Plant fibers contain valuable chemicals that can be used to make biofuels, plastics, medicines, and other products, but separating and purifying them is challenging, especially without using toxic solvents.
Now University of Wisconsin–Madison scientists have used machine learning to streamline the process of finding the best solvents for the job, balancing selectivity, efficiency, and environmental impact.
A collaboration between scientists with the Great Lakes Bioenergy Research Center (GLBRC) and Wisconsin Energy Institute, the process uses Bayesian experimental design, a framework used to make experiments more efficient and informative in uncertain situations.
The Bayesian framework uses statistical models to guess what a design space looks like based on existing knowledge and to decide which areas of the space to explore next. By balancing exploration of unknown areas and exploitation of promising ones, the framework can be used to improve predictive models.
That means instead of testing thousands of mixtures, researchers can instead focus on dozens of the most promising candidates, said Shannon Stahl, a professor of chemistry who led the project with Reid Van Lehn, Hunt-Hougen Associate Professor of chemical and biological engineering.
Van Lehn said the process, which combines computer modeling with lab experimentation, could speed innovation of bioproducts as well as pharmaceuticals.
“We think of this as a flexible technique that could be applied in multiple different contexts,” Van Lehn said. “And this is really just a proof of concept.”
Read more about their collaboration here.
Featured Image: Graduate student Surajudeen Omolabake with a liquid-handling robot used in a recent project that used machine learning to speed the selection of green solvent mixtures for extracting valuable molecules from plant biomass. Scientists trained computer model to select 40 mixtures predicted to have the best properties then used the liquid-handling robot to test them. The results of the experiments were then used to train the model. Photo Credit: Chelsea Mamott/Wisconsin Energy Institute