IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1483-d1055647.html
   My bibliography  Save this article

Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization

Author

Listed:
  • Xiaorui Liu

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Haiping Yang

    (State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jiamin Yang

    (School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Fang Liu

    (School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Torrefaction is an effective technology to overcome the defects of biomass which are adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential to characterize the properties of torrefied biomass. However, the preparation and characterization of torrefied biomass often consume a lot of time, costs, and manpower. Developing a reliable method to predict the fuel properties of torrefied biomass while avoiding various experiments and tests is of great value. In this study, a machine learning (ML) model of back propagation neural network (BPNN) hybridized with genetic algorithm (GA) optimization was developed to predict the important properties of torrefied biomass for the fuel purpose involving fuel ratio (FR), H/C and O/C ratios, high heating value (HHV) and the mass and energy yields (MY and EY) based on the proximate analysis results of raw biomass and torrefaction conditions. R 2 and RMSE were examined to evaluate the prediction precision of the model. The results showed that the GA-BPNN model exhibited excellent accuracy in predicting all properties with the values of R 2 higher than 0.91 and RMSE less than 1.1879. Notably, the GA-BPNN model is applicable to any type of biomass feedstock, whether it was dried or not before torrefaction. This study filled the gap of ML application in predicting the multiple fuel properties of torrefied biomass. The results could provide reference to torrefaction technology as well as the design of torrefaction facilities.

