Researchers at KAUST Propose a Data-Driven Artificial Intelligence (AI) Framework to Design Liquid Fuels Exhibiting Tailor-Made Properties for Combustion Engine Applications to Improve Efficiency and Lower Carbon Emissions

High-performance fuel design is essential to create cleaner burning and more efficient engine systems. To increase efficiency and reduce carbon emissions while designing liquid fuels for combustion engine applications, they present a data-driven artificial intelligence (AI) framework. The fuel design approach is a limited optimization challenge that combines two components:

•  A deep learning (DL) model to forecast the attributes of single components and mixes.
• Search algorithms to quickly move about the chemical space. 

Their method incorporates the mixing operator (MO) into the network architecture and provides the mixture-hidden vector as a linear combination of the vectors of each individual component in each blend. 

They show that the DL model predicts the attributes of pure components with comparable accuracy to rival computational techniques, whilst the search tool may produce a variety of candidate fuel combinations. The integrated framework was assessed to demonstrate the development of high-octane, low-smoke fuel that complies with gasoline specification requirements. Using an AI fuel design approach, fuel compositions may be developed quickly to increase engine efficiency and reduce emissions.

Most of the increase in global temperatures may be attributed to greenhouse gas emissions. The combustion of hydrocarbon fuels, such as gasoline, which power most vehicular engines, is a major source of CO2 emissions. Engineering transportation fuels with higher efficiency and reduced carbon emissions is a viable answer to these environmental problems.

Numerous methods for fuel screening have been developed; however, they are typically only proven on smaller blends or call for additional preprocessing, making these combinations unsuitable for inverse fuel design. According to the research group, “the key bottleneck is screening large mixes involving hundreds of components to forecast synergistic and antagonistic effects of species on the resultant combination attributes.”

To effectively screen, the researcher built a deep learning model consisting of numerous smaller networks dedicated to particular tasks. According to a researcher, “this problem was a strong fit for deep learning, which permits capturing nonlinear interactions between species.” The researchers used the inverse-design method to identify possible fuels by first defining combustion-related characteristics, such as fuel ignition quality and sooting propensity.

The researchers created a sizable database to train the model using experimental measures from the literature. The database included all kinds of pure substances, substitute fuel blends, and intricate mixtures, like gasoline.

The researchers had to incorporate vector representations into the model because no model could be modified for inverse fuel design. They created a mixing operator that directly connects hidden terms of pure compounds and mixes through linear combinations. This mixing operator was inspired by text processing methods that use hidden vectors to connect words to phrases. They also included search algorithms to find fuel mixes within a chemical space that match the predefined parameters.

The model correctly predicted the fuel ignition quality and the sooting propensity of different molecules and mixes. Additionally, it found several gasoline blends that met the predetermined standards.

This Article is written as a research summary article by Marktechpost Staff based on the research paper 'Artificial intelligence-driven design of fuel mixtures'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and reference article.

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