Computational study of propene selectivity and yield in the dehydrogenation of propane via process simulation approach

Abstract

Abstract Propene is a vital feedstock in the petrochemical industry with a vast range of applications. And there is a continuous rise in propene demand. To gain insight into how the on-purpose method could help meet the demand in the propene market, we investigated the impact of temperature (T) and pressure (P) on product distribution in terms of product yield and selectivity using the process simulation approach. Existing related studies were deployed to identify possible products that could be evaluated in the simulation. In the study, we used Gibbs minimization (with Gibb’s reactor) to predict the likely products obtained at different T and P. The impact of feed purity on product distribution was also evaluated. The study was aided by using the Aspen HYSYS process simulator, while Design Expert was used to search for the optimum conditions for higher conversion, yield, and selectivity. Results obtained for the modeling and simulation of the process show that operating the production process at a lower pressure would favor higher selectivity within the temperature range of 500–600 °C. In comparison, the one run at a higher pressure was predicted to be only promising, showing better selectivity within the range of 550–650 °C. The feed purity significantly impacts the propene amount, especially for one with sulfur impurity, leading to the formation of smaller olefins and sulfide compounds. Our study reveals the importance of reviewing feed purity before charging them into the dehydrogenation reactor to prevent poisoning, coking, and other activities, which do lead to undesired products like methane and ethylene. A catalyst can also be designed to efficiently dehydrogenate the propane to propene at a lower temperature to prevent side reactions.