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Ji-wei Bao, Zheng-gen Liu, Man-sheng Chu, Dong Han, Lai-geng Cao, Jun Guo, and Zi-chuan Zhao, Multi-objective collaborative optimization of metallurgical properties of iron carbon agglomerates using response surface methodology, Int. J. Miner. Metall. Mater., 28(2021), No. 12, pp.1917-1928. https://dx.doi.org/10.1007/s12613-020-2188-8
Ji-wei Bao, Zheng-gen Liu, Man-sheng Chu, Dong Han, Lai-geng Cao, Jun Guo, and Zi-chuan Zhao, Multi-objective collaborative optimization of metallurgical properties of iron carbon agglomerates using response surface methodology, Int. J. Miner. Metall. Mater., 28(2021), No. 12, pp.1917-1928. https://dx.doi.org/10.1007/s12613-020-2188-8
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基于响应曲面法的铁焦冶金性能多目标协同优化

Multi-objective collaborative optimization of metallurgical properties of iron carbon agglomerates using response surface methodology

Abstract: Iron carbon agglomerates (ICA) are used to realize low-carbon blast furnace ironmaking. In this study, the central composite design based on response surface methodology was used to synergistically optimize the compressive strength, reactivity, and post-reaction strength of ICA. Results show that the iron ore addition ratio significantly influences the compressive strength, reactivity, and post-reaction strength of ICA. The iron ore addition ratio and carbonization temperature or the iron ore addition ratio and carbonization time exert significant interaction effects on the compressive strength and reactivity of ICA, but it has no interaction effects on the post-reaction strength of ICA. In addition, the optimal process parameters are as follows: iron ore addition ratio of 15.30wt%, carbonization temperature of 1000°C, and carbonization time of 4.27 h. The model prediction results of compressive strength, reactivity, and post-reaction strength are 4026 N, 55.03%, and 38.24%, respectively, which are close to the experimental results and further verify the accuracy and reliability of the models.

 

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