LI Yan
,
MAO Zhi-zhong
,
WANG Yan
,
YUAN Ping
,
JIA Ming-xing
钢铁研究学报(英文版)
In electric arc furnace smelting, electrode regulator system is a key link. A good electrode control algorithm will reduce energy consumption effectively and shorten smelting time greatly. The offline design online synthesis model predictive control algorithm is proposed for electrode regulator system with input and output constraints. On the offline computation, the continuum of terminal constraint sets will be constructed. On the online synthesis, the time-varying terminal constraint sets will be adopted and at least one free control variable will be introduced to solve the min-max optimization control problem. Then Lyapunov method will be adopted to analyze closed-loop system stability. Simulation and field trial results show that the proposed offline design online synthesis model predictive control method is effective.
关键词:
electrode regulator system
,
model predictive control
,
time-varying terminal constraint set
,
Lyapunov stability
,
linear matrix inequalities (LMIs)
YUAN Ping
,
MAO Zhizhong
,
WANG Fuli
钢铁研究学报(英文版)
The endpoint parameters are very important to the process of EAF steelmaking, but their online measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines (MSVM). In this model, the input space was divided by subtractive clustering and a submodel based on LSSVM was built in each subspace. To decrease the correlation among the submodels and to improve the accuracy and robustness of the model, the submodels were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.
关键词:
endpoint prediction;EAF;soft sensor model;multiple support vector machine (MSVM);principal components regression (PCR)
