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Predictive modelling of ash particle deposition in a PF combustion furnace

Degereji, Mohammed Usman (2011) Predictive modelling of ash particle deposition in a PF combustion furnace. PhD thesis, University of Leeds.

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Slagging and fouling during the combustion of pulverised coal in boilers is a major problem as power generators strive to improve the efficiency of plants. The coal type has a major influence on the slagging propensity in furnaces. The correlation between predicted results using some of the existing slagging indices and the actual observations made in most conventional boilers has been poor, especially when their use is extended to different coals. In this thesis, a numerical model to predict coal ash particle deposition rate in pulverized coal boilers has been developed. The overall sticking probability of the particle is determined by its viscosity and its tendency to rebound after impaction. The deposition model has been implemented in the Fluent 12.1 software, and the effects of swirling motion ash particle viscosity on deposition rates have been investigated. The predicted results are in good agreement with the reported experimental measurements on the Australian bituminous coals. Also, a novel numerical slagging index (NSI) which is based on ash fusibility, ash viscosity and the content of ash in the coals has been developed. The incoming ash shows significant influence on slag accumulation in boilers. The results of assessment of the slagging potential using the NSI on a wide range of coals and some coal blends correlate very well with the reported field performance of the coals. The NSI has been modified to predict the slagging potential of some coal and biomass blends with <20% biomass ratio. The results of predictions using the modified index on coals blended with sewage sludge and saw-dust are in good agreement with the experimental data.

Item Type: Thesis (PhD)
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Chemical and Process Engineering (Leeds)
Other academic unit: Advanced Power Generation Technology Group, Centre for Computational Fluid Dynamics, School of Process, Environmental and Materials Engineering
Identification Number/EthosID: uk.bl.ethos.550806
Depositing User: Ethos Import
Date Deposited: 24 Aug 2018 13:58
Last Modified: 24 Aug 2018 13:58
URI: http://etheses.whiterose.ac.uk/id/eprint/21122

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