significantly improve the accuracy of ingredients，and effectively solve the
contradiction between ingredients weighing speed and accuracy．

Key words: ladle refining furnace, proportioning and weighing, particle swarm optimization, neural network, PID control