Multiobjective optimization of synthesis gas production using non-dominated sorting genetic algorithm

Synthesis gas finds wide applications in various chemical industries. Various routes for manufacture of synthesis gas have been reported such as steam reforming of methane, carbon dioxide reforming of methane, partial oxidation of methane and combination of both carbon dioxide reforming and partial oxidation of methane. However, very few studies have been reported on optimization of process parameters for synthesis gas production. These processes have multiple objective functions that are conflicting in nature and hence use of single objective optimization technique is not suitable. In this study therefore real parameter non-dominated sorting genetic algorithm has been used to obtain a Pareto optimal set of process parameters for production of synthesis gas from combined carbon dioxide reforming and partial oxidation of natural gas over a Pt/γ-Al2O3 catalyst. The objectives are to maximize the conversion of methane, maximize the selectivity of carbon monoxide and maintain the hydrogen to carbon monoxide mole ratio at approximately 1. The variables that have been taken are temperature, gas hourly space velocity and the oxygen to methane mole ratio. The results have been compared with that reported by other authors.