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467281 An Algorithm for Integrated Molecular and Process Design: Physically-Driven Domain Reduction for Liquid-Liquid Extraction

To address these challenges we have recently a developed a new algorithm for CAMPD of separation systems [3, 4]. The algorithm builds on the concept of embedding screening of molecules within an optimizer [5, 6]. Novel tests were introduced to screen both molecular *and *process variables, in the context of gas-liquid separation. In this approach, we first identify the process domain where the feed is stable. At each major iteration of an outer approximation (OA) algorithm [7], we find a reduced process domain for a solvent. The reduced process domain is an overestimation of the feasible region for that solvent. In a first test, the feasibility of solvent handling and storage is checked. Ina second test, the range of process variables over which the solvent and the feed can form a two-phase mixture is found. The range of process variables over which the required degree of purity of the treated stream may be attained is identified in a third test. If the solvent fails any of the tests it is eliminated. If the tests are feasible, the primal problem is solved using bounds identified by the tests and initial guesses within the reduced process domain. Information from the tests and the primal problem is used to construct the master problem for the OA algorithm.

The extension of this methodology to liquid-liquid extraction systems is presented here. The three tests, initially developed for absorption processes, are recast for the case of liquid-liquid extraction. The approach is illustrated with a case study of the separation of butanol from a fermentation broth. As the broth is dilute in butanol, separation of this mixture by distillation is highly energy intensive. Hence, an optimal solvent for extraction, and the corresponding optimal process variables, are designed using the algorithm proposed here. A group contribution equation of state, the group contribution version of the statistical associating fluid theory with a Mie potential, SAFT-γ Mie[8,9], is used in this work to predict the relevant thermodynamic properties. A systematic study of the performance of the algorithm is undertaken. The algorithm succeeds in avoiding expensive process evaluations for infeasible solvents and enhances convergence to the solution from multiple starting points.

1 Adjiman, C. S., Galindo, A., and Jackson, G. (2014). Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design FOCAPD 2014, edited by Eden M.R., Siirola J.D., Towler G.P., Computer Aided Chemical Engineering, Elsevier B.V.

2 Joback, K.G. and Stephanopoulos, G. (1995). In Advances in Chemical Engineering, 21: 257-311

3 Gopinath S, Galindo A, Jackson G, Adjiman CS (2016). Proceedings of the 26th European Symposium on Computer Aided Process Engineering, edited by Kravanja Z., Computer Aided Chemical Engineering. Elsevier B.V.

4 Gopinath S, Galindo A, Jackson G, Adjiman CS (2016). Computer-aided molecular and process design for absorption-desorption systems using physically-driven domain reduction (2016). Manuscript submitted

5 Buxton, A., Livingston, A. G., and Pistikopoulos, E. N. (1999). AIChE Journal, 45: 817-843

6 Giovanoglou, A., Barlatier, J., Adjiman, C. S., Pistikopoulos, E. N., and Cordiner, J. L. (2003). AIChE Journal, 49: 3095-3109

7 Duran, M. A. and Grossmann, I. E. (1986). Mathematical Programming, 36: 307-339

8 Papaioannou, V., Lafitte, T., Aveñdano, C., Adjiman, C. S., Jackson, G., Müller, E. A., and Galindo, A. (2014). The Journal of Chemical Physics, 140: 054107

9 Lafitte, T., Apostolakou, A., Avendaño, C., Galindo, A., Adjiman, C. S., Müller, E. A., and Jackson, G. (2013). The Journal of Chemical Physics, 139: 154504

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