Computer-aided solvent selection for the design of sustainable biorefinery processes
Laura König-Mattern, Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
Flash Presentation: Chemical & Biological (Re-)Synthesis
Video Conference Room (BigBlueButton): https://bbb.mpi-magdeburg.mpg.de/meeting/SmartPSE2022-KoenigMattern
Abstract: Climate change and the dependence on depleting fossil resources require the development of innovative processes based on renewable resources. Biorefineries aim at sustainably utilizing biomass as a feedstock for chemicals, fuel, pharmaceuticals, food and feed, and contribute to closing material loops. In terms of zero-waste production, unused biomass is minimised by breaking it down into its macromolecular components, which are subsequently converted into a variety of valuable products. In this contribution, we present a novel method enabling the rational selection of solvents for biomass fractionation, exemplified by microalgal and lignocellulosic biomass. In our approach, a database containing more than 8000 potential solvents is screened for melting and boiling point ranges matching the various process conditions required for recalcitrant or heat-sensitive biomolecules. Furthermore, environmental, health and safety properties are estimated using quantitative structure-activity relationship models to prevent the use of harmful solvents. We select representative molecules from each biomass fraction to predict their solubility in the identified solvent candidates using the quantum chemical-based method COSMO-RS. For biomass with high moisture content, the water solubility of the solvents significantly influences their accessibility to the target molecules. Therefore, we also consider the phase behavior of the solvent/water mixture by predicting its liquid-liquid-equilibrium and partition coefficients of the representative biomolecules using COSMO-RS. Using this approach, green solvents for the design of innovative biorefinery processes can be identified in a systematic manner as demonstrated for microalgal and lignocellulosic biomass.