Home African vultures optimization algorithm for optimization of shell and tube heat exchangers
Article
Licensed
Unlicensed Requires Authentication

African vultures optimization algorithm for optimization of shell and tube heat exchangers

  • Dildar Gürses

    Dildar Gürses received her BSc and MSc degree from the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. She is a Ph.D. candidate in the same department.

    , Pranav Mehta

    Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interests include metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

    , Sadiq M. Sait

    Sadiq M. Sait received his Bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his Master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia. He is a Senior Member of the IEEE. In 1981, he received the Best Electronic Engineer Award from the Indian Institute of Electrical Engineers, Bengaluru.

    and Ali Riza Yildiz

    Ali Riza Yildiz is a Professor in the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. His research interests are the finite element analysis of structural components, lightweight design, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

    EMAIL logo
Published/Copyright: August 5, 2022
Become an author with De Gruyter Brill

Abstract

Nature-inspired optimization algorithms named meta-heuristics are found to be versatile in engineering design fields. Their adaptability is also used in various areas of the Internet of things, structural design, and thermal system design. With the very rapid progress in industrial modernization, waste heat recovery from the power generating and thermal engineering organization is an imperative key point to reduce the emission and support the government norms. However, the heat exchanger is the component applied in various heat recovery processes. Out of the available designs, shell and tube heat exchangers (SHTHEs) are the most commonly adopted for the heat recovery process. Hence, cost minimization is the major aspect while designing the heat exchanger confirming various constraints and optimized design variables. In this study, cost minimization of the SHTHE is performed by applying a novel metaheuristic algorithm which is the African vultures optimization algorithm (AVOA). Adopting the AVOA for the best-optimized value (least cost of heat exchanger) and the design parameters are realized, confirming all the constraints. It was found that the AVOA is able to pursue the best results among the rest of them and can be used for the cost optimization of the plate-fin and tube-fin heat exchanger case studies.


Corresponding author: Ali Riza Yildiz, Department of Mechanical Engineering, Bursa Uludag University, Görükle bursa, 16059, Bursa, Turkey, E-mail:

About the authors

Dildar Gürses

Dildar Gürses received her BSc and MSc degree from the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. She is a Ph.D. candidate in the same department.

Pranav Mehta

Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interests include metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

Sadiq M. Sait

Sadiq M. Sait received his Bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his Master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia. He is a Senior Member of the IEEE. In 1981, he received the Best Electronic Engineer Award from the Indian Institute of Electrical Engineers, Bengaluru.

Ali Riza Yildiz

Ali Riza Yildiz is a Professor in the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. His research interests are the finite element analysis of structural components, lightweight design, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] P. Mehta, B. S. Yildiz, S. M. Sait, and A. R. Yildiz, “Hunger games search algorithm for global optimization of engineering design problems,” Mater. Test., vol. 64, no. 4, pp. 524–532, 2022, https://doi.org/10.1515/mt-2022-0013.Search in Google Scholar

[2] J. Li, Y. Xiang Li, S. S. Tian, and J. Zou, “Dynamic cuckoo search algorithm based on Taguchi opposition-based search,” Int. J. Bio-Inspired Comput., vol. 13, no. 1, pp. 59–69, 2019, https://doi.org/10.1504/IJBIC.2019.097728.Search in Google Scholar

[3] A. A. Ewees, M. A. El Aziz, and A. E. Hassanien, “Chaotic multi-verse optimizer-based feature selection,” Neural Comput. Appl., vol. 31, no. 4, pp. 991–1006, 2019, https://doi.org/10.1007/s00521-017-3131-4.Search in Google Scholar

[4] B. Brown and C. Singh, “Student understanding of the first law and second law of thermodynamics,” Eur. J. Phys., vol. 42, no. 6065702, 2021, https://doi.org/10.1088/1361-6404/ac18b4.Search in Google Scholar

[5] E. U. Schlunder, Heat Exchanger Design Handbook, United States, U.S.Department of Energy, Office of Scientific and Technical Information, 1983.Search in Google Scholar

[6] R. K. Shah and D. P. Sekuli, Fundamentals of Heat Exchanger Design, Hoboken, New Jersey, USA, John Wiley & Sons, 2003, https://doi.org/10.1002/9780470172605.Search in Google Scholar

