- Email: [email protected]
Multiscale Design and Analysis of CO2 Capture, Transport and Storage Networks
Available online at www.sciencedirect.com
Energy Procedia 37 (2013) 2552 – 2561
GHGT-11
Multiscale design and analysis of CO2 capture, transport and storage networks Ahmed Alhajaja, Niall Mac Dowella, Nilay Shaha* a
Centre For Process Systems Engineering, Imoerial College, London SW7 2AZ, UK
Abstract An integrated whole-system model of a CO2 capture, transport and storage (CCTS) network was developed in order to design the optimum network linking CO2 sources (e.g., power stations) with potential sinks (e.g., depleted oil reservoirs). This work is multiscale in nature, employing models describing system behaviour and interactions through a range of length and timescales. We used our model to determine the optimum location and operating conditions of each CO2 capture process while giving full consideration to the whole-system behaviour. Further, researchers assume a cost associated with a pre-specified 90% degree of capture. However, an important result of designing and analysing cost optimal CCTS networks for the UAE was that the cost optimal degree of capture is a site specific factor that depends on the flue gas characteristics, proximity to transportation networks and adequate geological storage capacity. The results of this study indicated an optimum capture rate lower than the one obtained by looking into account the economies of the capture plant alone. This conclusion serves to underscore the importance of a whole-system analysis of potential CCTS networks.
© 2013 The TheAuthors. Authors.Published PublishedbybyElsevier Elsevier Ltd. Ltd. Selection and/orpeer-review peer-reviewunder underresponsibility responsibility GHGT Selection and/or of of GHGT
Keywords: CO2 netwroks; Multiscale model; CCS model; CO2 transport;
1. Introduction Global warming is widely considered to be one of the most important challenges of the 21st century. An emerging countermeasure to combat global warming is to capture the CO 2 flue gas from large fixed point sources and transport it to a geological storage site where its long term stored is assured. This is a promising technology option as it allows us to continue the near to medium term exploitation of fossil
* Corresponding author. Tel.: +44 20 7594 6621. E-mail address: [email protected].
1876-6102 © 2013 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of GHGT doi:10.1016/j.egypro.2013.06.138
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
fuels whilst mitigating their environmental impact[1]. It also paves the way toward a smooth transition to renewable energy in the long term. From an energy system perspective, many studies analysed the penetration of CCS while meeting the energy demand of specific country considering existing feedstocks (e.g., power plant, fuel mix) in addition to renewable energy and CO2 emission reduction targets. The economic performance, public acceptance, natural resources endowment (e.g., wind) and the phase-out of existing power plants within the coming decade were taken into account in these studies [2-6]. CCS was seen as an economical option after 2020 when most of the measures for increasing the efficiency and productive sites for renewable energy were exploited [2, 3]. The real option theory was used in another study to assess power plant owners in investing in CCS as an option that maximize the profit of the company at the national level once a good price of CO2 is reflected in the market [7]. The main challenge associated with CCTS technology is a lack of large scale operating experience. Also there are a lot of site-specific factors that need to be borne in mind when applying this technology on a scale that minimizes the environmental impact of global warming: for instance, the proximity of the sources to geological storage sites having the ability to accommodate these emissions at a certain rate per year and for the life-time of the project. Most of the methods that have been suggested for distributing CO2 to geological storage sites overlook the interconnectedness of the CCTS components. Some studies of CCTS examine how different individual components can be optimised in terms of efficiency and cost. For instance, Rao & Rubin [8] and Abu-Zahra et.al [9, 10] examined the effect of degree of capture in the total cost of the capture plant. However, they do not consider the whole-system economics of the CCTS network. Other studies consider the whole system but neglect to give sufficient weight to certain factors. For instance, Van-Bergen et al. [11] highlights the economic benefit of CCTS, namely enhanced oil recovery (EOR) and coal bed methane, but they assume that the distance between storage and high CO2 content source is an important cost factor and so recommend that this should not exceed 100 km. A similar limitation is evident in the work of Brashdaw et al. [12]. They have applied a bottom-up approach, utilizing more storage site details such as risks, capacity, and injection depth to ensure better matching with the sources and hence better -115 Mt CO2/ year. But, once again, they limit the distance between appropriate storage sites and emission sources to less than 300 km. Other researchers have proposed a system optimisation tool that has the flexibility to link cost-optimal CO2 sources to geological storage site through a network of pipelines in a way that matches specific CO2 reduction targets [13, 14].However, they oversimplify the costing of the capture plant by failing to take into account site-specific factors such as degree of capture, the availability of water and energy cost. By contrast, our approach aims to integrate all the components of a CCTS to enable us to provide decision makers with a systematic tool that will help them find the optimum deployment of CCTS infrastructure that meet reduction targets at the national level. 2. Methodology An integrated whole-system model of a CO2 capture, transport and storage (CCTS) network was developed in order to design the optimum network t linking CO2 sources (e.g., power stations) with potential sinks (e.g., depleted oil reservoirs). This work is multiscale in nature, employing models
