Ji-wei Bao, Man-sheng Chu, Zheng-gen Liu, 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. https://doi.org/10.1007/s12613-020-2188-8
Cite this article as:
Ji-wei Bao, Man-sheng Chu, Zheng-gen Liu, 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. https://doi.org/10.1007/s12613-020-2188-8
Research Article

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

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  • Received: 30 July 2020Revised: 2 September 2020Accepted: 4 September 2020Available online: 5 September 2020
  • Iron carbon agglomerates (ICA) is considered to be an innovative charge to realize low carbon blast furnace (BF) ironmaking. In this study, the central composite Design (CCD) based on response surface methodology (RSM) was used to synergistically optimize the compressive strength, reactivity and post-reaction strength of ICA. The results show that the iron ore ratio has the most significant influence on compressive strength, reactivity and post-reaction strength. There are significant interactions on the compressive strength and reactivity between the iron ore ratio and carbonization temperature or the iron ore ratio and carbonization time, while the three variables do not interact with each other on the post-reaction strength. In addition, the optimal process parameters are iron ore ratio of 15.30%, carbonization temperature of 1000℃ and carbonization time of 4.27 h, and 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 verifies the accuracy and reliability of the models.
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