Deep Learning Artificial Intelligence for Optimal Production and Quality of Carbon Nano-fibers from Green-house Gases

Carbon dioxide and methane are two dominant green-house gases responsible for global warming—a topic of major concern nowadays. Efforts are intensifying in both academia and industry all around the world to come up with innovative and cost-effective solutions to combat global warming. One such effort is Carbonova’s technology
that transforms carbon dioxide and methane into carbon nano-fibers—a material that is lighter, stronger, and more flexible than steel, with applications in electronic devices, sensors, and more. However, Carbonova’s technology involves complex chemical reactions at two stages, where at each stage there exists non-linear relationship between the various process parameters and the output of the stage. Currently, the complex chemical reactions and non-linear relationships are determined experimentally which is time-intensive and costly. The research proposes to use deep learning artificial intelligence (DL/AI) to model the complex chemical reaction and nonlinear behavior of the process parameters on the output. The intern will create DL/AI models, and then train, test, and validate the models, which are then used to predict the production rate of carbon and quality of carbon nanofibers. The expected benefit of the research is a reduction in the number of experiments performed, which translates to savings in operational expenditure for Carbonova.

Faculty Supervisor:

Abraham O. Fapojuwo;Abraham Fapojuwo

Student:

Partner:

Carbonova

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Calgary

Program: