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A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

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  • Jaroslaw Krzywanski

    (Faculty of Science and Technology A. Krajowej 13/15, Jan Dlugosz University in Czestochowa, 42-200 Czestochowa, Poland)

Abstract

The paper introduces the artificial intelligence (AI) approach as a general method for the design and optimization study of heat exchangers. Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are applied in the paper. An AGENN model, combining Genetic Algorithms with Artificial Neural Networks, was developed and validated against the desired data on a large falling film evaporator. A broad range of operating conditions and geometric configurations are considered in the study. Four kinds of tubes are deliberated, including plain and enhanced tubes. Different tube pass arrangements, i.e., top-to-bottom, bottom-to-top, and side-by-side, are discussed. Finally, the effects of liquid refrigerant mass flow rate, as well as the number of flooded tubes on the performance of the evaporator, are analyzed. The total heat transfer rate of the evaporator, predicted by the model, is in good agreement with the desired data; the maximum error is lower than ±3%. The highest heat transfer rate of the evaporator is 1140.01 kW and corresponds to Turbo EHP tubes, and bottom-to-top tubes pass arrangements, which guarantee the best thermal energy conversion. The presented approach can be referred to as a complementary technique in heat exchanger design procedures, besides the common rating and sizing tasks. It is an effective and alternative method for the existing approaches, considering the complexity of analytical and numerical techniques as well as the high costs of experiments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4441-:d:289776
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    7. Rishikesh Sharma & Dipti Prasad Mishra & Marek Wasilewski & Lakhbir Singh Brar, 2023. "Application of Response Surface Methodology and Artificial Neural Network to Optimize the Curved Trapezoidal Winglet Geometry for Enhancing the Performance of a Fin-and-Tube Heat Exchanger," Energies, MDPI, vol. 16(10), pages 1-30, May.
    8. Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
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    10. Jaroslaw Krzywanski & Wojciech Nowak & Karol Sztekler, 2022. "Novel Combustion Techniques for Clean Energy," Energies, MDPI, vol. 15(13), pages 1-3, June.
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    12. Chen, Weixiong & Qian, Yiran & Tang, Xin & Fang, Huawei & Yi, Jingwei & Liang, Tiebo & Zhao, Quanbin & Yan, Junjie, 2023. "System-component combined design and comprehensive evaluation of closed-air Brayton cycle," Energy, Elsevier, vol. 278(C).
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    15. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.

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