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Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques

Author

Listed:
  • Bijendra Shrestha

    (Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Jetsada Posom

    (Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
    Center for Alternative Energy Research and Development, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Panmanas Sirisomboon

    (Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Bim Prasad Shrestha

    (Department of Mechanical Engineering, Kathmandu University, Dhulikhel P.O. Box 6250, Nepal
    Department of Bioengineering, University of Washington, Seattle, WA 98195, USA)

Abstract

In this study, partial least squares regression (PLSR) models were developed using no preprocessing, traditional preprocessing, multi-preprocessing 5-range, multi-preprocessing 3-range, a genetic algorithm (GA), and a successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage by employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis were compared, and the best model was selected based on the coefficient of determination of the validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on the model performance results, the proposed HHV model from GA-PLSR and the N models from the multi-preprocessing PLSR 5-range could be used for most applications, including research, whereas the C and H models from GA-PLSR and the O model from the multi-preprocessing PLSR 5 range method 5-range air performance and are applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving these models by 4.1839%, 8.1842%, 3.7587%, and 4.0085%, respectively. Therefore, this method can be considered a reliable and non-destructive alternative method for rapidly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for robust application.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5351-:d:1193106
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    References listed on IDEAS

    as
    1. Posom, Jetsada & Shrestha, Amrit & Saechua, Wanphut & Sirisomboon, Panmanas, 2016. "Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy," Energy, Elsevier, vol. 107(C), pages 464-472.
    2. 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.
    3. 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.
    4. Mahmudul Hasan & Yousef Haseli & Ernur Karadogan, 2018. "Correlations to Predict Elemental Compositions and Heating Value of Torrefied Biomass," Energies, MDPI, vol. 11(9), pages 1-15, September.
    5. Zhang, Ke & Zhou, Ling & Brady, Michael & Xu, Feng & Yu, Jianming & Wang, Donghai, 2017. "Fast analysis of high heating value and elemental compositions of sorghum biomass using near-infrared spectroscopy," Energy, Elsevier, vol. 118(C), pages 1353-1360.
    6. Han, Kuihua & Gao, Jie & Qi, Jianhui, 2019. "The study of sulphur retention characteristics of biomass briquettes during combustion," Energy, Elsevier, vol. 186(C).
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    1. 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.
    2. Bijendra Shrestha & Jetsada Posom & Pimpen Pornchaloempong & Panmanas Sirisomboon & Bim Prasad Shrestha & Hidayah Ariffin, 2024. "Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue," Energies, MDPI, vol. 17(6), pages 1-27, March.
    3. 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.

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