(657c) Discrete Element Method Model Calibration Using High-Speed Videos and Computer Vision for the Mechanochemical Grinding of Plastic Waste

Chemical recycling of polymers is a highly active topic of research that seeks to convert waste plastics to valuable chemicals through depolymerization. Conventional depolymerization pathways such as pyrolysis and solvolysis involve high energy consumption, use of chemicals or solvents and are capital intensive [1]. Such factors currently make depolymerization economically unviable. Therefore, the creation of new depolymerization processes is crucial in implementing sustainable practices and reducing negative environmental effects. One promising route is the mechanochemical depolymerization of plastic waste using ball-mill reactors, which has the potential to convert polymers in the solid state to valuable chemicals in a sustainable and economical way[2]. Physics-based models that accurately describe various phenomena in the ball mill reactor such as ball and particle interactions are necessary for process design, optimization, and reduction of energy and cost resources [3].

One promising route is Discrete Element Modeling (DEM)[4, 5]. While there is rich history of research in DEM simulations of ball mills for non-reactive applications such as in pharmaceuticals and mineral, studies on reactive ball mill systems like in the case of plastics depolymerization are virtually nonexistent. To address this gap, in this talk we will describe the use of high-speed video recordings of ball milling experiments in combination with computer vision algorithms to reproduce the kinematic interactions of a polymer milling system via DEM. A simulation-based optimization algorithm [6] is utilized to parametrize the DEM model, using kinematic data extracted from recordings of ball milling experiments. The resulting DEM simulation is exploited to identify phenomena most critical to the control/optimization of the mechanochemical depolymerization process. Combination of experimental results on the mechanochemical hydrolysis of polymer waste with energy information extracted from the DEM are used to construct relations between control parameters of the mill and depolymerization kinetics. Empirical correlations are used to relate the energy consumption of ball-mills reactors with operating costs. Further, the use of Reduced-Order Models (ROMs) is investigated to deal with the high computational cost of DEM simulations. Reduced-order models have played a significant role in connecting computationally expensive models with optimization algorithms [7], and in this work a ROM-DEM model is needed for embedding within a future flowsheet optimization study. In sum, this presentation aims to lay the foundation for DEM analysis of mechanochemical depolymerization inside a ball mill reactor.

Citations

[1] I. Vollmer et al., "Beyond Mechanical Recycling: Giving New Life to Plastic Waste," Angew Chem Int Ed Engl, vol. 59, no. 36, pp. 15402-15423, Sep 1 2020, doi: 10.1002/anie.201915651.

[2] V. Štrukil, "Highly Efficient Solid‐State Hydrolysis of Waste Polyethylene Terephthalate by Mechanochemical Milling and Vapor‐Assisted Aging," ChemSusChem, vol. 14, no. 1, pp. 330-338, 2021.

[3] M. H. Wang, R. Y. Yang, and A. B. Yu, "DEM investigation of energy distribution and particle breakage in tumbling ball mills," Powder Technology, vol. 223, pp. 83-91, 2012, doi: 10.1016/j.powtec.2011.07.024.

[4] L. M. Tavares, "A Review of Advanced Ball Mill Modelling," KONA Powder and Particle Journal, vol. 34, no. 0, pp. 106-124, 2017, doi: 10.14356/kona.2017015.

[5] N. Metta, M. Ierapetritou, and R. Ramachandran, "A multiscale DEM-PBM approach for a continuous comilling process using a mechanistically developed breakage kernel," Chemical Engineering Science, vol. 178, pp. 211-221, 2018/03/16/ 2018, doi: https://doi.org/10.1016/j.ces.2017.12.016.

[6] J. Zhai and F. Boukouvala, "Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization," Journal of Global Optimization, vol. 82, no. 1, pp. 21-50, 2021, doi: 10.1007/s10898-021-01045-8.

[7] F. Boukouvala, Y. Gao, F. Muzzio, and M. G. Ierapetritou, "Reduced-order discrete element method modeling," Chemical Engineering Science, vol. 95, pp. 12-26, 2013/05/24/ 2013, doi: https://doi.org/10.1016/j.ces.2013.01.053.