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describing system behaviour and interactions through a range of length and timescales. At the micro scale, a detailed molecular thermodynamic model based on SAFT-VR [15-19]that describes the thermophysical properties of the MEA-based solvent and flue gas was integrated with an equilibrium based mass transfer model while using the two film theory [20]. This was used to design the capture plant and the optimum operating conditions required to capture certain level of CO2. The predictive energetic and economic performance results of the capture plant were used to analyse high level decisions of the CO2 supply chain networks outlined in earlier studies [21-23]. This design is a difficult task due to the inherent complexity associated with the interactions between different models across the whole CCTS system. However, utilizing such a whole-system approach will avoid the problem of optimising one aspect of the system which can result in the sub-optimal operation of other parts of the network.
Fig. 1. Illustration of CCTS Multiscale modeling
2.1. Optimal steady-state design of CO2 networks A layout of the multi-scale detailed carbon capture and storage model is shown in figure 2. There are four macro components within the carbon capture and storage systems namely CO2 sources, CO2 capture
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plant, CO2 transportation and CO2 underground storage. Each component of the system is analyzed to identify any metrics and generate the necessary database to optimize the whole system.
Fig. 2. CCTS Multiscale modeling components
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The CO2 source plants (e.g., power plant, process plant) emit certain amounts of CO2 with variable concentration. This level of CO2 emissions might change in response to demand changes for services, products and utilities throughout the year. The energetic and economic performance of attaching a capture plant to different sources was exploited through the detailed amine based post-combustion CO2 capture plant developed in gPROMS. The simulations of the model were used to develop a database of information that supports designing CO2 networks at the high level while using mathematical based optimization model. The CO2 can be transported to a geological storage using pipeline with different diameters having a minimum and a maximum capacity. The cost of the pipelines (i.e., material, labour, right of way, miscellaneous) was estimated based on regression model developed for gas pipelines [24]. The CO2 can be stored in a permanent source such as depleted oil reservoir or it can be used for EOR projects. Each storage site has a certain capacity and injectivity that allow it to accommodate certain amount of CO2 per year. 2.1.1. Qualitative description of the mathematical model 2.1.1.1. Given Number, location, flue gas characteristics of CO2 sources Database of economic performance (i.e., capital and operating cost) of CO2 capture (for different degrees of capture) from all potential sources using the developed amine based postcombustion capture plant in gPROMSTM CO2 reduction target or potential market for EOR Number, location and capacity of each storage site Financial and technical data such as discount rate, capacity factor, life time of the plant 2.1.1.2. Decision variables Which CO2 source to select? Which degree of capture and operating conditions the capture plant is designed for? Where to establish pipeline and at which size? How to connect these pipelines in a network? Which CO2 storage site to exploit? 2.1.1.3. Objective Function The objective function is the minimization of the total levelized cost (i.e., capital and operating cost) of the whole system (i.e., CO2 capture, compression, transport and storage). 3. Case study We demonstrate the feasibility of our approach within the region of the UAE. As this region has the sixth highest carbon emissions per capita in the world, it has an ambitious primary target to reduce Abu a third through implementing large scale carbon capture, transport and storage (CCTS) networks for enhanced oil Recovery (EOR) [25]. The key drivers are the proximity of
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
large CO2 sources and reservoirs, the benefit of releasing natural gas currently being used for EOR and the opportunity of utilizing low cost fuel. A realistic scenario of capturing 5 million tons of CO2 from three different sources namely Boiler flue gas from Taweelah power company (TAWEELA), combined cycle gas turbine (CCGT) flue gas from Emirates Aluminum plant (EMAL) and a pure stream of CO2 from Emirates Steel Industry (ESI), shown in table 1 and figure 3 [25]. The aim here is to satisfy a portion of CO2 potential demand for EOR projects assuming 17% incremental oil production while employing CO2 in miscible floods while considering that1 ton of CO2 is capable to produce 2-6 barrels of oils per day [26]. Table 1. Case scenario represents phase 1 of announced UAE reduction target of 5 million tons of CO 2 per year by 2014.
