Affiliations: [a]
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| [b] Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing, China
| [c] HBIS Group Chengsteel Company, Hebei, China
Correspondence:
[*]
Corresponding author. Sen Zhang. E-mail: zhangsen@ustb.edu.cn.
Abstract: In order to improve the optimal setting temperature problem to achieve the global optimum of product performance, costs and benefits. In this article, a hierarchical structure optimal setting approach of production indexes for the rolling heating furnace temperature field (RHFTF) is proposed. It is composed of three layers with different functions to obtain the temperature control setting model of the RHFTF. In the first layer, the bi-feature Gaussian mixture model clustering (BFGMMC) algorithm of loading plan is proposed to optimize the setting of a limited number of slabs. In the second layer, the type-2 fuzzy rule interpolation (T2FRI) setting method is developed to obtain the optimal setting curve. Meanwhile, an improved KH (Kóczy-Hirota) α-cut distance (IKHCD) algorithm is proposed to get the miss information between any two adjacent interpolation points. In the third layer, knowledge feedforward compensation of rule matrices (KFCRM) algorithm is presented to improve the anti-interference ability of the setting model. The results of the study can demonstrate that the proposed method improves the accuracy of the model and optimizes the control strategy. Furthermore, the experimental results show that the proposed method meets the process technical requirements.
