Isaac, S., Kadurumba, C., Ugwu, H., Abam, F. (2025). Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems. , 43(1), 101-114. doi: 10.30684/etj.2024.155144.1846
S. O. Isaac; C. H. Kadurumba; H. U. Ugwu; F. I. Abam. "Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems". , 43, 1, 2025, 101-114. doi: 10.30684/etj.2024.155144.1846
Isaac, S., Kadurumba, C., Ugwu, H., Abam, F. (2025). 'Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems', , 43(1), pp. 101-114. doi: 10.30684/etj.2024.155144.1846
Isaac, S., Kadurumba, C., Ugwu, H., Abam, F. Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems. , 2025; 43(1): 101-114. doi: 10.30684/etj.2024.155144.1846

Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems

Engineering and Technology Journal
Volume 43, Issue 1, January 2025, Pages 101-114    PDF (1.13 M)
Document Type: Research Paper
DOI: 10.30684/etj.2024.155144.1846
Authors
S. O. Isaac* 1; C. H. Kadurumba2; H. U. Ugwu2; F. I. Abam3
1Mechanical Engineering Dept., Abia State University, PMB 2000 Uturu, Abia State, Nigeria. Mechanical Engineering Dept., Michael Okpara University, Umudike, Abia State, Nigeria.
2Mechanical Engineering Dept., Michael Okpara University, Umudike, Abia State, Nigeria.
3Mechanical Engineering Dept., University of Calabar, Calabar, Cross Rivers State, Nigeria.
Abstract
This study applied a population-based metaheuristic algorithm to optimize liquid nitrogen pumping in hydrocarbon and allied fluids piping systems to ensure optimal process efficiency, minimize energy consumption, and minimize operational costs without extensive computational tasks while considering uncertainties and variability. Liquid nitrogen pumping in hydrocarbon and allied fluids piping systems involves a heat and mass transfer process where by liquid nitrogen flows from the storage tank to the pump unit and is converted to the gaseous state in an integrated heat exchanger. The continuous high pressure from the triplex reciprocating pump pushes the gaseous nitrogen to the hydrocarbon and allied fluids piping systems. Hence, the pumping is for purging, inerting, or pneumatic testing of the piping systems. Thus, the pumping process was parameterized and parametrically characterized to define the performance and control parameters quantitatively. Surrogate optimization models were formulated using an efficient response surface method. Since the models are multiparametric, multi-objective genetic algorithms were utilized to provide global optimal solution sets for the models. The results show that 3.79% decrease in pressurized volume of the piping systems, 1.48% decrease in test pressure of the piping systems, 3.42% increase in maximum discharge pressure of the liquid nitrogen pump and 1.08% increase in maximum flow rate of the liquid nitrogen pump across test packs increased total volume of liquid nitrogen pumped by 40.00%, decreased test duration by 0.70%, decreased pumping duration by 0.70% and increased total volume of liquid nitrogen used by 8.00% and vice versa.

Highlights
  • Liquid nitrogen pumping is used for leak testing in hydrocarbon and allied fluid piping systems.
  • Process parameters were optimized using population-based metaheuristic techniques.
  • Multi-objective genetic algorithms were applied to achieve optimal solutions.
  • Optimal pumping performance and control parameters were identified.
  • The achieved process enhanced efficiency with minimal energy consumption and operational
Keywords
Liquid nitrogen pumping; Piping systems leak test; Metaheuristic optimization; Process efficiency; Energy; cost minimization
References
  1. Ushakov, I. A., Probabilistic Reliability Models, John Wiley & Sons Inc., Hoboken, New Jersey, 2012.
  2. Weise, T., Global Optimization Algorithms – Theory and Application,3rd Ed., 2011.
  3. Kaveh, A, Advances in Metaheuristic Algorithms for Optimal Design of Structures, Springer International Publishing, Switzerland, 2014.
  4. Voss, S., Metaheuristics, Floudas, C. A. and Pardalos, P. M., 2009, Encyclopaedia of Optimization, Eds., Springer Reference, 2nd ed., Springer Science + Business Media, New York.
  5. X. –S.Yang, S. F. Chien, and T. O. Ting, Computational Intelligence and Metaheuristic Algorithms with Applications, Sci. World J., 2014 (2014) 1- 4. https://doi.org/10.1155/2014/425853
  6. Luke, S., Essentials of Metaheuristics: A Set of Undergraduate Lecture Notes, 2nd , Online Version, 2015.
  7. Marinakis, Y., 2009, Metaheuristic Algorithms for the Vehicle Routing Problem, Eds., Floudas, C. A. and Pardalos, P. M., Encyclopaedia of Optimization, 2nd , Springer Science + Business Media, New York.