Boiler flue gas
Current reduction target (MMtCO2/year)
CO2 emitted (MMtCO2/year)
Comment
1.7
2.5
S1 CO2 content 8 mol %
CCGT
2.5
3
S2,S3
Iron plant
0.8
0.8
S4 No capture required, just compression and dehydration unit
Fig. 3. Location of CO2 sources and sinks of the case study
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4. Results and discussions The results of simulating 30wt% MEA-based capture plant system attached to all the sources in the case study at their optimum operating conditions while varying the degree of capture were used in the steady supply chain network. This was formulated as an MILP problem and solved in GAMS environment using CPLEX solver. The results of running Multiscale model to find the cost optimal layout of two reduction targets namely, 4.5 and 5.04 million tons of CO2 per year are shown in Fig.4 and Fig. 5 respectively. The optimum rate of capture from each selected source is shown in Table 2. The deployment of a compression plant at steel plant with highest degree of capture (i.e.,99%) is expected as it has a pure stream of CO2 that only need to be compressed and dried for transportation. However, a higher CO2 content in the boiler fired power plant compared to the CCGT does not mean that this source need to be exploited completely as can be seen in the results in Table 2. These results highlight the complexity of deploying an interconnected network to meet a specific reduction target. It also emphasises the need to explicitly account for the effect of optimum degree of CO2 capture from different sources which is a site specific factor that is highly interconnected with the availability and the cost of pipeline networks. Table 2. Degree of capture from each potential source DOC at 4.5 MMtCO2/year
DOC at 5.04 MMtCO2/year
S1
88
85
S2
79
75
S3 S4
72 99
99
Fig. 4.Results of capturing 4.2 million tons of CO2/year. Lines represent cost optimal deployment of 250 mm and 406 pipelines from three CO2 sources at different degree of capture.
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
Fig. 5. Results of capturing 5.04 million tons of CO 2/year. Lines represent cost optimal deployment of 250 mm and 406 pipelines from four CO2 sources at different degree of capture.
The computational statistics to solve this problem is shown in table 3. It shows that an optimum deployment of CCTS layout can be predicted through the model in very short time. Table 3. Computational results for the model. Number of equations
2107
Number of discrete variables
809
CPU time (sec)
0.129
5. Conclusion A detailed integrated multi-scale model for the design and analysis of the CCTS network has been developed and successfully implemented for a case study of the UAE. It emphasizes the need to take to take into account the effect of degree of capture, and site-specific factors such as flue gas characteristics in planning future CO2 reduction targets. Higher CO2 content does not necessarily indicate that the source needs to be exploited completely. Other factors such as the location of the CO2 source plant in the path of CO2 network is also another important factor that is ignored by the decision makers. The results of this study indicated an optimum capture rate lower than the one obtained by taking into account the economies of the capture plant alone. This conclusion serves to underscore the importance of a whole-system analysis of potential CCTS networks. It highlights that mass transfer and capture process details should be coupled and inherent in the design of CO2 networks.
