CHEMICAL ENGINEERING TRANSACTIONS VOL. 52, 2016 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Petar Sabev Varbanov, Peng-Yen Liew, Jun-Yow Yong, Jiří Jaromír Klemeš, Hon Loong Lam Copyright © 2016, AIDIC Servizi S.r.l., ISBN 978-88-95608-42-6; ISSN 2283-9216 Regional and Total Site CO2 Integration Considering Purification and Pressure Drop Wan Norlinda Roshana Mohd Nawia, Sharifah Rafidah Wan Alwia, Zainuddin Abdul Manana, Jiří Jaromír Klemešb, Petar Sabev Varbanov*b aProcess Systems Engineering Centre (PROSPECT), Research Institute on Sustainable Environment, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia bPázmány Péter Catholic University (PPKE), Faculty of Information Technology and Bionics, 1083 Budapest, Hungary varbanov.petar@itk.ppke.hu The application of Pinch Analysis (PA) targeting method has been recently explored to the design of CO2 emission reduction in Total Site planning of CO2 capture, utilisation and storage. The algebraic method based on Problem Table Algorithm (PTA) for Total Site CO2 Integration (TSCI) provides the designer with integrated CO2 capture, utilisation and storage (CCUS) for the optimal CO2 emission reduction. In TSCI, CO2 is captured with certain quality from various plants and supply into system header pipeline. The CO2 header could satisfy various CO2 demands for various industry located in the header, and only surplus CO2 is to be sent to storage. The extended methodology with consideration of purification and pressure drop in TSCI planning is proposed. The CO2 supply from the header could satisfy the purity demand through a process of purification. Purification is a process to upgrade the purity level have not been considered in the previously used in TSCI method. In addition, pressure drop during CO2 transportation in the pipeline system has been included to identify the implication of pressure drop in TSCI design and has resulted about 29.01 MPa of the total pressure drop (∆Pd). Therefore, 68.2 t/h of flow rate (FT) of 81 % purity level header is supplied and purified to satisfy the 50 t/h of demand, at 99 % purity level. The improved methodology of TSCI network has been further developed and provides a more realistic scenario for CCUS implementation. 1. Introduction CO2 capture, utilisation or sequestration has received considerable attention as a logical pathway to mitigate global warming effects. In year 2014, the concentration of CO2 (397 ppm) was about 40 % higher than the mid-1800s (IEA 2015). Energy-intensive industrial production is responsible for about 60 % of the total anthropogenic CO2 emissions, which include power plants, cement production, refineries, iron and steel industries, gas processing and petrochemicals (Čuček et al., 2015). Cost and further energy demand are the current major barrier for CO2 capture, sequestration or storage (CCS) (Rubin et al. 2013) included CO2 transfer from sources to dedicated storage. CCS project in Southeast United State (505 km-pipe length; 24 in- diameter) and Wyoming (373 km-pipe length; 20 in-diameter), CO2 pipeline transportation capital cost for these two locations is estimated about 370 M$ to 740 M$ for 42,320 t CO2/d capacity and 135 M$ to 430 M$ for 38,280 t CO2/d capacity (NETL 2013). Consequently planning and long-term strategies are needed to cluster the CO2 sources and develop CO2 pipeline networks (Jensen et al. 2013) such as source-to-sink transmission of CO2 at an optimal operation. PA or source-sink matching method has been developed from Heat Integration (Klemeš, 2013), extension to Total Site Heat Integration (Klemeš et al., 1977) and explored for CCS planning development (Klemeš et al., 2013). Ooi et al. (2013) introduced a planning method for CCS using PA. A graphical targeting tool is proposed to plan the CO2 captured from the power plants into CO2 storage facilities. To minimise the need to transport CO2 over long distances from the sources to storage, Diamante et al. (2014) highlighted the injectivity constraint of sinks and time availability of potential sources and sinks using graphical