(325a) Discrete Element Method Analysis for the Mechanochemical Grinding of Polymers

Plastic and synthetic polymer usage has increased at an accelerated rate since their mass production started in the 1950s. Since then, a cumulative volume of 6.3 billion tons (in 2015) has been produced and only 9% of them have been recycled [1]. Plastic monomers from fossil hydrocarbons generally cannot be consumed by microorganisms, which leads to an increased accumulation of plastics in landfills and oceans [2]. Thus, identifying new technical pathways that can efficiently process and recycle plastic waste is one of the greatest challenges of the next years for preserving the environment [1]. One pathway is mechanochemical depolymerization wherein the polymer chain is deconstructed by the application of some form of mechanical energy which then activates the chemical reaction. Traditionally, these reactions are performed in ball mills—tumbling mills where the grinding media are balls—which are known to have high energy consumption and hence are very expensive to operate. Therefore, accurate models, that describe the particles’ interactions, based on physics, mechanics and chemistry are necessary for the optimization of their operation and reduction of expenses [3].

One promising route is Discrete Element Modeling (DEM). While there is rich history of research investigating DEM simulations for grinding applications such as in pharmaceuticals [4] and mining [5], such studies for depolymerizing plastics are as of yet very sparse. This submission adapts commonly used calibration tests to reproduce the kinematic interactions of a polymer ball-milling system via DEM. The resulting DEM simulation is exploited to identify phenomena most critical to and mechanisms uniquely apart of the control and optimization of polymer mechanocatalysis. Further, various reduced order modeling techniques are employed to develop faster and accurate representations of the DEM data, suitable for integration with population balance models for flowsheet modeling and optimization. Reduced-order models have played a significant role in connecting computational expensive models with optimization algorithms [6]. The techniques that will be presented employ Machine Learning regression and dimensionality reduction methods. In sum, this presentation aims to lay the foundation for DEM analysis and reduced-order modeling of polymer mechanocatalysis using ball-milling devices.

References

1. Å trukil, V., Highly Efficient Solid-State Hydrolysis of Waste Polyethylene Terephthalate by Mechanochemical Milling and Vapor-Assisted Aging. ChemSusChem, 2021. 14(1): p. 330-338.
2. Geyer, R., J.R. Jambeck, and K.L. Law, Production, use, and fate of all plastics ever made. Science Advances, 2017. 3(7): p. e1700782.
3. Wang, M.H., R.Y. Yang, and A.B. Yu, DEM investigation of energy distribution and particle breakage in tumbling ball mills. Powder Technology, 2012. 223: p. 83-91.
4. Sen, M., et al., A Multi-Scale Hybrid CFD-DEM-PBM Description of a Fluid-Bed Granulation Process. Processes, 2014. 2(1): p. 89-111.
5. Tavares, L.M., A Review of Advanced Ball Mill Modelling. KONA Powder and Particle Journal, 2017. 34: p. 106-124.
6. Boukouvala, F., et al., Reduced-order discrete element method modeling. Chemical Engineering Science, 2013. 95: p. 12-26.