A Multiobjective Optimization Approach for CCS Infrastructure

Article pubs.acs.org/IECR

A Multiobjective Optimization Approach for CCS Infrastructure Considering Cost and Environmental Impact Jae-Uk Lee, Jee-Hoon Han,* and In-Beum Lee Department of Chemical Engineering, POSTECH, Pohang, Korea ABSTRACT: In this study, we address the design of a carbon capture and storage (CCS) infrastructure with economic and environmental concerns. Given a set of available technologies to capture, sequestrate, and transport CO2, the problem consists of determining the optimal planning of the CCS infrastructure capable of satisfying a predefined CO2 reduction target. The planning task is formulated as a multiobjective mixed-integer linear programming (moMILP) problem, which simultaneously accounts for the minimization of cost and environmental impact. The environmental impact is measured through all contributions made by operation and installation of the CCS infrastructure. The emissions considered in the environmental impact analysis are quantified according to the principles of Life Cycle Assessment (LCA), specifically the Eco-indicator 99 method. The multiobjective optimization problem was solved by using the ε-constraint method. The capability of the proposed modeling framework is illustrated and applied to a real case study based on Korea, for which valuable insights are obtained.

1. INTRODUCTION Carbon capture and storage (CCS) is receiving increasing interest as a key technology for reducing greenhouse gas (GHG) emissions.1 A major challenge for the use of CCS is the need for a widespread infrastructure to capture, sequestrate, and transport CO2. As the requirement of reducing CO2 emissions grows, costeffective strategies should be found to construct the CCS infrastructure. Several papers have considered the design and operation of cost-effective CCS infrastructure, including a mathematical model for various activities such as capture, sequestration, and transportation of CO2,2−4 a stochastic model considering uncertainty in CO2 emission,5 and a multiperiod model which addresses the variation of CO2 emissions over a long time interval.6 Although CO2 emissions are reduced by operation of a CCS system, previous studies confirmed that large amounts of raw materials and energy are used and pollutant substances are emitted when the CCS system is established and operated.7−9 In other words, other environmental pollutions excepting global warming are caused by the CCS system. Thus, the concern of environment impact of the CCS system has been an important factor to design the overall CCS system. Several recent studies also indicate that both economic and environmental concerns have been essential decision-making factors in establishing investment strategies with planning a new process design. Hugo and Pistikopoulos proposed an environmentally conscious planning model of supply chain networks with multiobjective programming.10 Guillén-Gosálbez and Grossmann suggested a bicriterion optimization for planning of hydrogen supply chains with environmental and economic concerns.11 Cristóbal, J. performed a similar approach to compare carbon capture technologies considering economic and environmental criteria with multiobjective programming.12,13 In this work, the environmental effect of a whole CCS system is assessed by the following principles of Life Cycle Assessment (LCA) employed from Hugo and Guillén-Gosálbez’s works.10,11 © 2012 American Chemical Society

The two advantages of the LCA approach are that (i) it concerns the entire life cycle from CO2 capture procedures to CO2 storage procedures and (ii) it induces a damage model that cover the emissions released, raw materials extracted, and waste generated from the overall CCS infrastructure installation and system operation. Therefore, this study aims to address a holistic approach to suggest the optimal planning of the CCS infrastructure with environmental and economic concerns. Specifically, the main objective of this study is to develop a multiobjective mathematical model that considers the total cost and life cycle impact of CCS infrastructure simultaneously. Hence, the εconstraint method is also presented to expedite the search for the Pareto solutions of the model. First, we will state the formal definition of the problem. Then, the detailed mathematical model follows. Finally, the capability of the proposed model is illustrated through its application to a real case study based on Korea.

