Statistical Model for Prediction of Ash Fusion Temperatures from Additive Doped Biomass

Joanna Wnorowska, Waldemar Gądek and Sylwester Kalisz
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Joanna Wnorowska: Department of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Waldemar Gądek: Department of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Sylwester Kalisz: Department of Power Engineering and Turbomachinery, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44-100 Gliwice, Poland

Energies, 2020, vol. 13, issue 24, 1-21

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: ash fusion temperature (AFT); biomass combustion; fuel additives; AFT statistic model; prediction of ash temperature (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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