Machine learning could fast-track the formulation of tailor-made mixtures, including “greener” fuels.
An inverse mixture-design approach based on machine learning can teach computers to create mixtures from a set of target properties. Developed by KAUST, this could help find high-performance transport fuels that burn efficiently while releasing little carbon dioxide (CO2) into the atmosphere.
Greenhouse gas emissions are major contributors to rising global temperatures. A large proportion of CO2 emissions comes from the combustion of hydrocarbon fuels, such as gasoline, that power most automotive engines. A promising solution to these environmental issues is to engineer transport fuels that offer enhanced efficiency and lower carbon emissions.
There are several methods developed for fuel screening, but they are usually validated only on smaller blends, or require additional preprocessing, which makes these configurations unsuitable for inverse fuel design. “The key bottleneck is screening complex mixtures containing hundreds of components to predict synergistic and antagonistic effects of species on the resultant mixture properties,” says first author Nursulu Kuzhagaliyeva, a Ph.D. student in Mani Sarathy’s research group.
Kuzhagaliyeva, Sarathy and coworkers constructed a deep learning model — comprising multiple smaller networks dedicated to specific tasks — to screen fuels efficiently. “This problem was a good fit for deep learning that allows capturing nonlinear interactions between species,” Kuzhagaliyeva says. In the inverse-design approach, the researchers first defined combustion-related properties, such as fuel ignition quality and sooting propensity, and then determined potential fuels according to these properties.
Read more at KAUST Discovery.