2. PROBLEM DESCRIPTION The objective of this paper is to address the optimal planning of a CCS infrastructure for reducing CO2 emissions with the goal of minimizing the total cost and life cycle impact simultaneously. This infrastructure network model includes three main components: capture facilities, sequestration facilities, and transport modes (see Figure 1.). The planning network includes a set of c facility types which capture CO2, and a set of s sequestration facilities where CO2 is sequestrated finally being delivered by a set of l transportation means to other sequestration facilities in other regions. All capture and sequestration types can be included in this superstructure. On the other hand, the only transport mode is the pipeline because it is more economical than Received: Revised: Accepted: Published: 14145

April 12, 2012 September 5, 2012 October 10, 2012 October 10, 2012 dx.doi.org/10.1021/ie3009583 | Ind. Eng. Chem. Res. 2012, 51, 14145−14157

Industrial & Engineering Chemistry Research

Article

Figure 1. CCS infrastructure planning superstructure.

other means.1 Specifically, this network planning superstructure is based on the work by Han and Lee,4 which proposed the design of a CCS infrastructure for Korea. The CCS technologies concerned in the superstructure can be established in a set g of potential regions which are distributed all over the nation of interest. Among these regions, the regions which have CO2 emission sources can have the CO2 capture facilities only. Similarly the CO2 sequestration facilities can be established in regions which can sequestrate CO2 geologically. The decision-maker must provide the technological capability of the CCS of each region. The environmentally concerned CCS infrastructure planning can be stated as follows: (1) The goal is to design an optimal CCS infrastructure configuration that minimizes the cost and environmental impact. The cost objective function includes the investment and operating costs. In contrast, the environmental impact objective function is based upon the impact from the entire life cycle of the CCS process over the entire planning horizon. The principles of the LCA approach are used in this model. (2) Given conditions are a fixed time horizon, total mandated reduction of CO2 over all the time period, investment costs, operating costs, the capacity limitation of each CCS technology, and its environmental data. (3) The major decisions are the number, location, type, and capacity of capture and sequestration facilities; the total amount of CO2 captured, transported and sequestrated in each region and the size and type of transportation means. The mathematical formulation proposed to solve this problem is described in the next section.

3.1. Total Annual Cost. The detailed explanations for the first objective and its constraints were described by Han and Lee,4 but those which are relevant to this part of the paper are summarized below. 3.1.1. Objective function. TAC, the total annual cost, is calculated as the sum of the capital installation costs of capture and sequestration facilities FCC and transportation modes TCC and the operation costs of the facilities FOC and the transportation modes TOC for the CCS infrastructure. (1)

TAC = FCC + TCC + FOC + TOC

FCC, the facility capital cost, is the total cost of building capture and sequestration facilities. FCC =

⎡ CCR

∑ ⎢⎢ g

facility

LR

⎣

∑ (∑ ∑ ∑ CCCi ,c ,si,sp ,gBCi ,c ,si,sp ,g i

c

si

sp

⎤

+

∑ SCCi ,sNSi ,s , g )⎥⎥ ⎦

s

(2)

TCC, the transport capital cost, is calculated as a sum of costs of establishing transportation modes through onshore TCConshore and offshore TCCoffshore. (3)

TCC = TCConshore + TCCoffshore

3. MODEL FORMULATION The mathematical formulation of the CCS infrastructure model will be presented as two objective functions and several constraints. The addressed model is based on the work in ref 4 in which the authors proposed a “deterministic” formulation for CCS infrastructure planning focused on economic concerns. Specifically, the mathematical formulation of this study extends the original one in order to include the environmental concerns. This consideration led to a multiobjective optimization approach to the problem and made a solution set of Pareto optimal points that show trade-offs between cost and environmental impact. The detailed model will be described below. The notation of the model is summarized in Table 1.

TCConshore =

⎛ CCR pipeline

∑ ∑ ∑∑∑⎜ i

l ∈ {pipe}

g

g′

d

⎝

LR

⎞ (TPICondLonl , g , g ′NTPoni , l , g , g ′ , d)⎟ ⎠

TCCoffshore =

⎛ CCR pipeline

∑ ∑ ∑∑∑⎜ i

l ∈ {pipe}

g

g′

d

⎝

LR

⎞ (TPICoff dLoffl , g , g ′NTPoffi , l , g , g ′ , d)⎟ ⎠ 14146

(4)

