The first part of this thesis deals with greenhouse gas (GHG) emissions from fossil fuel-fired power stations. The GHG emission estimation from fossil fuel power generation industry signifies that emissions from this industry can be significantly reduced by fuel switching and adaption of advanced power generation technologies. In the second part of the thesis, steady-state models of some of the advanced fossil fuel power generation technologies are presented. The impacts of various parameters on the solid oxide fuel cell (SOFC) overpotentials and outputs are investigated. The detail analyses of operation of the hybrid SOFC-gas turbine (GT) cycle when fuelled with methane and syngas demonstrate that the efficiencies of the cycles with and without anode exhaust recirculation are close, but the specific power of the former is much higher. The parametric analysis of the performance of the hybrid SOFC-GT cycle indicates that increasing the system operating pressure and SOFC operating temperature and fuel utilization factor improves cycle efficiency, but the effects of the increasing SOFC current density and turbine inlet temperature are not favourable. The analysis of the operation of the system when fuelled with a wide range of fuel types demonstrates that the hybrid SOFC-GT cycle efficiency can be between 59% and 75%, depending on the inlet fuel type. Then, the system performance is investigated when methane as a reference fuel is replaced with various species that can be found in the fuel, i.e., H₂, CO₂, CO, and N₂. The results point out that influence of various species can be significant and different for each case. The experimental and numerical analyses of a biodiesel fuelled micro gas turbine indicate that fuel switching from petrodiesel to biodiesel can influence operational parameters of the system. The modeling results of gas turbine-based power plants signify that relatively simple models can predict plant performance with acceptable accuracy. The unique feature of these models is that they are developed based on similar assumptions and run at similar conditions; therefore, their results can be compared. This work demonstrates that, although utilization of fossil fuels for power generation is inevitable, at least in the short- and mid-term future, it is possible and practical to carry out such utilization more efficiently and in an environmentally friendlier manner.
Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study