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Acknowledgements A.A. thanks Masdar Abu Dhabi for funding this research project. References [1] N. MacDowell, N. Florin, A. Buchard, J. Hallett, A. Galindo, G. Jackson, C.S. Adjiman, C.K. Williams, N. Shah, P. Fennell, An overview of CO2 capture technologies, Energy & Environmental Science, 3 (2010) 1645-1669. [2] M. Odenberger, F. Johnsson, Pathways for the European electricity supply system to 2050 The role of CCS to meet stringent CO2 reduction targets, International Journal of Greenhouse Gas Control, 4 (2010) 327-340. [3] M. Odenberger, T. Unger, F. Johnsson, Pathways for the North European electricity supply, Energy Policy, 37 (2009) 1660-1677. [4] P. Rafaj, S. Kypreos, Internalisation of external cost in the power generation sector: Analysis with Global Multi-regional MARKAL model, Energy Policy, 35 (2007) 828-843. [5] M. Wise, J. Dooley, R. Dahowski, C. Davidson, Modeling the impacts of climate policy on the deployment of carbon dioxide capture and geologic storage across electric power regions in the United States, International Journal of Greenhouse Gas Control, 1 (2007) 261-270. [6] M.A. Wise, J.J. Dooley, The value of post-combustion carbon dioxide capture and storage technologies in a world with uncertain greenhouse gas emissions constraints, International Journal of Greenhouse Gas Control, 3 (2009) 39-48. [7] L. Zhu, Y. Fan, A real options based CCS investment evaluation model: Case study generation sector, Applied Energy, 88 (2011) 4320-4333. [8] A.B. Rao, E.S. Rubin, Identifying Cost-Effective CO2 Control Levels for Amine-Based CO2 Capture Systems, Industrial & Engineering Chemistry Research, 45 (2005) 2421-2429. [9] M.R.M. Abu-Zahra, J.P.M. Niederer, P.H.M. Feron, G.F. Versteeg, CO2 capture from power plants: Part II. A parametric study of the economical performance based on mono-ethanolamine, International Journal of Greenhouse Gas Control, 1 (2007) 135-142. [10] M.R.M. Abu-Zahra, L.H.J. Schneiders, J.P.M. Niederer, P.H.M. Feron, G.F. Versteeg, CO2 capture from power plants: Part I. A parametric study of the technical performance based on monoethanolamine, International Journal of Greenhouse Gas Control, 1 (2007) 37-46. [11] F. van Bergen, J. Gale, K.J. Damen, A.F.B. Wildenborg, Worldwide selection of early opportunities for CO2-enhanced oil recovery and CO2-enhanced coal bed methane production, Energy, 29 (2004) 1611-1621. [12] J. Bradshaw, G. Allinson, B.E. Bradshaw, V. CO2 geological storage potential and matching of emission sources to potential sinks, Energy, 29 (2004) 1623-1631. [13] R.S. Middleton, J.M. Bielicki, A scalable infrastructure model for carbon capture and storage: SimCCS, Energy Policy, 37 (2009) 1052-1060. [14] A. Alhajaj, Design and analysis of CO2 networks, in: Chemical Engineering and Chemical Technology, Imperial College London, London, 2008. [15] A. Galindo, L.A. Davies, A. Gil-Villegas, G. Jackson, The thermodynamics of mixtures and the corresponding mixing rules in the SAFT-VR approach for potentials of variable range, Molecular Physics, 93 (1998) 241-252. [16] A. Gil-Villegas, A. Galindo, P.J. Whitehead, S.J. Mills, G. Jackson, A.N. Burgess, Statistical associating fluid theory for chain molecules with attractive potentials of variable range, J. Chem. Phys., 106 (1997) 4168-4186.
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[17] N. Mac Dowell, F. Llovell, C.S. Adjiman, G. Jackson, A. Galindo, Modeling the Fluid Phase Behavior of Carbon Dioxide in Aqueous Solutions of Monoethanolamine Using Transferable Parameters with the SAFT-VR Approach, Industrial & Engineering Chemistry Research, 49 (2010) 1883-1899. [18] N. Mac Dowell, F.E. Pereira, F. Llovell, F.J. Blas, C.S. Adjiman, G. Jackson, A. Galindo, Transferable SAFT-VR Models for the Calculation of the Fluid Phase Equilibria in Reactive Mixtures of Carbon Dioxide, Water, and n-Alkylamines in the Context of Carbon Capture, The Journal of Physical Chemistry B, 115 (2011) 8155-8168. [19] J. Rodriguez, N. Mac Dowell, F. Llovell, C.S. Adjiman, G. Jackson, A. Galindo, Modelling the fluid phase behaviour of aqueous mixtures of multifunctional alkanolamines and carbon dioxide using transferable parameters with the SAFT-VR approach, Molecular Physics, 110 (2012) 1325-1348. [20] R. Taylor, R. Krishna, Multicomponent mass transfer, John Wiley, New York ; Chichester, 1993. [21] A. Almansoori, N. Shah, Design and Operation of a Future Hydrogen Supply Chain: Snapshot Model, Chemical Engineering Research and Design, 84 (2006) 423-438. [22] N. Shah, Process industry supply chains: Advances and challenges, Computers & Chemical Engineering, 29 (2005) 1225-1236. [23] P. Tsiakis, N. Shah, C.C. Pantelides, Design of Multi-echelon Supply Chain Networks under Demand Uncertainty, Industrial & Engineering Chemistry Research, 40 (2001) 3585-3604. [24] S.T. McCoy, E.S. Rubin, An engineering-economic model of pipeline transport of CO2 with application to carbon capture and storage, International Journal of Greenhouse Gas Control, 2 (2008) 219229. [25] S. Nader, Paths to a low-carbon economy The Masdar example, Energy Procedia, 1 (2009) 39513958. [26] D.J. Beecy, V.A. Kuuskraa, Basin strategies for linking CO2 enhanced oil recovery and storage of CO2 emissions, in: E.S. Rubin, D.W. Keith, C.F. Gilboy, M. Wilson, T. Morris, J. Gale, D.W.K.C.F.G.M.W.T.M.J.G. K. ThambimuthuA2 - E.S. Rubin, K. Thambimuthu (Eds.) Greenhouse Gas Control Technologies 7, Elsevier Science Ltd, Oxford, 2005, pp. 351-359.
