Date of Graduation

5-2023

Document Type

Thesis

Degree Name

Bachelor of Science in Chemical Engineering

Degree Level

Undergraduate

Department

Chemical Engineering

Advisor/Mentor

Walters, Keisha B.

Committee Member/Reader

Chem, David

Committee Member/Second Reader

Walker, Heather

Committee Member/Third Reader

Beitle, Bob

Abstract

Lignin, an abundant biopolymer, is a waste byproduct of the paper and pulp industry. Despite its renewable nature and potential applicability in various products, such as plastics and composites, the development of lignin-based materials has been impeded by the cumbersome, Edisonian process of trial and error. This research proposes a novel approach to forecasting the properties of lignin-based copolymers by utilizing a recurrent neural network (RNN) based on the Keras models previously created by Tao et al. Example units of modified lignin were synthesized via esterification and amination functional group modifications. To increase the efficiency and accuracy of the prediction model, the Keras models were redeveloped as a PyTorch RNN model. The PyTorch RNN model produced similar or superior precision with a minimum decrease in execution time of 60%. Furthermore, the PolyInfo database was investigated for its potential use as a universal copolymer data set, and a complimentary interpreter was developed. While the interpreter produced a data set from basic copolymers, assembling the framework and composition of more intricate and ambiguous copolymers proved unreliable. Further exploration and development are required to construct a comprehensive copolymer data set from the PolyInfo database.

Keywords

Recurrent Neural Network, Machine Learning, Structure property relationships, polymer modeling, Python, modulus

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