Suppr超能文献

使用离散单元法模拟在球磨机反应器中聚对苯二甲酸乙二酯的机械化学解聚

Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations.

作者信息

Anglou Elisavet, Chang Yuchen, Bradley William, Sievers Carsten, Boukouvala Fani

机构信息

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta , Georgia 30332, United States.

Renewable Bioproducts Institute, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

出版信息

ACS Sustain Chem Eng. 2024 Jun 4;12(24):9003-9017. doi: 10.1021/acssuschemeng.3c06081. eCollection 2024 Jun 17.

Abstract

Developing efficient and sustainable chemical recycling pathways for consumer plastics is critical for mitigating the negative environmental implications associated with their end-of-life management. Mechanochemical depolymerization reactions have recently garnered great attention, as they are recognized as a promising solution for solvent-free transformation of polymers to monomers in the solid state. To this end, physics-based models that accurately describe the phenomena within ball mills are necessary to facilitate the exploration of operating conditions that would lead to optimal performance. Motivated by this, in this paper we develop a mathematical model that couples results from discrete element method (DEM) simulations and experiments to study mechanically-induced depolymerization. The DEM model was calibrated and validated via video experimental data and computer vision algorithms. A systematic study on the influence of the ball-mill operating parameters revealed a direct relationship between the operating conditions of the vibrating milling vessel and the total energy supplied to the system. Moreover, we propose a linear correlation between the high-fidelity DEM simulation results and experimental monomer yield data for poly(ethylene terephthalate) depolymerization, linking mechanical and energetic variables. Finally, we train a reduced-order model to address the high computational cost associated with DEM simulations. The predicted working variables are used as inputs to the proposed mathematical expression which allows for the fast estimation of monomer yields.

摘要

为消费型塑料开发高效且可持续的化学回收途径对于减轻与其生命周期末端管理相关的负面环境影响至关重要。机械化学解聚反应最近备受关注,因为它们被认为是将聚合物在固态下无溶剂转化为单体的一种有前景的解决方案。为此,需要基于物理的模型来准确描述球磨机内的现象,以促进对能实现最佳性能的操作条件的探索。受此启发,在本文中我们开发了一个数学模型,该模型将离散元法(DEM)模拟结果与实验相结合,以研究机械诱导解聚。通过视频实验数据和计算机视觉算法对DEM模型进行了校准和验证。对球磨机操作参数影响的系统研究揭示了振动研磨容器的操作条件与系统所供应的总能量之间的直接关系。此外,我们提出了聚对苯二甲酸乙二酯解聚的高保真DEM模拟结果与实验单体产率数据之间的线性相关性,将机械变量和能量变量联系起来。最后,我们训练了一个降阶模型来解决与DEM模拟相关的高计算成本问题。预测的工作变量被用作所提出数学表达式的输入,该表达式允许快速估计单体产率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/574d/11187622/c51eee076198/sc3c06081_0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验