Development of a Multiperiod Model for Planning CO2 Disposal and

Article pubs.acs.org/IECR

Development of a Multiperiod Model for Planning CO2 Disposal and Utilization Infrastructure Jee-Hoon Han,† Jae-Uk Lee,† and In-Beum Lee*,† †

Department of Chemical Engineering, POSTECH, Pohang, Korea ABSTRACT: Reducing CO2 emissions economically and efficiently necessitates the construction of carbon capture and storage (CCS) infrastructure as a comprehensive network that is capable of disposing of, and of utilizing CO2. Most of the early attempts to design and model the future CCS infrastructure were either limited to examining an individual component of the CCS infrastructure or focused on design and operation of a deterministic, steady-state network in which CO2 emissions are constant over time. In this paper, a multiperiod model for planning CCS infrastructure is developed to consider variation of the emission reduction target, as well as the variation of CO2 emissions over a long-term planning interval, thus leading to phased infrastructure development. The proposed model can help determine where and how much CO2 to capture, store, transport, utilize or sequester for the purpose of maximizing the total profit of the CCS infrastructure while meeting the CO2 mitigation target during each time period of the planning interval. The features and capabilities of the model are illustrated by application of the future CCS infrastructure on the east coast of Korea. The results will be helpful to multiperiod planning of the development of a CCS infrastructure.

1. INTRODUCTION Carbon capture and storage (CCS) technologies constitute a promising solution to the problem of reducing GHG emissions.1 CCS technologies can be classified into two categories: those that dispose of CO2 and those that utilize CO2.2 CCS technologies to dispose of CO2 separate it from various emission sources, transport it to a storage location, and isolate it from the atmosphere for a long period. CCS technologies to utilize CO2 use it as fuel, a chemical, or a nutrient for bioreactors. CCS technologies will contribute about 19% of total CO2 reductions in 2050 and without CCS, overall costs to halve emissions will rise by 70% by 2050.3 To achieve the targets included in the CCS roadmap, various countries have called for tremendous efforts to effectively design and model the future CCS infrastructure. The CCS infrastructure is a network of integrated technology components (for utilization, capture, storage, sequestration, transportation) that are mutually connected and which interact in a specific way. Our research2,4,5 has been conducted to study the framework of a network in which all components of the CCS infrastructure are analyzed comprehensively and independently. The framework was developed using a mathematical modeling approach and solved using an optimization technique. We proposed an optimization model for CCS infrastructure that generates a fully integrated, profit-maximizing CCS system. Previous studies did not consider variance of CO2 emission and evolution of emission reduction target over a long time interval; to obtain more-realistic results, CCS infrastructure models must consider the these factors, as well as the possibility of selecting different scales of utilization, capture, storage, sequestration and transportation technologies. This study addresses the problem of designing a CCS infrastructure model that considers the effects of (i) different sizes of utilization, capture, storage, and sequestration facilities and (ii) evolution of the CO2 reduction target over a long-term © 2012 American Chemical Society

planning interval. The CCS infrastructure is modeled as a mixed-integer linear programming (MILP) optimization problem. The objective of the model is to maximize the profit of the CCS infrastructure, taking into consideration a number of design and operational decisions including: (i) number, location, capacity, and type of utilization, capture, storage, and sequestration facilities and (ii) CO2 flow rate and type of transportation links to be established. An illustrative case study using the east coast of Korea is provided to demonstrate the applicability of the model.

2. PROBLEM DESCRIPTION The mathematical model described in our previous paper2 addressed the design of a steady-state CCS infrastructure at a single point in time to achieve a particular CO2 reduction target. However, the model neglects two essential elements traditionally considered in the design of CCS infrastructures: (i) the scale of utilization, capture, storage, and sequestration facilities; and (ii) the evolution of the network over time. To predict the future evolution of the CCS infrastructure and the network operation over a long time, both of these elements will be included in the proposed optimization problem. The superstructure of the proposed model (Figure 1, notation: Table A1) is composed of regions enclosing different types c and sizes j of CO2 capture technologies, regions enclosing different types and sizes of CO2 sequestration technologies, and different types and sizes of CO2 utilization technologies. The CO2 captured by the capture technologies established in various emission sources within regions should be matched by disposal activities such as sequestration or utilization activities Received: Revised: Accepted: Published: 2983

July 8, 2011 January 17, 2012 January 17, 2012 January 17, 2012 dx.doi.org/10.1021/ie201460v | Ind. Eng. Chem. Res. 2012, 51, 2983−2996

Industrial & Engineering Chemistry Research

Article

Figure 1. Superstructure of the multiperiod model.

the difference between total annual benefit and total annual cost. This benefit is the income from selling products made by utilizing CO2; this cost was obtained by dividing the sum of capital and operating costs during each time period by the total number of time periods. Capital cost is associated with the establishment of transportation links and utilization, capture, storage, and sequestration facilities. Operating cost is the cost of utilizing, capturing, storing, sequestering, and transporting CO2. The benefit and cost terms that constitute the overall profit function are elaborated in the following subsections. 3.1.1. Total Annual Benefit. The total annual benefit TAB over the entire planning interval is the income from selling products made by utilizing CO2 in utilization facilities during each time period t. The benefit of the utilization facilities is the product of the unit selling benefit USBe,p and the production amount Pe,p,g:

such as production of biobutanol and green polymers. If the regions containing the capture technologies do not contain sequestration or utilization technologies, these regions must be connected to the sequestration or utilization technology in other regions by different potential transportation modes (truck, railcar, or ship). Regions containing different types and sizes of intermediate storage technologies only exist to collect CO2 captured from emission sources within particular region, to load CO2 for delivery by different transport modes. Capture technology regions are directly connected to the sequestration or utilization technology regions by pipeline-based transportation without intermediate storage technologies. The resulting model structure contains all possible configurations of the CCS infrastructure, as well as the interactions among the various components such as utilization, capture, storage, sequestration, and transportation. From this structure, the optimization algorithm will search for the best design option by eliminating inefficient combinations of utilization, capture, storage, sequestration facilities and transportation links. Our search procedure solves for all time periods simultaneously, and the predicted network structure will be built up over a long-term planning interval. In the following section, each component of the model structure will be addressed in more detail.

TAB =

∑

∑ ∑ ∑ USBe , pPe , p , g , t

e ∈ {greenpolymer,biobutanol} p

g

t

(1)

3.1.2. Total Annual Cost. The total annual cost TAC over the entire planning interval is the sum of facility capital cost FCC, transport capital cost TCC, facility operating cost FOC, and transport operating cost TOC:

3. MODEL FORMULATION The mathematical formulation is composed of an objective function and constraints. The following subsections discuss the objective function and the model constraints in more detail. 3.1. Objective Function. The aim of the optimization problem is to maximize the average annual profit of the CCS infrastructure over a long-term planning interval. This profit is

TAC = FCC + TCC + FOC + TOC

(2)

3.1.2.1. Facility Capital Cost. FCC over the entire planning interval is related to the establishment of utilization, capture, storage, and sequestration facilities at each time period t. It is 2984

dx.doi.org/10.1021/ie201460v | Ind. Eng. Chem. Res. 2012, 51, 2983−2996


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The number of pipelines is

the product of the capital cost of each facility and the total number of facilities

QPi , l , g , g ′ , d , t ≤ TPCapi , l , d NTPoni , l , g , g ′ , d , t ∀ i , l , d , g , g ′, t ; g ≠ g ′, l ∈ {pipe}

⎡ ⎢ CCRf FCC = ∑ ∑ ⎢ ( ∑ ∑ ∑ PCCe , p , jIPe , p , j , g , t LR g t ⎢ e p j ⎣ +

i

+

QPi , l , g , g ′ , d , t ≤ TPCapi , l , d NTPoffi , l , g , g ′ , d , t ∀ i , l , d , g , g ′, t ; g ≠ g ′, l ∈ {pipe}

