Biomass pyrolysis over ZIF-67 catalyst: thermogravimetric, pyrolysis vapors, kinetics, thermodynamics and artificial neural network modelling
| dc.Affiliation | October University for modern sciences and Arts MSA | |
| dc.contributor.author | Samy Yousef | |
| dc.contributor.author | Vilmantė Kudelytė | |
| dc.contributor.author | Nerijus Striūgas | |
| dc.contributor.author | Mohammed Ali Abdelnaby | |
| dc.date.accessioned | 2026-07-31T08:18:26Z | |
| dc.date.issued | 2026-07-22 | |
| dc.description | SJR 2025 1.132 Q1 H-Index 222 Subject Area and Category: Agricultural and Biological Sciences Agronomy and Crop Science Forestry Energy Renewable Energy, Sustainability and the Environment Environmental Science Waste Management and Disposal | |
| dc.description.abstract | Zeolite Imidazole Frame-67 (ZIF-67), a thermally stable subclass of metal-organic frameworks (MOFs), has recently emerged as a promising flexible catalyst for upgrading biomass pyrolysis process and reducing reaction complexity. This research explores the catalytic pyrolysis behavior of wood pellets (WP) biomass over ZIF-67 using thermogravimetric analysis (TGA), TG-Fourier Transform Infrared (TG-FTIR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS). Thermal decomposition kinetics and thermodynamic parameters were evaluated using serval modelling approaches, while artificial neural network (ANN) technique was employed to predict pyrolysis behaviour of WP and ZIF-67/WP samples under untested conditions. Biomass samples containing 10, 20 and 30 wt% ZIF-67 were examined to determine the optimum catalyst loading. The TGA results revealed that biomass underwent thermal decomposition below 600 °C, with a mass loss of 77-86 wt%, followed by the decomposition of ZIF-67 at higher temperatures. The addition of ZIF-67 significantly influenced the composition of the evolved vapors, with 20 wt% catalyst providing the highest catalytic activity by promoting the formation of naphthalene, 1,2,3,4 tetrahydro-2-phenyl-rich vapor, which accounted for approximately 85% of the total identified aromatic GC-MC peak area under the investigated conditions. Kinetics analysis demonstrated that the optimum ZIF-67 loading reduced the activation energy from 233 to 427 kJ/mol (WP) to 159-295 kJ/mol, indicating a substantial reduction in the energy barrier of pyrolysis. Furthermore, the catalyst lowered thermodynamics parameters (enthalpy, Gibbs free energy, and entropy), confirming improved reaction feasibility. The constructed ANN models accurately predicted the pyrolysis behaviour of both WP and ZIF-67/WP samples, achieving R2 exceeding 0.9999. These results demonstrate that incorporating 20 wt% of ZIF-67 into the biomass pyrolysis process effectively reduces the reaction energy requirements and enhances the selectivity of pyrolysis vapors towards valuable aromatic hydrocarbons, highlighting its potential as an efficient catalyst for sustainable biomass valorisation and the production of advanced biofuels and renewable chemicals. | |
| dc.description.uri | https://www.scimagojr.com/journalsearch.php?q=28810&tip=sid&clean=0 | |
| dc.identifier.citation | Yousef, S., Kudelytė, V., Striūgas, N., & Abdelnaby, M. A. (2026). Biomass pyrolysis over ZIF-67 catalyst: thermogravimetric, pyrolysis vapors, kinetics, thermodynamics and artificial neural network modelling. Biomass and Bioenergy, 109868. https://doi.org/10.1016/j.biombioe.2026.109868 | |
| dc.identifier.doi | https://doi.org/10.1016/j.biombioe.2026.109868 | |
| dc.identifier.other | https://doi.org/10.1016/j.biombioe.2026.109868 | |
| dc.identifier.uri | https://repository.msa.edu.eg/handle/123456789/6815 | |
| dc.language.iso | en_US | |
| dc.publisher | Elsevier Ltd | |
| dc.relation.ispartofseries | Biomass and Bioenergy ; Article number 109868 | |
| dc.subject | Artificial neural networks | |
| dc.subject | Catalytic pyrolysis | |
| dc.subject | Kinetics | |
| dc.subject | Metal-organic frameworks | |
| dc.subject | Zeolitic imidazolate frameworks | |
| dc.title | Biomass pyrolysis over ZIF-67 catalyst: thermogravimetric, pyrolysis vapors, kinetics, thermodynamics and artificial neural network modelling | |
| dc.type | Article |
