A Pinch-Based Approach for Targeting of Carbon Capture, Utilization

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Process Systems Engineering

A Pinch-Based Approach for Targeting of Carbon Capture, Utilization, and Storage (CCUS) Systems Sonal K. Thengane, Raymond R. Tan, Dominic Chwan Yee Foo, and Santanu Bandyopadhyay Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b06156 • Publication Date (Web): 30 Jan 2019 Downloaded from http://pubs.acs.org on February 5, 2019

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Industrial & Engineering Chemistry Research

A Pinch-Based Approach for Targeting of Carbon Capture, Utilization, and Storage (CCUS) Systems

Sonal K. Thengane a, Raymond R. Tan b, Dominic C.Y. Foo c, Santanu Bandyopadhyay d,*

a

Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India (Currently with Department of Mechanical Engineering, Massachusetts Institute of Technology, United States) b

Chemical Engineering Department, Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 1004 Manila, Philippines c

Department of Chemical & Environmental Engineering, Centre of Excellence for Green Technologies, The University of Nottingham, Malaysia Campus, Selangor 43500, Malaysia d Department

of Energy Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

* Corresponding Author. Tel.: +91-22-25767894. Fax: +91-22-25726875. Email:

(S.K.T.)

[email protected];

(R.R.T.)

[email protected];

(D.C.Y.F.) [email protected]; (S.B.) [email protected].

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract Carbon capture and storage (CCS) reduces carbon dioxide (CO2) emissions by sequestration of captured CO2 for long-term storage whereas carbon capture and utilization (CCU) offers resource conservation benefits by displacing the need for extracted CO2 from natural sources. The integration of these two results in carbon capture, utilization, and storage (CCUS) system, which either uses CO2 for profitable applications or stores it in the reservoirs. One of the key problems in CCS systems is to optimally match the sources (e.g., CO2 captured by fossil-fueled power plants) and the sinks (e.g., available geological reservoirs for storing the captured CO2). In practice, the geological storage sites may be available at different times and have limitations on the maximum CO2 storage capacity and the injectivity rate, subject to other geological characteristics. This work proposes an improved pinch analysis-based methodology by simultaneously considering the injectivity constraints and variable availability of all sources and sinks. Two types of CO2 storage are considered in this work, i.e., sinks with fixed life and sinks with fixed capacity. A new CCUS Mapping Diagram is presented to show the capture of CO2 to the individual sinks. Four illustrative examples demonstrate the applicability of the proposed methodology to the CCUS systems where purity is not a constraint in CO2 utilization. In some cases, the improved methodology overcomes the pitfall of the previous method by identifying rigorous CCUS targets. Keywords: CO2 capture; pinch analysis; source-sink matching; targeting; process integration


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Graphical Abstract



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1. Introduction There is mounting international concern about climate change, and an urgent need to mitigate CO2 emissions. The global energy-related CO2 emissions reached 32.5 Gt in 20171, and are expected to grow further due to economic and demographic trends. Thus, radical technologies will be needed to stabilize the climate to safe levels in the coming decades. Carbon capture and storage (CCS) systems and carbon capture and utilization (CCU) systems are looked upon as the potentially high impact solutions to deal with the issue of climate change by reducing industrial greenhouse gas (GHG) emissions.2 In CCS systems, CO2 is captured mainly from fossil-fuel combustion sources and is then stored in depleted oil and gas wells, inaccessible coal seams, saline aquifers, and other geological structures which would act as reservoirs.3 Negative emissions technologies such as direct air capture and bioenergy with CCS can also be integrated into these systems to achieve further reductions in atmospheric CO2 levels.2 One of the main criticisms of CCS is that the capture and storage of CO2 adds to capital as well as operating costs. Hence, the commercial utilization of captured CO2 in different sectors have been explored, e.g. enhanced oil recovery (EOR) in oil and gas industry, as solvent and preservative in food and drinks industry, as an intermediate in the pharmaceutical industry, and as a reactant for the synthesis of chemical products.4 The high cost and uncertainties in long-term geological storage have also shifted attention towards including utilization in CCS systems.5 Furthermore, the main benefit of CCU results from the displacement of extracted CO2 by captured CO2 in different applications.6 CCU has the potential to drive industrial innovation and make energy-intensive industries competitive without sacrificing climate goals.7 The integration of CCS and CCU systems can be described as carbon capture, utilization, and storage (CCUS) system which mainly consists of three stages, i.e. the capture of CO2, transportation, and final solution either being utilized or stored in a geological reservoir. Such systems may include storage options in permanent sinks or utilization via different technology platforms.8 However, it has been pointed out that the utilization options are limited by both scalability and non-permanence of carbon sequestration system.9 The optimized CCUS system has the potential to utilize captured CO2 or/and store in secure geological reservoirs, thus enabling the use of fossil fuels while controlling CO2 emitted into the atmosphere.10 The planning for a typical CCS system could take over decades based on the planned 4 ACS Paragon Plus Environment