(5)

dx.doi.org/10.1021/ie3009583 | Ind. Eng. Chem. Res. 2012, 51, 14145−14157


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Table 1. Model Notation of CCS Infrastructure indices b1 b2 c d g g′ i k l n p s si sp x CCCi c si sp g CCRpipeline CCR

facility

Loffl g g′ Lonl g g′ LR MCCi m g SCCi s g TPICoffd TPICond TPOCoffd TPOCond UCCi c si UMCi m USCi s ωobCa1 c ωobTr1 l d ωobSq1 s υn x b1 ωibCa2 c

parameters ωiTr b2 l d

environment burdens from operation environment burdens from installation type of capture facility pipeline diameter geographical region geographical region (g′ ≠ g) physical form of CO2 technology set type of transport mode damage category type of utilization facility or production facility type of sequestration facility type of source industry source plant name impact category parameters capital cost of building CO2-capture facility type c capturing in source plant sp of industry type si in region g capital charge rate of pipelinesthe rate or return required on invested capital cost capital charge rate of facilitiesthe rate or return required on invested capital cost average delivery distance between regions g and g′ by transport mode l offshore average delivery distance between regions g and g′ by transport mode l onshore learning rate−cost reduction as technology manufacturers accumulate experience capital cost of establishing intermediate storage facility type m storing CO2 in physical form i in region g capital cost of establishing CO2 sequestration facility type s sequestrating CO2 in physical form i in region g total capital cost of installing pipeline with pipe diameter d offshore total capital cost of installing pipeline with diameter d onshore total operating cost of pipeline with pipe diameter d offshore total operating cost of pipeline with pipe diameter d onshore unit capture cost for CO2 captured in physical form i by capture facility type c in source industry si unit storage cost for CO2 in physical form i stored by intermediate storage facility type m unit sequestration cost for CO2 sequestered in physical form i by sequestration facility type s entry of emission inventory from operation b1 associated with the capture per one unit of CO2 by capture facility type c entry of emission inventory from operation b1 per one unit of CO2 mass transported one unit of distance by pipelines with diameter d entry of emission inventory from operation b1 associated with the sequestration of one unit of CO2 by sequestration facility type s damage factor of environment burden b1 in terms of damage category n and impact category x entry of emission inventory from installation b2 from installing one capture facility of type c

entry of emission inventory from installation b2 per unit of distance from installing pipelines with diameter d entry of emission inventory from installation b2 from installing one sequestration facility of type s damage factor of environment burden b2 in terms of damage category n and impact category x normalization factor for damage categories belonging to set n weighting factor for each normalized damage category n according to perspective categories r binary variables

ωiSq b2 s υn x b2 ηn ϑr n

BCi c si sp g Xi l g g′

$

NTPoni l g g′ d NTPoffi l g g′ d

km·trip−1

Ci c si sp g

0 ≤ LR ≤ 1 $ $

FCC FOC Mi m g

$·km−1

Qpipelinei l g g′ d

$·km−1

Si s g

$·km−1·t CO2−1 $ km−1·t CO2−1 $·t CO2−1

TAC TCC TCCoffshore TCConshore TOC TOCoffshore

$·t CO2−1 $·t CO2−1

TOConshore

kg·tCO2−1

IOkn x g

kg·km−1 ·tCO2−1

IIknx g

kg·tCO2−1 Dg n Eco99

kg kg

investment of capture facility type c capturing CO2 in physical form i in source plant sp of industry type si in region g 1 if CO2 in physical form i is to be transported from region g to g′ by transport mode l, 0 otherwise integer variables

NSi s g

0≤ CCRpipeline ≤1 0≤ CCRfacility ≤1 km·trip−1

kg·km−1

number of well or injection facilities of type s sequestering CO2 in region g number of pipelines with diameter d for transporting CO2 in physical form i between regions g and g′ onshore number of pipelines with diameter d for transporting CO2 in physical form i between regions g and g′ offshore continuous variables amount of CO2 in physical form i captured by capture facility type c in source plant sp of industry type si in region g facility capital cost facility operating cost inventory of CO2 in physical form i stored by intermediate storage facility type m in region g flow rate of CO2 in physical form i transported by pipelines with diameter d between regions g and g′ Amount of CO2 in physical form i sequestered by sequestration facility type s in region g total annual cost transport capital cost transport capital cost for CO2 offshore transport capital cost for CO2 onshore transport operating cost total transportation operating cost of pipeline offshore total transportation operating cost of pipeline onshore environment impact of operation of technology set k in terms of damage category n and impact category x in region g environment impact of installation of technology set k in terms of damage category n and impact category x in region g environment damage score of the damage category n in region g total environment impact score

t CO2·y−1

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

Impact·y−1

Damage·y−1 Score·y−1

kg

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The sequestration rate Si,s,g is bounded by the minimum sequestration capacity Scap mini,s and the and maximum sequestration capacity Scapmaxi,s:

