Superstructure Reaction Network Design for the Synthesis of Biobased Sustainable Nitrogen-Containing Polymers

Abstract

Biomass-derived polymer production processes have been extensively studied to substitute petrochemical-based polymer synthesis routes. However, previous research predominantly focused on the synthesis of nitrogen-free polymers. Therefore, a novel superstructure reaction network was constructed in this study to convert different types of biomass waste into a range of commercial nitrogen-containing polymers. Trade-offs between process profit and greenness were analyzed to screen the large size of bioderived reaction pathways. Both mixed-integer programming and multiobjective optimization were formulated to effectively identify the best polymer candidates and their associated optimal synthesis route. Several commercial nitrogen-containing polymers including urea-formaldehyde and Nylon 1,6 were found to be promising candidates for future bioproduction. Moreover, the stability of current optimization results with respect to the availability of external nitrogen and hydrogen sources was also thoroughly analyzed. The results of this study can be used to guide the development of future renewable polymer production processes.

Tom Savage

Applied Data-Driven Optimisation

I am a PhD student at Imperial College London & 2023 Enrichment student at the Alan Turing Institute. I have a background in Chemical Engineering and still enjoy teaching labs at Imperial College. Alongside my work in process systems engineering, I am affiliated with Winchester School of Art producing installations with the Tate on the intersection between AI and art. My interests include Bayesian optimisation, human-in-the-loop machine learning, cricket 🏏, and darts 🎯.