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introduction of new carbon sources, and evaluation and availability of geological sinks. In practice, many of the methods developed for planning CCS systems can be adapted for use in CCUS systems. CO2 sinks in commercial operations will typically have a fixed operating life and demand (i.e., flow rate), and thus cannot be treated in the same manner as geological sinks of fixed capacity. Furthermore, the geological surveys that usually evaluate reservoirs to store CO2 have some inherent uncertainties depending on the magnitude of the survey and the type of site.11 The site properties such as permeability and porosity of geological sinks vary signiﬁcantly across the actual reservoir and can influence the overall capacity estimation in a significant way. This heterogeneity can affect the design and planning of the overall CCS system.6 Matching of CO2 sources (e.g., CO2 captured by fossil-fueled power plants equipped with CCS) with the appropriate sinks (e.g., the available geological reservoirs for storing the captured CO2) is one of the key problems in planning the deployment of CCUS systems. The appropriate matching of sources and sinks help in the economic evaluation of the deployment of technology before detailed engineering design.12 In the past decade, various process integration techniques have been developed for the deployment of CCS and CCUS systems. These techniques have been majorly applied in process design with the objective of attaining efficient resource utilization and minimizing emissions. One of the promising tools of process integration, i.e. Pinch Analysis, has emerged as an effective design tool for various resource conservation systems such as heat exchanger network13, production supply chain14, water resources15, cogeneration systems16, integrated bio-refineries17, emission reduction through appropriate management of municipal solid waste18, carbon management networks for biochar utilization19, etc. A detailed review on different process integration techniques for various emission- and footprint-related problems is presented in Foo and Tan20. Tan and Foo21 developed the carbon emission pinch analysis (CEPA) procedure by extending the use of pinch analysis into energy sector planning with carbon emissions constraints. Since the development of the CEPA procedure21, pinch analysis-based methods have been increasingly applied to CCS and CCUS systems. Lee et al.22 introduced two important extensions of the original CEPA21 by applying graphical targeting approach to locate minimum consumption of low-carbon source and an automated targeting approach to determine the optimum allocation of energy sources 5 ACS Paragon Plus Environment


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in a segregate planning scenario. Tan et al.23 later reported a graphical pinch analysis technique for the planning of CCS systems for power generation sector, with the aim to identify the minimum retrofit target. An algebraic targeting approach was developed later by Sahu et al.24 as an alternative to Tan et al.23, for the planning of grid-wide CCS retrofit considering emission targets with significant emissions from compensatory power. The methodology has been extended to CCUS application by Mohd Nawi et al.25 in which total site CO2 integration-based planning is aimed to maximize the utilization of CO2 sources to satisfy CO2 demands across the total site, and minimize the amount of CO2 sent to storage or reservoir. Manan et al.26 developed another algebraic approach to achieve minimum extracted CO2 (fresh CO2 feed) requirement for any range of CO2 concentration in an industrial sector such as refinery site that involves a large number of emission point sources. The low CO2 industrial site planning involved matching of CO2 sources with CO2 utilization options as demands, subject to purity constraints.27 Shenoy and Shenoy28 targeted the energy allocation for multiple CCS sources along with clean-carbon resources by profile matching. Recently, Putra et al.29 applied the pinch design method30 for multi-regional CCS network using simultaneous and sequential approaches, and found the sequential approach promising if the quantity of capturable CO2 is preferred over the cost. Another related work is the planning of high CO2 gas field development, where minimum CO2 removal is targeted with graphical and automated approaches.31 Ooi et al.32 proposed a graphical source-sink matching procedure to address the planning problem of the storage of captured CO2 from power plants into corresponding reservoirs and developed a Grand Composite Curve (GCC) for scheduling of storage capacity surplus or deficit. This earlier study considered capacity and time of availability of the sinks, as they need to be developed before storage, but did not account for the injection rate limits of the sinks, nor did it consider the nature of the sink. An alternative procedure that accounts for both capacity and injectivity constraints of the sinks was proposed by Diamante et al.33. However, the existence of all sources and sinks was assumed to be at the start of the planning period.33 This was generalized by Diamante et al.34 where sources and sinks start operation at any time within the planning horizon. It should be noted that the unified pinch model proposed by Diamante et al.34 accounts for uncertain properties and injectivity consideration of the CCS planning problem. The model focused on the physical issue of maximizing capture of CO2 emissions for the temporal, flow rate, 6 ACS Paragon Plus Environment

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and storage constraints of the system components. However, as will be shown in the latter part of the present paper, this methodology does not give the correct targets in some specific cases, especially when the capacity of some sinks remain underutilized at the end of their lives. This deviation could be mainly attributed to the possible local capture of CO2 from source to demand, usually represented by the pockets in the GCC. The local capture within a pocket is possible if some storage capacity is present, as in the case of inventory in production supply chain problems.14 Desai and Bandyopadhyay35 confirmed that for process GCC with a pocket, it may be possible for the heat absorbing profile of the organic Rankine cycle to go into the pocket and further enhance the work output by taking some heat from the pocket. Other than the conceptual approaches of pinch analysis, mathematical optimization approaches have also been proposed to address the optimal planning of CCS and CCUS systems. Pękala et al.36 proposed a source–sink framework based mathematical model for the systems of optimum biofuels production with consideration of multiple footprints, and the deployment of cost-effective CCS retrofit. Tan et al.37 formulated the improved discrete mathematical programming models for planning the retrofit of power plants at the regional, sectoral or national level. These models considered carbon footprint reduction targets, cost limits and power losses, for retrofitting the power plants with the appropriate carbon capture technique. In their later work, Tan et al.38 developed a mixed integer linear programming (MILP) formulation using a continuous time domain without considering injectivity constraints. Simplified formulation of this model with a reduced number of variables and constraints was subsequently proposed.39 Shaik and Kumar40 reformulated and simplified the models proposed by Tan et al.38 and Lee and Chen39 leading to a smaller problem size with fewer variables and constraints. Tan et al.41 proposed a multi-period MILP model for matching CO2 sources and sinks under temporal, injection rate, and storage capacity constraints. He et al.42 developed a continuous-time uncertain MILP model with physical and temporal constraints and described uncertainties as interval and uniform distributed stochastic parameters for CCS planning. Ooi et al.43 proposed an optimization-based automated targeting model (ATM) for carbon-constrained energy planning (CCEP), focusing on the deployment of CCS. The model incorporated the advantages of both insight-based pinch techniques and mathematical optimization approach. Hasan et al.44 proposed a multi-scale framework for the optimal design of CCUS supply chain network and found that about 3% of the total stationary CO2 7 ACS Paragon Plus Environment


