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Volume 13 Issue 4
Apr.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(4): 955-965
Y. Shan, H. Dong, Z. Han, J. Zhao, and H. Liu, “An approach integrating data-driven and mechanistic models for predicting and optimizing heating flue temperature of coke ovens,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 955–965, Apr. 2026. doi: 10.1109/JAS.2025.125771
Citation: Y. Shan, H. Dong, Z. Han, J. Zhao, and H. Liu, “An approach integrating data-driven and mechanistic models for predicting and optimizing heating flue temperature of coke ovens,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 955–965, Apr. 2026. doi: 10.1109/JAS.2025.125771
shu

An Approach Integrating Data-Driven and Mechanistic Models for Predicting and Optimizing Heating Flue Temperature of Coke Ovens

doi: 10.1109/JAS.2025.125771
Funds:  This work was supported by the National Natural Science Foundation of China (62125302, 62394345, 62473073)
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    Abstract
  • As one of the most important parameters for coke oven of steel industry, heating flue temperature plays a pivotal role in obtaining quality-guaranteed final product. While the complexity such as nonlinearity, time-delay, and coupling relationship with its heating fuel, in particular, blast furnace gas (BFG), brings about challenges for heating flue temperature prediction and optimization. As such, a data-mechanism combined driven systematic approach considering both internal and external influencing factors of coke oven is proposed in this study. To provide a solid dataset, a density-based spatial clustering of applications with noise (DBSCAN) based outlier detection algorithm is designed at first for preprocessing, which accommodates the data characteristics in practice. Then, taking full consideration of the periodic and trend features of flue temperature data, a neural network (NN) based multi-channel prediction model is constructed for temperature forecasting. In order to establish dynamic rather than static constraints for the following temperature optimization, a mechanism based controllable region assessment method is proposed. Finally, the flue temperature is optimized via a well-designed fuzzy-based approach along with swarm and evolutionary algorithms for parameter determination. Based on the real data, the simulation results demonstrate the superiority of the proposed systematic approach compared with other partially applied methods, so as to manifest its benefits for the operational optimization of coke oven in steel industry.

     

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