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Abstract
The large-scale deployment of carbon capture, transport and storage (CCTS) systems will be capital intensive and complex. Therefore, it is necessary to design a network infrastructure that can meet a specific CO2 reduction target and is at the same time optimized to minimize cost and operation problems. The objective of this work is to develop a multiscale modeling approach that uses flue gas characterization data as an input to simulate and size a post-combustion capture plant model. The profile of the bare capturing cost of CO2 against degree of capture is used in designing and analyzing the cost optimal CO2 infrastructure layout that matches CO2 sources and sinks in capacity and time. This will help generate insights of whole-system integration issues and its performance as function of design variables. Thus the whole system is optimized rather than optimizing individual components, which leads to sup-optimal CCTS design.

Typically, researchers assume a cost associated with a pre-specified 90% degree of capture, which is claimed to be the cost optimal capture plant. The results of designing and analyzing cost optimal CCTS networks for a UAE case study show that the cost optimal degree of capture is a site specific factor that depends on the flue gas characteristics and proximity to transportation networks and geological storage that can accommodate certain amount of CO2 per year.

Introduction
As the UAE has the second highest carbon emissions per capita in the world, it has an ambitious primary target to reduce Abu Dhabi’s carbon footprint by third through implementing large scale carbon capture, transport and storage (CCTS) networks for enhanced oil Recovery (EOR) (Nader, 2009). The key drivers are the proximity of large CO2 sources and reservoirs, the benefit of releasing natural gas currently being used for EOR and the opportunity of utilizing low cost fuel.

The large-scale deployment of CCTS systems will be capital intensive and complex. Therefore, it is necessary to design a network infrastructure that can meet a specific CO2 reduction target and is at the same time optimized to minimize cost and operation problems. The network comprises a number of CO2 sources at fixed locations and a number of potential CO2 storage sites. The decisions to be determined include from which sources it is appropriate to capture CO2 and the cost-optimal degree-of-capture (DOC) for a given source and the infrastructural layout of the CO2 transmission network. Typically, researchers assume a cost associated with a pre-specified 90% degree of capture, which is claimed to be the cost optimal capture plant. There are only few studies in the literature that individually takes into account the effect of degree of capture in the total cost of the capture plant (Rao & Rubin, 2006; Abu-Zahra et.al 2006; Abu-Zahra et.al 2007). We argue that the cost optimal degree of capture is a site specific factor that not only depends on the economies of scale of the capture plant but also its characteristics and proximity to transportation networks and geological storage that can accommodate certain amount of CO2 per year.

The objective of this work is to develop a multiscale modeling approach that uses flue gas characterization data as an input to simulate and size a post-combustion capture plant model. The profile of the bare capturing cost of CO2 against degree of capture is used in designing and analyzing the cost optimal CO2 infrastructure layout that matches CO2 sources and sinks in capacity and time. This will help generate insights of whole-system integration issues and its performance as function of design variables. Thus the whole system is optimized rather than optimizing individual components, which leads to sup-optimal CCTS design.