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emissions in the United States could be eliminated at zero net cost. Sun and Chen45 developed a multi-stage mixed integer programming (MIP) model for carbon source and sink matching in China CCUS Decision Support System to design a better CO2 pipeline layout. Recently, Zhang et al.46 developed a CCUS supply chain superstructure for better management of integrated CO2 capture, compression, transportation, utilization, and storage infrastructure. They also proposed a MILP model to optimize the strategic CCUS deployment in Northeast China. Simplified MILP formulations have also been proposed for generic CCUS47 and enhanced oil recovery48 problems. CCUS models based on a supply chain or network formulations have been proposed for largescale problems.49 Patricio et al.50 developed a generic matrix based novel methodological framework to identify potential partnerships between companies producing CO2 and companies reusing CO2 as input for their industrial process. Another recent study established a comprehensive evaluation method of CO2 storage potential using the mass balance approach, which includes storage site screening, storage mechanism analysis, and storage capacity evaluations.51 An extensive review of quantitative techniques for planning CCUS systems was recently published.12 Generally, all sources and sinks defined in CCS or CCUS systems are assumed to be in sufficiently close geographic proximity to make all possible matches economically viable. In addition to power plants and industrial sites from which CO2 is captured from flue gases, the sources as defined here can also include direct air capture (DAC) facilities52. The sources are usually associated with uncertainties in the flow and quality parameters due to environmental, operational, feed, and market conditions. Though such kind of uncertainties in sources may be incorporated in pinch analysis53, the present study does not consider these uncertainties. The CO2 sinks considered in Diamante et al.34 are characterized by a fixed life and an upper limit for CO2 storage capacity, for which a maximum rate of injection is fixed. The objective of the present work is to propose a methodology to accurately match the CO2 sources and sinks, where sinks are of two types, i.e., one with fixed life and fixed injectivity, and other with fixed capacity and fixed injectivity. The sinks with fixed life cannot be used for storage at a later period and can correspond to the utilization options such as EOR. On the other hand, the sinks with fixed capacity can be used for storing CO2 at a later time, if some of its capacity is unutilized (subject to the injectivity constraint); this represents pure geological storage options. The novelty of the present study lies in effectively utilizing these characteristics of two types of sinks in the proposed methodology for the planning 8 ACS Paragon Plus Environment

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of CCUS systems. These CCUS systems are assumed to utilize CO2 through techniques such as EOR having comparable timescales as CCS and no constraints of purity; utilization options that involve CO2 being used as a process feedstock are beyond the scope of this methodology. The rest of this paper is organized as follows. A formal problem statement is given in the following section, followed by a description of the proposed targeting algorithm. This is then followed by four illustrative examples to demonstrate the application of the proposed methodology. Finally, conclusions are drawn at the end of the paper.

2. Problem Statement The formal problem statement addressed in this work is as follows:  The CCUS system is assumed to be comprised of m CO2 sources, (n+k) CO2 sinks; the sources and sinks may start operation at any given time. All CO2 streams are assumed to be of equivalent purity, based on typical requirements for pipeline transportation.  Each CO2 source i (i = 1, 2, ..., m) is characterized by fixed captured CO2 flow rate (Si) that corresponds to the maximum potential removal from the power plant’s flue gas emissions. Furthermore, the operating life of each source i is also defined in terms of start (tstarti) and end time (tendi). It is assumed that capture may commence at any known time during the life of a source, after which the CO2 stream flowrate will be constant until the plant ceases to operate.  There are two different types of CO2 sinks: one which is characterized by fixed life (called fixed life sinks), and other characterized by fixed CO2 storage capacity (called fixed capacity sinks). Each fixed life CO2 sink j (j = 1, 2, ..., n) is characterized by a fixed life (with known start (tstartj) and end time (tendj)) to store CO2 and a maximum rate at which CO2 may be injected into each sink. Each fixed capacity sink j (j = n+1, n+2, ..., n+k) is characterized by an upper limit for CO2 storage capacity; the maximum rate at which CO2 may be injected into each sink is also given. The fixed life sinks cannot be used at a later time for storage purpose whereas the fixed capacity sinks can be used for storing CO2 at a later time if some storage capacity is unutilized subject to the injectivity constraint. All of these characteristics are based either on the 9 ACS Paragon Plus Environment