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GHGT-11
Multiscale design and analysis of CO2 capture, transport and storage networks Ahmed Alhajaja, Niall Mac Dowella, Nilay Shaha* a
Centre For Process Systems Engineering, Imoerial College, London SW7 2AZ, UK
Abstract An integrated whole-system model of a CO2 capture, transport and storage (CCTS) network was developed in order to design the optimum network linking CO2 sources (e.g., power stations) with potential sinks (e.g., depleted oil reservoirs). This work is multiscale in nature, employing models describing system behaviour and interactions through a range of length and timescales. We used our model to determine the optimum location and operating conditions of each CO2 capture process while giving full consideration to the whole-system behaviour. Further, researchers assume a cost associated with a pre-specified 90% degree of capture. However, an important result of designing and analysing cost optimal CCTS networks for the UAE was that the cost optimal degree of capture is a site specific factor that depends on the flue gas characteristics, proximity to transportation networks and adequate geological storage capacity. The results of this study indicated an optimum capture rate lower than the one obtained by looking into account the economies of the capture plant alone. This conclusion serves to underscore the importance of a whole-system analysis of potential CCTS networks.
© 2013 The TheAuthors. Authors.Published PublishedbybyElsevier Elsevier Ltd. Ltd. Selection and/orpeer-review peer-reviewunder underresponsibility responsibility GHGT Selection and/or of of GHGT
Keywords: CO2 netwroks; Multiscale model; CCS model; CO2 transport;
1. Introduction Global warming is widely considered to be one of the most important challenges of the 21st century. An emerging countermeasure to combat global warming is to capture the CO 2 flue gas from large fixed point sources and transport it to a geological storage site where its long term stored is assured. This is a promising technology option as it allows us to continue the near to medium term exploitation of fossil
* Corresponding author. Tel.: +44 20 7594 6621. E-mail address: [email protected].
1876-6102 © 2013 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of GHGT doi:10.1016/j.egypro.2013.06.138
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
fuels whilst mitigating their environmental impact[1]. It also paves the way toward a smooth transition to renewable energy in the long term. From an energy system perspective, many studies analysed the penetration of CCS while meeting the energy demand of specific country considering existing feedstocks (e.g., power plant, fuel mix) in addition to renewable energy and CO2 emission reduction targets. The economic performance, public acceptance, natural resources endowment (e.g., wind) and the phase-out of existing power plants within the coming decade were taken into account in these studies [2-6]. CCS was seen as an economical option after 2020 when most of the measures for increasing the efficiency and productive sites for renewable energy were exploited [2, 3]. The real option theory was used in another study to assess power plant owners in investing in CCS as an option that maximize the profit of the company at the national level once a good price of CO2 is reflected in the market [7]. The main challenge associated with CCTS technology is a lack of large scale operating experience. Also there are a lot of site-specific factors that need to be borne in mind when applying this technology on a scale that minimizes the environmental impact of global warming: for instance, the proximity of the sources to geological storage sites having the ability to accommodate these emissions at a certain rate per year and for the life-time of the project. Most of the methods that have been suggested for distributing CO2 to geological storage sites overlook the interconnectedness of the CCTS components. Some studies of CCTS examine how different individual components can be optimised in terms of efficiency and cost. For instance, Rao & Rubin [8] and Abu-Zahra et.al [9, 10] examined the effect of degree of capture in the total cost of the capture plant. However, they do not consider the whole-system economics of the CCTS network. Other studies consider the whole system but neglect to give sufficient weight to certain factors. For instance, Van-Bergen et al. [11] highlights the economic benefit of CCTS, namely enhanced oil recovery (EOR) and coal bed methane, but they assume that the distance between storage and high CO2 content source is an important cost factor and so recommend that this should not exceed 100 km. A similar limitation is evident in the work of Brashdaw et al. [12]. They have applied a bottom-up approach, utilizing more storage site details such as risks, capacity, and injection depth to ensure better matching with the sources and hence better -115 Mt CO2/ year. But, once again, they limit the distance between appropriate storage sites and emission sources to less than 300 km. Other researchers have proposed a system optimisation tool that has the flexibility to link cost-optimal CO2 sources to geological storage site through a network of pipelines in a way that matches specific CO2 reduction targets [13, 14].However, they oversimplify the costing of the capture plant by failing to take into account site-specific factors such as degree of capture, the availability of water and energy cost. By contrast, our approach aims to integrate all the components of a CCTS to enable us to provide decision makers with a systematic tool that will help them find the optimum deployment of CCTS infrastructure that meet reduction targets at the national level. 2. Methodology An integrated whole-system model of a CO2 capture, transport and storage (CCTS) network was developed in order to design the optimum network t linking CO2 sources (e.g., power stations) with potential sinks (e.g., depleted oil reservoirs). This work is multiscale in nature, employing models
