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 , 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.
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