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characteristics of the CO2 utilization capability or on the geological characteristics of the storage site. The earliest time of availability of each sink is also specified (tstartj). Based on such physical and temporal data, the characteristic end time of a fixed capacity sink, or the earliest time that it can be filled to capacity, may be deduced from the injectivity (Dj) (i.e., the time at which storage capacity is used up, assuming that the sink receives CO2 at maximum flowrate throughout its operating life and that it begins receiving CO2 as soon as it is available). This end time is denoted as tendj.  The objective is to maximize the capture of CO2 by matching CO2 sources and sinks; given these specified temporal and physical constraints. It may be noted that if a suitable sink could not be found to allocate captured CO2 from any source, the remaining amount of CO2 can either be exported or not captured at all. If captured, it is recommended to arrange an additional storage for CO2 or find a site for utilizing this captured CO2. The time for the requirement of the additional storage could be determined during sourcesink matching exercise. If the CO2 is not captured at all, it will add to atmospheric emissions. However, for a retrofitted site (e.g. power plant) with CCS, these emissions would be lower than the original site. The carbon footprints associated with compression of the flow streams and construction of the entire network are neglected in this work. Furthermore, no restrictions on network topology or network evolution are imposed.

3. Targeting Algorithm The algorithm to capture maximum possible CO2 for a CCUS system is described in this section. The methodology makes use of both algebraic and graphical techniques that complement each other to find the accurate targets. The algebraic targeting technique is known as the carbon capture and storage cascade analysis (CCSCA), and its procedure is given by Steps 1 – 7 that follows, with results shown in the generic cascade table in Table 1. The preliminary targets of the CCUS system can be identified from the CCSCA. Steps 8 – 10 next describe the graphical technique of GCC, which is used to determine the actual targets of the CCUS system.


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Table 1. A generic cascade table for CCSCA 1 2 Time Sinks (y) j

3 4 5 Total sink Sources Total injectivity i source (Mt/y) flow (Mt/y)

6 Net flow (Mt/y)

7 CO2 load (Mt)

t1  t2

 

TD1



TD1 – TS1

 

TD2

t3



⁞



⁞



tq



⁞





TDq



TS2

TD2 – TS2

⁞

Feasible Cascade (Mt)

Cum.Δm1=0

Δms

Cum.Δm2

Cum.Δm2 + Δms

Cum.Δm3

Cum.Δm3 + Δms

⁞

⁞

Cum.Δmq

Cum.Δmq + Δms

Δm2

 ⁞

9

Δm1 = 0

 

 

tp-1

TS1

8 Cumulative CO2 load (Mt)

⁞

TSq

TDq – TSq

Δmq

⁞

⁞

⁞

⁞

⁞

⁞

Cum.Δmp-1

Cum.Δmp-1 + Δms

TDp-1

TSp-1

TDp-1 – TSp-1

Δmp-1

tp 1) The start (tstart) and end times (tend) of all CO2 sources and sinks are tabulated in increasing order in the first column. If a particular time value occurs more than once, the same need not be repeated. Without loss of generality, it can be said that the time for q-th row is denoted as tq such that

t1  t2  ...  tq  ...  t p

(1)

The CO2 sinks and sources are then located at their respective time intervals in the second and fourth columns respectively. 2) Total injectivity of all CO2 sinks, present during a time interval, is tabulated in the third column. For the q-th row, total sink injectivity is denoted as TDq (Table 1) and is expressed as follows: 11 ACS Paragon Plus Environment


D

TDq  t q t startj

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(2)

j j AND t endj t q

where Dj is total injectivity of sink j. 3) The total flow rate of all CO2 sources, present during a time interval, is tabulated in the fifth column. For q-th row, total source flow is denoted as TSq (Table 1) and is expressed as follows:

S

TSq  t q t starti

(3)

i i AND t endi t q

where, Si is total CO2 emissions from source i. 4) The net flow rates, the difference between the total injectivity of CO2 sinks and total flow rate of CO2 sources, are tabulated in the sixth column. 5) The first entry of the seventh column is arbitrary assigned zero. The CO2 load for each time interval is tabulated in this column. The CO2 load is determined as the product of the net flow rate with the duration of the respective time interval, using Equation (4). The positive CO2 load in this column indicates the excess capacity of the sink, while the negative value indicates excess CO2 (uncaptured) for that particular interval. Δmq is the CO2 load for q-th row and is given by

m q  ( TDq  TSq )( t q  t q 1 )

(4)

6) Cumulative CO2 loads are tabulated in the eighth column. Summation of CO2 loads for all previous rows ( rq1 m r ) denotes the cumulative flows for the q-th row. 7) For a feasible solution, all entries in the eighth column should take non-negative values (negative values mean CO2 loads are cascaded from later to earlier time intervals, which is 12 ACS Paragon Plus Environment

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infeasible). For cases where negative values are observed in the last column, the absolute value of the largest negative number (i.e. the smallest number in the eighth column; denoted as m s ) is added to every entry in the eighth column to produce the feasible cascade (as shown in the ninth column of Table 1). The first entry of the feasible CO2 load cascade column indicates uncaptured CO2 load, while the last entry of the column indicates unutilized storage capacity of the CO2 sinks; these are known as the preliminary targets of the CCUS system. 8) The graphical tool, i.e. GCC of the system is plotted based on the CCSCA Table. Figure 1 shows a generic representation of the GCC, where time (first column of Table 1) is plotted versus the feasible CO2 load (last column of Table 1). Note that the time is plotted in reverse order to keep consistency with CCSCA Table. The opening at the upper section of GCC corresponds to uncaptured CO2 load, and the opening at the lower section of GCC corresponds to unutilized sink capacity identified in Step 7 (see Figure 1). These are called the preliminary targets.