∑ ( ∑ ∑ ∑ (CCCi , c , si , jICi , c , si , j , sp , g , t ) c

si sp

∑ ∑ MCCi , m , jIMi , m , j , g , t m

where TPCapi,l,d is the transport capacity of the pipelines, which depends on their diameter. Multiple parallel pipelines between regions are not common for practical reasons. Thus, the number of pipelines between different regions is bounded by the constraints

j

⎤ ⎥ + ∑ ∑ SCCi , s , jISi , s , j , g , t ))⎥ ⎥⎦ s j

(3)

∑ NTPoni , l , g , g ′ , d , t ≤ α

where the first part of the right-hand-side denotes the capital cost of utilization facilities, and the second part denotes the capital cost of disposal facilities including capture, storage, and sequestration facilities. To find the cost per year, the capital costs of facilities are multiplied by the capital charge rate CCRf of facilities. The learning rate LR is introduced to anticipate the reduction in the cost of capture, storage, and sequestration technologies as experience accumulates over time. This coefficient is defined as6

LR = 1 + (κ(t − 1))

∑ NTPoffi , l , g , g ′ , d , t ≤ α

(7)

where α is a small number to limit the number of pipelines and is assumed to be two. Also, the total flow of CO2 in physical form i by pipeline between different regions is equal to the flow rate of all pipelines of type d that are established

(4)

Q i,l ,g ,g′,t =

∑ QPi , l , g , g ′ , d , t d

∀ i , l , g , g ′, t ; g ≠ g ′, l ∈ {pipe}

(8)

As a result, the total transportation capital cost is ⎛

TCC =

⎞ CCR l ⎟ (NTUoni , l , t TMCi , l )⎟ ⎜ LR ⎟ i l ∈ {railcar,truck} t ⎝ ⎠

∑

∑

∑ ⎜⎜

⎛

⎞ CCR l ⎟ (NTUoffi , l , tTMCi , l )⎟ ⎜ LR ⎟ i l ∈ {ship} t ⎝ ⎠

+

∑ ∑ ∑ ⎜⎜

+

∑ ∑ l ∈ {pipe} ∑ ∑ ∑ ∑ i

g

g′ d

t

⎛ ⎜ CCR l ×⎜ (INTPoni , l , g , g ′ , d , t Long , g ′TPIConi , l , d ⎜ LR ⎝

∑∑

Q i , l , g , g ′ , t ⎛ 2Loffg , g ′ ⎞ ⎜⎜ + LUTl⎟⎟ TMAlTCapi , l ⎝ SPl ⎠ g g′ ∀ i , l , t ; l ∈ {ship}

∀ i , l , g , g ′, t ; g ≠ g ′, l ∈ {pipe}

d

Q i , l , g , g ′ , t ⎛ 2Long , g ′ ⎞ + LUTl⎟ ⎜ TMAlTCapi , l ⎝ SPl ⎠ g g′ ∀ i , l , t ; l ∈ {railcar, truck}

NTUoffi , l , t =

∀ i , l , g , g ′ , t ; g ≠ g ′ , l ∈ {pipe}

d

where t − 1 represents the cost reduction over time and κ is the percentage of the corresponding reduction per year, assumed to be 5%·y−1. 3.1.2.2. Transportation Capital Cost. TCC is obtained by multiplying the number of transport units, that is, truck, railcar, ship, or pipeline, by the cost of each respective form and then summing. This cost is divided into onshore and offshore costs. The onshore transportation cost represents the cost of transporting CO2 between various regions onshore; the offshore transportation cost represents the cost of transporting CO2 from the harbor to the final sequestration region offshore. The number of transport units excluding pipelines is NTUoni , l , t =

(6)

⎞ ⎟ + INTPoffi , l , g , g ′ , d , t Loffg , g ′TPICoffi , l , d)⎟ ⎟ ⎠

∑∑

(9)

where the capital cost TMCi,l of transport modes excluding pipelines is the sum of transport container cost, undercarriage cost, and cab cost. The total pipeline installation cost onshore TPIConi,l,d and offshore TPICoffi,l,d each consist of pipeline material cost, pipeline miscellaneous cost, right-of-way cost and labor cost. To find the cost per year, the capital costs of transport modes are multiplied by the capital charge rate CCRl of transport mode l. 3.1.2.3. Facility Operating Cost. FOC over the entire planning interval is related to the cost required to operate CCS facilities efficiently. It is obtained by multiplying the unit costs of utilization, capture, storage, and sequestration

(5)

where the first term represents the number of transport units, that is, truck or railcar, required between regions onshore, and the second term corresponds to the number of ships used to transport CO2 from the harbor to the final sequestration region offshore. Here, NTUoni,l,t and NTUoffi,l,t depend on the average distance between different regions, the transport unit capacity, the flow rate of CO2 between the regions, the availability of the transportation modes, their average speed, and loading/ unloading time. 2985



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Table 1. Analysis Conditions Selected for the Case Study description transportation capture

storage

con-figur-ation

MEA

LCO2

pipe

onshore

1a 2b

○ ○

○

○ ○

offshore

rail ○

truck

pipe

○

○ ○

sequestration

utilization

ship

DGR

SAS

biofuel

GPolymer

○

○ ○

○ ○

○ ○

○ ○

a

Case 1: Capture of liquid CO2 (LCO2) using monoethanolamine (MEA) in different sizes of capture facilities, Delivery of liquid CO2 (LCO2) via different sizes of pipeline delivery onshore and offshore, Sequestration of liquid CO2 (LCO2) via different sizes of depleted gas reservoir (DGR) and saline aquifer storage (SAS), Utilization of liquid CO2 (LCO2) via different sizes of biobutanol and ‘green’ polymer plants. bCase 2: Similar to the first scenario, however, tanker truck, railcar, pipeline onshore, and tanker ship, pipeline offshore will be used for transportation.

by the corresponding amounts of utilization, capture, storage, and sequestration

The annual general cost GC is

∑ ∑ ∑ ( ∑ ∑ UPCe , p , jPe , p , j , g , t

FOC =

j

g

t

e

GC =

i

c

si sp

m

s

∑

∑

g′

⎛ 2Loffg , g ′Q ⎞ i,l ,g ,g′,t ⎟ ⎟ FElTCapi , l ⎝ ⎠

∑ ∑ ∑ ∑ ∑ FPl⎜⎜

+

i l ∈ {ship} g

g′

t

∑

∑

⎡Q

∑ ∑ ∑ DWl⎢⎢

TOCP =

i l ∈ {railcar,truck} g

g′

t

+

∑

TOC = TOCEP + TOCP

(12)

⎛ 2Long , g ′Q


+

g′

⎝

t

⎛ 2Loffg , g ′Q

∑ ∑ ∑ ∑ ∑ MEl⎜⎜ i l ∈ {ship} g

g′

t

⎝

⎞ ⎟ ⎠

⎞

TCapi , l

1 (TAB − TAC) δ

max TAPave

i,l ,g ,g′,t ⎟

⎟ ⎠

(17)

(18)

To find the average profit of the network over the entire planning interval, the right-hand-side of eq 18 is divided by the number of time periods δ. Finally, the corresponding profit is maximized by the optimization

i,l ,g ,g′,t ⎟

TCapi , l

t

3.1.3. Annual Profit. The average annual profit TAPave is calculated by the difference between total annual benefit TAB and total annual cost TAC TAPave =

∑ ∑ ∑ MEl⎜⎜

g′ d

where the total pipeline operating cost TPOCi,l,d consists of labor and utility cost in such activities as compressing and pumping. As a result