and algebraic DOI: 10.3303/CET1652196 Please cite this article as: Mohd Nawi W. N. R., Wan Alwi S. R., Manan Z. A., Klemeš J. J., Varbanov P. S., 2016, Regional and total site co2 integration considering purification and pressure drop, Chemical Engineering Transactions, 52, 1171-1176 DOI:10.3303/CET1652196 1171 approaches. Zhang et al. (2015) extended the Process Integration methods for optimal operation characteristics in the CCS system by targeting Pinch Temperature to integrate heat pumps and refrigerator for waste heat recovery. Krishna Priya and Bandyopadhyay (2015) have introduced a PA based approach for the viability of capture technology using the concept of prioritised cost for retrofitting into existing power plants. Kim et al. (2015) made an attempt to introduce GHG Footprint Composite Curves and Giaouriset al. (2015) extended Power Grand Composite Curves approach introduced by Wan Alwi et al. (2012) into adaptive operation of renewable energy smart grids. Total Site CO2 Integration (TSCI) planning tool is introduced (Mohd Nawi et al. 2015) to maximise the CO2 exchange between CO2 sources and demands using central pipeline header before sending the excess CO2 to storage. The main challenge in TSCI is the cost to integrate the sources, demands and storage and CO2 transfer across distance. Besides pipeline distance, other major components for cost estimation are compressor power and additional CO2 purifier. An extended TSCI with consideration of purification and pressure drop during CO2 transfers is proposed in this study. Purification processes is used to upgrade the concentration to satisfy a high purity CO2 demand and have been widely used in the hydrogen network (Wang et al. 2016) to reduce production load. Pressure drop is highlighted to ensure that process transfer of CO2 in the pipeline is function normally and an unanticipated pressure drop may resulted in leakage (Noothout et al. 2013). This extended methodology with consideration of these two important parameters in CO2 transportation via pipeline would give a realistic scenario for TSCI implementation. 2. Methodology with illustrated case study There are currently about 6,500 km of CO2 pipeline worldwide, which most of them are linked to EOR operations that associated with or under development for CO2 storage (Noothout et al. 2013). Dense phase or supercritical condition for CO2 is the most efficient way to transport via pipeline and it is required to maintain the pressure in the pipeline above the critical point of CO2 (Wetenhall et al. 2014). In the Gas Processors Suppliers Association (GPSA) Engineering Data book (GPSA 1998), the critical point of CO2 occurs at a pressure of 7.38 MPa and a temperature of 31.4 °C and most widely used operating pressure is between 7.4 and about 21 MPa to ensure CO2 single-phase flow in the pipeline (Dakota Gasification Company 2016). High inlet pressure or booster stations installation at every 100 km to 150 km are required to make up the pressure losses (Wong 2013) as pressure drop increases with the increasing of flow rate (Seevam et al. 2010). In this study, single phase or supercritical CO2 is assumed in the pipeline system transportation. The critical point properties of CO2 are at 31 °C and 7.37 MPa and density of CO2 at this point is assumed 467.69 kg/m3 (Fenghour et al. 1998). 2.1 Identify CO2 sources and demands Eight sources of potential CO2 captured points and four potential of CO2 demands in a potential area (Mohd Nawi et al. 2016) have been identified for an integrated network. The developed methodology had targeted the CO2 purity at each point of header to optimal CO2 utilisation, minimum fresh CO2 supply needed and a minimum of CO2 sent to storage. Tables 1 and 2 show the CO2 sources and CO2 demands and their corresponding data. Table 1: Data for CO2 sources Source Description FT (t/h) PCO2 Distance (km) FCO2 (t/h) FOG (t/h) S1 Cement 138.8 0.90 410 124.9 13.9 S2 Refineries/ chemical 608.5 0.70 390 425.9 182.5 S3 Power (coal based) 1,174.3 0.85 360 998.2 176.1 S4 Power (NG based) 101.5 