Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Qin, Shiyi; Omolabake, Surajudeen; Diaby, Aminata; Li, Jianping; González, Leonardo D.; Holland, Christopher M.; Zavala, Victor M.; Stahl, Shannon S.; Van Lehn, Reid C.

doi:10.1021/acssuschemeng.4c07423

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Journal Article · Fri Dec 13 00:00:00 EST 2024 · ACS Sustainable Chemistry & Engineering

DOI:https://doi.org/10.1021/acssuschemeng.4c07423· OSTI ID:2507032

Qin, Shiyi ^[1]; Omolabake, Surajudeen ^[1]; Diaby, Aminata ^[1]; ^[1]; ^[1]; Holland, Christopher M. ^[1]; ^[1]; ^[2]; ^[2]

University of Wisconsin - Madison, WI (United States)
University of Wisconsin - Madison, WI (United States); Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

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. Furthermore, 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.

View Accepted Manuscript (DOE)

Research Organization:: Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018409

OSTI ID:: 2507032

Journal Information:: ACS Sustainable Chemistry & Engineering, Journal Name: ACS Sustainable Chemistry & Engineering Journal Issue: 52 Vol. 12; ISSN 2168-0485

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

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