A number of Carbon Capture and Storage projects (CCS) are under way around the world, but the technology's high capital and operational costs act as a disincentive to large-scale deployment. In the case of both oxycombustion and post-combustion CO 2 capture, the CO 2 compression and purication units (CO 2 CPU) are vital, but costly, process elements needed to bring the raw CO 2 product to a quality that is adequate for transport and storage. Four variants of the CO 2 CPU were modelled in Aspen HYSYS each of which provide dierent CO 2 product purities at dierent capital and operating costs. For each unit, a price of CO 2 is calculated by assuming that it is an independent entity in which to invest and the internal rate of return (IRR) must be greater or equal to the minimum rate of return on investment. In this study, we test the hypothesis that, owing to the fact that CO 2 will likely be transported in multi-source networks, not all CO 2 streams will need to be of high purity, and that it may be possible to combine several sources of varying purity to obtain an end-product that is suitable for storage. We nd that, when considering study generated costs for an example network in the UK, optimally combining these dierent sources into one multi-source transport network subject to a minimum CO 2 purity of 96% can reduce the price of captured CO 2 by 17%.
A techno-economic assessment of power plants with CO2 capture technologies with a focus on process scenarios that deliver different grades of CO2 product purity is presented.The three leading CO2 capture technologies are considered, namely; oxyfuel combustion, pre-combustion and post-combustion capture. The study uses a combination of process simulation of flue gas cleaning processes, modelling with a power plant cost and performance calculator and literature values of key performance criteria in order to evaluate the performance, cost and CO2 product purity of the considered CO2 capture options. For oxyfuel combustion capture plants, three raw CO2 flue gas processing strategies of compression and dehydration only, double flash system purification and distillation purification are considered. Analysis of pre-combustion capture options is based on integrated gasification combined cycle plants using physical solvent systems for capturing CO2 and sulfur species via three routes; co-capture of sulfur impurities with the CO2 stream using Selexol TM solvent, separate capture of CO2 and sulfur impurities using Dedication: This paper is dedicated to the memory of our friend and colleague, Dr. Robert M. Woolley, who made a significant input to the CO2QUEST project and whose expertise, commitment and great humour will never be forgotten.
Using carbon dioxide for enhanced oil recovery (CO2-EOR) has been widely cited as a potential catalyst for gigatonne-scale carbon capture and storage (CCS) deployment. Carbon dioxide enhanced oil recovery could provide revenues for CO2 capture projects in the absence of strong carbon taxes, providing a means for technological learning and economies of scale to reduce the cost of CCS. We develop an open-source techno-economic Model of Iterative Investment in CCS with CO2-EOR (MIICE), using dynamic technology deployment modeling to assess the impact of CO2-EOR on the deployment of CCS. Synthetic sets of potential CCS with EOR projects are created with typical field characteristics and dynamic oil and CO2 production profiles. Investment decisions are made iteratively over a 35 year simulation period, and long-term changes to technology cost and revenues are tracked. Installed capacity at 2050 is used as an indicator, with 1 gigatonne per year of CO2 capture used as a benchmark for successful large-scale CCS deployment. Results show that current CO2 tax and oil price conditions do not incentivize gigatonne-scale investment in CCS. For current oil prices ($45 per bbl–$55 per bbl), the final CO2 tax must reach $70 per tCO2 for gigatonne-scale deployment. If oil price alone is expected to induce CCS deployment and learning, oil prices above $85 per bbl are required to promote the development of a gigatonne-scale CCS industry. Nonlinear feedbacks between early deployment and learning result in large changes in final state due to small changes in initial conditions. We investigate the future of CCS in five potential ‘states of the world’: an optimistic ‘Base Case’ with a low CO2 tax and low oil price, a ‘Climate Action’ world with high CO2 tax, a ‘High Oil’ world with high oil prices, a ‘Depleting Resources’ world with an increasing deficit in oil supply, and a ‘Forward Learning’ world where mechanisms are in place to drive down the cost of CCS at rates similar to other clean energy technologies. Through multidimensional sensitivity analysis we outline combinations of conditions that result in gigatonne-scale CCS. This study provides insight levels of taxes, learning rates, and oil prices required for successful scale-up of the CCS industry
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