(547g) Design of Extractive Distillation Processes Using Simulated Annealing and a Rigorous Process Simulator

Extractive distillation is a widely-used method for the separation of azeotropic mixtures, and many researchers have reported on the design and optimization of extractive distillation processes [1–7]. Due to the large number of design variables, most optimization work is either based on simplified column models, or is conductive iteratively one variable at a time. The former method is less accurate due to the simplified column model and the latter method is susceptible to being trapped in a local minimum.

In this work, continuous extractive distillation processes were optimized using simulated annealing (SA) and simulation in a rigorous process simulator (Aspen Plus). Connection between the two programs was achieved by means of a client-server architecture, with Matlab as the automation client and Aspen Plus as the automation server. Three separation problems were considered: separation of acetone and methanol, separation of hexane and ethyl acetate and the separation of n-hexane and tetrahydrofuran. For the separation of acetone and methanol, previous results for the process design are available in the literature for comparison. For the latter two separations, to our knowledge there are no reports of continuous process designs for the separation in the open literature. For each separation, several different candidate entrainers were considered and the costs were compared.

For the separation of acetone and methanol, the TAC of the process for each entrainer considered was lower than the initial condition taken from the literature. For the separation of n-hexane and ethyl acetate, NMP, 2-methylpyridine, 3-methylpyridine, DMF, and pyrrole were considered as possible entrainers. The results show that DMF offers the best performance and economic benefit. For the separation of n-hexane and tetrahydrofuran, the candidate entrainers were DMF, NMP, and 2-methylpyridine. DMF was again found to be the most suitable entrainer. The results show that the SA algorithm has the advantage of running automatically and has a high probability to obtain a design near the global optimum.

[1] Luyben, W. L., & Chien, I. L. (2011). Design and control of distillation systems for separating azeotropes. John Wiley & Sons.

[2] Widagdo, S., & Seider, W. D. (1996). Journal review. Azeotropic distillation. AIChE Journal, 42(1), 96-130.

[3] Andersen H W, Laroche L, Morari M. Effect of design on the operation of homogeneous azeotropic distillation. Computers & chemical engineering, 1995, 19(1): 105-122.

[4] Knapp, J. P., & Doherty, M. F. (1990). Thermal integration of homogeneous azeotropic distillation sequences. AIChE journal, 36(7), 969-984.

[5] Kossack, S., Kraemer, K., Gani, R., & Marquardt, W. (2008). A systematic synthesis framework for extractive distillation processes. chemical engineering research and design, 86(7), 781-792.

[6] Kossack, S., Kraemer, K., & Marquardt, W. (2006). Combining shortcut methods and rigorous MINLP optimization for the design of distillation processes for homogeneous azeotropic mixtures. In INSTITUTION OF CHEMICAL ENGINEERS SYMPOSIUM SERIES (Vol. 152, p. 122). Institution of Chemical Engineers; 1999.

[7] Garcia-Herreros, P. and J. M. Gomez (2011). "Optimization of the Design and Operation of an Extractive Distillation System for the Production of Fuel Grade Ethanol Using Glycerol as Entrainer." Industrial & Engineering Chemistry Research 50(7): 3977-3985.