On the Integration Role of Solvents in Process Synthesis-Design-Intensification: Application to Dmc/MeOH Separation

On the Integration Role of Solvents in Process Synthesis-Design-Intensification: Application to DMC/MeOH separation

 

Deenesh K. Babi, Rafiqul Gani

 

Department of Chemical and Bio-chemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, DK-2800, Kgs. Lyngby Denmark.

*rag@kt.dtu.dk

 

Solvents (mass separating agents) play an important role in separation-based processes. For example, consider the separation of an azeotropic mixture. If the azeotrope is not pressure dependent, then a feasible separation technique that can be employed for separation of the azeotrope is usually extractive distillation. In extractive distillation the solvent affects the relative volatility of the two key compounds to be separated. In other words, for a two column distillation sequence configuration, the lighter boiling compound is obtained as the top product of the first distillation column and the heavier boiling compound is obtained as the top product of the second distillation column where the solvent is recovered (for re-use and recycle).

Therefore, the solvent design problem can be defined as follows, given an azeotropic mixture to be separated into two pure streams that utilizes a mass separating agent, find the best (optimal or near-optimal) solvent candidate (or mixture) that can perform the separation subject to economic, environmental and thermo-physical property constraints. This design problem inherently is a mixed integer non-linear programming problem because the property-process models used can be linear, non-linear or a combination of both and, numerous solvents (or solvent mixtures) can in principle be selected (Lei et al., 2015).

In this work, the generation, screening and verification of the solvent candidate follows a three stage approach, in order to, decompose the solvent design problem into manageable sub-problems. In the first stage, a number of solvent candidates are generated based on pre-defined structural constraints, for example, acyclic, cyclic and/or aromatic compounds, etc. In the second stage, the solvent candidates are screened using property constraints, for example, temperature/non-temperature dependent properties and environmental properties. In stage 3, the selected feasible solvent candidates are verified through simulation for selection of the best (optimal) solvent candidate (mixture). In stages 1-3, property models play an integration role, service plus advice role and service role respectively (Kontogeorgis and Gani, 2004).

Application of the method is highlighted for a typical azeotropic mixture separation. Di-methyl carbonate (DMC) is an important chemical because it can be used as a fuel additive and is therefore considered to be one of the better replacements for methyl tert-butyl ether. Methanol (MeOH) is used as a common raw material in the production of DMC, for example, using phosgene with hydrochloric acid as the by-product, using carbon monoxide and oxygen with water as the by-product, using a cyclic carbonate with a glycol as the by-product, etc. Therefore, recovery/separation of MeOH/DMC is an important separation sequence encountered in generating more sustainable process alternatives for the production of DMC (Babi et al., 2015, Holtbruegge et al., 2014) using MeOH as the raw material. The objective of this presentation is to present the best separation system, with the focus on solvent generation, screening and verification for extractive distillation for the separation of MeOH and DMC. The three stage approach will be presented and it will be shown that existing solvent candidates found in the literature are already generated in the ‘'generation'' stage plus new solvent candidates. In the ‘'screening'' and ‘'verification'' stages, it will be shown that two solvent candidates (not previously reported) are selected that satisfy the structural, property and environmental constraints for the effective separation and recovery of MeOH and DMC. Finally, a design of experiments method will be presented in order to cover the design and pilot testing of the best solvent candidate.

 

References:

Babi, D. K., Holtbruegge, J., Lutze, P., Gorak, A., Woodley, J. M., & Gani, R. (2015). Sustainable Process Synthesis-Intensification. Computers & Chemical Engineering. doi:10.1016/j.compchemeng.2015.04.030

 

Gani, T. & O'Connell, J. P. (2004). Computer Aided Property Estimation for Process and Product Design. Computer Aided Chemical Engineering (Vol. 19). Elsevier. doi:10.1016/S1570-7946(04)80004-X

 

Holtbruegge, J., Kuhlmann, H., & Lutze, P. (2014). Process analysis and economic optimization of intensified process alternatives for simultaneous industrial scale production of dimethyl carbonate and propylene glycol. Chemical Engineering Research and Design. doi:10.1016/j.cherd.2014.05.002

 

Zhang, L., Cignitti, S., & Gani, R. (2015). Generic Mathematical Programming Formulation and Solution for Computer-Aided Molecular Design. Computers & Chemical Engineering. doi:10.1016/j.compchemeng.2015.04.022