0.88 290 89.3 12.2 S5 Agricultural 69.9 0.65 270 45.4 24.4 S6 Petrochemical 615.4 0.80 210 492.3 123.1 S7 Gas processing 36.5 0.90 190 32.8 3.6 S8 Iron & steel 27.9 0.95 150 26.5 1.4 Table 2: Data for CO2 demands Demands Description FT (t/h) PCO2 Distance (km) FCO2 (t/h) FOG (t/h) D1 Beverage plant 50.0 0.99 340 49.5 0.5 D2 Enhance oil recovery 208.3 0.80 240 166.6 41.7 D3 Methanol production 83.3 0.50 110 41.7 41.7 D4 Micro algae production 220.0 0.10 100 22.0 198.0 1172 FT is the total flow rate of flue gas and PCO2 is the purity of CO2 in the total flue gas which gives the value of CO2 flow rate (FCO2) and other gases flow rate (FOG). Beside CO2 has been also other gases some of them strong GHG - (FOG) such as N2, CO, NOx, SOx and N2 included in the flue gas. Distance (km) is estimated from the point of sources or demands through a header to the storage. 2.2 CO2 Total Site – Problem Table Algorithm The CO2 sources and demands are matched by targeting the maximum CO2 utilisation before the remaining capture CO2 is sent to storage. Noted that one header is constructed in this study and Figure 1 illustrates the TSCI network. The step by step construction of CO2 Total Site Problem Table Algorithm (CTS-PTA) methodology is described by Mohd Nawi et al. (2016). The sources and demands are arranged based on their location along the header (Column 1). FT is extracted in Column 2 followed by PCO2 and FCO2 in Columns 3 and 4. The positive value of flow rate represents sources and negative is for demands. Next step is to match the sources and demands. Cumulative FT in Column 5 and FCO2 in Column 6 are cascading downwards and cumulative PCO2 (Column 7) is indicated by dividing the cumulative FCO2 with FT cumulative as shown in Table 3. In order to match CO2 sources and demand, the FT header would be directly supplied to the demands if the required demand purity is lower or equal to the header purity. However, if the demand requires higher purity than the header purity, a purification process is proposed to satisfy the demand. Figure 1: CO2 sources (S) and demands (D) through a header 2.3 Calculate purification process For a demand that requires a higher purity than the CO2 purity in the header, a purifier is considered to utilise CO2 from header to demand site. The purification process generates two outputs (Zhang et al. 2011) – one of which with higher purity as the product, FDi and the other one is by product or tail gas (FGi). The cumulative flow rate from the header to satisfy D1, Fin,(H-Di) is calculated by using Eq(1) as stated in Column 5. Eq(2) is used to calculate the tail gas flow rate (FGi) of the process and Eq(3) to determine the purity of the tail gas of the system. Figure 2: Mass balance for purification process The tail gas is supplied back into the header and purity of FGi is indicated as PGi. Figure 2 illustrates this arrangement. The recovery efficiency (R) of the purification process is assumed 0.9. 1173 𝐹𝑖𝑛,𝐻−𝐷𝑖 = 𝐹𝑇,𝐷𝑖 𝑥 𝑃𝐶𝑂2,𝐷𝑖 𝑅(𝑃𝐶𝑂2,𝐻 ) (1) 𝐹𝐺𝑖 = 𝐹𝑖𝑛,𝐻−𝐷𝑖 − 𝐹𝐷𝑖 (2) 𝐹𝑖𝑛𝐻−𝐷𝑖 𝑃𝐻−𝐷𝑖 = 𝐹𝐷𝑖 𝑃𝐷𝑖 + 𝐹𝐺𝑖 𝑃𝐺𝑖 (3) For a demand that requires equal or lower purity (PCO2) than CO2 purity (PH) in the header, Fin,(H-Di) is directly supplied per demand required without purifier installation as stated in TSCI purity rule concept (Mohd Nawi et al. 2016). Eq(4) and Eq(5) represent the direct supply of flow rate from header to demand. 𝐹𝑖𝑛,𝐻−𝐷𝑖 = 𝐹𝐷𝑖 (4) 𝐹𝑖𝑛𝐻−𝐷𝑖 𝑃𝐻−𝐷𝑖 = 𝐹𝐷𝑖 𝑃𝐷𝑖 (5) 2.4 Calculate pressure drop Pressure drop due to friction along CO2 pipeline transportation is calculated as pressure is the most important to ensure that CO2 transportation function normally. Eq(6) is pressure drop estimation (Fox and McDonald 1992), where f is the friction factor (0.0165), m is mass flow rate (kg/s), ρ is the fluid density (kg/m3), L is pipe length (km) and D is pipe diameter (m). For turbulent pipe flow that typically fluids flow in a plant, f depends on the Reynolds number and relative roughness Ɛ/D, ratio of a mean height of roughness of the pipe to the pipe diameter. The value is following the Colebrook equation, Eq(7) and