Suggested Citation

  • Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2023. "Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization," Energies, MDPI, vol. 16(3), pages 1-11, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1483-:d:1055647
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1483/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1483/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. González-Arias, J. & Gómez, X. & González-Castaño, M. & Sánchez, M.E. & Rosas, J.G. & Cara-Jiménez, J., 2022. "Insights into the product quality and energy requirements for solid biofuel production: A comparison of hydrothermal carbonization, pyrolysis and torrefaction of olive tree pruning," Energy, Elsevier, vol. 238(PC).
    2. Tu, Ren & Sun, Yan & Wu, Yujian & Fan, Xudong & cheng, Shuchao & Jiang, Enchen & Xu, Xiwei, 2022. "The fuel properties and adsorption capacities of torrefied camellia shell obtained via different steam-torrefaction reactors," Energy, Elsevier, vol. 238(PC).
    3. Onsree, Thossaporn & Tippayawong, Nakorn & Phithakkitnukoon, Santi & Lauterbach, Jochen, 2022. "Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass," Energy, Elsevier, vol. 249(C).
    4. Niu, Yanqing & Lv, Yuan & Lei, Yu & Liu, Siqi & Liang, Yang & Wang, Denghui & Hui, Shi'en, 2019. "Biomass torrefaction: properties, applications, challenges, and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    5. Lin, Yi-Li & Zheng, Nai-Yun, 2021. "Torrefaction of fruit waste seed and shells for biofuel production with reduced CO2 emission," Energy, Elsevier, vol. 225(C).
    6. Onsree, Thossaporn & Tippayawong, Nakorn, 2021. "Machine learning application to predict yields of solid products from biomass torrefaction," Renewable Energy, Elsevier, vol. 167(C), pages 425-432.
    7. Tian, Zhen & Gan, Wanlong & Zou, Xianzhi & Zhang, Yuan & Gao, Wenzhong, 2022. "Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 254(PB).
    8. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2022. "Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    2. Maja Ivanovski & Aleksandra Petrovič & Darko Goričanec & Danijela Urbancl & Marjana Simonič, 2023. "Exploring the Properties of the Torrefaction Process and Its Prospective in Treating Lignocellulosic Material," Energies, MDPI, vol. 16(18), pages 1-20, September.
    3. 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.
    4. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
    5. Abdulyekeen, Kabir Abogunde & Umar, Ahmad Abulfathi & Patah, Muhamad Fazly Abdul & Daud, Wan Mohd Ashri Wan, 2021. "Torrefaction of biomass: Production of enhanced solid biofuel from municipal solid waste and other types of biomass," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    6. Maja Ivanovski & Darko Goričanec & Danijela Urbancl, 2023. "The Evaluation of Torrefaction Efficiency for Lignocellulosic Materials Combined with Mixed Solid Wastes," Energies, MDPI, vol. 16(9), pages 1-15, April.
    7. Giuseppe Maggiotto & Gianpiero Colangelo & Marco Milanese & Arturo de Risi, 2023. "Thermochemical Technologies for the Optimization of Olive Wood Biomass Energy Exploitation: A Review," Energies, MDPI, vol. 16(19), pages 1-17, September.
    8. Wang, Chu & Yuan, Xinhua & Li, Shanshan & Zhu, Xifeng, 2021. "Enrichment of phenolic products in walnut shell pyrolysis bio-oil by combining torrefaction pretreatment with fractional condensation," Renewable Energy, Elsevier, vol. 169(C), pages 1317-1329.
    9. Yajing He & Shihong Zhang & Dongjing Liu & Xing Xie & Bin Li, 2023. "Effect of Biomass Particle Size on the Torrefaction Characteristics in a Fixed-Bed Reactor," Energies, MDPI, vol. 16(3), pages 1-14, January.
    10. Yan, Beibei & Li, Songjiang & Cao, Xingsijin & Zhu, Xiaochao & Li, Jian & Zhou, Shengquan & Zhao, Juan & Sun, Yunan & Chen, Guanyi, 2023. "Flue gas torrefaction integrated with gasification based on the circulation of Mg-additive," Applied Energy, Elsevier, vol. 333(C).
    11. Kung, Kevin S. & Thengane, Sonal K. & Ghoniem, Ahmed F. & Lim, C. Jim & Cao, Yankai & Sokhansanj, Shahabaddine, 2022. "Start-up, shutdown, and transition timescale analysis in biomass reactor operations," Energy, Elsevier, vol. 248(C).
    12. Niu, Qi & Ronsse, Frederik & Qi, Zhiyong & Zhang, Dongdong, 2022. "Fast torrefaction of large biomass particles by superheated steam: Enhanced solid products for multipurpose production," Renewable Energy, Elsevier, vol. 185(C), pages 552-563.
    13. Jagadale, Manisha & Gangil, Sandip & Jadhav, Mahesh, 2023. "Enhancing fuel characteristics of jute sticks (Corchorus Sp.) using fixed bed torrefaction process," Renewable Energy, Elsevier, vol. 215(C).
    14. Chen, Ying-Chu & Jhou, Sih-Yu, 2020. "Integrating spent coffee grounds and silver skin as biofuels using torrefaction," Renewable Energy, Elsevier, vol. 148(C), pages 275-283.
    15. Onsree, Thossaporn & Tippayawong, Nakorn & Phithakkitnukoon, Santi & Lauterbach, Jochen, 2022. "Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass," Energy, Elsevier, vol. 249(C).
    16. Chi-Hung Tsai & Yun-Hwei Shen & Wen-Tien Tsai, 2023. "Effect of Alkaline Pretreatment on the Fuel Properties of Torrefied Biomass from Rice Husk," Energies, MDPI, vol. 16(2), pages 1-10, January.
    17. Shahbeik, Hossein & Rafiee, Shahin & Shafizadeh, Alireza & Jeddi, Dorsa & Jafary, Tahereh & Lam, Su Shiung & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes," Renewable Energy, Elsevier, vol. 199(C), pages 1078-1092.
    18. Djandja, Oraléou Sangué & Duan, Pei-Gao & Yin, Lin-Xin & Wang, Zhi-Cong & Duo, Jia, 2021. "A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 232(C).
    19. Donghoon Shin & Akhil Francis & Purushothaman Vellayani Aravind & Theo Woudstra & Wiebren de Jong & Dirk Roekaerts, 2022. "Numerical Evaluation of Biochar Production Performance of Downdraft Gasifier by Thermodynamic Model," Energies, MDPI, vol. 15(20), pages 1-18, October.
    20. Guo, Feihong & Chen, Jun & He, Yi & Gardy, Jabbar & Sun, Yahui & Jiang, Jingyu & Jiang, Xiaoxiang, 2022. "Upgrading agro-pellets by torrefaction and co-pelletization process using food waste as a pellet binder," Renewable Energy, Elsevier, vol. 191(C), pages 213-224.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1483-:d:1055647. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.