[7] P. Wildi-Tremblay and L. Gosselin, “Minimizing shell-and-tube heat exchanger cost with genetic algorithms and considering maintenance,” Int. J. Energy Res., vol. 31, no. 9, pp. 867–885, 2007, https://doi.org/10.1002/er.1272.Search in Google Scholar

[8] B. Dandotiya and H. K. Sharma, “Climate change and its impact on terrestrial ecosystems,” in Research Anthology on Environmental and Societal Impacts of Climate Change, I. R. Management Association, Ed., IGI Global, 2022, pp. 88–101.10.4018/978-1-6684-3686-8.ch005Search in Google Scholar

[9] L. Abualigah, M. A. Elaziz, A. M. Khasawneh et al.., “Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results,” Neural Comput. Appl., vol. 34, no. 6, pp. 4081–4110, 2022, https://doi.org/10.1007/s00521-021-06747-4.Search in Google Scholar

[10] N. Panagant, N. Pholdee, S. Bureerat, A. R. Yıldız, and S. M. Sait, “Seagull optimization algorithm for solving real-world design optimization problems,” Mater. Test., vol. 62, no. 6, pp. 640–644, 2020, https://doi.org/10.3139/120.111529.Search in Google Scholar

[11] E. Demirci and A. R. Yıldız, “A new hybrid approach for reliability-based design optimization of structural components,” Mater. Test., vol. 61, no. 2, pp. 111–119, 2019, https://doi.org/10.3139/120.111291.Search in Google Scholar

[12] B. S. Yıldız, N. Pholdee, S. Bureerat, M. U. Erdaş, A. R. Yıldız, and S. M. Sait, “Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry,” Mater. Test., vol. 63, no. 4, pp. 356–359, 2021, https://doi.org/10.1515/mt-2020-0053.Search in Google Scholar

[13] A. R. Yildiz and M. U. Erdaş, “A new hybrid taguchisalp swarm optimization algorithm for the robust design of real-world engineering problems,” Mater. Test., vol. 63, no. 2, pp. 157–162, 2021, https://doi.org/10.1515/mt-2020-0022.Search in Google Scholar

[14] M. Jahangiri, M. A. Hadianfard, M. A. Najafgholipour, M. Jahangiri, and M. R. Gerami, “Interactive autodidactic school: a new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems,” Comput. Struct., vol. 235, no. 106268, 2020, https://doi.org/10.1016/j.compstruc.2020.106268.Search in Google Scholar

[15] S. Kumar, G. G. Tejani, N. Pholdee, S. Bureerat, and P. Mehta, “Hybrid heat transfer search and passing vehicle search optimizer for multi-objective structural optimization,” Knowl. Base Syst., vol. 212, no. 106556, 2021, https://doi.org/10.1016/j.knosys.2020.106556.Search in Google Scholar

[16] H. Yi, Q. Duan, and T. W. Liao, “Three improved hybrid metaheuristic algorithms for engineering design optimization,” Appl. Soft Comput., vol. 13, no. 5, pp. 2433–2444, 2013, https://doi.org/10.1016/j.asoc.2012.12.004.Search in Google Scholar

[17] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, “Honey badger algorithm: new metaheuristic algorithm for solving optimization problems,” Math. Comput. Simulat., vol. 192, pp. 84–110, 2022, https://doi.org/10.1016/j.matcom.2021.08.013.Search in Google Scholar

[18] M. Yıldız, N. Panagant, N. Pholdee et al.., “Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems,” Mater. Test., vol. 63, no. 6, pp. 547–551, 2021, https://doi.org/10.1515/mt-2020-0091.Search in Google Scholar

[19] B. S. Yıldız, V. Patel, N. Pholdee, S. M. Sait, S. Bureerat, and A. R. Yıldız, “Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design,” Mater. Test., vol. 63, no. 4, pp. 336–340, 2021, https://doi.org/10.1515/mt-2020-0049.Search in Google Scholar

[20] B. Abdollahzadeh, F. SoleimanianGharehchopogh, and S. Mirjalili, “Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems,” Int. J. Intell. Syst., vol. 36, no. 10, pp. 5887–5958, 2021, https://doi.org/10.1002/int.22535.Search in Google Scholar

