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

Bijendra Shrestha, Jetsada Posom (), Panmanas Sirisomboon and Bim Prasad Shrestha
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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
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

Energies, 2023, vol. 16, issue 14, 1-26

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.

Keywords: higher heating value; near-infrared spectroscopy; partial least squares regression; spectral multi-preprocessing method; ultimate analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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