  8. Koziel, S., Yang, X.S. ‏ Computational Optimization, Methods and Algorithms Studies in Computational Intelligence, Springer Science + Business Media, New York, 2011. https://doi.org/10.1007/978-3-642-20859-1
  9. Gosavi, A. Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning. Second Edition; Springer Science + Business Media, New York, 2015
  10. Radosavljević, J., Metaheuristic Optimization in Power Engineering, The Institution of Engineering and Technology, London, 2018.
  11. Talbi, E.-G., Metaheuristics: From Design to Implementation, John Wiley & Sons, New Jersey, 2009.
  12. M. Raheleh and H. Reza, Proposing a Meta-Heuristic Algorithm for Enhanced Oil Recovery Using CO2 Injection, Open J. Yangtze Gas Oil, 1 (2-16) 47 – 58. https://doi.org/10.4236/ojogas.2016.13007
  13. Reeves, C., 2010, Genetic Algorithms, Handbook of Metaheuristics, Glover, F. and Kochenberger, G. A., , Ed., Kluwer Academic Publishers, Dordrecht.
  14. A. B. Majid, M. H. and M. R. Arshad, A Combined systematic and metaheuristic approach for cooperative underwater acoustic source localization by a group of autonomous surface vehicles, Indian J. Geo Mar. Sci., 46 (2017) 2434 – 2443.
  15. Apitzsch, T., Kloffer, C., Jochem, P., Doppelbauer, M. and Fichtner, W., Metaheuristics for online drive train efficiency optimization in electric vehicles, Karlsruhe Institute of Technology, 2016.
  16. Delahaye, D., Chaimatanan, S., Mongeau, M. 2019. Simulated Annealing: From Basics to Applications. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, Vol. 272, pp. 1–35. Springer, Cham. https://doi.org/10.1007/978-3-319-91086-4_1
  17. Gendreau, M. and Potvin, J.-Y., Tabu Search, and Potvin, J.-Y., 2019, Handbook of Metaheuristics, Eds., 3rd ed., Springer International Publishing AG.
  18. Hansen, P., Mladenovic, N., Brimberg, J. and Perez, J. A. M., 2019, Variable Neighborhood Search, Gendreau, M. and Potvin, J.-Y., Handbook of Metaheuristics, Eds., 3rd ed., Springer International Publishing AG.
  19. Gandomi, A. H., Yang, X.-S., Talatahari, S. and Alavi, A. H., 2013, Metaheuristic Algorithms in Modelling and Optimization, Gandomi, A. H., Yang, X.-S., Talatahari, S. and Alavi, A. H. (Eds.), Metaheuristic Applications in Structures and Infrastructure, Elsevier Inc.
  20. J. O. Agushaka and A. E. Ezugwu, Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review, Appl. Sci., 12 (2022) 896. https://doi.org/10.3390/app12020896.
  21. F. N. Arici, and E. Kaya, Comparison of Metaheuristic Algorithms on Benchmark Functions, 7th Int. Symp, Innovative Technologies in Engineering and Science, 22 – 24 November, 2019. https://doi.org/10.33793/acperpro.02.03.41
  22. M. W. Ahmad, M. Mourshe, B. Yuce, and Y. Rezgui, Computational Intelligence Techniques for HVAC Systems: A Review, Build. Simul., 9 (2016) 359 – 398. https://doi.org/10.1007/s12273-016-0285-4
  23. Coello, C. A. C., Lamot, G. B. and Van Veldhuizen, D. A., Evolutionary algorithms for solving multi-objective problems, 2nd ed, Springer Science + Business Media, New York, 2007.
  24. C. Lagos, B. Crawford, E. Cabrera, R. Soto,J.-M. Rubio, and F. Paredes, Comparing evolutionary strategies on a biobjective cultural algorithm, Sci. World J., 2014 (2014) 1-10. https://doi.org/10.1155/2014/745921
  25. Deb, K., Multi-objective optimization using evolutionary algorithms: an introduction, KanGAL report number 2011003, 2011.
  26. Casas, N., Genetic Algorithms for Multimodal Optimization: A Review, arXiv preprint, 2015. https://doi.org/10.48550/arXiv.1508.05342
  27. S. Gupta, and A. Jawdekar, Performance Measurement on Multi-Objective Optimization with its Techniques, Int. J. Database Theory Appl., 9 (2016) 173 – 186. http://dx.doi.org/10.14257/ijdta.2016.9.4.16
  28. C. M. Fonseca, and P. J. Fleming, Genetic Algorithms for Multi-Objective Optimization: Formulation, Discussion and Generalization, 5th Conf. San Mateo, Canada, (1993).
