The replacement of fossil-based products by renewables will require advanced processes with high economic and ecological performance. To that end, a promising option is to use gamma-valerolactone (GVL), a biomass-derived solvent, to solubilize all fractions of lignocellulosic biomass. This approach was successfully used to produce soluble carbohydrates, levulinic acid, and furfural [1,2,3]. To separate GVL effectively, a co-solvent can be used to switch the miscibility of the system by lowering the temperature inside the process. Thereby, the carbohydrates are immediately separated in an aqueous stream, while the GVL is recycled back to the reactor . The resulting aqueous stream is then further deprived of GVL in a separation section, including an extraction and distillation column . A key to unlock the potential of such a process lies in the right (co-)solvent choice. Today, the solvent selection is still mainly conducted experimentally and decoupled from process design.
In this work, we combine computational and experimental methods to increase the molecular set of co-solvent candidates and simultaneously decrease the manual effort to conduct experiments, compared to conventional solvent selection. A quantum mechanical approach  to predict physical properties allows us to investigate a broad range of molecules. Integration of reduced-order process models [7,8], embedded in an optimization, enables evaluation of each solvent candidate on the minimal process operating costs. Also, environmental, health, and safety (EHS)  are evaluated. The suggested approach allows us to identify the co-solvent, leading to the best process performance from over 4 600 molecules. We test the best system experimentally for the co-solvent hydrolysis reaction and confirm the predicted phase compositions through IR spectroscopy. The results show toluene and ethylbenzene as promising co-solvents. Ethylbenzene shows a lower experimental extraction efficiency than toluene and would not be a leading candidate when the choice is solely based on experimental evaluation. We show that ethylbenzene is even better than the conventionally selected toluene with operating costs savings of 15% and a lower EHS score. The presented approach allows the rapid in silico assessment of a large number of co-solvents on the process level and therefore balances the trade-offs leading to optimal process performance.
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