Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used
technique
for the separation and purification of liquid-phase products with
applications in various industries, including pharmaceuticals, petrochemicals,
and renewable chemistry. A critical step in the design of an LLE process
is the selection of appropriate solvents. This study presents a new
methodology for identifying solvent mixtures for bioproduct separation
using Bayesian experimental design (BED). Motivated by the need for
environmentally friendly and effective separation methods, we address
the challenge of selecting solvent systems that balance separation
efficiency, selectivity, and environmental impact while also tackling
the difficulty of separating multiple bioproducts using complex solvent
systems. Our approach specifically seeks to predict product partition
coefficients (log10 Kp values)
as thermodynamic parameters underlying solvent selection. The iterative
approach integrates Bayesian optimization with experimental measurements
to guide solvent selection and leverages COSMO-RS simulations to enhance
high-throughput experimentation. Using the design of solvent systems
for the separation of lignin-derived aromatic products via centrifugal
partition chromatography (CPC) as a case study, we show that within
seven iterations/cycles of the methodology, we can identify new mixtures
of green solvents that align with CPC design principles. These results
demonstrate the efficacy of the BED framework in optimizing green
solvent systems for complex separations, highlighting the potential
of this method to advance the field of green chemistry and contribute
to the development of sustainable industrial processes.