(11)

The annual maintenance cost MC is

∑

t

(16)

⎞⎤ + LUTl⎟⎥ + ∑ ∑ ∑ ∑ ∑ DWl ⎠⎥⎦ i l ∈ {ship} g g ′ t

MC =

g′ d

∑ ∑ ∑ ∑ ∑ ∑ TPOCoffi , l , dQPi , l , g , g ′ , d , t i l ∈ {pipe} g

SPl

⎡Q ⎞⎤ i , l , g , g ′ , t ⎛ 2Loffg , g ′ ⎜⎜ ×⎢ + LUTl⎟⎟⎥ ⎢⎣ TCap ⎝ SPl ⎠⎥⎦ i,l

∑ ∑ ∑ ∑ ∑ ∑ TPOConi , l , dQPi , l , g , g ′ , d , t i l ∈ {pipe} g

i , l , g , g ′ , t ⎛ 2Lon g , g ′

⎜ ⎣ TCapi , l ⎝

(15)

The transportation operating cost of pipeline TOCP is also classified into onshore and offshore costs

The annual labor cost LC for all time periods is LC =

(14)

TOCEP = FC + LC + MC + GC

⎛ 2Long , g ′Q ⎞ i,l ,g ,g′,t ⎟ ⎟ FElTCapi , l ⎝ ⎠

t

t

Finally, the total transportation operating cost excluding pipelines TOCEP is

∑ ∑ ∑ FPl⎜⎜


g′

⎛ 2Loffg , g ′ ⎞⎤ × ⎜⎜ + LUTl⎟⎟⎥ ⎝ SPl ⎠⎥⎦

(10)

3.1.2.4. Transportation Operating Cost. TOC excluding pipelines is categorized into fuel, labor, maintenance, and general costs. In a similar manner to the transportation capital cost, the operating cost is further classified into onshore and offshore costs. In the following four equations, the first term of the right-hand-side denotes the onshore operating cost, and the second term the offshore operating cost. The annual fuel cost FC during all time periods is

FC =

⎡ Q i , l , g , g ′ , t ⎛ 2Long , g ′ ⎜ ⎣ TMAlTCapi , l ⎝ SPl

∑ ∑ ∑ GEl⎢⎢

⎡ Q ⎞⎤ i ,l ,g ,g′,t + LUTl⎟⎥ + ∑ ∑ ∑ ∑ ∑ GEl⎢ ⎢⎣ TMAlTCap ⎠⎥⎦ i ,l i l ∈ {ship} g g ′ t

∑ UMCi , m , jMi , m , j , g , t + ∑ USCi , s , jSi , s , j , g , t ))

+

∑


p

∑ ( ∑ ∑ ∑ UCCi , c , si , jCi , c , si , j , sp , g , t

+

∑

(13)

(19)

The maximization is subject to all constraints stated below. 2986



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Table 2. CO2 Emissions of Each Plant and Time Period (t CO2·y−1) time period, t

a

industry type

plant

region

t1 (2011−2015)a

t2 (2016−2020)b

t3 (2021−2025)b

t4 (2026−2030)b

power power power power power power power power steel steel steel steel steel oil refinery oil refinery oil refinery oil refinery oil refinery petrochemical petrochemical

Youngdong #1 Unit Youngdong #2 Unit Samcheonpo #1 Unit Samcheonpo #2 Unit Samcheonpo #3 Unit Samcheonpo #4 Unit Samcheonpo #5 Unit Samcheonpo #6 Unit Posco #1 Unit Posco #2 Unit Posco #3 Unit Posco #4 Unit Posco #5 Unit Skenergy #1 Unit Skenergy #2 Unit Skenergy #3 Unit Skenergy #4 Unit Skenergy #5 Unit KPIC Skchem

Gangwon Gangwon Gyeongnam Gyeongnam Gyeongnam Gyeongnam Gyeongnam Gyeongnam Pohang Pohang Pohang Pohang Pohang Ulsan Ulsan Ulsan Ulsan Ulsan Ulsan Ulsan

875 277 1 400 444 4 485 188 4 648 286 4 403 639 4 729 834 3 995 895 4 158 992 1 588 632 2 421 506 4 472 846 8 189 936 5 444 534 201 893 370 137 572 030 807 572 874 870 899 569 1 646 020

952 249 1 523 598 4 879 613 5 057 054 4 790 893 5 145 774 4 347 292 4 524 732 1 814 995 2 766 546 5 110 180 9 356 916 6 220 323 225 101 412 684 637 783 900 400 975 434 978 677 1 790 770

1 035 989 1 657 583 5 308 724 5 501 769 5 212 202 5 598 291 4 729 591 4 922 635 2 073 613 3 160 749 5 838 327 10 690 178 7 106 653 250 975 460 121 711 095 1 003 899 1 087 558 1 064 741 1 948 250

1 127 094 1 803 350 5 775 571 5 985 592 5 670 561 6 090 602 5 145 509 5 355 529 2 369 081 3 611 123 6 670 227 12 213 416 8 119 277 279 824 513 010 792 833 1 119 295 1 212 570 1 158 374 2 119 578

Han and Lee.2 bAverage annual growth rate (%) − power industry, 1.7; steel industry, 2.7; oil refinery industry, 2.2; petrochemical industry, 1.7.9

Table 3. Sequestration Planned and Time Period sequestration type, s

depleted gas reservoir t1, t2 (2011−2020)

time period, t candidate region for CO2 sequestration GSCapi,s,g,t (106 t CO2)

Ulleung Basin 1

saline aquifer storage

t3, t4 (2021−2030)

Pohang Basin 1

Ulleung Basin 10

Pohang Basin 10

t1, t2 (2011−2020)

t3, t4 (2021−2030)

Norway 10

Norway 100

Table 4. Production Capacities and Costs of CO2 Utilization Facilitiesa plant type, p

biobutanol

green polymer

plant size, j

small

medium

large

small

medium

large

PCapmine,p,j (106) PCapmaxe,p,j (106) PCCe,p,j ($ × 106) UPCe,p,j

7.5 L·y−1 150 L·y−1 25 0.78 $·L−1

75 L·y−1 1,500 L·y−1 100 0.62 $·L−1

150 L·y−1 3,000 L·y−1 1,000 0.31 $·L−1

10 kg·y−1 200 kg·y−1 360 1.78 $·kg−1

50 kg·y−1 1,000 kg·y−1 940 0.94 $·kg−1

100 kg·y−1 2,000 kg·y−1 1,400 0.71 $·kg−1

a

CO2 use factor − biobutanol plant, 0.00279 t CO2·L−1; green polymer plant, 0.431 t CO2·t−1.2

3.2.1. Constraints. Capture Facilities Constraints. Assuming steady-state operation during each time period, the total mass balance in a region is

the minimum and maximum capture limits associated with c and j CCapimin , c , si , jNCi , c , si , j , sp , g , t ≤ Ci , c , si , j , sp , g , t

∑ ∑ ∑ ∑ ηc ·Ci , c , si , j , sp , g , t c

si

=

j

≤ CCapimax , c , si , jNCi , c , si , j , sp , g , t

sp

∑ ∑ (Q i , l , g , g ′ , t − Q i , l , g ′ , g , t ) + ∑ ∑ Si , s , j , g , t l

+

g′

s

∑ ∑ Ui , p , j , g , t p

j

∀ i , c , si , j , sp , g , t

(21)

The total amount Ci,c,si,j,sp,g,t of CO2 captured by all capture facility of type c and size j established in each source plant type sp in region g is determined by the CO2 emissions Ei,si,sp,g,t from the source plant type sp in region g

j

∀ i, g , t

(20)