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describing system behaviour and interactions through a range of length and timescales. At the micro scale, a detailed molecular thermodynamic model based on SAFT-VR [15-19]that describes the thermophysical properties of the MEA-based solvent and flue gas was integrated with an equilibrium based mass transfer model while using the two film theory [20]. This was used to design the capture plant and the optimum operating conditions required to capture certain level of CO2. The predictive energetic and economic performance results of the capture plant were used to analyse high level decisions of the CO2 supply chain networks outlined in earlier studies [21-23]. This design is a difficult task due to the inherent complexity associated with the interactions between different models across the whole CCTS system. However, utilizing such a whole-system approach will avoid the problem of optimising one aspect of the system which can result in the sub-optimal operation of other parts of the network.
Fig. 1. Illustration of CCTS Multiscale modeling
2.1. Optimal steady-state design of CO2 networks A layout of the multi-scale detailed carbon capture and storage model is shown in figure 2. There are four macro components within the carbon capture and storage systems namely CO2 sources, CO2 capture
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
plant, CO2 transportation and CO2 underground storage. Each component of the system is analyzed to identify any metrics and generate the necessary database to optimize the whole system.
Fig. 2. CCTS Multiscale modeling components
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The CO2 source plants (e.g., power plant, process plant) emit certain amounts of CO2 with variable concentration. This level of CO2 emissions might change in response to demand changes for services, products and utilities throughout the year. The energetic and economic performance of attaching a capture plant to different sources was exploited through the detailed amine based post-combustion CO2 capture plant developed in gPROMS. The simulations of the model were used to develop a database of information that supports designing CO2 networks at the high level while using mathematical based optimization model. The CO2 can be transported to a geological storage using pipeline with different diameters having a minimum and a maximum capacity. The cost of the pipelines (i.e., material, labour, right of way, miscellaneous) was estimated based on regression model developed for gas pipelines [24]. The CO2 can be stored in a permanent source such as depleted oil reservoir or it can be used for EOR projects. Each storage site has a certain capacity and injectivity that allow it to accommodate certain amount of CO2 per year. 2.1.1. Qualitative description of the mathematical model 2.1.1.1. Given Number, location, flue gas characteristics of CO2 sources Database of economic performance (i.e., capital and operating cost) of CO2 capture (for different degrees of capture) from all potential sources using the developed amine based postcombustion capture plant in gPROMSTM CO2 reduction target or potential market for EOR Number, location and capacity of each storage site Financial and technical data such as discount rate, capacity factor, life time of the plant 2.1.1.2. Decision variables Which CO2 source to select? Which degree of capture and operating conditions the capture plant is designed for? Where to establish pipeline and at which size? How to connect these pipelines in a network? Which CO2 storage site to exploit? 2.1.1.3. Objective Function The objective function is the minimization of the total levelized cost (i.e., capital and operating cost) of the whole system (i.e., CO2 capture, compression, transport and storage). 3. Case study We demonstrate the feasibility of our approach within the region of the UAE. As this region has the sixth highest carbon emissions per capita in the world, it has an ambitious primary target to reduce Abu a third through implementing large scale carbon capture, transport and storage (CCTS) networks for enhanced oil Recovery (EOR) [25]. The key drivers are the proximity of
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
large CO2 sources and reservoirs, the benefit of releasing natural gas currently being used for EOR and the opportunity of utilizing low cost fuel. A realistic scenario of capturing 5 million tons of CO2 from three different sources namely Boiler flue gas from Taweelah power company (TAWEELA), combined cycle gas turbine (CCGT) flue gas from Emirates Aluminum plant (EMAL) and a pure stream of CO2 from Emirates Steel Industry (ESI), shown in table 1 and figure 3 [25]. The aim here is to satisfy a portion of CO2 potential demand for EOR projects assuming 17% incremental oil production while employing CO2 in miscible floods while considering that1 ton of CO2 is capable to produce 2-6 barrels of oils per day [26]. Table 1. Case scenario represents phase 1 of announced UAE reduction target of 5 million tons of CO 2 per year by 2014.
