(652e) Leveraging Discrete Element Method Simulations for Studying Mechanochemical Depolymerization of Polystyrene Waste

Mechanochemical depolymerization is a novel chemical recycling route that seeks to convert solid waste plastics into their constituent monomers through the application of mechanical forces and energy [1]. Polymers are loaded into mechanically agitated ball mills where they experience local high-energy collisions with grinding media. Subsequently, those events cause successive breakage of the chemical bonds of the polymer chains. The high dissipated energies and local temperatures experienced obviate the need for costly heating and large solvent volumes associated with traditional depolymerization methods thereby reducing the total environmental footprint [2, 3].

Understanding the mechanisms of depolymerization within mechanochemical recycling processes is critical to improving energetic efficiency, informing industrial scale-up studies and ultimately optimizing the mechanochemical process [4, 5]. This presentation focuses on the investigation of the kinetic rates and mechanisms of depolymerizing polystyrene (PS) waste in a lab-scale vibratory ball mill [6]. The inside of the mill contains many high-velocity particles and multiple grinding media, so the in-situ examination of particle kinetics and heat dissipation is experimentally infeasible [7]. To estimate the particle motions and energetics within the mill, a Discrete Element Method (DEM) simulation was developed and validated. The outcome of those simulations is a detailed representation (e.g., every tenth of a millisecond) of the movement and forces of both grinding media and polymer particles within the moving mill. Building off extensive particle comminution literature that leverage DEM outputs such as collision energy, heat dissipation, and compressive forces to predict particle breakage kinetics, we develop DEM informatics that predict depolymerization reactions [8, 9]. We leverage machine learning methods to analyze large, simulated datasets of high dimensionality to consider and compare potential indicators of depolymerization. Furthermore, by distinguishing particle-particle, particle-ball, and other collision types, as well as by tracking the successive collisions experienced by individual plastic particles, we use the DEM simulations to gain insight into the depolymerization reaction mechanism.

Finally, results from the DEM simulations are subsequently used as inputs to a population balance model (PBM) to form a hybrid model from which we can elucidate the temporal evolution of the PS molecular weight distribution (MWD) during depolymerization. The hybrid PBM retains the structure of traditional milling PBMs, but it is parameterized by physically relevant outputs of DEM simulation that predict depolymerization rates. The hybrid PBM is further informed by diverse experimental data and utilizes recently advanced machine learning methods to better predict MWDs at unseen conditions and to better understand kinetic patterns within the mill. Gaining a better understanding of the kinetics within the mechanochemical depolymerization process and capturing the dynamic evolution of MWDs throughout depolymerization are necessary tools for the effective optimization and industrial scaling of the mechanochemical recycling solution.

Citations

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

[2] A. W. Tricker, G. Samaras, K. L. Hebisch, M. J. Realff, and C. Sievers, “Hot spot generation, reactivity, and decay in mechanochemical reactors,” Chem. Eng. J., vol. 382, p. 122954, Feb. 2020, doi: 10.1016/j.cej.2019.122954.

[3] A. W. Tricker et al., “Stages and Kinetics of Mechanochemical Depolymerization of Poly(ethylene terephthalate) with Sodium Hydroxide,” ACS Sustain. Chem. Eng., vol. 10, no. 34, pp. 11338–11347, Aug. 2022, doi: 10.1021/acssuschemeng.2c03376.

[4] E. Anglou, Y. Chang, A. Ganesan, S. Nair, C. Sievers, and F. Boukouvala, “Discrete Element Simulation and Economics of Mechanochemical Grinding of Plastic Waste at an Industrial Scale,” in Computer Aided Chemical Engineering, vol. 52, A. C. Kokossis, M. C. Georgiadis, and E. Pistikopoulos, Eds., in 33 European Symposium on Computer Aided Process Engineering, vol. 52. , Elsevier, 2023, pp. 2405–2410. doi: 10.1016/B978-0-443-15274-0.50382-6.

[5] E. Anglou et al., “Process development and techno-economic analysis for mechanochemical recycling of poly(ethylene terephthalate),” Chem. Eng. J., vol. 481, p. 148278, Feb. 2024, doi: 10.1016/j.cej.2023.148278.

[6] Y. Chang et al., “Kinetic Phenomena in Mechanochemical Depolymerization of Poly(styrene),” ACS Sustain. Chem. Eng., vol. 12, no. 1, pp. 178–191, Dec. 2023, doi: 10.1021/acssuschemeng.3c05296.

[7] F. Delogu and L. Takacs, “Information on the mechanism of mechanochemical reaction from detailed studies of the reaction kinetics,” J. Mater. Sci., vol. 53, no. 19, pp. 13331–13342, Oct. 2018, doi: 10.1007/s10853-018-2090-1.

[8] M. H. Wang, R. Y. Yang, and A. B. Yu, “DEM investigation of energy distribution and particle breakage in tumbling ball mills,” Powder Technol., vol. 223, pp. 83–91, Jun. 2012, doi: 10.1016/j.powtec.2011.07.024.

[9] A. Datta and R. K. Rajamani, “A direct approach of modeling batch grinding in ball mills using population balance principles and impact energy distribution,” Int. J. Miner. Process., vol. 64, no. 4, pp. 181–200, Apr. 2002, doi: 10.1016/S0301-7516(01)00044-8.