Authors : Orlu Ndamati Moses; Akuma Oji; Muwarure Peter

Volume/Issue : Volume 11 - 2026, Issue 10 - October

---

Google Scholar : https://tinyurl.com/etpaz3ds

DOI : https://doi.org/10.38124/ijisrt/26Oct1063

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Conventional multi-objective optimisation (MOO) of methane pyrolysis reactors assumes model parameters as precisely known, while many crucial factors are highly unknown. This research uses NSGA-II and Monte Carlo sampling to produce and analyse robust Pareto fronts under uncertainty using a surrogate-assisted robust multi-objective optimisation framework. Kriging surrogates trained on 300 Latin hypercube designs with 500 Monte Carlo samples compare mean–variance, worst-case, and chance-constrained formulations.

Keywords : Robust Optimization; Multi-Objective Optimization; NSGA-II; Surrogate-Assisted Optimization; Methane Pyrolysis; Fluidized Bed; Chance Constraints.

References :

1. International Energy Agency, The Future of Hydrogen: Seizing Today’s Opportunities. Paris, France: IEA, 2019.
2. International Energy Agency, Global Hydrogen Review 2023. Paris, France: IEA, 2023.
3. N. Z. Muradov and T. N. Veziroğlu, “From hydrocarbon to hydrogen–carbon to hydrogen economy,” Int. J. Hydrogen Energy, vol. 30, no. 3, pp. 225–237, 2005.
4. H. F. Abbas and W. M. A. Wan Daud, “Hydrogen production by methane decomposition: A review,” Int. J. Hydrogen Energy, vol. 35, no. 3, pp. 1160–1190, 2010.
5. N. Sánchez-Bastardo, R. Schlögl, and H. Ruland, “Methane pyrolysis for zero-emission hydrogen production: A potential bridge technology from fossil fuels to a renewable and sustainable hydrogen economy,” Ind. Eng. Chem. Res., vol. 60, no. 32, pp. 11855–11881, 2021.
6. D. Kunii and O. Levenspiel, Fluidization Engineering, 2nd ed. Boston, MA: Butterworth-Heinemann, 1991.
7. P. Ammendola, R. Chirone, G. Ruoppolo, G. Russo, and R. Solimene, “Some issues in modelling methane catalytic decomposition in fluidized bed reactors,” Int. J. Hydrogen Energy, vol. 33, no. 11, pp. 2679–2694, 2008.
8. R. A. Dagle, V. Dagle, M. D. Bearden, J. D. Holladay, T. R. Krause, and S. Ahmed, “An overview of natural gas conversion technologies for co-production of hydrogen and value-added solid carbon products,” Argonne National Laboratory and Pacific Northwest National Laboratory, Rep. ANL-17/11; PNNL-26726, 2017.
9. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. Chichester, U.K.: Wiley, 2001.
10. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.
11. T. Keipi, H. Tolvanen, and J. Konttinen, “Economic analysis of hydrogen production by methane thermal decomposition: Comparison to competing technologies,” Energy Convers. Manage., vol. 159, pp. 264–273, 2018.
12. H.-G. Beyer and B. Sendhoff, “Robust optimization – A comprehensive survey,” Comput. Methods Appl. Mech. Eng., vol. 196, no. 33–34, pp. 3190–3218, 2007.
13. K. Deb and H. Gupta, “Introducing robustness in multi-objective optimization,” Evol. Comput., vol. 14, no. 4, pp. 463–494, 2006.
14. A. I. J. Forrester, A. Sóbester, and A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide. Chichester, U.K.: Wiley, 2008.
15. A. Bhosekar and M. Ierapetritou, “Advances in surrogate based modeling, feasibility analysis, and optimization: A review,” Comput. Chem. Eng., vol. 108, pp. 250–267, 2018.
16. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
17. P. Virtanen et al., “SciPy 1.0: Fundamental algorithms for scientific computing in Python,” Nat. Methods, vol. 17, pp. 261–272, 2020.
18. J. Blank and K. Deb, “pymoo: Multi-objective optimization in Python,” IEEE Access, vol. 8, pp. 89497–89509, 2020.
19. Q. Zhang and H. Li, “MOEA/D: A multiobjective evolutionary algorithm based on decomposition,” IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712–731, 2007.
20. A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, and E. Tsang, “Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion,” in Proc. 2006 IEEE Congr. Evol. Comput., 2006, pp. 892–899.
21. C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications. Berlin, Germany: Springer, 1981.
22. A. Charnes and W. W. Cooper, “Chance-constrained programming,” Manage. Sci., vol. 6, no. 1, pp. 73–79, 1959.
23. N. V. Sahinidis, “Optimization under uncertainty: State-of-the-art and opportunities,” Comput. Chem. Eng., vol. 28, no. 6–7, pp. 971–983, 2004.
24. A. Ben-Tal and A. Nemirovski, “Robust convex optimization,” Math. Oper. Res., vol. 23, no. 4, pp. 769–805, 1998.
25. D. R. Jones, M. Schonlau, and W. J. Welch, “Efficient global optimization of expensive black-box functions,” J. Global Optim., vol. 13, no. 4, pp. 455–492, 1998..