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Title: Development of ash deposition prediction models through the CFD methods and the ash deposition indices
Author: Yang, Xin
ISNI:       0000 0004 6350 4783
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2017
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Pulverised coal-fired power generation technologies are important for meeting the electricity consumption worldwide, especially for the developing countries. Changing fuels (coal blending, co-combustion, new fuels, etc.) is common practice in the power stations, which may result in the change of ash deposition behaviours. Ash deposition issues can reduce the heat transfer and have a negative effect on the long-term operation of the combustion systems. Therefore, prediction of ash deposition behaviours is significant for the efficient operation of boilers. In this thesis, new ash deposition prediction models based on particle impaction and sticking behaviours, ash melting behaviour and multi-slagging routes have been developed in order to understand ash deposit formation and predict the slagging propensities through using Computational Fluid Dynamics (CFD) methods and ash deposition indices. Regarding the CFD methods, an ash deposition model has been proposed to predict the ash deposit formation on an uncooled probe for the co-combustion of South African coal and palm kernel expeller in an entrained flow reactor. A new revised particle impaction sub-model has been developed in order to minimize the numerical related errors without excessive meshing. The molten fraction model obtained from the chemical equilibrium calculations was employed to predict the particle sticking behaviour. The simulation results show that the revised particle impaction model is suitable to accurately resolve particle impaction without using a prohibitive meshing size. Particle impaction and sticking properties dictate the ash deposit formation. In addition, a CFD-based dynamic ash deposition model has been developed to predict the slagging formation on a cooled probe under high furnace temperatures of Zhundong lignite (rich in alkali and alkaline earth metal elements) combustion in a pilot-scale furnace. The developed model is based on the inertia impaction, the thermophoresis and the direct alkali vapour condensation and incorporates the influence of the heat transfer rate. The results show that particle deposition from the inertia impaction and the thermophoresis dictates ash deposit formation under high furnace temperatures. The deposition caused by the direct alkali vapour condensation is less significant. As deposition time increases, particle impaction efficiency decreases and sticking efficiency increases due to the thermophoresis and the local temperature conditions. In addition, the ash deposition characteristics are influenced under different furnace temperatures, due to the changes in the particle impaction and sticking behaviours. Further, a new method for building the ash deposition indice has been proposed to predict the slagging propensities of coals/blends combustion in utility boilers. The method is based on the initial slagging routes and the sintered/slagging route. Two types of initial slagging routes are considered, namely (i) pyrite-induced initial slagging on the furnace wall, and (ii) fouling caused by the alkaline/alkali components condensing in the convection section. In addition, the sintered/slagging route is considered by the liquids temperature, which represents the melting potential of the main ash composition and is calculated using the chemical equilibrium methods. The partial least square regression (PLSR) technique, coupled with a cross validation method, is employed to obtain the correlation for the ash deposition indice. The results obtained show that the developed indice yields a higher success rate in classifying the overall slagging potential in boilers than some of the typical slagging indices. In addition, both SiO2 and Al2O3 can reduce the slagging potential, but the drop in slagging propensity is more significant by adding Al2O3 compared to SiO2.
Supervisor: Pourkashanian, Mohamed ; Ingham, Derek B. ; Lin, Ma Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available