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

A more efficient fuel design is needed to create a cleaner combustion and a more efficient engine system. To optimize 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:

  • An in-depth study model (DL) to predict the characteristics of a single compound and blend.
  • Find algorithms to move quickly about chemical space.

Their method integrates the melt operator (MO) into the network architecture and provides a hidden vector of the mixture, a linear combination of vectors of individual components in each mix.

They show that the DL model predicts the attributes of pure components with accuracy comparable to rival calculation techniques, while the search tool can generate variations of a candidate’s fuel combination. The combined framework is evaluated to demonstrate the development of low-octane, high-octane fuels that meet specific gasoline requirements. Using AI fuel design methods, fuel components can be quickly developed to increase engine efficiency and reduce emissions.

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Most of the increase in global temperature can be caused by greenhouse gas emissions. Combustion of hydrocarbon fuels, such as gasoline, which powers most car engines is a major source of CO2 emissions. Highly efficient, engineered and reduced carbon emissions are the viable answer to these environmental problems.

Several methods for fuel control have been developed; However, they are usually only shown on small mixtures, or so-called pre-refining, making these combinations unsuitable for reverse fuel design. According to the research team, “the main hurdle is examining large-scale mixtures involving hundreds of compounds to predict the overall and antitrust effects of a species on the resulting combination attributes.”

To effectively examine, the researchers developed an in-depth study model with several sub-networks dedicated to specific tasks. According to one researcher, “This problem is very suitable for in-depth learning, which allows to capture asymmetric interactions between species.” Researchers used a reverse design approach to identify potential fuels by identifying initial combustion-related characteristics, such as the quality of the ignition and the propensity to melt.

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Researchers have developed a large database to train models using experimental measures from the literature. The database includes all kinds of pure substances, alternative fuels and complex mixtures such as gasoline.

Researchers need to include vector representations in the model because none of the models can be modified for reverse fuel design. They developed mixers that directly linked the secretions of pure components and blended through linear combinations. This melting operation is inspired by a text processing method that uses hidden vectors to attach words to phrases. They also incorporate search algorithms to find fuel mixtures in the chemical space that correspond to predefined parameters.

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This model accurately predicts the ignition quality of the fuel and the inclination of the various molecules and mixtures. In addition, it has found many gasoline mixtures that meet 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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Ashish Kumar is an intern at MarktechPost. He is currently pursuing his Btech studies from the Indian Institute of Technology (IIT), Kanpur. He is eager to learn about the latest advances in technology and their real-life applications.


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