has been simplified into Moody chart to present Darcy friction factor for circular pipe flow (Cengel and Cimbala 2006). A roughness value (Ɛ) of 0.0457 mm has been used as the recommended value for commercial steel pipelines (Wetenhall et al. 2014) and diameter of the pipe is assumed to be 27-in (Noothout et al. 2013) to estimate the pressure drop in this study. Note that L (km) in Column 11 is the pipe length between each of source or demand points. ∆𝑃𝑑 = 𝑓 𝑚2 𝜌 𝐿 𝐷5 8,000 𝛱2 (6) 1 √𝑓 = −1.8 log [ 6.9 𝑅𝑒 + ( Ɛ/𝐷 3.7 ) 1.11 ] (7) The CTS-PTA with purifier installation and estimation of pressure drop due TSCI network is given in Table 3. The last row in Cum FT and Cum FCO2 gives the minimum target to be sent to CO2 storage for permanently stored. Total pressure drop is 29.01 MPa as shown in the last row of Column 14 (Cum ∆Pd). Three points of compression are considered in the TSCI design network to transport CO2 along the header as shown in Figure 3. Each of the compression points is assumed to make up about 10 MPa of pressure losses along the header and 0.085 MW capacity of each compressor is required, based on 8.5 kWh/t of energy consumption is required for 1 MPa CO2 compression (Wong, 2013). Figure 3: TSCI design with purifier and compressors Injection to Storage20 km 30 km 20 km 50 km 20 km 30 km 30 km 20 km 40 km 40 km 10 km 100 km S1 100 km S2 S3 S4 S5 S6 S7 S8 D1 D2 D3 D4 100 km 100 km 110 km Purifier 1174 3. Conclusion TSCI has a potential to integrate major CO2 emitter supplies into a centralised system and able to supply to any potential demands along the header. This paper is an improved TSCI methodology for further development in CCUS planning. A unit of purification is identified to satisfy the demand that requires higher CO2 purity than the header. The total pressure drop (∆Pd) of TSCI network in this study is 29.01 MPa at the range of 410 km of pipeline length. The improved TSCI methodology with consideration of purification process and compression is seen as realistic assessment for CCUS development. Further works can include cost tariff analysis of TSCI to estimate the cost of sequestration for each plant that wants to supply their CO2 into centralised header system. Table 3: The CTS-PTA and pressure drop calculation 1 5 6 7 8 9 10 11 12 13 14 i S/D Cum FT Cum FCO2 Cum PCO2 Fin, (H-D) FG PG L (km) Cum FT or m (kg/s) ∆Pd (MPa) Cum ∆Pd (MPa) 1 S1 138.79 124.91 0.90 20 38.55 0.01 2 S2 0.01 747.29 550.86 0.74 30 207.58 0.22 3 S3 0.23 1,921.59 1,549.02 0.81 20 533.78 0.97 4 D1 68.23 18.23 0.30 1.20 1,853.36 1,494.02 0.81 50 514.82 2.26 5 S4 3.45 1,954.86 1,583.34 0.81 20 543.02 1.00 6 S5 4.46 2,024.76 1,628.77 0.80 30 562.43 1.62 7 D2 208.30 - - 6.07 2,233.06 1,796.33 0.80 30 620.29 1.97 8 S6 8.04 2,848.41 2,288.61 0.80 20 791.23 2.13 9 S7 10.17 2,884.87 2,321.43 0.80 40 801.35 4.38 10 S8 14.55 2,912.77 2,347.93 0.81 40 809.10 4.46 11 D3 83.30 - - 19.01 2,829.47 2,280.79 0.81 10 785.96 1.05 12 D4 220.00 - - 20.06 2,609.47 2,103.45 0.81 100 724.85 8.95 29.01 Acknowledgement The authors gratefully acknowledge financial support from Ministry of Higher Education Malaysia, Universiti Teknologi Malaysia, and Pázmány Péter Catholic University, Faculty of Information Technology and Bionics. Reference Cengel Y.A., Cimbala J.M., 2006. Fluid Mechanics. Fundamental and applications. 1sted, McGraw Hill, New York, USA. Čuček L., Klemeš J.J., Varbanov P.S., Kravanja Z., 2015. Significance of environmental footprints for evaluating sustainability and security of development. Clean Technologies and Environmental Policy, 17(8), 2125-2141. Dakota Gasification Company, 2016. CO2 Pipeline. , accessed 03.06.2016. Diamante J.A.R., Tan R.R., Foo D.C.Y., Ng D.K.S., Aviso K.B., Bandyopadhyay S., 2014. Unified pinch approach for targeting of carbon capture and storage (CCS) systems with multiple time periods and regions. Journal of Cleaner Production, 71, 67-74, doi:10.1016/j.jclepro.2013.11.027. 1175 Fenghour A., Wakeham W.A., Vesovic V., 1998. The viscosity of carbon dioxide, Journal of Physical and Chemical Reference Data, 27, 31-44. Fox R.W., McDonald A.T., 1992. Introduction to Fluid Mechanics. 