In eq 20, the left-hand-side represents the total amount of CO2 captured in a region and ηc is CO2 capture efficiency for a type of capture facility. The first term of the right-hand-side is the total remaining flow rate, the second term is the total amount of CO2 sequestered in the region, and the third term represents the total amount of CO2 used in the region. The capture rate of a capture facility of type c and size j established in a source plant sp in region g during time period t is constrained by the number of capture facilities and

∑ ∑ Ci , c , si , j , sp , g , t ≤ Ei , si , sp , g , t c

j

∀ i , si , sp , g , t (22)

3.2.2. Intermediate Storage Facilities Constraints. An important complication in the operation of this network that if the CO2 is to be transported by truck, railcar, or ship, then storage facilities must collect CO2 captured from source plants in a given region, whereas for pipeline-based transportation no 2987



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intermediate storage is required.7 Storage facilities could be built either locally within a specific region or outside the region, away from the emission source. The total average inventory of CO2 in physical form i in a region g during time period t is a function of the total flow rate Qi,l,g,g′,t of CO2 in physical form i leaving region g multiplied by a safety stock factor SSF

∑ ∑ Mi , m , j , g , t = SSF( m

j

∑

∑ Q i ,l ,g ,g′,t)

sequestration facilities and the minimum and maximum sequestration limits associated with s and j max Scappimin , s , j NSi , s , j , g , t ≤ Si , s , j , g , t ≤ Scapi , s , j NSi , s , j , g , t

(29)

The total amount Si,s,j,g,t of CO2 sequestered by all sequestration facilities of type s and size j in region g is determined by the available geological capacity GSCapi,s,g,t for sequestering CO2 in region g

∀ i, g , t

l ∈ {railcar,truck,ship} g ′

(23)

∑ Si , s , j , g , t ≤ GSCapi , s , g , t

SSF is introduced to allow operational flexibility in case of unanticipated events such as the closing of a capture plant. In capture facilities, the average inventory of CO2 in physical form i stored in storage facility of type m and size j during time period t cannot exceed certain limits

∀ i , m, j , g , t

Ui , p , j , g , t =

(24)

∑ ∑ Xi , l , g ′ , g , t ≤ 1 i

l

∀ e , p, j , g , t

(32)

3.2.6. CO2 Reduction Target Constraints. The target amount T of CO2 to be reduced by CCS facilities is the product of the mandated reduction of CO2 emissions LMRi,t, the utilization rate UCCSi,t of CCS as CO2 reduction technology, and the total amount Ei,si,sp,g,t of CO2 emissions from all sources: Tt =

∑ ∑ ∑ ∑ LMR i , tUCCSi , t Ei , si , sp , g , t i

si sp

(33)

g

The total amount of CO2 sequestered and used in all regions cannot be less than the target amount T of CO2 to be reduced by CCS facilities

∑ ∑ ∑ ( ∑ Si , s , j , g , t + ∑ Ui , p , j , g , t ) ≥ Tt

ug ′ − ug + nXi , l , g , g ′ , t ≤ n − 1

i

j

g

s

(34)

p

(26)

3.2.7. Time Evolution Constraints. As the network evolves over time, new CCS facilities will be constructed to meet the increased the mandated reduction of CO2 emissions. Therefore, the number of utilization, capture, storage, and sequestration facilities in each region and during the subsequent time periods is equal to the number of the previously established facilities plus the number of newly constructed facilities. This is expressed by the following constraints:

∀ g , g ′, t ; g ≠ g ′ (27)

l

(31)

max Pcapemin , p , j NPe , p , j , g , t ≤ Pe , p , j , g , t ≤ Pcape , p , j NPe , p , j , g , t

where Xi,l,g,g′,t is binary variable representing whether or not transport exists. CO2 in physical form i can flow only from a source to a sequestration facility or utilization facility. A harbor region can both import CO2 from neighboring source regions, and export CO2 to other sequestration or utilization regions. To formulate the flow mathematically, subtour elimination constraints are employed as follows:8

i

∀ i , p, j , g , t

The production rate Pe,p,j,g,t of a product form e by any utilization facility of type p and size j in region g during time period t is constrained by the number of production facilities and the minimum and maximum production limits associated with p and j

(25)

∑ ∑ Xi , l , g , g ′ , t ≤ 1

∑ CUFi , e , pPe , p , j , g , t e

∀ i , l , g , g ′, t ; g ≠ g ′

∀ i , l , g , g ′, t ; g = 2, ... n , g ′ = 2, ... n; g ≠ g ′

(30)

3.2.5. Utilization Facilities Constraints. The amount Ui,p,j,g,t of CO2 used by a utilization facility of type p and size j in region g during time period t is calculated by multiplying the CO2 use factor CUFi,e,p of product form e by the production rate Pe,p,j,g,t of product form e in any utilization facility of type p in region g

Equation 24 implies that the average inventory of CO2 in physical form i stored in a region g during time period t is constrained by the number NMi,m,j,g,t of storage facilities. 3.2.3. Transportation Constraints. To fulfill the mandated reduction of CO2 emissions, flow of CO2 among regions must be continuous. Flow of CO2 in physical form i from a region g to a different region g′ is possible only if the transportation mode has been established. Also, minimum and maximum flow rates of CO2 are always needed to justify the establishment of a transportation mode between two regions in the network max Q imin , l Xi , l , g , g ′ , t ≤ Q i , l , g , g ′ , t ≤ Q i , l Xi , l , g , g ′ , t

∀ i, s, g , t

j

MCapimin , m , jNM i , m , j , g , t ≤ Mi , m , j , g , t ≤ MCapimax , m , j NM i , m , j , g , t

∀ i, s, j, g , t

∀ g , g ′, t ; g ≠ g ′ (28)

3.2.4. Sequestration Facilities Constraints. Sequestration is storage of CO2 in geological or ocean reservoirs for long periods of time. The decision to sequester CO2 should consider geopolitical factors such as the location and capacity of the sequestration site. The sequestration rate of facility type s and size j established in region g during time period t is constrained by the number of

NPe , p , j , g , t = NPe , p , j , g , t − 1 + IPe , p , j , g , t

∀ e , p, j , g , t (35)

NCi , c , si , j , sp , g , t = NCi , c , si , j , sp , g , t − 1 + ICi , c , si , j , sp , g , t ∀ i , c , si , j , sp , g , t 2988

(36)



Article

Table 5. Capture Capacities and Costs of CO2 Capture Facilities capture type, c

MEA

source industry type, si

power

facility sizea, j CCapmini,c,si,j (106 t CO2·y−1) CCapmax i,c,si,j (106 t CO2·y−1) CCCi,c,si,j ($ × 106) UCCi,c,si,j ($·(t CO2)−1) a

S 0.2 0.8 230 63.64

steel

M 1 4 605 33.43

L 3 12 1170 21.54

S 0.2 0.8 302 63.15

M 1 4 792 33.18

petrochemical L 3 12 1531 21.38

S 0.2 0.8 498 63.54

M 1 4 1308 33.38

oil refinery L 3 12 2528 21.51

S 0.2 0.8 245 88.21

M 1 4 644 46.34

L 3 12 1245 29.86

Plant size: S (small), M (medium), L (large).

Meanwhile, the life cycle of pipelines is as long as that of facilities, thus pipeline should also apply the time evolution constraint.