Figure 1. GCC without any pocket



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9) Inspection is performed on the GCC, to verify if the preliminary targets identified in Step 7 are correct. If the GCC does not contain any pocket (see Figure 1), the preliminary targets remain valid and are confirmed as the actual targets of the CCUS system. 10) For cases where a pocket is found in the GCC (Figure 2), local CO2 capture from earlier to later time intervals is possible. A good analogy may be derived from the supply chain management problem, where the transfer of any inventory within a pocket is possible only if some inventory is present.14 For a CCS system, such a case is only possible if a CO2 sink is not completely utilized in an earlier time interval, and is not present during a later time interval (this is applicable for fixed capacity sinks only). The sink may remain unutilized if the total amount of CO2 from the sources is less than the capacity of a sink in the earlier time interval, or if no source is present during that interval. If such a sink is present in a later time interval, then it is infeasible to increase the maximum capacity of a fixed capacity sink in a particular time interval. In a GCC, line segments with negative slope indicate the excess capacity of the sinks during the time interval of interest. In contrast, line segments with positive slope in a GCC indicate the excess CO2 (uncaptured) from the sources during the time interval. Note that the excess capacity and excess CO2 were also observed in the seventh column of the CCSCA (see discussion in Step 5). The steps to analyze a pocket in the GCC are given as follows: i) Fixed capacity sinks that are present in every line segment of GCC pockets are identified. ii) If a fixed capacity sink is present in a line segment with negative slope but is absent in any segment with a positive slope at a later time interval, this sink can be used to capture CO2 for the later time interval. Once such sink is identified, its excess storage capacity is then identified from the horizontal length of the pockets. If the excess capacity of such sink in the previous time interval is enough to capture the CO2 load, the preliminary targets identified in Step 7 hold. In other words, there is no excess CO2 load in the pocket. This case is illustrated in Figure 2(a). In the latter, there exists a negative slope line immediately after the Pinch Point. Two fixed capacity sinks, i.e. Sink 1 (SK1) and Sink 2 (SK2), are present in this segment from t3 to t4. Immediately after, there are segments from t4 to t5 and t5 to t6 with positive slopes, indicating 14 ACS Paragon Plus Environment

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excess CO2. However, only SK1 is present in this positive slope segment. Therefore, the excess capacity of SK2 can be used to capture CO2 during this time interval. Note that the excess capacity of SK2 can be identified from the CCSCA, sink characteristic data, and the GCC. If the capacity of SK2 is sufficient to capture this additional CO2 (say until t6), the preliminary targets identified in Step 7 hold for this case, i.e. become the actual targets. If the excess capacity of SK2 is insufficient to completely capture the excess CO2, the exact targets are given by adding remaining uncaptured CO2 load to the preliminary targets.

(a)

(b)

Figure 2. GCC with a CO2 pocket iii) In a different scenario, if a fixed capacity sink is absent in a GCC (with pockets), length of the pocket represents the additional uncaptured CO2 load. The latter has to be added to the preliminary targets identified in Step 7. This case is illustrated in Figure 2(b). In this case, there exists a negative slope segment immediately after the Pinch Point. A fixed capacity sink SK1 is present in the segment with a negative slope from t3 to t4 and also in the next segment with a positive slope from t4 to t5. Even if the capacity of SK1 is not fully utilized until t4, its excess capacity cannot be utilized to capture additional CO2 beyond t4, even though it is present from 15 ACS Paragon Plus Environment


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t4 to t5. This is because it is not feasible to increase the maximum capacity of a fixed capacity sink in a particular time interval due to the injectivity constraint (indicated by the slope). Hence, the preliminary targets identified in Step 7, i.e. uncaptured CO2 and unutilized capacity, have to be revised; the exact targets are given by the summation of the preliminary targets and the horizontal length of the CO2 pocket.

Figure 3. Flowchart for proposed targeting methodology The procedure mentioned in Step 10 is applicable irrespective of the number of pockets and their location in the GCC. Figure 3 represents the flowchart for the proposed methodology. In the following section, four examples having different numbers of pockets at different locations are


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considered to illustrate this point. It is also possible that the preliminary targets may or may not change due to pockets, depending on the problem data.

