LAPSE:2023.28472
Published Article
LAPSE:2023.28472
Statistical Model for Prediction of Ash Fusion Temperatures from Additive Doped Biomass
April 11, 2023
Abstract
The prediction of phase transformation of biomass ashes is challenging due to the highly variable composition of these fuels as well as the complex processes accompanying phase transformations. The AFT (Ash Fusion Temperature) model was performed in Statistica 13.1 software. This model was divided into three separate submodels, which were designed to predict the characteristic ash melting temperatures for raw and modified biomass. It is based on the chemical composition of fuel and ash as obtained using ash analysis standards. For the discussed models, several coefficients describing multiple regression parameters are presented. The AFT model discussed in this article is suitable for predicting ash fusion temperatures for biomass and allows for the prediction of the temperature with an average error of <±70.05 °C for IDT; <±51.98 °C for HT; <±47.52 °C for FT for raw biomass. For some of the additionally tested biomass, a value higher than the average difference between the measured temperature and the designated model was observed (<90 °C). Moreover, morphological analyses of the structure SEM-EDS for ash samples with and without additive were performed.
Keywords
AFT statistic model, ash fusion temperature (AFT), biomass combustion, fuel additives, prediction of ash temperature
Suggested Citation
Wnorowska J, Gądek W, Kalisz S. Statistical Model for Prediction of Ash Fusion Temperatures from Additive Doped Biomass. (2023). LAPSE:2023.28472
Author Affiliations
Wnorowska J: Department of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland [ORCID]
Gądek W: Department of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Kalisz S: Department of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland [ORCID]
Journal Name
Energies
Volume
13
Issue
24
Article Number
E6543
Year
2020
Publication Date
2020-12-11
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13246543, Publication Type: Journal Article
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LAPSE:2023.28472
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https://doi.org/10.3390/en13246543
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