Table 6. Storage Capacities and Costs of Intermediate Storage Facilities storage type, m

liquid CO2 storage

storage size, j

small

medium

large

MCapmini,m,j (103 t CO2·y−1) MCapmax i,m,j (103 t CO2·y−1) MCCi,m,j ($ × 106) UMCi,m,j ($·(t CO2)−1)

0.5 17 0.9 4.98

10 250 4 1.66

200 1,000 10 0.72

NTPoni , l , g , g ′ , d , t = NTPoni , l , g , g ′ , d , t − 1 + INTPoni , l , g , g ′ , d , t ∀ i , l , g , g ′, d , t

NTPoffi , l , g , g ′ , d , t = NTPoffi , l , g , g ′ , d , t − 1 + INTPoffi , l , g , g ′ , d , t ∀ i , l , g , g ′, d , t

Table 7. Sequestration Capacities and Costs of Sequestration Facilities sequestration type, s

depleted gas reservoir

sequestration size, j

small medium large

small medium

0.5 20 0.12 30.6

10 30 0.002 18.4

SCapmini,s,j (103 t CO2·y−1) SCapmax i,s,j (103 t CO2·y−1) SCCi,s,j ($ × 106) USCi,s,j ($·(t CO2)−1)

10 57 0.58 7.6

200 1086 54 1.9

large 1000 324 665 52 1.8

Table 8. Capital Costs and Unit Transport Costs of Pipeline with Diametera diameter (in)

6.88 onshoreb

TPCapi,l,d (103 t CO2·y−1) TPIConi,l,d ($·km−1) TPOConi,l,d ($·t CO2−1·y−1)

730 216 900 5.15

∑ ∑ ∑ LSFp , jIPe , p , j , g , t ≤ ALg p

9.27

offshorec 303 660 7.21

onshoreb

1460 292 247 409 146 3.34 4.68

t

(41)

4. CASE STUDY: AN INDUSTRIAL COMPLEX ON THE EAST COAST OF KOREA The case study in ref 2 is revised to accommodate the different sizes of utilization, capture, storage, and sequestration facilities, as well as the mandated reduction of CO2 emissions and infrastructure evolution over a long-term planning interval. This case study makes the following assumptions: (i) CO2 is emitted from four different industrial sources, power, steel, oil refinery, and petrochemical plants; (ii) after CO2 is captured using these different capture technologies, it is transported onshore by truck, railcar or pipeline, or a combination of these modes, and is transported offshore by pipeline or ship, or both; (iii) CO2 is either disposed of at stationary sequestration facilities of different types and sizes, or is used at stationary utilization facilities of different types and sizes. The mathematical model derived (section 3) will be used to analyze two different configurations (Table 1) of CCS infrastructure used on the east coast of Korea. To use the model, the parameters are divided into three categories: (i) the benefit parameters, including selling benefits of biobutanol or of green polymer; (ii) the cost parameters, including utilization costs, capture costs, storage costs, sequestration costs, transportation costs; and (iii) other parameters that describe the system information, including CO2 emissions,

a

Common design bases plant capacity factor [%]: 80. Pipeline inlet pressure [MPa]: 15.2. Pipeline outlet pressure [MPa]: 10.3. CO2 temperature [°C]: 25. CO2 density [kg/m3]: 884. CO2 viscosity [N-s/ m2]: 6.06 × 10−5. Common economic bases reference cost year: 2010. Conversion of euro to dollar [$/€]: 1.20. Operational lifetime [years]: 20. Discount rate [%]: 10. Electricity cost [$/kWh]: 0.04. Location factor: Japan/Korea = 1.0. Terrain factor: onshore = 1.0/offshore = 1.3. bEstimated based on McCollum and Ogden.14 cEstimated based on Hedle et al.15

NM i , m , j , g , t = NM i , m , j , g , t − 1 + IM i , m , j , g , t

NSi , s , j , g , t = NSi , s , j , g , t − 1 + ISi , s , j , g , t

j

∀ e, g

where LSFp,j denotes a land size factor for utilization facility type p and size j, and ALg is the available land sizes on each region g.

offshorec

∀ i , m, j , g , t

(40)

where NTPoni,l,g,g′,d,t−1 and NTPoffi,l,g,g′,d,t−1 are, respectively, the number of pipelines onshore and offshore at the end of the previous time period; and INTPoni,l,g,g′,d,t and INTPoffi,l,g,g′,d,t are, respectively, the number of pipelines onshore and offshore constructed during the current time period. 3.2.8. Site Constraints. The number and size of facilities constructed in a region is limited by the size of the site. This limit is calculated by multiplying the required number of facilities by their individual land requirements:

saline aquifer storage 200 3417 3.4 4.6

(39)

(37)

∀ i, s, j, g , t (38)

where NPe,p,j,g,t−1, NCi,c,si,j,sp,g,t−1, NMi,m,j,g,t−1, and NSi,s,j,g,t−1 are, respectively, the number of utilization, capture, storage, and sequestration facilities at the end of the previous time period and IPe,p,j,g,t, ICi,c,si,j,sp,g,t, I,m,j,g,t, and ISi,s,j,g,t are, respectively, the number of utilization, capture, storage, and sequestration facilities constructed during the current time period. 2989



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Table 9. Costs and Sizes of Sites for CO2 Utilization Facilities biobutanol

green polymer

region

land price ($/km2)

small (2 km2, $)

medium (5 km2, $)

large (10 km2, $)

small (0.072 km2, $)

medium (0.24 km2, $)

large (0.9 km2, $)

available land (km2)

Ulsan Pohang

3.09 × 107 8.30 × 106

6.18 × 107 1.66 × 107

1.55 × 108 4.15 × 107

3.09 × 108 8.30 × 107

2.22 × 106 0.60 × 106

7.42 × 106 1.99 × 106

2.78 × 107 7.47 × 106

1 5

5. RESULTS AND DISCUSSION The model presented is used to map out two different configurations for CCS infrastructure, especially the type of transportation modes. Configuration 1 only allows pipelines onshore and offshore for CO2 transportation; Configuration 2 allows all transportation modes onshore, and both tanker ships and pipelines offshore. Because these configurations show the CO2 reduction target evolution over 20 years divided into four periods, two distinct networks were obtained. The proposed model used to outline the two examined network configurations was computed using CPLEX 9.0 (GAMS) in on a computer equipped with a Pentium 4 chip, operating at 3.16 GHz. The short times required for solving the two different configurations and the low optimality gaps (Table 10) are satisfactory.

CO2 reduction targets, upper and lower boundary capacity of each CCS technology, transport distances between regions. This study used data and parameters from,2 which collected them from previous studies and national reports, and applied several engineering-oriented methods, such as cost estimation. However, the addition of two extra features, that is, time or size, to the model presented in paper2 makes the process of collecting data even more complicated. The input data used additionally for the model that considers time or size are summarized below. 4.1. Data with Respect to Time. To estimate the CO2 emissions in each period, the planning interval is divided into four time periods from 2011 to 2030. The length of each period is assumed to be five years. The CO2 emissions in the first period (Table 2) were obtained from ref 2. Because the CO2 emissions during the first time period are known beforehand, the CO2 emissions at the next time period (Table 2) can be determined by using the economic analysis of2 and the economic growth rate at source plants of,9 that is, 1.7% in power plants, 2.7% in steel plants, 2.2% in oil plants, and 1.7% in petrochemical plants. Because CO2 emissions tend to increase as an economy expands, the level of mandated reduction in CO2 emissions must be increased by a certain future time and be set differently during each period. This paper sets that the levels of mandated reduction LMRi,t of CO2 emissions are 10%, 30%, 32%, 35%9 from the first period to the final period, and assumes that utilization of CCS UCCSi,t as CO2 reduction technology is 5%, 12.5%, 20%, and 30% from the first period to the final period, respectively. Candidate regions (Table 3) for CO2 sequestration and total sequestration planned in the region during each period are offered by the economic assessment of ref 10. 4.2. Data with Respect to Size. The unit costs of utilization, capture, storage, and sequestration are directly proportional to the size of corresponding units. These unit costs benefit from economies of scale. Therefore, as the capacity of utilization, capture, storage, or sequestration increases, the unit cost decreases. To estimate the cost of CCS technologies with respect to size, we used the six-tenths factor rule11 ⎛ SB ⎞0.6 CB = CA⎜ ⎟ ⎝ SA ⎠

