Maximizing the hydrogen content for methanol steam reforming processes by using the novel pareto-based multi-objective evolutionary algorithms

This research focuses on the methanol-steam reforming (MSR) process to produce hydrogen-rich syngas. A thermodynamic equilibrium reactor was designed for the process, using the Peng-Robinson fluid package for all liquid and gas components. This research aims to reveal the collective impacts of three main parameters—reaction temperature (RT) (100–500 °C in 50 °C intervals), reactor pressure (RP) (1–7 atm in 2 atm intervals), and methanol-to-water (MtW) molar ratio (0. 25, 0. 5, 1, 2, and 4 atm) —on syngas composition. Additionally, Pareto-based multi-objective evolutionary algorithms (MOEAs), including Multimodal Multi-Objective Differential Evolution with Improved Crowding Distance (MMODEICD), Multi-Objective Slime Mould Algorithm (MOSMA), and Improved Multi-Objective Manta-Ray Foraging Optimization (IMOMRFO), were used to maximize hydrogen composition at the reactor outlet. Using these algorithms, the operating parameters for the MSR were optimized. The highest hydrogen content achieved under these conditions was 67. 90% among syngases. However, it could be increased by 7. 22% with MMODEICD, 6. 92% with MOSMA, and 4. 71% with IMOMRFO algorithms. Furthermore, the algorithms predicted actual data with error margins of 1. 1% for MMODEICD, 0. 28% for MOSMA, and 3. 52% for IMOMRFO. In conclusion, this research demonstrates that Pareto-based multi-objective evolutionary algorithms are very effective tools for increasing hydrogen production in MSR processes. • Worked on the methanol steam reforming process to maximize the hydrogen content among syngas. • Used novel three Pareto-based multi-objective evolutionary algorithms to optimize the operation parameters. • Optimized the reactor temperature, pressure, and methanol-to-water molar ratio. • Noticed that the methanol-to-water molar ratio is the most effective parameter of the hyrogen content. • Increased the hydrogen composition between 4. 71% and 7. 22% with the optimized parameters.