Multi-objective optimization for methane, glycerol, and ethanol steam reforming using lichtenberg algorithm

The growing energy demand is causing the energy sector to look for new sources of efficient and environmentally acceptable fuels. Although hydrogen is traditionally produced through steam reforming of fossil fuels, such as natural gas, different fuels and applications, have also been considered over the years. In this sense, several studies have focused on finding the best operational conditions for enhancing hydrogen production for each particular cycle. This study provides a statistically detailed analysis of hydrogen production using Response Surface Methodology and Lichtenberg Algorithm, aiming to develop a methodology that can quickly optimize steam reforming cycles respecting process limitations, different feedstock compositions, and other particularities. Process optimization was conducted by creating a direct and interactive link between the thermodynamic simulation software and the optimization algorithm. Lichtenberg Algorithm proved to be an efficient multi-objective optimization tool for quickly optimizing steam reforming cycles, finding Pareto fronts with substantial convergence and coverage. Finally, comparison with other optimization studies showed that previously suggested optimal conditions are close to points obtained from the Lichtenberg algorithm, thereby proving that this new methodology offers a quick and consistent method for optimizing steam reforming and potentially other thermodynamic cycles.