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Automated machine learning-assisted analysis of biomass catalytic pyrolysis for selective production of benzene, toluene, and xylene

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  • Zhang, Zihang
  • Liu, Jinlong
  • Yi, Weiming
  • Wang, Shurong

Abstract

Biomass pyrolysis catalyzed by metal-modified zeolites presents a viable pathway for producing benzene, toluene, and xylene (BTX), yet the complexity of interactions among feedstock characteristics, processing conditions, and catalyst properties presents substantial optimization challenges. To address these complexities, this study employed advanced automated machine learning (AutoML) techniques, specifically the tree-based pipeline optimization tool (TPOT) and H2O AutoML, to model the catalytic pyrolysis process. These AutoML approaches demonstrated successful prediction capabilities, with R2 values exceeding 0.912 for BTX production. Through model explainability analysis, this paper elucidated the quantitative mechanisms linking feedstock, process, and catalyst characteristics to performance outcomes, as well as the structure-activity relationships of the catalysts. Operating parameters were found to have the greatest impact on BTX production, contributing 40.5%, followed by catalyst descriptors at 34.1% and feedstock characteristics at 25.4%. Additionally, by integrating the particle swarm optimization (PSO) algorithm, this study conducted reverse process design and catalyst screening, identifying Zn-modified zeolite as the optimal catalyst, with the optimal process conditions including a catalytic temperature of 550–650 °C, metal loading of 1–3 wt%, Si/Al ratio of 30–40, and feedstock H/C ratio of 1.4–1.6. Overall, this research underscored the potential of AutoML to advance catalytic pyrolysis technology by facilitating targeted product regulation.

Suggested Citation

  • Zhang, Zihang & Liu, Jinlong & Yi, Weiming & Wang, Shurong, 2025. "Automated machine learning-assisted analysis of biomass catalytic pyrolysis for selective production of benzene, toluene, and xylene," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s036054422501031x
    DOI: 10.1016/j.energy.2025.135389
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    References listed on IDEAS

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    1. Li, Xiaohua & Wang, Junlong & Yu, Yongbo & Cao, Yu & Shao, Shanshan & Wu, Shiliang, 2024. "Promoted production of aromatic hydrocarbons in biomass catalytic pyrolysis over the coupled catalysts of carbon-reduced waste lithium battery cathode materials and HZSM-5," Energy, Elsevier, vol. 304(C).
    2. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Chen, Yong, 2024. "Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning," Energy, Elsevier, vol. 290(C).
    3. Yang, Ke & Wu, Kai & Zhang, Huiyan, 2022. "Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions," Energy, Elsevier, vol. 254(PB).
    4. Xiaoqin Si & Rui Lu & Zhitong Zhao & Xiaofeng Yang & Feng Wang & Huifang Jiang & Xiaolin Luo & Aiqin Wang & Zhaochi Feng & Jie Xu & Fang Lu, 2022. "Catalytic production of low-carbon footprint sustainable natural gas," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Jia, Liangyuan & Shao, Wanyun & Wang, Jingjing & Qian, Yingying & Chen, Yingquan & Yang, Qingchun, 2024. "Machine learning-aided prediction of bio-BTX and olefins production from zeolite-catalyzed biomass pyrolysis," Energy, Elsevier, vol. 306(C).
    6. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    7. Leng, Lijian & Zhou, Junhui & Zhang, Weijin & Chen, Jiefeng & Wu, Zhibin & Xu, Donghai & Zhan, Hao & Yuan, Xingzhong & Xu, Zhengyong & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2024. "Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes," Energy, Elsevier, vol. 288(C).
    8. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
    9. Potnuri, Ramesh & Suriapparao, Dadi V. & Sankar Rao, Chinta & Sridevi, Veluru & Kumar, Abhishankar, 2022. "Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach," Renewable Energy, Elsevier, vol. 197(C), pages 798-809.
    10. Wang, Jia & Jiang, Jianchun & Li, Dongxian & Meng, Xianzhi & Zhan, Guowu & Wang, Yunpu & Zhang, Aihua & Sun, Yunjuan & Ruan, Roger & Ragauskas, Arthur J., 2022. "Creating values from wastes: Producing biofuels from waste cooking oil via a tandem vapor-phase hydrotreating process," Applied Energy, Elsevier, vol. 323(C).
    11. Zhao, Chenxi & Lu, Xueying & Jiang, Zihao & Ma, Huan & Chen, Juhui & Liu, Xiaogang, 2024. "Prediction of bio-oil yield by machine learning model based on 'enhanced data' training," Renewable Energy, Elsevier, vol. 225(C).
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