Boiler flue gas
Current reduction target (MMtCO2/year)
CO2 emitted (MMtCO2/year)
Comment
1.7
2.5
S1 CO2 content 8 mol %
CCGT
2.5
3
S2,S3
Iron plant
0.8
0.8
S4 No capture required, just compression and dehydration unit
Fig. 3. Location of CO2 sources and sinks of the case study
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4. Results and discussions The results of simulating 30wt% MEA-based capture plant system attached to all the sources in the case study at their optimum operating conditions while varying the degree of capture were used in the steady supply chain network. This was formulated as an MILP problem and solved in GAMS environment using CPLEX solver. The results of running Multiscale model to find the cost optimal layout of two reduction targets namely, 4.5 and 5.04 million tons of CO2 per year are shown in Fig.4 and Fig. 5 respectively. The optimum rate of capture from each selected source is shown in Table 2. The deployment of a compression plant at steel plant with highest degree of capture (i.e.,99%) is expected as it has a pure stream of CO2 that only need to be compressed and dried for transportation. However, a higher CO2 content in the boiler fired power plant compared to the CCGT does not mean that this source need to be exploited completely as can be seen in the results in Table 2. These results highlight the complexity of deploying an interconnected network to meet a specific reduction target. It also emphasises the need to explicitly account for the effect of optimum degree of CO2 capture from different sources which is a site specific factor that is highly interconnected with the availability and the cost of pipeline networks. Table 2. Degree of capture from each potential source DOC at 4.5 MMtCO2/year
DOC at 5.04 MMtCO2/year
S1
88
85
S2
79
75
S3 S4
72 99
99
Fig. 4.Results of capturing 4.2 million tons of CO2/year. Lines represent cost optimal deployment of 250 mm and 406 pipelines from three CO2 sources at different degree of capture.
Ahmed Alhajaj et al. / Energy Procedia 37 (2013) 2552 – 2561
Fig. 5. Results of capturing 5.04 million tons of CO 2/year. Lines represent cost optimal deployment of 250 mm and 406 pipelines from four CO2 sources at different degree of capture.
The computational statistics to solve this problem is shown in table 3. It shows that an optimum deployment of CCTS layout can be predicted through the model in very short time. Table 3. Computational results for the model. Number of equations
2107
Number of discrete variables
809
CPU time (sec)
0.129
5. Conclusion A detailed integrated multi-scale model for the design and analysis of the CCTS network has been developed and successfully implemented for a case study of the UAE. It emphasizes the need to take to take into account the effect of degree of capture, and site-specific factors such as flue gas characteristics in planning future CO2 reduction targets. Higher CO2 content does not necessarily indicate that the source needs to be exploited completely. Other factors such as the location of the CO2 source plant in the path of CO2 network is also another important factor that is ignored by the decision makers. The results of this study indicated an optimum capture rate lower than the one obtained by taking into account the economies of the capture plant alone. This conclusion serves to underscore the importance of a whole-system analysis of potential CCTS networks. It highlights that mass transfer and capture process details should be coupled and inherent in the design of CO2 networks.
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