[21] A. R. Yildiz, N. Kaya, N. Öztürk, and F. Öztürk, “Hybrid approach for genetic algorithm and Taguchi’s method based design optimization in the automotive industry,” Int. J. Prod. Res., vol. 44, pp. 4897–4914, 2006, https://doi.org/10.1080/00207540600619932.Search in Google Scholar

[22] Y. Chen, P. Lundqvist, and P. Platell, “Theoretical research of carbon dioxide power cycle application in automobile industry to reduce vehicle’s fuel consumption,” Appl. Therm. Eng., vol. 25, no. 1415, pp. 2041–2053, 2005, https://doi.org/10.1016/j.applthermaleng.2005.02.001.Search in Google Scholar

[23] C. Iwendi, P. K. R. Maddikunta, T. R. Gadekallu, K. Lakshmanna, A. K. Bashir, and Md. J. Piran, “A metaheuristic optimization approach for energy efficiency in the IoT networks,” Software Pract. Ex., vol. 51, no. 12, pp. 2558–2571, 2021, https://doi.org/10.1002/spe.2797.Search in Google Scholar

[24] V. Patel, B. Raja, V. Savsani, and A. R. Yildiz, “Qualitative and quantitative performance comparison of recent optimization algorithms for economic optimization of the heat exchangers,” Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 2881–2896, 2021, https://doi.org/10.1007/s11831-020-09479-1.Search in Google Scholar

[25] C. Elsido, A. Cremonesi, and E. Martelli, “A novel sequential synthesis algorithm for the integrated optimization of Rankine cycles and heat exchanger networks,” Appl. Therm. Eng., vol. 192, p. 116594, 2021, https://doi.org/10.1016/j.applthermaleng.2021.116594.Search in Google Scholar

[26] M. S. Dehaj and H. Hajabdollahi, “Fin and tube heat exchanger: constructal thermo-economic optimization,” Int. J. Heat Mass Transfer, vol. 173, no. 121257, 2021, https://doi.org/10.1016/j.ijheatmasstransfer.2021.121257.Search in Google Scholar

[27] V. K. Patel, B. D. Raja, V. J. Savsani, and N. B. Desai, “Performance of recent optimization algorithms and its comparison to state-of-the-art differential evolution and its variants for the economic optimization of cooling tower,” Arch. Comput. Methods Eng., vol. 28, no. 7, pp. 4523–4535, 2021, https://doi.org/10.1007/s11831-021-09529-2.Search in Google Scholar

[28] R. Deharkar, A. Mudgal, and V. K. Patel, “Investigation on a small‐scale vertical tube evaporator multieffect desalination system: modeling, analysis, and optimization,” Heat Tran., vol. 50, no. 6, pp. 5332–5355, 2021, https://doi.org/10.1002/htj.22126.Search in Google Scholar

[29] C. Li, G. Chen, G. Liang, F. Luo, J. Zhao, and Z. Y. Dong, “Integrated optimization algorithm: a metaheuristic approach for complicated optimization,” Inf. Sci., vol. 586, pp. 424–449, 2022, https://doi.org/10.1016/j.ins.2021.11.043.Search in Google Scholar

[30] I. Ahmadianfar, A. Asghar Heidari, S. Noshadian, H. Chen, and A. H. Gandomi, “INFO: an efficient optimization algorithm based on weighted mean of vectors,” Expert Syst. Appl., vol. 195, no. 116516, 2022, https://doi.org/10.1016/j.eswa.2022.116516.Search in Google Scholar

[31] L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. A. Al-qaness, and A. H. Gandomi, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Comput. Ind. Eng., vol. 157, no. 107250, 2021, https://doi.org/10.1016/j.cie.2021.107250.Search in Google Scholar

[32] T. RahkarFarshi, “Battle royale optimization algorithm,” Neural Comput. Appl., vol. 33, no. 4, pp. 1139–1157, 2021, https://doi.org/10.1007/s00521-020-05004-4.Search in Google Scholar

[33] A. Kaveh, “Thermal exchange metaheuristic optimization algorithm,” in Advances in Metaheuristic Algorithms for Optimal Design of Structures, Cham, Springer International Publishing, 2021, pp. 733–782, https://doi.org/10.1007/978-3-030-59392-6_23.Search in Google Scholar

