Abstract
Catalytic pyrolysis of plastic waste is a promising approach for producing value-added fuels and chemicals; however, achieving high liquid yield and olefin fraction under mild conditions remains challenging. Here, Mo–Zn/γ-Al₂O₃ catalysts were systematically investigated for the catalytic pyrolysis of high-density polyethylene (HDPE), with emphasis on the effects of metal composition, reaction temperature, and calcination temperature in a fixed-bed reactor. The bimetallic catalysts promoted dehydrogenation and cracking, improving both liquid production and olefin formation. Under the optimal experimental conditions, Mo5Zn5 produced a liquid yield of 65.74±0.57 wt%, an olefin fraction of 57.81±1.03 area%, and a higher heating value of 43.62±0.16 MJ kg⁻¹ . Machine-learning models were further employed to predict the pyrolysis performance, with the optimized ANN model showing the highest predictive accuracy, achieving R values of 0.9926, 0.9810, and 0.9762 for liquid yield, HHV, and olefin fraction, respectively. These results demonstrate that the integration of catalyst design and machine learning provides an effective strategy for optimizing plastic pyrolysis processes, offering a promising pathway toward the rational design of catalytic systems for sustainable fuel production.
| Original language | English |
|---|---|
| Article number | 108128 |
| Journal | Journal of Analytical and Applied Pyrolysis |
| Volume | 200 |
| DOIs | |
| State | Published - Nov 2026 |
Bibliographical note
Publisher Copyright:© 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Artificial neural network
- Catalytic dehydrogenation cracking
- Fixed-bed reactor
- HDPE pyrolysis
- Olefin recovery
ASJC Scopus subject areas
- Analytical Chemistry
- Fuel Technology
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