Process design and 4E analysis of different distillations of ethyl acetate/ethanol/cyclohexane separation based on extractive and pressure-swing properties

In this study, we proposed and compared extractive distillation, extractive pressure-swing distillation (EPSD), and pressure-swing distillation (PSD) techniques for the efficient separation of the ethyl acetate/ethanol/ cyclohexane pressure-sensitive azeotropic mixture. Based on the influence of the extractant on the relative volatility and the analysis of the ternary phase diagram, dimethyl sulfoxide was selected as the extractant for extractive distillation. The pressure range for PSD was also determined. We employed the non-dominated sorting genetic algorithm II to optimize different processes, considering equipment and operation costs as the objective functions. After the multi-objective optimization, the decision solution was selected using the minimum distance method. A heat exchange network was also used to explore the energy-saving effects of different processes. The aforementioned processes were comprehensively evaluated based on economic, energy, environmental, and exergy analyses. The process results were correlated with the phase diagram based on the normal distance. The total annual cost, energy consumption, gas emissions, and energy loss of the improved processes decreased by more than 10%, 15%, 16%, and 3%, respectively, compared to those of the conventional process. Among the conventional and improved processes, EPSD and heat-integrated EPSD were identified as the best, respectively.

Automated summary

In this study, extractive distillation, extractive pressure-swing distillation (EPSD), and pressure-swing distillation (PSD) techniques were proposed and compared for the separation of the ethyl acetate/ethanol/cyclohexane azeotropic mixture. The impact of extractant on relative volatility and the analysis of the ternary phase diagram were considered, and dimethyl sulfoxide was selected as the extractant. Non-dominated sorting genetic algorithm II was used for process optimization, considering equipment and operation costs as objective functions. The improved processes showed significant reductions in cost, energy consumption, gas emissions, and energy loss compared to the conventional process.