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Modeling and Optimization for Piercing Energy Consumption

XIAO Dong , PAN Xiaoli , YUAN Yong , MAO Zhizhong , WANG Fuli

钢铁研究学报(英文版)

Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square method. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the optimization method based on the energy consumption prediction model can obtain the optimum process parameters effectively and also provide reliable evidences for practical production.

关键词: seamless tube;piercing energy consumption;mean value staged multiway partial least

Endpoint Prediction of EAF Based on Multiple Support Vector Machines

YUAN Ping , MAO Zhizhong , WANG Fuli

钢铁研究学报(英文版)

The endpoint parameters are very important to the process of EAF steelmaking, but their online 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 submodel based on LSSVM was built in each subspace. To decrease the correlation among the submodels and to improve the accuracy and robustness of the model, the submodels 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)

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