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• W4384202119 endingPage "5341" @default.
• W4384202119 startingPage "5341" @default.
• W4384202119 abstract "Emerging global environmental pollution issues have caused a reduction in coal utilization, leading to an increased research focus on biomass use as an alternative. However, due to the low heat values of biomass, studies in this field are still in progress. Biomass primarily comprises cellulose, hemicellulose, and lignin. To determine the composition of these three components, the measurement methods recommended by TAPPI (Technical Association of the Pulp and Paper Industry) and NREL (National Renewable Energy Laboratory) are typically employed involving equipment such as HPLC. However, these methods are time consuming. In this study, we proposed a model for predicting cellulose, hemicellulose, and lignin contents based on elemental and industrial analyses. A dataset comprising 174 samples was used to develop this model. This was validated using 25 additional samples. The R2P values for cellulose, hemicellulose, and lignin were 0.6104–0.6362, 0.4803–0.5112, and 0.7247–0.7914, respectively; however, the R2CV values obtained from the validation results were 0.7387–0.7837, 0.3280–0.4004, and 0.7427–0.7757, respectively. The optimal models selected for cellulose, lignin, and hemicellulose were C1, L2, and 100-(C1-L2) or H2, respectively. Our predictions for woody and herbaceous biomass, including torrefied samples, should be applied with caution to other biomass types due to the potential accuracy limitations. To enhance the prediction accuracy, future research should broaden the range of biomass types considered and gather more data specifically related to woody and herbaceous biomass." @default.
• W4384202119 created "2023-07-14" @default.
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• W4384202119 date "2023-07-13" @default.
• W4384202119 modified "2023-09-29" @default.
• W4384202119 title "Development of a Biomass Component Prediction Model Based on Elemental and Proximate Analyses" @default.
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• W4384202119 doi "https://doi.org/10.3390/en16145341" @default.
• W4384202119 hasPublicationYear "2023" @default.
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