Abstract

Combustion of coal is one of the primary sources of electricity generation worldwide today. Coal contains different chemicals that cause particulate matter(PM) and sulfur dioxide (SO2) emissions. These are health hazards and are responsible for deteriorating the ambient air quality. Particulate matter also forms ash deposits inside the coal combustor, which in turn decreases the energy efficiency of the power plants. Using biomass as a fuel in these utility boilers can potentially reduce the problems of particulate matter emissions and ash deposition, and can significantly reduce the SO2 emissions. However, biomass needs to be pretreated to make its properties similar to coal in terms of energy density, grindability, and durability before it can be fired in utility boilers. Steam explosion is one of the leading biomass pretreatment methods that enhances the physicochemical properties of biomass. A comprehensive review of the steam explosion process, its product properties, its comparison with other treatment processes, as well as its economic analysis and lifecycle assessment, have been explored in this work. Steam-exploded biomass has been co-combusted with bituminous coal in a 1500 kWth combustor to analyze the ash aerosol particle size distribution, composition, and deposition behavior. The primary results of these tests showed that both particulate matter emissions and ash deposition amount reduced significantly as more biomass was co-fired with coal. The submicron-sized particulate matter concentration showed a high correlation with the final mass of ash deposits (R2 > 0.96). Predicting ash deposition rates is important during the combustion of solid fuels. A Machine Learning tool was applied and trained with a fuel composition database of 92 fuels obtained from a thermodynamic equilibrium software (FactSage). When fully operational, this model should be integrated with an existing ash deposition model, which should make it self-sufficient in terms of generating equilibrium composition data. SO2 emissions were analyzed during the co-combustion of biomass and coal, and a synergistic decrease in SO2 emissions was observed with higher biomass blends. Experiments were conducted in a full-scale 471 MWe furnace to analyze the SO2 emissions, and an 85%-15% blend of coal and biomass was responsible for a 28.1% reduction in emissions and 22.1% reduction in the lime slurry utilization in the flue gas desulfurization (FGD) towers compared to pure coal combustion. Ash deposit characterizations by energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) combined with thermodynamic equilibrium simulations revealed that calcium and potassium were responsible for this synergistic reduction as these metals captured the SO2 from the flue gases and retained them in the ash phase. The SO2 research was important since the current literature is deficient in research conducted at suspension-fired full-scale utility boilers to reduce SO2 emissions by co-firing coal and biomass blends. The research in this dissertation should provide valuable insights to the energy industries that are considering a transformation of fuel portfolio from coal to biomass and explore how the mineral matter present in pretreated biomass would behave inside a utility boiler. The primary conclusions are that during the co-combustion of coal and biomass, ash deposition mass and particulate matter ash load decreased, and SO2 emission saw a synergistic reduction in emissions due to higher calcium and potassium content in biomass compared to pure coal combustion.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Chemical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2024-04-23

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd13215

Keywords

biofuel, renewable energy, steam explosion, milling, ash deposition modeling, machine learning, fouling, synergistic effect, pollution control, co-combustion, flue gas desulfurization, power generation

Language

english

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