Table 10. Summary of Computational Results for the Examined Model configuration quantity number of constraints number of integer variables number of continuous variables optimality gap (%) CPU time (s)

1 12 628 20 856 33 322 0.0 3.37

2 14 949 21 282 37 663 0.0 3.79

The optimal network structures (Figures 2−5) for configuration 1 for the four time periods of the planning interval each have a distinctive characteristic. The reason behind this is variation of CO2 reduction target over a long-term planning interval. The CO2 reduction during the introductory stage of CCS infrastructure is fulfilled by only one capture facility, one utilization facility and two pipelines for an annual reduction target of 277 477 t CO2·y−1. Because the CO2 capture efficiency of monoethanolamine (MEA) is assumed to be 90%, the total amount of CO2 captured in a region onshore is ∼0.31 Mt CO2·y−1. Of the power industries in the Gangwon region, only KOSEP5 #1 Unit has a small MEA capture facility. The CO2 captured should be matched by disposal activities such as sequestration or utilization activities like production of biobutanol and green polymers. One medium green polymer plant in the Ulsan region are established to fulfill the utilization of CO2, and all of total CO2 captured are used in production of green polymers without the disposal activities. Two pipelines are established to transport liquid CO2 from the point of capture to the point of utilization; the amount of CO2 transported by two 9.27-in. pipelines from the Gangwon region to the Ulsan region are ∼0.28 Mt CO2·y−1. Although the Ulsan region has large industrial sources of CO2 such as oil refineries and petrochemical plants, no capture facilities are established to reduce CO2 in the region, because of the relative high cost per unit of capture for the oil and petrochemical industries in the Ulsan region, compared with other industries. This means that establishing long transportation links among regions is cheaper

(40)

where CB = the approximate cost (US$) of equipment having size SB, CA = is the known cost (US$) of equipment having size SA. After application of the six-tenths factor rule to the economic analysis,2 we estimated the cost with respect to size of each technology for CCS infrastructure (Tables 4−8). Moreover, the costs and sizes of sites for facilities are a significant element in overpopulated regions. This study estimates the costs of sites for each different type of facility based on assumptions and parameters proposed by Han et al.12 The costs and sizes of sites for utilization facilities are summarized in Table 9. 2990



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Figure 2. Optimal structure of case 1 for CCS infrastructure during the first time period.

Figure 4. Optimal structure of case 1 for CCS infrastructure during the third time period.

Figure 3. Optimal structure of case 1 for CCS infrastructure during the second time period.

Figure 5. Optimal structure of case 1 for CCS infrastructure during the fourth time period.

than building a new capture facility. Due to the low reduction target during the first time period, the model produced a centralized solution. As the reduction target rate increased, reaching 2 311 053 t CO2·y−1 at the second time period, the utilization capacity of utilization facilities available from the previous time period increased to cope with the new reduction target (Figure 3). Three additional medium green polymer plants in the Ulsan region are established and one new medium biobutanol plant is established in the Pohang region to fulfill the utilization of CO2.

The total amount of CO2 used in production of biobutanol and green polymers is ∼0.59 Mt CO2·y−1 and ∼1.72 Mt CO2·y−1, respectively; these are ∼26% and 74% of the total CO2 used. In case 1, green polymer production is the main method of utilizing CO2. In addition, many small to medium capture facilities were built in the Gangwon and Gyeongnam regions. Due to the introduction of these facilities, one transportation link was established additionally; the amount of CO 2 transported by a 9.27-in. pipeline from the Gyeongnam region to the Ulsan region is ∼1.46 Mt CO2·y−1. 2991



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Moving to the third time period, reaching 4,382,501 t CO2·y−1, we see denser network structure (Figure 4). During this time period, the first large capture facility was established in the Ulsan region while no utilization facility and transport link was established; these satisfy the CO2 utilization demand within the region rather than importing CO2 from neighboring regions, unlike in the first and second time periods. During the four time period, reaching 7,993,526 t CO2·y−1, a fully saturated CCS infrastructure are assumed to be created (Figure 5). It is remarkable to note that the CO2 reduction method has changed drastically compared to the one for three time periods; the total amount of CO2 treated by disposal activities such as sequestration or utilization activities like production of biobutanol and green polymers is ∼2.08 Mt CO2·y−1 and ∼5.91 Mt CO2·y−1, respectively; these are ∼26% and 74% of the total CO2 reduced. Most of these changes took place in the Pohang Basin and Ulleung Basin; eleven medium scale and Depleted Gas Reservoir (DGR) type of sequestration facilities are built in the Pohang Basin, and one large and seven medium scale DGR are built in the Ulleung Basin to satisfy the CO2 reduction target. This means that when the reduction target rate is high, the CCS infrastructure requires more disposal activities to fulfill the required reduction demand, due to the high cost of utilization activities despite its high benefit, than when it is low. Moreover, the other main reason behind this is a limit on the size of the site where the CO2 utilization facilities constructed realistically. From the four network structures examined, it can be inferred that CO2 reduction demand at an early stage will be fulfilled mainly by distributed, small-scale capture facilities and medium-scale green polymer plants. As time progresses and more CO2 are reduced, centralized CO2 capture via medium or large-scale plants will be introduced gradually especially in areas with high CO2 emission density. Moreover, distributed CO2 sequestration via medium or large-scale DGR facilities will be deployed gradually as well as centralized CO2 utilization facilities. At a buoyant CCS economy, a combination of small and large facilities will be utilized to meet the annual demand of CO2 reduction. So far, this discussion has focused on examining the network structure (Configuration 1) obtained from using pipelines onshore and offshore as the only means of CO2 transport. The second network structure of interest (Configuration 2) studies how the arrangement of transportation links is affected by considering of utilizing all transportation alternatives. The resulting network structures during the two time periods of the planning interval (Figures 6−9) have very different locations of CCS facilities and transportation links than does Configuration 1. To illustrate this, consider both network configurations for the first and second time period as an example. During the first time period of Configuration 2 (Figure 6), one small scale MEA capture facility in POSCO # 1 unit is built in Pohang region to satisfy the CO2 reduction target, unlike Configuration 1. During the second time period of Configuration 2 (Figure 7), more centralized CO2 capture via only one medium-scale MEA facility presented, unlike Configuration 1. Another difference between Configurations 1 and 2 is that Configuration 2 uses railcars for short delivery distances onshore because of economical benefits. Configuration 1 has more capture facilities than does Configuration 2. The main reason behind this is the high cost of pipelines for short delivery distances onshore, although pipelines are preferable for large quantities and long distances onshore.2 Furthermore, Configuration 2 does not require extra transportation modes, i.e. tanker truck and tanker ship, in the optimal solution (Figures 6−9), because they do not offer financial benefits.

Figure 6. Optimal structure of case 2 for CCS infrastructure during the first time period.

Figure 7. Optimal structure of case 2 for CCS infrastructure during the second time period.

