An Explainable Classification Framework for Determining and Understanding the Suitability of Solvent Extraction for Bioproduct Recovery

Lignocellulosic biomass is an abundant feedstock for producing sustainable fuels and chemicals. However, a key challenge in most biomass utilization strategies is the recovery of products from a dilute, typically aqueous, phase. In this respect, liquid-liquid extraction, which relies on a solvent to transfer a product of interest from one liquid phase to another (solvent-rich) phase, is a technology that can reduce the energy requirements for product recovery. To reduce solvent consumption, liquid-liquid extraction needs to be combined with another separation method (e.g., distillation) to recycle the solvent. Despite the research on solvent extraction, there are limited system-wide methods that allow us to determine when extraction is well suited to carry out a specific separation. Accordingly, we present a classification framework to predict whether extraction, coupled with distillation, is feasible and more economical than distillation. Our framework is based on features such as feed composition, liquid-liquid equilibrium constants, relative volatilities, and solvent price, and leads to trained classifiers that show good prediction accuracy. We further study how specific features influence the suitability of extraction. To showcase the applicability of the framework, we use it to analyze the separation of acetic acid from water.