4. Illustrative Examples The proposed targeting algorithm is illustrated with four different examples in the following section. Example 1: Planning problem with no pocket In this example adapted from Diamante et al.34, there are three CO2 sources and two CO2 sinks, with relevant data as shown in Table 2. Sink 1 is of the fixed life type; while sink 2 is of fixed capacity type. The sources generate a combined 550 Mt of CO2 throughout the entire planning period, while the combined storage capacity of the sinks is 600 Mt. Thus, it may seem at first that it will be possible to capture and store all of the CO2 in the system. Table 2. Source and sink characteristics in example 1 Source 1 2 3

Sink 1 2

CO2 flowrate (Mt/y) 10 10 5 Total Injectivity (Mt/y) 10 15 Total

CO2 load (Mt) 300 400 150 550

Storage capacity (Mt) 300 300 600

Start time (y) 0 10 20

Earliest available time (y) 10 30

Characteristic end time (y) 40 50

End time (y) 30 50 50

Type of sink Fixed life Fixed capacity

The previously described CCSCA was performed for Example 1, and its result is shown in the cascade table in Table S1. The latter shows that the pinch occurs at year 30, with uncaptured CO2 being identified as 350 Mt and the unutilized capacity as 100 Mt. These preliminary targets may also be found by plotting the GCC, by following the procedure outlined in Section 3. Figure 4 shows that the GCC may also be generated from the CO2 capture and storage pinch diagram33, 17 ACS Paragon Plus Environment


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which verifies the various targets, i.e. pinch point, uncaptured CO2 and unutilized capacity of the system. The pinch gives the point in time before which there is a deficit of CO2 storage and/or utilization capacity, and beyond which there is a surplus. In cases with multiple pinch points, the total available CO2 sources exactly match the total capacity of the sinks in any time interval occurring between pinch points.

Figure 4. GCC and CCSPD for Example 1


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Figure 5. CCUS mapping diagram for Example 1 In Figure 4, the GCC does not contain any pocket and hence sinks need not be identified for local CO2 capture. Hence, the preliminary targets identified by the CCSCA and GCC remain valid, i.e. 350 Mt uncaptured CO2 load and 100 Mt unutilized storage capacity of the CO2 sinks. In other words, it is not possible to store all the CO2 in the system. These results are in agreement with those reported in Diamante et al.34. Figure 5 shows the CCUS Mapping Diagram that shows the capture of various CO2 sources in the sinks, with the enclosed area indicating captured CO2 and storage capacity. The uncaptured CO2 from SR1 – SR3 and the unutilized storage capacity of SK1 (years 30-40) match those obtained with CCSCA and GCC earlier.



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Example 2: Planning problem with single pocket This example is adapted from Diamante et al.33 with four CO2 sources and two CO2 sinks, with relevant data shown in Table 3. The sink SK1 is of fixed life type and sink SK2 is of fixed capacity type. The sources generate a combined 800 Mt of CO2 throughout the entire planning period, while the combined storage capacity of the sinks is 1000 Mt. Table 3. Source and sink characteristics in Example 2 Source 1 2 3 4

Sink 1 2

CO2 flowrate (Mt/y) 5 10 8 5 Total Injectivity (Mt/y) 15 25 Total

Storage capacity (Mt) 750 250 1000

CO2 load (Mt) 200 300 200 100 800 Earliest available (y) 0 0

time

Start time (y) 0 0 0 0

Characteristic end time (y) 50 10

End time (y) 40 30 25 20 Type of sink Fixed life Fixed capacity

Table S2 shows the cascade table for Example 2. The first entry of the last column indicates 50 Mt of uncaptured CO2 load and the last entry indicates 250 Mt of the unutilized storage capacity of the CO2 sinks. These targets match with those reported in Diamante et al.33. The GCC is plotted following the procedure in Section 3 and is shown in Figure 6. The GCC in Figure 6 shows that a pocket is present in the interval years of 0 – 19.2. Table S2 shows that both sinks SK1 and SK2 are present in the first decade (corresponds to a segment of negative slope in between 0 – 10 years in the GCC pocket in Figure 6). Note however that SK2 is absent in interval years of 10 – 19.2. This means that in the latter interval years, SK2 can be used to capture the excess CO2 in the CO2 pocket. We then proceed to check if the excess capacity of SK2 is sufficient in capturing the CO2 load in the identified pocket.


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Figure 6. GCC for Example 2

Figure 7. CCUS mapping diagram for Example 2



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Column 3 of Table S2 indicates the total capacity of all sinks within each time interval. One can calculate the individual capacity of the sink from the product of the sink injectivity with the interval duration. For instance, in interval years 0 – 10, SK1 can store a maximum of 150 Mt CO2 (= 15 x (10 – 0) Mt), and SK2 can store up to 250 Mt CO2 (= 15 x (10 – 0) Mt) thereby resulting in total 400 Mt capacity. Similarly, the overall CO2 load of the sources in these interval years may be calculated as 280 Mt (= 28 x (10 – 0) Mt). Hence, 150 Mt CO2 (of the total of 280 Mt) may be stored in SK1, while the remaining 130 Mt CO2 is stored in SK2. This results in 120 Mt (= 250 Mt – 130 Mt) excess capacity remaining unutilized for SK2 in 0 – 10 years. In the following interval years, i.e. 10 – 20 years, only SK1 is present in this interval, which has a capacity of 150 Mt CO2 (= 15 x (20 – 10) Mt). The total CO2 load of all sources in these interval years is determined as 280 Mt (= 28 x (20 – 10) Mt). Hence, this results in an excess CO2 load of -130 Mt (= 150 – 280 Mt; Column 7). SK2 is a fixed capacity sink that is present during the earlier interval years but is absent in this interval. Hence, its unutilized capacity of 120 Mt CO2 can be used for storing the equivalent excess CO2 out of 130 Mt. This leaves an excess CO2 of 10 Mt (= 120 – 130 Mt), indicated by -10 Mt CO2 in Column 8 of Table S2. This excess CO2 load of 10 Mt is then added to another excess 40 Mt CO2 in the interval years 20-25 thus resulting in a total of 50 Mt uncaptured CO2. In other words, the preliminary targets of 50 Mt uncaptured CO2 and 250 Mt unutilized sink capacity, remain valid as reported in the cascade table (Table S2) and the GCC (Figure 6). One of the possible CO2 allocation networks for this example is shown as the CCUS Mapping Diagram in Figure 7. The uncaptured CO2 (from SR3, years 18.75 – 25) and unutilized capacity (SK1) match those obtained from CCSCA and GCC. Note that the CO2 load from SR3 and SR4 in years 10 – 20 are transferred to SK2 that exists earlier in interval years 0 – 10.

