Nik Norizam, N.N.A. orcid.org/0000-0001-8793-2940, Yang, X., Ingham, D. et al. (5 more authors) (2023) An improved index to predict the slagging propensity of woody biomass on high-temperature regions in utility boilers. Journal of the Energy Institute, 109. 101272. ISSN 1743-9671
Abstract
Ash deposition-related issues adversely affect the thermal transfer and cause corrosion to biomass fired utility boilers. Various models have been proposed to estimate the slagging propensities for firing biomass fuels of different origins. However, there is no reliable general applicable method that is available for assessing biomass fuel slagging propensities without carrying out extensive experimental testing. In addition, empirical correlations developed for coal produce large inaccuracies when applied to biomass. In this paper, a predictive slagging index, In, has been built by analysing the slagging formations of a range of woody biomass fuels, using the thermodynamic equilibrium modelling tool FactSage, together with the partial least square regression (PLSR) coupled with cross-validation. The new index has been validated and supported by experimental observations from various literatures. The results obtained with the new index showed a substantially greater success rate in predicting the woody biomass ash slagging propensity when compared with the experimental observations from the literature than the other five conventional slagging indices. The predictive index, In, also successfully predicts the slagging propensities with high accuracy when extended to the application of herbaceous biomass and blended fuel between woody biomass and peat.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier Ltd on behalf of Energy Institute. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Woody biomass; Slagging propensity; Thermodynamic equilibrium model; Slagging indices |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 May 2023 08:45 |
Last Modified: | 07 Jun 2023 08:28 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.joei.2023.101272 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199505 |