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000877625 0247_ $$2doi$$a10.1016/B978-0-12-818634-3.50070-9
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000877625 0247_ $$2ISSN$$a2543-1331
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000877625 037__ $$aFZJ-2020-02340
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000877625 1001_ $$0P:(DE-HGF)0$$aGertig, Christoph$$b0
000877625 1112_ $$a29th European Symposium on Computer Aided Process Engineering$$cEindhoven$$d2019-06-16 - 2019-06-19$$wThe Netherlands
000877625 245__ $$aIntegrated Design of Solvents and Processes based on Reaction Kinetics from Quantum Chemical Prediction Methods
000877625 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2019
000877625 300__ $$a415 - 420
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000877625 4900_ $$aComputer Aided Chemical Engineering$$v46
000877625 520__ $$aThe choice of the employed solvent often strongly influences the performance of chemical processes. To obtain optimal process designs, we propose a method for the integrated in silico design of solvents and reaction-based processes. The search space of possible solvent molecules is explored by a genetic optimization algorithm which is directly linked to gradient-based process optimization. Thereby, the process performance of the designed solventis evaluated. While most approaches for such integrated design problems are based on group contribution methods and limited to equilibrium properties, we here propose a quantum mechanics-based approach to capture reaction kinetics. The integrated design method is successfully applied to the design of solvent and process for a carbamate cleavage reaction. The presented method allows for efficient design of a large number of promising solvents within the integrated reaction solvent and process design.
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000877625 7001_ $$0P:(DE-HGF)0$$aLeonhard, Kai$$b1
000877625 7001_ $$0P:(DE-Juel1)172023$$aBardow, André$$b2$$eCorresponding author$$ufzj
000877625 773__ $$a10.1016/B978-0-12-818634-3.50070-9
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