Example 3: Planning problem with multiple pockets This example consists of four CO2 sources and two CO2 sinks, with relevant data as shown in Table 4. Table S3 shows the cascade table for the example, while the GCC is shown in Figure 8. The cascade table and the GCC identify the uncaptured CO2 and unutilized storage capacity as 200 Mt and 450 Mt, respectively. It should also be noted that the same targets can be obtained if 22 ACS Paragon Plus Environment

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one were to utilize the CO2 capture and storage pinch diagram (CCSPD)34 for the targeting task (see Figure 9). However, these preliminary targets will have to be examined to see if they are the actual targets. Table 4. Source and sink characteristics in Example 3 Source 1 2 3 4

Sink 1 2

CO2 flowrate (Mt/y) 5 15 25 25 Total

CO2 load (Mt) 150 300 250 500 1200

Injectivity Storage (Mt/y) capacity (Mt) 15 25 Total

1200 250 1450

Earliest available (y) 0 0


Characteristic time end time (y) 80 10

End time (y) 30 20 20 50

Type of sink Fixed life Fixed capacity

As shown in the GCC in Figure 8, there are two pockets that exist before the pinch, i.e. year 50. The first pocket presents in the interval years 0 – 20, while the second pocket in years 20 – 40. Similar to the case in Example 2, SK1 and SK2 are present in interval years 0-10 (segment of negative slope), but SK2 (fixed capacity type) is absent in the next decade (segment with positive slope). Hence, SK2 can be used as storage to capture CO2 for the later time interval (10 – 20 years). We next examine if the excess capacity of SK2 is sufficient for future storage. In the interval years, 0 – 10, SK1 and SK2 have the capacity of storing 150 and 250 Mt CO2 (product of injectivity and duration) respectively. On the other hand, only 200 Mt CO2 (= 20 x (10 – 0) Mt) need to be stored among all sources present in that interval years. Hence, 150 Mt of CO2 load may be stored in SK1 (its maximum capacity), while the remaining 50 Mt CO2 is stored in SK2. This results in 200 Mt of excess capacity (= 250 Mt – 50 Mt) of SK2 remaining unutilized in 0-10 years.



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Figure 8. GCC for Example 3 In the interval years 10 – 20, the value -300 Mt (Table S3, Column 7) indicates the total uncaptured CO2 of 300 Mt for the interval. The sink SK2 that was present in earlier interval years with 200 Mt excess capacity, can be used in this interval. This leaves an excess capacity of 100 Mt CO2 (Column 7 of Table S3). In other words, this pocket does not contribute to additional uncaptured CO2 target. For the second pocket that is present in years 20 – 40, since SK1 is present throughout the pocket and is a fixed life sink (Figure 8), it cannot be used as a local storage to capture CO2 for the later time interval (50-60 years). The length of the pocket indicates that the excess CO2 load is 100 Mt; this value is to be added to the uncaptured CO2 target reported by the CCSCA earlier, i.e. 200 Mt, resulting in an actual uncaptured CO2 load of 300 Mt (= 200 + 100 Mt). The equivalent quantity of 100 Mt is also added to the unutilized capacity target identified earlier, thereby resulting in a total of 550 Mt (= 450 + 100 Mt) unutilized CO2 sink capacity. Thus, the preliminary targets identified earlier (Table S3) are not the actual targets of the CCUS system. In other words, the CO2 capture and storage pinch diagram34 (Figure 9) also fails to locate the correct targets. 24 ACS Paragon Plus Environment

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After considering the CO2 capture in the pockets of GCC, the actual targets of the CCUS system are identified as 300 Mt and 550 Mt for the uncaptured CO2 and unutilized storage capacity, respectively. Hence, the present methodology is more generic and accurate than the previous one, which otherwise may not identify correct targets in some specific cases. Figure 10 shows the CCUS mapping diagram for this example based on one of the possible networks. The actual uncaptured CO2 (from SR3) and unutilized capacity (in SK1) may be verified from the associated areas.

Figure 9. CCSPD showing solution for Example 3



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Figure 10. CCUS mapping diagram for Example 3 Example 4: Planning problem with multiple pockets This example consists of four CO2 sources and three CO2 sinks, with relevant data shown in Table 5. Table S4 shows the cascade table for Example 4, while the GCC is shown in Figure 11. The cascade table and GCC identify the preliminary targets as 100 Mt uncaptured CO2 and 150 Mt unutilized sink capacity. We next examine if the preliminary targets will remain valid as the actual targets.


