With an increased urgency for global action towards climate change mitigation, this research was undertaken with the aim of evaluating post-combustion CO2 capture as an emission abatement strategy for gas-fired power plants. A dynamic rate-based model of a capture plant with MEA solvent was built, with imposed chemical equilibrium, and validated at pilot scale under transient conditions. The model predicted plant behaviour under multiple process inputs and disturbances. The validated model was next used to analyse the process and it was found that CO2 absorption is mass transfer limited. The model was then improved by explicitly adding reactions rate in the model continuity, the first such dynamic model to be reported for the capture process. The model is again validated and is observed to provide better predictions than the previous model. Next, high fidelity models of a gas-fired power plant, a scaled-up capture plant and a compression train were built and integrated for 90% CO2 capture. Steam for solvent regeneration is extracted from the power plant IP/LP crossover pipe. Net efficiency drops from 59% to 49%, with increased cooling water demand. A 40% exhaust gas recirculation resulted in a recovery of 1% efficiency, proving that enhanced mass transfer in the capture plant reduces solvent regeneration energy demands. Economic analysis reveals that overnight cost increases by 58% with CO2 capture, and cost of electricity by 30%. While this discourages deployment of capture technology, natural gas prices remain the largest driver for cost of electricity. Other integration approaches – using a dedicated boiler and steam extraction from the LP steam drum – were explored for operational flexibility, and their net efficiencies were found to be 40 and 45% respectively. Supplementary firing of exhaust gas may be a viable option for retrofit, as it is shown to minimise integrated plant output losses at a net efficiency of 43.5%. Areas identified for further study are solvent substitution, integrated plant part load operation, flexible control and use of rotating packed beds for CO2 capture.

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Phänologie, die Lehre der periodisch wiederkehrenden Entwicklungserscheinungen in der Natur, hat sich in den letzten Jahrzehnten zu einem wichtigen Teilgebiet der Klimaforschung entwickelt. Einer der Haupteffekte der globalen Erwärmung ist die Veränderung der Wachstumsmuster und Fortpflanzungsgewohnheiten von Pflanzen, und somit veränderte Phänologie. Um die Auswirkungen der Klimaveränderung auf wildwachsende sowie Kulturpflanzen vorherzusagen, werden phänologische Modelle angewendet, verbessert und validiert. Dabei ist Wissen über den aktuellen Stand der Vegetation notwendig, welches aus Beobachtungen und fernerkundliche Messungen gewonnen wird. Die hier präsentierte Arbeit befasst sich mit dem Verständnis der Zusammenhänge zwischen fernerkundlichen Messungen und phänologischen Stadien und somit den Herausforderungen der modernen phänologischen Forschung: Der Vorhersage der Phänologie durch Modellierungsansätze, der Beobachtung der Phänologie mit optischen boden- und satellitengestützten Sensoren und der Validierung phänologischer Produkte. / Phenology, the study of recurring life cycle events of plants and animals has emerged as an important part of climate change research within the last decades. One of the main effects of global warming on vegetation is altered phenology, since plants have to modify their growth patterns and reproduction habits as reaction to changing environmental conditions. Forecasting phenology, thus phenological modelling, is a timely challenge given the necessity to predict the impact of global warming on wild-growing species and agricultural crops. However, assessing the present state of vegetation, thus phenological monitoring, is essential to update and validate model results. An improved comprehension of the relationships between plant phenology and remotely sensed products is crucial to interpret these results. Consequently, the presented thesis deals with the main challenges faced in modern phenology research, covering phenological forecasting with a modelling approach, satellite-based phenology extraction, and near-surface long-term monitoring of phenology.