Exploring Iterative Applications of Machine Learning on Pyrolysis of Plastics

Authors: Eric Himebaugh; Owen Ferrara; Matthew Rando; Chris Skangos

Advisor: Michael Timko

Category: Undergraduate

Increasing amounts of plastic waste has prompted research into alternative chemical recycling solutions. Pyrolysis could prove to be a more sustainable way to recycle plastics and produce oil. By using a machine learning algorithm to analyze a pyrolysis data set, oil yields can be predicted from relevant independent variables such as feed composition, temperature, and reaction time. These predictions indicate that it is possible to optimize process parameters, thereby creating a more circular process and reducing energy waste.

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