(165a) 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