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Model predictive control for improving waste heat recovery in coke dry quenching processes

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  • Sun, Kai
  • Tseng, Chen-Ting
  • Shan-Hill Wong, David
  • Shieh, Shyan-Shu
  • Jang, Shi-Shang
  • Kang, Jia-Lin
  • Hsieh, Wei-Dong

Abstract

CDQ (coke dry quenching) is a widely used method for recovering waste heat in the steel industry. We have developed a novel, data driven modeling approach and model based control for a CDQ unit to increase steam generation in a cogeneration system. First, the correlation between steam generation and TCGB (the temperature of circulation gas entering the associated boiler) was confirmed. Subsequently, a nonlinear variable selection method was employed to build models of TCGB and the carbon monoxide concentration of the circulation gas. The models obtained were implemented to achieve MPC (model predictive control) for regulating the supplementary gas to maximize steam generation in an existing steelmaking plant. Upon comparison of the original process and the proposed modified operation, the effectiveness of the implementation of MPC was justified. The results showed that steam generation was increased by 7%. In our approach, the large amount of available operational data stored electronically was used to establish the models. Modification of the established system is not required. Taking into account that no capital investment is required, the process improvement is remarkable in terms of its return on investment.

Suggested Citation

  • Sun, Kai & Tseng, Chen-Ting & Shan-Hill Wong, David & Shieh, Shyan-Shu & Jang, Shi-Shang & Kang, Jia-Lin & Hsieh, Wei-Dong, 2015. "Model predictive control for improving waste heat recovery in coke dry quenching processes," Energy, Elsevier, vol. 80(C), pages 275-283.
    • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:275-283
    DOI: 10.1016/j.energy.2014.11.070
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    References listed on IDEAS

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    1. Yin, Xiaohong & Wang, Xinli & Li, Shaoyuan & Cai, Wenjian, 2016. "Energy-efficiency-oriented cascade control for vapor compression refrigeration cycle systems," Energy, Elsevier, vol. 116(P1), pages 1006-1019.
    2. Jiacheng Cui & Gang Meng & Kaiqiang Zhang & Zongliang Zuo & Xiangyu Song & Yuhan Zhao & Siyi Luo, 2025. "Research Progress on Energy-Saving Technologies and Methods for Steel Metallurgy Process Systems—A Review," Energies, MDPI, vol. 18(10), pages 1-26, May.
    3. Liu, Changxin & Xie, Zhihui & Sun, Fengrui & Chen, Lingen, 2017. "Exergy analysis and optimization of coking process," Energy, Elsevier, vol. 139(C), pages 694-705.
    4. Zhang, Junxia & Zhong, Junfeng & Yang, Li & Wang, Zehua & Chen, Dongrui & Wang, Qiaoli, 2024. "Enhancement effect of semicoke waste heat on energy conservation and hydrogen production from biomass gasification," Renewable Energy, Elsevier, vol. 236(C).
    5. Brage Rugstad Knudsen & Hanne Kauko & Trond Andresen, 2019. "An Optimal-Control Scheme for Coordinated Surplus-Heat Exchange in Industry Clusters," Energies, MDPI, vol. 12(10), pages 1-22, May.
    6. Lin Lu & Zhipeng Yan & Xilong Yao & Yunfei Han, 2025. "An Investigation into the Effects of Coke Dry Quenching Waste Heat Production on the Cost of the Steel Manufacturing Process," Sustainability, MDPI, vol. 17(10), pages 1-30, May.
    7. Zhang, Kai & Du, Shiqi & Sun, Peng & Zheng, Bin & Liu, Yongqi & Shen, Yingkai & Chang, RunZe & Han, Xiaobiao, 2021. "The effect of particle arrangement on the direct heat extraction of regular packed bed with numerical simulation," Energy, Elsevier, vol. 225(C).

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