Process optimisation of solar thermal technology with power station carbon capture
thesisposted on 01.03.2017 by Lambert, Tristan Hiley
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
In chemical engineering design, process plants are often capable of turndown and flexible operation to maximise profitability depending on demand and price signals, and the optimisation of these processes needs to take into account the variability in the plant operation. By performing the optimisation with flexible operating conditions the overall plant performance can be improved. The optimisation process makes use of two computational modelling techniques: multi-objective optimisation (MOO) and surrogate modelling. Multi-objective optimisation allows for the optimisation of two or more competing variables simultaneously. In the cases studied here, the long individual simulation time coupled with the large number of individual simulations required meant that the total simulation time was prohibitive for dynamic MOO. In order to reduce computation time surrogate models of the processes are developed using artificial neural networks. A statistical dynamic modelling framework within a multi-objective optimisation methodology is developed to optimise a process plant’s performance. Here the dynamic modelling is used to optimise the economic and environmental performance of natural gas combined cycle power plant systems fitted with carbon capture and storage (CCS) and solar thermal energy (STE) in a time-integrated MOO framework. This framework allows for variable process inputs and flexible operation based on external signals, such as product price. By introducing a probabilistic approach to the modelling of flexible operation allows for more data to be included in the analysis. The methodology for dynamic modelling of flexible operation could be applied to other industrial processes that face similar dynamic economic and/or environmental conditions. From a CCS standpoint dynamic MOO was used to show that variable capture can be used to make the CCS plant more profitable over steady state operation. At a capture rate of 80 % the NPV of a gas turbine combined cycle with CCS improved by 38 m$AU by utilising flexible operation of the capture process. This also allows for various economic scenarios to incentivise the reduction of CO₂ emissions from natural gas based power plants, with the use of CCS and STE used to reduce the greenhouse emissions. The economic strategies of a carbon price and governmental grants are investigated to determine the levels of policy help to incentivise implementing CO₂ reduction technologies. In order to achieve a financial incentive for limiting CO₂ emissions from a combined cycle power plants equipped with CCS a government co-investment grant of 50 % of the capital costs and a carbon price of 83 $AU/tCO₂ is required.