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Table 5. Source and sink characteristics in example 4 Source 1 2 3 4

Sink 1 2 3

CO2 flowrate (Mt/y) 15 10 5 20 Total

CO2 load (Mt) 450 200 200 400 1250

Injectivity Storage (Mt/y) capacity (Mt) 20 15 10 Total

600 300 400 1300

Earliest available (y) 10 0 40

time


Characteristic end time (y) 40 20 80

End time (y) 30 30 40 70

Type of sink Fixed capacity Fixed capacity Fixed life

The GCC in Figure 10 shows that a first CO2 pocket exists in the interval years 10 – 25 and the second pocket exists in the interval years 33.3 – 70. For the first pocket in years 10 – 25, Table S4 shows that both sinks SK1 and SK2 are present in years 10 – 20. In the following decade (years 20 – 30), a fixed capacity sink SK2 is absent; which means that SK2 can be used for CO2 storage in a later time interval. In the interval years 10 – 20, column 7 of Table S4 indicates that the respective capacities of SK1 and SK2 are determined as 200 and 150 Mt (product of respective injectivity and interval duration), while the total CO2 load of the three sources is 300 Mt (= 30 x (20 – 10) Mt). Hence, SK2 has an excess capacity of 50 Mt (= 200 + 150 – 300) in this interval. Since SK2 is absent in the following interval years 20 – 30, its excess capacity can be used to store the excess CO2 load of 100 Mt (Column 7), thereby leaving 50 Mt uncaptured CO2.



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Figure 11. GCC for Example 4 A similar situation also occurs for CO2 pocket in the interval years 33.3 – 70. The GCC in Figure 11 shows that the SK1 and SK3 are present in between 33.3 – 50 years (segment with negative slope), and only SK3 is present throughout the later years (segment with positive slope). Hence, SK1 can be used to store the excess CO2 in the latter interval years. We next examine if its capacity is sufficient for use. In the interval years 30 – 40, SK1 has a capacity of 200 Mt (= 20 x (40 – 30) Mt; Table S4, Column 2). In the same interval, there exists CO2 load from SR3 of 50 Mt (= 5 x (40 – 30) Mt). Hence, the excess capacity of SK1 from interval years 30 – 40 is determined as 150 Mt (= 200 – 50 Mt). The next positive slope stretches between interval years 50 – 70, where the fixed life SK3 is present, with an excess CO2 load of 200 Mt (Table S4, Column 7). Hence, the excess capacity of SK1 from interval years 30 – 40 (150 Mt) can be used to store a big portion of this 200 Mt CO2 load, thereby leaving 50 Mt (= 150 – 200 Mt) uncaptured CO2 at the end of year 70. Thus, in this case, the preliminary targets of uncaptured CO2 and unutilized capacity remain valid.


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The possible CO2 allocation network for this example is shown in Figure 12. As shown, the uncaptured CO2 (from SR1 and SR3) is before the pinch year 30, while the unutilized capacity (SK1 and SK3) is found after year 30.

Figure 12. CCUS mapping diagram for Example 4

5. Conclusion A pinch analysis-based approach for optimal matching of CO2 sources and sinks in CCUS systems has been developed, by simultaneously considering injectivity constraint and availability of all sources and sinks. CCUS is one of the potentially important technological solutions to mitigate CO2 emissions; thus, the capability to rigorously plan integrated CCUS systems is a significant contribution to address climate change. The utilization or storage of captured CO2 needs to be optimally planned, by considering the physical and temporal characteristics of the sources and the sinks. The proposed methodology makes use of the algebraic technique to determine preliminary CCUS targets (i.e., uncaptured CO2 load and unutilized storage capacity). The graphical GCC tool is then used to verify those targets; it can also identify the possibility of local CO2 capture, where 29 ACS Paragon Plus Environment


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the previously unutilized excess storage capacity of a CO2 sink is utilized at a later time within the planning period. This feature is essential to enable long-term planning of CCS and CCUS systems across timeframes that span multiple decades. Four examples were presented to demonstrate the applicability of the various aspects of the proposed methodology. It was found that the proposed method rigorously identifies accurate targets, which may be different from preliminary targets, depending on the nature of the sources and sinks in the problem. The proposed method is applicable in general to the CCUS systems where CO2 utilization is not dependent on purity constraints. Furthermore, the present work involved long enough time scales (years or decades) where short-term fluctuations (days or weeks) are not significant and may not be considered. However, we propose to look into flexible capture problem in the future with shorter time scales. Additional constraints could be introduced in future research to address factors such as purity and pressure of the streams, and the impact on the network configuration of the CCUS system. The CO2 footprint of transport through the development of a pinch approach that combines the sourcesink matching and grid power loss aspects of CCS planning could also be considered.

Notes The authors declare no competing financial interest. Acknowledgments

Notation i = source index j = sink index Parameters tq = time for q-th row TSq = total source flowrate for q-th row TDq = total sink injectivity for q-th row Dj = injectivity of sink j (Mt/y) 30 ACS Paragon Plus Environment

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Si = total CO2 emissions from source i (Mt) Δmq = CO2 load for q-th row Δms = smallest number in the eighth column of cumulative CO2 load

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Supporting Information The Cascade Tables for examples 1-4, referred as Tables S1-S4, respectively, in the manuscript are provided as the supporting information. This information is available free of charge via the Internet at http://pubs.acs.org/


A Pinch-Based Approach for Targeting of Carbon Capture, Utilization

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