This sensitivity of the CCS infrastructure design to the allowed modes is an important result for policy makers: licensing efficient short distance modes onshore such as railcar enables a greater degree of capture centralization and locations than does licensing only long distance modes. This insight (well-known in location science and economic geography) is an important outcome of this modeling process. The values of the benefit, cost and profit terms that constitute the objective function of network Configurations 1 and 2 were summarized (Tables 11 and 12) for each time 2992



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Table 11. Benefits, Costs, and Profit for the First Network Configuration quantity time period, t

configuration 1 t1

benefits (million $·y−1) selling benefit of biobutanol selling benefit of green polymer 1288 total annual benefits (million $·y−1) 1288 costs (million $·y−1) capital cost capture, storage, sequestration facilities 34 utilization facilities 140 transportation modes 18 total capital cost 192 operating cost capture, storage, sequestration facilities 20 utilization facilities 605 transportation modes 2 total operating cost 627 total annual cost (million $·y−1) 819 profits (million $·y−1) total annual profit (million $·y−1) 469 Network wide profit ($·t CO2−1) 1690 network average profit (million $·y−1)

Figure 8. Optimal structure of case 2 for CCS infrastructure during the third time period.

t2

t3

t4

252 8000 8252

1143 8000 9143

1800 8000 9800

118 420 4 542

206

8

206

10 18

114 3891 9 4014 4556

133 4351 7 4491 4697

228 4690 19 4937 4955

3696 4446 1599 1014 3363.97

4845 606

Table 12. Benefits, Costs, and Profit for the Second Network Configuration quantity time period, t

configuration 1 t1

benefits (million $·y−1) selling benefit of biobutanol selling benefit of green polymer 1288 total annual benefits (million $·y−1) 1288 costs (million $·y−1) capital cost capture, storage, sequestration facilities 45 utilization facilities 140 transportation modes 9 total capital cost 194 operating cost capture, storage, sequestration facilities 19 utilization facilities 605 transportation modes 1 total operating cost 625 total annual cost (million $·y−1) 819 profits (million $·y−1) total annual profit (million $·y−1) 469 network wide profit ($· t CO2−1) 1690 network average profit (million $·y−1)

Figure 9. Optimal structure of case 2 for CCS infrastructure during the fourth time period.

t2

t3

t4

252 8000 8252

1143 8000 9143

1800 8000 9800

90 421 7 518

206

8

4 210

14 22

108 3891 12 4011 4529

126 4351 9 4486 4696

221 4690 15 4926 4948

3723 4447 1611 1015 3371.89

4852 607

shorter distances. This is different from the results of other mathematical models (i.e., deterministic static model, stochastic static model) in2,13 which the single-mode network of pipeline make a fairly well-established economic CCS infrastructure. To illustrate the capabilities of the proposed model compared to the other mathematical models, we performed a sensitivity analysis with the change in the levels of mandated reduction (Table 13). The difference of total network profit between the mathematical models for CCS infrastructure of each case ranged from −9.2 to −12.5% (Figure 10). The deterministic static and stochastic static model, regardless of the decision

period along with the total annual benefit, cost and profit of the network at each time period and the average annual profit of the network for the entire planning interval. The average annual profit of the multimode network (Configuration 2) is higher than that of the single-mode network (Configuration 1); considering the multimode network resulted in a 0.24% (∼$ 7.9 million) increase in total network profit. The considerable difference in the network profits indicates that the multimode network is more favorable than the single-mode one; pipelines, which are profitable for transportation across larger distances are thereby used in conjunction with railcars which are profitable for transportation of CO2 across 2993



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structure over time interval, resulted in 9.2% and 10.2% decrease in total network profit in case 1 (i.e., 10% of the mandated reduction of CO2 emissions) compared to the proposed deterministic dynamic model. Most of the decrease is a result of increases in facility capital cost and in transportation capital cost, which occur because the invariant decision structure over time enforce more CO2 capture, storage, sequestration facilities, and transportation modes to satisfy the mandated reduction of CO2 emissions during the planned period. As reduction target increased, the difference of total network profit the mathematical models for CCS infrastructure increased. In particular, the difference of total network cost the mathematical models for CCS infrastructure increased largely, but the one of total network benefit decreased slightly. This is because the limitation in total production capacity of the utilization facilities. When planning additional construction of utilization facilities as reduction target increases, the gap of total network profit the mathematical models for CCS infrastructure will increase more and more.

Table 13. Sensitivity Analysis Conditions Selected for the Case Studya description case

model b

levels of mandated reduction (%)

time (year)

1

deterministic static stochastic staticc deterministic dynamic (multiperiod)

10 10 10

2030 2030 2011−2030

2

deterministic staticb stochastic staticc deterministic dynamic (multiperiod)

20 20 20

2030 2030 2011−2030

3

deterministic staticb stochastic staticc deterministic dynamic (multiperiod)

30 30 30

2030 2030 2011−2030

a

Major feature of the mathematical models: deterministic static, CO2 emissions are constant over time; stochasitc static, uncertainties in the variation of CO2 emissions, that is, from −20% to 20% in the average emissions; Deterministic dynamic, variation of CO2 emissions over a long-term planning interval (Table 2). Common conditions: utilization of CCS, 30%, all CCS technologies (the case 2 in Table 1). bHan and Lee.2 cHan and Lee.13

6. CONCLUSIONS The model developed in this paper extends the one examined in ref 2 to include several features of great importance in the area of CCS infrastructure design and operation. These features include (i) different sizes of utilization, capture, storage, and sequestration facilities and (ii) evolution of CO2 reduction target over a long-term planning interval. The model described is driven by a predefined CO2 emissions profile over a 20-year planning interval. During each time period of the timing interval, the model was capable of predicting the investment required to establish the future CCS infrastructure. The model was also able to reveal the network configuration by a number of strategic decisions including (i) number, location, capacity, and type of utilization, capture, storage, and sequestration facilities; (ii) CO2 flow rate and type of transportation links to be established. These decisions were determined by formulating a model as an MILP problem. The optimization problem uses profit as a performance objective to predict the optimal design of the future CCS infrastructure configurations. To examine the feasibility of the derived model, two different CO2 network configurations were designed. These configurations consist of (i) a single-mode network using pipeline onshore and offshore; and (ii) a multiple-mode network using tanker trucks, railcars, and pipelines onshore, and tanker ships and pipeline offshore. It was applied to the east coast of Korea. The result obtained from of these networks illustrates that CO2 will be reduced during the introductory phase from a medium scale capture facility established in power plant as well as used from three large scale utilization facilities. As the reduction target rate increased, the vast demand of CO2 reduction was met by dispersed small to large-scale CCS facilities. Especially, disposal facilities such as depleted gas reservoir sequestration would be utilized to reduce CO2. Also, pipelines are preferable over a long-term planning interval, regardless of the sensitivity of the CCS infrastructure. The solution of the proposed model gives a good indication that the model considers the interactions among various components of the CCS infrastructure as well as the network evolution over a long-range planning interval. Also, more costeffective CCS technologies with scalable capacity in the construction of CCS infrastructure may bring many benefits.

■

APPENDIX Table A1 gives the model notation of the CCS infrastructue.

