IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v172y2021icp1341-1350.html
   My bibliography  Save this article

Near infrared spectroscopy model for analyzing chemical composition of biomass subjected to Fenton oxidation and hydrothermal treatment

Author

Listed:
  • Jeong, So-Yeon
  • Lee, Eun-Ju
  • Ban, Se-Eun
  • Lee, Jae-Won

Abstract

Herein, near infrared (NIR) spectroscopy, rapid, accurate, and non-destructive method, was employed to analyze biomass composition. Calibration and prediction models for various types of biomass were developed from NIR data by applying the partial least squares method. Cellulose, hemicellulose, and lignin in a total of 75 samples were analyzed by a wet chemical method and NIR spectroscopy. The NIR model developed for hardwood accurately predicted the lignin content with a particle size of 20–80 mesh with a correlation coefficient (R2) of >0.95, low root mean square error (0.68), high ratio of error range (22.23), and high residual predictive deviation (6.07). On the other hand, the models for other compositions exhibited relatively low prediction accuracy. Different biomass particle sizes (20–80 mesh, >40 mesh, and <40 mesh) led to statistically significant differences in NIR spectra based on the root mean square value. Although preprocessing (via smoothing, first and second derivatives) was performed to improve the prediction accuracy and reduce differences based on biomass particle size, a significant improvement was not achieved.

Suggested Citation

  • Jeong, So-Yeon & Lee, Eun-Ju & Ban, Se-Eun & Lee, Jae-Won, 2021. "Near infrared spectroscopy model for analyzing chemical composition of biomass subjected to Fenton oxidation and hydrothermal treatment," Renewable Energy, Elsevier, vol. 172(C), pages 1341-1350.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:1341-1350
    DOI: 10.1016/j.renene.2020.12.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120319467
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.12.020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xue, Junjie & Yang, Zengling & Han, Lujia & Liu, Yuchen & Liu, Yao & Zhou, Chengcheng, 2015. "On-line measurement of proximates and lignocellulose components of corn stover using NIRS," Applied Energy, Elsevier, vol. 137(C), pages 18-25.
    2. José Luis Fernández & Felicia Sáez & Eulogio Castro & Paloma Manzanares & Mercedes Ballesteros & María José Negro, 2019. "Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy," Energies, MDPI, vol. 12(13), pages 1-10, June.
    3. Sirisomboon, Panmanas & Funke, Axel & Posom, Jetsada, 2020. "Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition," Renewable Energy, Elsevier, vol. 147(P1), pages 1921-1931.
    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. Pitak, Lakkana & Sirisomboon, Panmanas & Saengprachatanarug, Khwantri & Wongpichet, Seree & Posom, Jetsada, 2021. "Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis," Energy, Elsevier, vol. 220(C).
    2. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    3. Aleksandra Minajeva & Algirdas Jasinskas & Rolandas Domeika & Edvardas Vaiciukevičius & Egidijus Lemanas & Stanisław Bielski, 2021. "The Study of the Faba Bean Waste and Potato Peels Recycling for Pellet Production and Usage for Energy Conversion," Energies, MDPI, vol. 14(10), pages 1-14, May.
    4. Sirisomboon, Panmanas & Funke, Axel & Posom, Jetsada, 2020. "Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition," Renewable Energy, Elsevier, vol. 147(P1), pages 1921-1931.
    5. Peng, Wei & Beggio, Giovanni & Pivato, Alberto & Zhang, Hua & Lü, Fan & He, Pinjing, 2022. "Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    6. Sirisomboon, Panmanas & Posom, Jetsada, 2019. "On-line measurement of activation energy of ground bamboo using near infrared spectroscopy," Renewable Energy, Elsevier, vol. 133(C), pages 480-488.
    7. Jinming Liu & Changhao Zeng & Na Wang & Jianfei Shi & Bo Zhang & Changyu Liu & Yong Sun, 2021. "Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics," Energies, MDPI, vol. 14(5), pages 1-17, March.
    8. Bijendra Shrestha & Jetsada Posom & Panmanas Sirisomboon & Bim Prasad Shrestha, 2023. "Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques," Energies, MDPI, vol. 16(14), pages 1-26, July.
    9. Bijendra Shrestha & Jetsada Posom & Panmanas Sirisomboon & Bim Prasad Shrestha & Axel Funke, 2024. "Effect of Combined Non-Wood and Wood Spectra of Biomass Chips on Rapid Prediction of Ultimate Analysis Parameters Using near Infrared Spectroscopy," Energies, MDPI, vol. 17(2), pages 1-22, January.
    10. Liang Song & Shanjun Liu & Wenwen Li, 2019. "Quantitative Inversion of Fixed Carbon Content in Coal Gangue by Thermal Infrared Spectral Data," Energies, MDPI, vol. 12(9), pages 1-17, May.
    11. Zhang, Zhicai & Zheng, Huihua & Qian, Jingya, 2023. "Pretreatment with a combination of steam explosion and NaOH increases butanol production of enzymatically hydrolyzed corn stover," Renewable Energy, Elsevier, vol. 203(C), pages 301-311.
    12. Posom, Jetsada & Sirisomboon, Panmanas, 2017. "Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy," Energy, Elsevier, vol. 121(C), pages 147-158.
    13. Bijendra Shrestha & Jetsada Posom & Panmanas Sirisomboon & Bim Prasad Shrestha & Pimpen Pornchaloempong & Axel Funke, 2024. "NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models," Energies, MDPI, vol. 17(4), pages 1-27, February.

    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:eee:renene:v:172:y:2021:i:c:p:1341-1350. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.