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.