  29. A. Jabri, A. El Barkany, and A. El Khalfi, Multi-Objective Optimization using Genetic Algorithms of Multi-Pass Turning Process, Engineering, 5 (2014) 601- 610. http://dx.doi.org/10.4236/eng.2013.57072
  30. J. K. Arthur, E. A. Frimpong, and J. O. Adjei, Optimization Algorithms for Solving Combined Economic Emission Dispatch: A Review, Proc. World Congress on Engineering and Computer Science, October 22 – 24, San Francisco, 2019.
  31. S. A. Sirigu, L. Foglietta, G. Giorgi, M. Bonfanti, G. Cervelli, G. Bracco, and G. Mattiazzo, Techno-Economic Optimization for a Wave Energy Converter via Genetic Algorithm, J. Mar. Sci. Eng., 8 (2020) 482. http://dx.doi.org/10.3390/jmse8070482
  32. T. Sibalija, Parametric Optimization of Integrated Circuit Assembly Process: An Evolutionary Computing-Based Approach, Proceedings of CECNet ,345, 2021, 239 - 246. http://dx.doi.org/10.3233/FAIA210408
  33. Y. Yang, and R. Li, Techno-Economic Optimization of an Off-Grid Solar/Wind/Battery Hybrid System with a Novel Multi-Objective Differential Evolution Algorithm, Energies, 13 (2020) 1585. https://doi.org/10.3390/en13071585
  34. J. M. Weaver-Rosen, P. B. C. Leal, D. J. Hartl, and R. J. Malak, Parametric optimization for morphing structures design: application to morphing wings adapting to changing flight conditions, Struct. Multidiscip. Optim., 62 (2020) 2995 – 3007. https://doi.org/10.1007/s00158-020-02643-y
  35. V. Kumar, and S. M. Yadav, A State-of-the-Art Review of Heuristic and Metaheuristic Optimization Techniques for the Management of Water Resources, Water Supply, 22 (2022) 3702–3728. https://doi.org/10.2166/ws.2022.010
  36. S.V. Konstantinov, A.A. Baryshnikov, Comparative Analysis of Evolutionary Algorithms for the Problem of Parametric Optimization of PID Controllers, Procedia Comput. Sci., 103 (2017) 100 – 107. https://doi.org/10.1016/j.procs.2017.01.021
  37. X. Shaoa , C. Lia ,S. Zhanga D. Menga , Optimal a Partial Emission Circulation Pump in Cryogenic Systems Based on Reducing Hydraulic Loss and Improving Cavitation, SSRN J., (2023). https://dx.doi.org/10.2139/ssrn.4390479
  38. S.P. Sivasree, B. Nitin, Optimal Design of Coolant Jacket for Cryogen Transfer Pipelines, The Canadian J. Chem. Eng.,102 (2024) 3867 – 3878. https://doi.org/10.1002/cjce.25368
  39. H. Tan, H. Wu, Q. Zhang, G. Lei, Q. Chen, Surrogate-Assisted Multi-Objective Optimization of a Liquid Oxygen Vacuum Subcooling System Based on Ejector and Liquid Ring Pump, Processes, 10 (2022) 1188. https://doi.org/10.3390/pr10061188
  40. S. Asghari, N.J. Navimipour, Review and Comparison of MetaHeuristic Algorithms for Service Composition in Cloud Computing, Majlesi J. Multimedia Process., 4 (2015) 28-34.
  41. N. Zlobinsky, D.L. Johnson, A.K. Mishra, A.A. Lysko‏, Comparison of metaheuristic algorithms for interface-constrained channel assignment in a hybrid dynamic spectrum access – Wi-Fi infrastructure WMN, IEEE Access 10 (2022) 26654 – 26680. https://doi.org/1109/ACCESS.2022.3155642
  42. M. K. Tana, H. S. Ee Chuo, G. Lim, R. K. Yin Chin, S. S. Yang, K. T. Kin Teo, A Comparison Study of Deterministic and Metaheuristic Algorithms for Stochastic Traffic Flow Optimization under Saturated Condition, J. Soft Comput., 10 (2020) 2117- 2123. https://doi.org/10.21917/ijsc.2020.0301
  43. Smith, J. E., 2002, Genetic Algorithms, Pardalos, P. M. and Romeijn, H. E. Handbook of Global Optimization Volume 2, (Eds.) Springer Science+Business Media Dordrecht.
  44. B. Xue, L. Cervante, L. Shang, W. N. Browne, M. Zhang, Multi-Objective Evolutionary Algorithms for Filter Based Features Selection in Classification, Int. J. Artif. Intell. Tools, 22 (2013) 1-31. https://doi.org/10.1142/S0218213013500243
  45. Sivanandam, S. N. and Deepa, S. N. 2008. Introduction to Genetic Algorithms, Springer-Verlag, Berlin Heidelberg, pp. 15–37. https://doi.org/10.1007/978-3-540-73190-0_2
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