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Optimization design of heat recovery systems on rotary kilns using genetic algorithms

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  • Yin, Qian
  • Du, Wen-Jing
  • Cheng, Lin

Abstract

Heat loss from rotary kilns accounts for certain amounts of the total energy consumption in chemical and metallurgical industries. To reduce the heat loss, a parallel and a series-parallel heat recovery systems with nine heat recovery exchangers are proposed to preheat the cold water in this paper. Experimental measurements are carried out to determine the heat transfer coefficient equations of each heat recovery exchanger. Then, the heat recovery systems are analyzed to deduce the mathematic relation between the design parameters and the system requirements, i.e. the temperatures and heat transfer rates of the nine heat recovery exchangers. The total heat transfer area, the total power consumption and the entropy generation due to heat transfer and fluid flow are set as the objective functions in four multi-objective optimization (MOO) cases. With the aid of the genetic algorithm in the Matlab 2015, the optimized operational and structural parameters are obtained. Finally, the MOO results are compared with that of the single objective optimization (SOO) method and the original values. The optimization results show that the MOO method are more suitable for the operational parameters design of the heat recovery systems compared with the SOO method. The required total heat transfer area and the total power consumption are decreased by at least 12.1% and 13.7%, respectively. Besides, as the entropy generation due to heat transfer and fluid flow decrease in the MOO cases, the corresponding heat transfer area and power consumption of the heat recovery system decrease, respectively.

Suggested Citation

  • Yin, Qian & Du, Wen-Jing & Cheng, Lin, 2017. "Optimization design of heat recovery systems on rotary kilns using genetic algorithms," Applied Energy, Elsevier, vol. 202(C), pages 153-168.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:153-168
    DOI: 10.1016/j.apenergy.2017.05.072
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    Cited by:

    1. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    2. Matelli, José Alexandre & Goebel, Kai, 2018. "Conceptual design of cogeneration plants under a resilient design perspective: Resilience metrics and case study," Applied Energy, Elsevier, vol. 215(C), pages 736-750.
    3. Chen, Xi & Chen, Qun & Chen, Hong & Xu, Ying-Gen & Zhao, Tian & Hu, Kang & He, Ke-Lun, 2019. "Heat current method for analysis and optimization of heat recovery-based power generation systems," Energy, Elsevier, vol. 189(C).

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