[34] M. Fesanghary, E. Damangir, and I. Soleimani, “Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm,” Appl. Therm. Eng., vol. 29, nos. 5–6, pp. 1026–1031, 2009, https://doi.org/10.1016/j.applthermaleng.2008.05.018.Search in Google Scholar

[35] R. Selbaş, Ö. Kızılkan, and M. Reppich, “A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view,” Chem. Eng. Process: Process Intensif., vol. 45, no. 4, pp. 268–275, 2006, https://doi.org/10.1016/j.cep.2005.07.004.Search in Google Scholar

[36] V. K. Patel and R. V. Rao, “Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique,” Appl. Therm. Eng., vol. 30, nos. 11–12, pp. 1417–1425, 2010, https://doi.org/10.1016/j.applthermaleng.2010.03.001.Search in Google Scholar

[37] J. Guo, L. Cheng, and M. Xu, “Optimization design of shell-and-tube heat exchanger by entropy generation minimization and genetic algorithm,” Appl. Therm. Eng., vol. 29, nos. 14–15, pp. 2954–2960, 2009, https://doi.org/10.1016/j.applthermaleng.2009.03.011.Search in Google Scholar

[38] S. Sanaye and H. Hajabdollahi, “Multi-objective optimization of shell and tube heat exchangers,” Appl. Therm. Eng., vol. 30, nos. 14–15, pp. 1937–1945, 2010, https://doi.org/10.1016/j.applthermaleng.2010.04.018.Search in Google Scholar

[39] A. ŞencanŞahin, B. Kılıç, and U. Kılıç, “Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm,” Energy Convers. Manage., vol. 52, no. 11, pp. 3356–3362, 2011, https://doi.org/10.1016/j.enconman.2011.07.003.Search in Google Scholar

[40] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems,” Comput. Ind. Eng., vol. 158, no. 107408, 2021, https://doi.org/10.1016/j.cie.2021.107408.Search in Google Scholar

[41] A. R. Yildiz and P. Mehta, “Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components,” Mater. Test., vol. 64, no. 5, pp. 706–713, 2022, https://doi.org/10.1515/mt-2022-0012.Search in Google Scholar

[42] P. Mehta, B. S. Yildiz, S. M. Sait, and A. R. Yildiz, “Gradient-based optimizer for economic optimization of engineering problems,” Mater. Test., vol. 64, no. 5, pp. 690–696, 2022, https://doi.org/10.1515/mt-2022-0055.Search in Google Scholar

Published Online: 2022-08-05
Published in Print: 2022-08-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Effect of heat treatment on the electrical and mechanical properties of a Cu–Ni–Si cast alloy
  3. Effect of isothermal heat treatments under Ms temperature on the microstructures and mechanical properties of commercial high-silicon spring steel
  4. Effect of austenitizing temperature on microstructure and properties of a high-speed cobalt steel
  5. Effect of hot rolling process parameters on the microstructure and mechanical properties of continuously cooled low-carbon high-strength low-alloy (HSLA) steel
  6. Mechanical and tribological properties of a WC-based HVOF spray coated brake disc
  7. Microstructure and mechanical properties of AISI 304/DUROSTAT 500 steel double-sided TIG welds
  8. A Nelder Mead-infused INFO algorithm for optimization of mechanical design problems
  9. Modeling of hexagonal honeycomb hybrids for variation of Poisson’s ratio
  10. Effect of elevated test temperature on the tensile strength and failure mechanism of hot-pressed dissimilar joints of laser ablation-treated AA5754-H111 and thermoplastic composite
  11. Steel shot peening effects on friction stir welded AA2014-T6 aluminum alloys
  12. Improvement of incremental sheet metal forming with the help of a pressurised fluid system
  13. Nugget formation, microstructural features and strength of resistance spot welded cold-rolled dual-phase steel lap joints for automotive applications
  14. African vultures optimization algorithm for optimization of shell and tube heat exchangers
  15. Effect of welding current on properties of activated gas tungsten arc super duplex stainless steel welds
Downloaded on 10.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/mt-2022-0050/html
Scroll to top button