In iron and steel industry, the sintering process requires huge carbon consumption. Achieving accurate and dynamic prediction of carbon consumption in this process is of evident significance to protect the environment and raise the economic efficiency of iron and steel industry. This article develops an original dynamic modeling framework, including automatic identification of operating conditions, modeling in different conditions, and dynamic prediction model of sintering carbon consumption. First, an automatic kernel-based fuzzy C-means algorithm is presented for the automatic identification of operating conditions. Dynamic relationships between the inputs and output of the model are analyzed by copula entropy, and then, the relevant production data in each operating condition are determined by a just-in-time learning method. Next, broad learning models are established under different operating conditions. Furthermore, a dynamic prediction model of sintering carbon consumption is designed, and the prediction error is adopted to quantify the performance of the model and as one criterion to determine the update of production database. Finally, results of experiments using actual production data demonstrate the advantage and validity of the developed model compared with some advanced modeling methods. The developed model considers complex characteristics of the sintering process and presents better dynamic performance and information mining performance.

This work was supported in part by the National Natural Science Foundation of China under Grant 61210011, in part by the 111 project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2021CFB145 and Grant 2015CFA010, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant CUG2106210.

National Natural Science Foundation of China [61210011]; 111 project [B17040]; Hubei Provincial Natural Science Foundation of China [2021CFB145, 2015CFA010]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG2106210]