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Describing biomass pyrolysis kinetics using a generic hybrid intelligent model: A critical stage in sustainable waste-oriented biorefineries

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  • Aghbashlo, Mortaza
  • Almasi, Fatemeh
  • Jafari, Ali
  • Nadian, Mohammad Hossein
  • Soltanian, Salman
  • Lam, Su Shiung
  • Tabatabaei, Meisam

Abstract

The pyrolysis process is one of the most widely practised thermochemical pathways for converting biomass into biofuel. The most challenging aspect of the pyrolysis conversion is modelling the thermal decomposition kinetics of lignocellulosic biomass. Therefore, this study aimed to develop a generic hybrid intelligent model to describe biomass pyrolysis kinetics based on the ultimate analysis (carbon, hydrogen, oxygen, nitrogen, sulfur content) and process heating rate. First, an analytical model was fitted to the experimental data from thermogravimetric analysis reported in the published literature to determine the pyrolysis kinetic parameters of a wide range of biomass feedstocks. The derived kinetic parameters of biomass pyrolysis (i.e., reaction order, frequency factor, activation energy) were then modelled using three exclusive Adaptive Neuro-Fuzzy Inference System (ANFIS) models tuned by genetic algorithm (GA). The capability of the GA-ANFIS approach in modelling the kinetic parameters of biomass was also compared with that of the classical ANFIS model. The obtained results showed that the GA-ANFIS approach outperformed the classical ANFIS model in estimating the pyrolysis kinetic parameters of biomass. Generally, the highly nonlinear and extremely complex kinetic parameters of biomass thermal degradation were satisfactorily estimated using the GA-ANFIS models with a coefficient of determination exceeding 0.940 and a mean absolute error lower than 0.096. The pyrolysis reaction kinetics of five biomass materials, unexploited during the development of the GA-ANFIS models,‏ were estimated with a correlation coefficient higher than 0.811 and a mean absolute error lower than 0.7376 using the generic hybrid intelligent model. The promising agreement between the predicted and experimental kinetic data suggested that the generic hybrid intelligent model could be an alternative to the laborious experimental thermogravimetric measurements, thereby allowing pyrolysis process optimization, monitoring, and controlling to be more effectively conducted. Finally, an easy-to-use software package was developed based on the developed generic hybrid intelligent model to describe the devolatilization behaviour of biomass.‏

Suggested Citation

  • Aghbashlo, Mortaza & Almasi, Fatemeh & Jafari, Ali & Nadian, Mohammad Hossein & Soltanian, Salman & Lam, Su Shiung & Tabatabaei, Meisam, 2021. "Describing biomass pyrolysis kinetics using a generic hybrid intelligent model: A critical stage in sustainable waste-oriented biorefineries," Renewable Energy, Elsevier, vol. 170(C), pages 81-91.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:81-91
    DOI: 10.1016/j.renene.2021.01.111
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    Cited by:

    1. Xie, Wen & Su, Jing & Zhang, Xiangkun & Li, Tan & Wang, Cong & Yuan, Xiangzhou & Wang, Kaige, 2023. "Investigating kinetic behavior and reaction mechanism on autothermal pyrolysis of polyethylene plastic," Energy, Elsevier, vol. 269(C).
    2. Ahmed, Gaffer & Kishore, Nanda, 2024. "Synergistic effects on properties of biofuel and biochar produced through co-feed pyrolysis of Erythrina indica and Azadirachta indica biomass," Renewable Energy, Elsevier, vol. 227(C).
    3. Ma, Cheng & Zhao, Yuzhen & Lang, Tingting & Zou, Chong & Zhao, Junxue & Miao, Zongcheng, 2023. "Pyrolysis characteristics of low-rank coal in a low-nitrogen pyrolysis atmosphere and properties of the prepared chars," Energy, Elsevier, vol. 277(C).
    4. Zhou, Yufang & Gao, Mingqiang & Miao, Zhenyong & Cheng, Cheng & Wan, Keji & He, Qiongqiong, 2024. "Physicochemical properties and combustion kinetics of dried lignite," Energy, Elsevier, vol. 289(C).
    5. Ahmed, Gaffer & Kishore, Nanda, 2023. "Fuel phase extraction from pyrolytic liquid of Azadirachta indica biomass followed by subsequent characterization of pyrolysis products," Renewable Energy, Elsevier, vol. 219(P1).
    6. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    7. Mishra, Garima & Bhaskar, Thallada, 2022. "Insights into the decomposition kinetics of groundnut shell: An advanced isoconversional approach," Renewable Energy, Elsevier, vol. 196(C), pages 1-14.
    8. Deng, Jun & Qu, Gaoyang & Ren, Shuaijing & Wang, Caiping & Su, Hui & Yuan, Yu & Duan, Xiadan & Yang, Nannan & Wang, Jinrui, 2024. "Effect of water soaking and air drying on the thermal effect and heat transfer characteristics of coal oxidation at the low-temperature oxidation stage," Energy, Elsevier, vol. 288(C).
    9. He, Yifeng & Liu, Ronghou & Yellezuome, Dominic & Peng, Wanxi & Tabatabaei, Meisam, 2022. "Upgrading of biomass-derived bio-oil via catalytic hydrogenation with Rh and Pd catalysts," Renewable Energy, Elsevier, vol. 184(C), pages 487-497.
    10. Qiao, Yanyu & Chen, Zhichao & Wu, Xiaolan & Li, Zhengqi, 2023. "Investigation on co-combustion of semi-coke and bituminous coal in oxygen-enriched atmosphere: Combustion, thermal conversion, and kinetic analyses," Energy, Elsevier, vol. 269(C).

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