Improvement of HHV prediction model of biomass based on the ultimate analysis

This paper presents an improved prediction model for the higher heating value (HHV) of biomass based on the ultimate analysis by using the standard least squares method. This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of li...

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Bibliographic Details
Published in:Journal of renewable and sustainable energy 2021-09, Vol.13 (5)
Main Authors: Kim, Se Ung, Yun, Jong Sun, Ri, Jong Sim, Hong, Kwang Il, Chon, Ok Sim, Ri, Jin Hyok, Ri, Jang Mi
Format: Article
Language:English
Subjects:
Biomass
Calorific value
Fitness
Least squares method
Neural networks
Prediction models
Regression models
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Summary:This paper presents an improved prediction model for the higher heating value (HHV) of biomass based on the ultimate analysis by using the standard least squares method. This study intends for us to predict the HHV of biomass within a wide range of elemental distributions rather than the range of literature in order to create an optimal prediction model. To this end, many experimental data, comprising a wide range of biomass elements, regression models, and neural networks, are used for its comparative validation. As a result, the proposed prediction model, HHV = 2.8799 + 0.2965 * C + 0.4826 * H – 0.0187 * O demonstrates a better HHV prediction performance for biomass in a comparative validation of 250 samples presented in the literature, and the fitness model using a neural network shows a high fitness in the training, validation, and testing for 430 samples.
ISSN:1941-7012
1941-7012
DOI:10.1063/5.0059376