Machine Learning Tools for Biofuel Creation and Purification using Ionic Fluids

Orlando Acevedo of the University of Miami is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop artificial intelligence tools that will aid in the design of biomass-derived fuels. Biomass has significant potential as a renewable source of energy. For example, it has been estimated that by 2030, 20% of transportation fuel and 25% of chemicals will be produced from biomass.

Despite an annual production of around 170 billion metric tons, no more than 5% of this produced biomass is used for diverse purposes. The potential market for value-added chemicals and high-quality fuel products produced from plant materials is immense. However, at present it is more cost-effective to drill and refine mineral diesel oil than grow, extract, and purify biodiesel.

Environmentally friendly solvents known as ionic liquids (ILs) and deep eutectic solvents (DESs) have the potential to serve as a significant part of the solution in the creation of economically viable biofuels. Dr. Acevedo will develop and apply artificial intelligence-based software (i.e., machine learning) to design new ILs and DESs that efficiently convert biomass-derived building blocks into liquid fuel and extract impurities from diesel and biodiesel fuels.

This project will help accelerate the transition from fossil fuels to biomass-derived energy by emphasizing the use of cost-effective and recyclable ionic fluids. The computational tools developed by Dr. Acevedo will be freely available to the scientific community.

In addition, multiple community outreach events will highlight the use of artificial intelligence in chemistry through demonstrations at science museums located in Ft. Lauderdale and Miami, Florida, research networking social gatherings, and an illustrated science poem contest for K-12 grades.

In the first major research thrust, Dr. Acevedo will study the conversion of the highly versatile biomass-derived 5-hydroxymethylfurfural (HMF) compound into (a) 5,5’-di(hydroxymethyl)furoin (DHMF) using N-heterocyclic carbene (NHC) catalysis, and (b) 2,5-bis(hydroxymethyl)furan (BHMF) via a Cannizzaro reaction. Both methods are efficient, mild, and eco-friendly.

To predict solvent-derived rate and selectivity enhancements upon these HMF transformations, a new mixed machine learning, quantum mechanics, and molecular mechanics (ML/QM/MM) methodology will be developed here that provides the accuracy of ab initio QM calculations with dramatically increased calculation speeds. In the second major research thrust, Dr.

Acevedo will explore the use of ILs and DESs to extract (a) dibenzothiophene and other aromatic sulfur-compounds from diesel, and (b) glycerol from biodiesel, a waste product developed during the transesterification of plant triglyceride oil to biofuel. Both ILs and DESs have properties ideally suited for impurity extraction from transportation fuel: insoluble in oil, non-toxic, and environmentally benign.

To study the origin of the favorable intermolecular interactions between these fuel-based impurities and ILs/DESs, our alternative method will train artificial neural networks (ANNs) using representative ab initio molecular dynamics data and couple the newly optimized ANN potentials to Monte Carlo simulations. Machine learning based methodology developed will be broadly applicable to other areas of chemical research and all software/products generated as part of this project will be readily available to the scientific community.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.