Figure 10. Comparison of profits, benefits, and costs of the mathematical models for CCS infrastructure. 2994



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Table A1. Model Notation of CCS Infrastructure indices c d e g g′ i j l m p s si sp t ALg max Ccapi,c,si,j min Ccapi,c,si,j

CCCi,c,si,j CCRf CCRl LR CUFi,e,p DWl Ei,si,sp,g,t FEl FPl GEl GScapi,s,g,t

Long,g′ LMRi,t Loffg,g′ LSFp,j LUTl max Mcapi,m,j min Mcapi,m,j

MCCi,m,j MEl

parameters max Pcape,p,j

type of capture facility pipeline diameter product form geographical region geographical region (g′ ≠ g) physical form of CO2 size of utilization, capture, storage and sequestration facilities type of transport mode type of intermediate storage facility type of utilization facility or production facility type of sequestration facility type of source industry source plant name time period of the planning interval parameters available land sizes on each region g maximum CO2 capture capacity of facility type c and size j of industry type si minimum CO2 capture capacity of facility type c and size j of industry type si capital cost of building CO2-capture facility type c and size j of industry type si capital charge rate of facilities − the rate or return required on invested capital cost capital charge rate of transport mode l − the rate or return required on invested capital cost learning rate−cost reduction as technology manufacturers accumulate experience CO2 use factor of product form e in utilization facility type p driver wage of transport mode l amount of CO2 emitted from source plant sp of industry type si in region g during time period t fuel economy of transport mode l fuel price of transport mode l general expenses of transport mode l available geological capacity for CO2 in physical form i sequestered by sequestration facility type s in region g during time period t average delivery distance between regions g and g′ onshore level of mandated requirement of reducing CO2 emissions during time period t average delivery distance from harbor region g onshore to sequestration region g′ offshore land size factor for utilization facility type p and size j load/unload time of transport mode l maximum storage capacity of facility type m and size j to store CO2 in physical form i minimum storage capacity of facility type m and size j to store CO2 in physical form i capital cost of establishing intermediate storage facility type m and size j storing CO2 in physical form i maintenance expenses of transport mode l

min Pcape,p,j

PCCe,p,j Q i,l max Q i,lmin max Scapi,s,j min Scapi,s,j

SCCi,s,j

km2 t CO2 y−1

SPl SSF

t CO2 y−1

TCapi,l $ Tt 0 ≤ CCRfacility≤ 1 TMAl TMCi,l

0 ≤ CCRpipeline ≤ 1

TPCapi,l,d

0 ≤ LR ≤ 1

TPICoffi,l,d t CO2·t−1 or t CO2·L−1 $ h−1 t CO2·y−1

TPIConi,l,d

km·L−1 $ L−1 $ y−1 t CO2·y−1

TPOCoffi,l,d

km·trip−1

UCCi,c,si,j

TPOConi,l,d

0 ≤ LMRi,t ≤ 1 UCCSi,t km·trip

−1

UMCi,m,j

km2

UPCe,p,j

h·trip−1 t CO2·y−1

USBe,p

t CO2·y−1

USCi,s,j

$

α

$·km−1

δ ηc 2995

maximum CO2 production capacity of facility type p and size j minimum CO2 production capacity of facility type p and size j capital cost of establishing utilization facility type p and size j producing product form e maximum flow rate of CO2 in physical form i transported by transport mode l minimum flow rate of CO2 in physical form i transported by transport mode l maximum CO2 sequestration capacity of facility type s and size j minimum CO2 sequestration capacity of facility type s and size j capital cost of establishing CO2 sequestration facility type s and size j average speed of transport mode l safety stock factor of CO2 inventory within a intermediate storage facility capacity of transport mode l to transport CO2 in physical form i. target amount of CO2 to be reduced by CCS facilities during time period t availability of transport mode l cost of establishing transport mode l to transport CO2 in physical form i capacity of pipeline with diameter d to transport CO2 in physical form i total capital cost of installing pipeline transport mode l with diameter d offshore transporting CO2 in physical form i total capital cost of installing pipeline transport mode l with diameter d onshore transporting CO2 in physical form i total operating cost of pipeline transport mode l with pipe diameter d offshore transporting CO2 in physical form i total operating cost of pipeline transport mode l with pipe diameter d onshore transporting CO2 in physical form i unit capture cost for CO2 captured by capture facility type c and size j in source industry si utilization of CCS as CO2 reduction technology during time period t unit storage cost for CO2 in physical form i stored by intermediate storage facility type m and size j unit production cost for product form e produced by utilization facility type p and size j unit selling benefit of product form e produced by utilization facility p unit sequestration cost for CO2 sequestered by sequestration facility type s and size j small number to limit the number of pipelines number of time periods CO2 capture efficiency of capture facility c

t·y−1 or L·y−1 t·y−1 or L·y−1 $ t CO2·y−1 t CO2·y−1 t CO2·y−1 t CO2·y−1 $ km·h−1 % t CO2·trip−1 t CO2·y−1 h·y−1 $ t CO2·y−1 $·km−1

$·km−1

$·km−1·t CO2−1

$ km−1·t CO2−1

$·t CO2−1 0 ≤ UCCSi,t ≤ 1 $·t CO2−1 $·t−1 or $·L−1 $·t−1 or $·L−1 $·t CO2−1

0 ≤ ηc ≤ 1



Article

Table A1. continued binary variables 1 if CO2 in physical form i is to be transported from region g to g′ by transport mode l during time period t, 0 otherwise continuous variables

Xi,l,g,g′,t continuous variables Ci,c,si,j,sp,g,t

FC FCC FOC GC LC MC Mi,m,j,g,t Pe,p,g,t Qi,l,g,g′,t QPi,l,g,g′,d,t Si,s,j,g,t TAB TAC TAPave TCC TOC TOCEP

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amount of CO2 in physical form i captured by capture facility type c and size j in source plant sp of industry type si in region g during time period t fuel cost for CO2 transportation facility capital cost facility operating cost general cost for CO2 transportation labor cost for CO2 transportation maintenance cost for CO2 transportation inventory of CO2 in physical form i stored by intermediate storage facility type m and size j in region g during time period t amount of product form e produced by utilization facility p in region g during time period t flow rate of CO2 in physical form i transported by transport mode l between regions g and g′ during time period t flow rate of CO2 in physical form i transported by pipelines with diameter d between regions g and g′ during time period t amount of CO2 in physical form i sequestered by sequestration facility type s and size j in region g during time period t total annual benefit total annual cost total average profit of the network over the entire planning interval transport capital cost transport operating cost total transportation operating cost excluding pipeline

t CO2·y−1

TOCP ug Ui,p,j,g,t

−1

$·y $·y−1 $·y−1 $·y−1 $·y−1 $·y−1 t CO2·y−1

ICi,c,si,j,sp,g,t IMi,m,j,g,t INTPoffi,l,g,g′,d,t INTPoni,l,g,g′,d,t IPe,p,j,g,t

tonne·y−1 or L·y−1 t CO2·y−1

ISi,s,j,g,t

t CO2·y−1

NCi,c,si,j,sp,g,t

t CO2·y−1

NMi,m,j,g,t

$·y−1 $·y−1 $·y−1

NPe,p,j,g,t NSi,s,j,g,t

$·y−1 $·y−1 $·y−1

NTPoffi,l,g,g′,d,t NTPoni,l,g,g′,d,t NTUoffi,l,t NTUoni,l,t

$·y−1 t CO2·y−1

investment of capture facility type c and size j capturing CO2 in physical form i in source plant sp of industry type si in region g during time period t investment of storing facility type m and size j storing CO2 in physical form i in region g during time period t investment of pipelines offshore during time period t investment of pipelines onshore during time period t investment of plants of type p and size j producing product form e in region g during time period t investment of sequestration facility type s and size j sequestrating CO2 in physical form i in region g during time period t number of capture facilities type c and size j capturing CO2 in physical form i in source plant sp of industry type si in region g during time period t number of intermediate storage facilities of type m and size j storing CO2 in physical form i in region g during time period t number of utilization facilities type p and size j of product for e in region g during time period t number of well or injection facilities of type s and size j sequestering CO2 in region g during time period t number of pipelines offshore number of pipelines onshore number of transport units offshore excluding pipeline number of transport units onshore excluding pipeline

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +82-54-279-2274. Fax: +82-54-279-5528. E-mail: iblee@ postech.ac.kr. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This paper was supported by the Korea Research Foundation Grant funded by the Korea Government (MOEHRD, Basic Research Promotion Fund) (KRF-2008-313-D00178).

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total transportation operating cost of pipeline number of regions visited after visiting region g amount of CO2 used by utilization facility p with size j in region g during time period t integer variables

REFERENCES

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Development of a Multiperiod Model for Planning CO2 Disposal and

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