化学回收中混合热塑性塑料和废塑料热解的能源需求:模型预测和中试验证

Niklas Netsch*, Michael Zeller, Frank Richter, Britta Bergfeldt, Salar Tavakkol* and Dieter Stapf, 
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摘要

塑料废弃物热解是关闭人为碳循环的一项关键技术。与既有的回收途径相比,这一内热过程的能量需求(ED)是评估其效益的关键因素。热解的 ED 可以通过实验确定。不过,这种方法很复杂,而且可移植性有限。现有模型适用于实验室规模的原始塑料或碳氢化合物热塑性混合物。在此,我们开发了一种基于原始聚合物数据叠加的热塑性混合物 ED 计算模型。材料数据,如热容量、相变焓和反应焓,是通过差示扫描量热法确定的。在 1 公斤/小时的螺杆反应器中进行了中试实验。这些实验数据与模型计算结果进行了比较。热解的原料特定 ED 与塑料类型无关。它约占原料净热值的 4-6%。验证结果表明,原生塑料和烃类塑料混合物的 ED 非常吻合。包含杂原子的混合物的模型 ED 被系统性低估,这表明降解机制发生了变化。该模型可以解决导致热解 ED 的若干现象。通过对发生现象的深入了解来简单计算 ED,可以进行更可靠的工艺设计、优化和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Energy Demand for Pyrolysis of Mixed Thermoplastics and Waste Plastics in Chemical Recycling: Model Prediction and Pilot-Scale Validation

Pyrolysis of plastic waste is a key technology for closing the anthropogenic carbon cycle. The energy demand (ED) of this endothermic process is a crucial factor to evaluate its benefits compared to established recycling pathways. The pyrolysis ED can be determined experimentally. However, this is elaborate and limited in transferability. Existing models cover virgin plastics or hydrocarbon thermoplastic mixtures on a laboratory scale. Here, a model for calculating the ED of thermoplastic mixtures based on the superposition of virgin polymer data is developed. The material data, such as heat capacity, phase transition enthalpy, and reaction enthalpy, are determined using differential scanning calorimetry. Pilot-scale experiments are performed in a 1 kg/h screw reactor. These experimental data are compared to model calculations. The feedstock-specific ED for pyrolysis is plastic-type independent. It amounts to approximately 4–6% of the feedstocks’ net calorific value. The validation shows excellent accordance for virgin plastics and hydrocarbon plastics mixtures. The modeled ED of mixtures including heteroatoms is systematically underestimated, which indicates changes in the degradation mechanism. The model allows for resolving several phenomena contributing to the pyrolysis ED. The simple calculation of the ED with in-depth information on occurring phenomena enables more reliable process design, optimization, and evaluation.

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