Massive, automated solvent screening for minimum energy demand in hybrid extraction-distillation using COSMO-RS

An automated approach for large-scale solvent screening is presented based on a comprehensive process-level assessment. In this solvent screening approach, COSMO-RS is used to efficiently predict physical properties for large numbers of solvents without the need for experimental data. The predicted thermodynamical behavior is used in pinch-based separation models for a thermodynamically sound and robust calculation of the minimum energy demand. With this approach, the performance of a hybrid extraction-distillation process is evaluated fully automated for more than 4600 solvents. The massive solvent screening approach is successfully applied to purification of the bio-based platform chemical gamma-valerolactone (GVL). Novel promising solvents are identified. A reduction of 63% is achieved in minimum energy demand using the best predicted solvent in comparison to the literature benchmark. Restricting the approach to known classes of solvents, we still find a reduction of 31%. The process-level assessment overcomes the limitations of heuristics based on physical properties only, and allows for efficient and robust solvent screening. (C) 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.