4th edn. Wiley, New York, USA. Giaouris, D., Papadopoulos, A.I., Voutetakis, S., Papadopoulou, S., Seferlis, P., 2015, A power grand composite curves approach for analysis and adaptive operation of renewable energy smart grids, Clean Technologies and Environmental Policy, 17 (5), 1171-1193. GPSA, 1998. Fluid flow and piping vol 17. GPSA Engineering Data Book, 11thed. GPSA, Tulsa, OK, USA. IEA, 2015. CO2 Emissions From Fuel Combustion Highlights. Intenational Energy Agency. , accessed 19.06.2016. Jensen M.D., Pei P., Snyder A.C., Heebink L.V., Gorecki C.D., Steadman E.N., Harju J.A., 2013. A Phased Approach to Building a Hypothetical Pipeline Network for CO2 Transport During CCUS. Energy Proc, 37: 3097-3104. Kim M., Kim M-J., Pyo S-H., Lee S-C., Ghorbannezhad P., Foo D.C.Y., Yoo C-K., 2015, Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies, Clean Techn Environ Policy, doi:10.1007/s10098-015-1063-1 Klemeš J.J. (Ed), 2013. Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions, Woodhead/Elsevier, Cambridge, UK, 1184 ps. ISBN – 987-0-85709-0. Klemeš J.J., Varbanov P.S., Kravanja Z., 2013. Recent Developments in Process Integration, Chemical Engineering Research and Design, 91(10), 2037–2053. Klemeš J., Dhole V.R., Raissi K., Perry S.J., Puigjaner L., 1997. Targeting and Design Methodology for Reduction of Fuel, Power and CO2 on Total Sites. Applied Thermal Engineering, 17(8-10), 993 – 1003. Krishna Priya G.S., Bandyopadhyay S., 2015. A Pinch Analysis based approach to power system planning with carbon capture. Chemical Engineering Transactions, 45, 1603-1608, doi:10.3303/CET1545268. Mohd Nawi W.N.R., Wan Alwi S.R., Manan Z.A., Klemeš J.J., 2015. A New Algebraic Pinch Analysis Tool for Optimising CO2 Capture, Utilisation and Storage. Chemical Engineering Transactions, 45, 265-270. Mohd Nawi W.N.R., Wan Alwi S.R., Manan Z.A., Klemeš J.J., 2016. Pinch Analysis targeting for CO2 Total Site planning. Clean Technologies and Environmental Policy, doi:10.1007/s10098-016-1154-7. NETL, 2014. Carbon Dioxide Transport and Storage Costs in NETL studies, US DoE, , accessed 30.06.2016. Noothout P, Wiersma F, Hurtado O, Roelofsen P, Macdonald D, 2013. CO2 pipeline infrastucture. IEAGHG, Cheltenham, UK, accessed 04.07.2016. Ooi R.E.H., Foo D.C.Y., Ng D.K.S., Tan R.R., 2013. Planning of carbon capture and storage with pinch analysis techniques. Chemical Engineering Research and Design, 91, 2721–2731 doi:10.1016/j.cherd.2013.04.007. Rubin E.S., Short C., Booras G., Davison J., Ekstrom C., Matuszewski M., McCoy S., 2013. A proposed methodology for CO2 capture and storage cost estimates. Int J Greenhouse Gas Control, 17, 488-503. Seevam P., Race J., Downie M., Barnett J., Cooper R., 2010. Capturing Carbon Dioxide: The Feasibility of Re-Using Existing Pipeline Infrastructure to Transport Anthropogenic CO2, 8th Int. Pipeline Conference, Vol 2, Calgary, Alberta, Canada, 129-142, doi:10.1115/ipc2010-31564. Wan Alwi S R, Mohammad Rozali N. E., Abdul-Manan Z, Klemeš JJ, A process integration targeting method for hybrid power systems, Energy, 44 (1) 2012, 6-10 Wang Y., Zheng M., Liu G., Zhang D., Zhang Q., 2016. Graphical method for simultaneous optimization of the hydrogen recovery and purification feed. International Journal of Hydrogen Energy, 41, 2631-2648. Wetenhall B., Race J.M., Downie M.J., 2014. The Effect of CO2 Purity on the Development of Pipeline Networks for Carbon Capture and Storage Schemes. International Journal of Greenhouse Gas Control, 30, 197-211. Wong S., 2013. CO2 Compression and Transportation to Storage Reservoir (Module 4), , accessed 28.06.2016. Zhang K., Liu Z., Huang S., Li Y., 2015. Process Integration analysis and improved options for an MEA CO2 capture system based on the Pinch Analysis. Applied Thermal Engineering, 85, 214-224. Zhang Q., Feng X., Liu G., Chu K.H., 2011. A novel graphical method for the integration of hydrogen distribution systems with purification reuse. Chemical Engineering Science, 66, 797-809. 1176