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Title: CFD modelling of pulverised coal and biomass combustion
Author: Stechly, Katarzyna
ISNI:       0000 0004 8506 423X
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2019
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The energy sector faces a challenge of providing energy with limited carbon dioxide (CO2) emissions due to its contribution to climate change. Currently the energy mix is dominated by fossil fuels and coal is the major contributor of CO2 emissions. Since biomass is classified as a renewable source of energy, its combustion is considered to have a near–zero CO2 emission, and thus it supports the climate change targets. Therefore, to reduce emissions from coal combustion, coal can be depleted by the usage of biomass by either co-firing or full conversion to biomass. Nevertheless, biomass combustion needs more investigations and development in order to increase its efficiency and to reduce the potential operational problems. Computational fluid dynamics (CFD) assists with the predictions of the combustion process and therefore, gives an opportunity to develop and design the efficient biomass conversion and this enables the retrofitting of coal fired boilers to biomass fuel. In this thesis pulverised coal and biomass combustion experiments on the 250 kW UKCCRC PACT Combustion Test Facility have been performed. The experimental data have been used for validation of the CFD model of coal combustion. Further, an extensive sensitivity study of the sub-models for coal combustion has been performed to provide an accurate and reliable CFD model. The validated CFD model has been employed to biomass combustion to investigate the limitation of the CFD predictions. It has been found that irregular shape and large size of the biomass particles causes major impact on the biomass combustion discrepancies in the CFD model. Thus, an in-house code has been developed and the effect of the implementation of irregular shape and large size biomass particles compared with experimental data presented improvement in the CFD predictions.
Supervisor: Pourkashanian, Mohamed ; Ingham, Derek Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available