Work

Surrogate-Assisted Robust Multi-Objective Optimization of a Fluidized-Bed Methane Pyrolysis Reactor Under Technical and Market Uncertainty

Year: 2026

Type: article

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.

Source: International Journal of Innovative Science and Research Technology (IJISRT)

DOI: 10.38124/ijisrt/26oct1063

Authors: Orlu Ndamati Moses, Akuma Oji, Muwarure Peter

Institution: University of Port Harcourt

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W7223797233