The facility operating cost FOC is determined by multiplying the unit operating costs of capture and sequestration by the corresponding amounts of CO2: FOC =

Scapimin NSi , s , g ≤ Si , s , g ≤ Scapimax NSi , s , g ,s ,s

∑ ∑ (∑ ∑ ∑ UCCi ,c ,siCi ,c ,si,sp ,g g

+

i

c

si

sp

A minimum flow rate Q i,l and a maximum flow rate of CO2 Qmaxi,l are needed to justify the establishment of a transportation mode between two regions:

(6)

s

Q imin Xi , l , g , g ′ ≤ Q i , l , g , g ′ ≤ Q imax Xi , l , g , g ′ ,l ,l

Like in eq 3, the transport operating cost TOC is classified into operating cost of transport modes onshore TOConshore and offshore TOCoffshore.

g

g′

ug ′ − ug + nXi , l , g , g ′ ≤ n − 1

d

∀ i , l , g , g ′; g = 2, ···, n , g ′ = 2, ···, n; g ≠ g ′

Qpipelinei , l , g , g ′ , d TOCoffshore =

(16)

∑ ∑ ∑ ∑ ∑ TPOCond l ∈ {pipe}

(8)

l ∈ {pipe}

g

g′

∑ ∑ Xi , l , g , g ′ ≤ 1

d

i

Qpipelinei , l , g , g ′ , d

(9)

i

∀ g , g ′; g ≠ g ′ (18)

l

∑ ∑ Xi , l , g ′ , g ≤ 1

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

(17)

All transport modes with all physical forms of CO2 leaving or entering region g are bounded by the constraints:

∑ ∑ ∑ ∑ ∑ TPOCoffd i

∀ i , l , g , g ′; g ≠ g ′

The transportation of CO2 in physical form i must occur only from a source to a sequestration facility or utilization facility:

(7)

TOC = TOConshore + TOCoffshore

i

(15)

min

∑ USCi ,sSi ,s , g )

TOConshore =

∀ i, s, g

∀ g , g ′; g ≠ g ′

l

(19)

3.2. Total Environmental Impact. The environmental impact of a whole CCS system is estimated by principles of LCA (Figure 2). LCA consists of three steps as follows: Goal and

∑ ∑ ∑ ∑ LMR iUCCSiEi ,si,sp ,g i

si

sp

(10)

g

Mass balance of individual regions should consider rates of total annual capture Ci,c,si,sp,g, transport Qi,l,g,g′, and sequestration Si,s,g:

∑ ∑ ∑ Ci ,c ,si,sp ,g = ∑ ∑ (Q i ,l ,g ,g ′ − Q i ,l ,g ′ ,g ) c

si

+

l

sp

∑ Si ,s ,g

g′

∀ i, g (11)

s

Moreover, the total inventory Mi,m,g of CO2 in physical form i of all storage facilities in region g is a function of the total flow rate Qi,l,g,g′ of CO2 in physical form i leaving region g multiplied by a safety stock factor SSF:

∑ Mi ,m,g = SSF( m

∑

∑ Q i ,l ,g ,g ′)

Figure 2. Life cycle assessment procedure.

∀ i, g

l ∈ {truck,railcar,ship} g ′

Scope Definition, Inventory Analysis, and Impact Assessment. In the goal and scope definition step, system boundary and functional unit are determined. Next, in inventory analysis step, materials and energy uses of the system are investigated. In impact assessment step, the environmental impact is aggregated into one single score or calculated in several impact scores according to their categories. In this work, the Eco-indicator 99 method is used for estimating the total environmental impact score. It is categorized into (i) three main categories of damage indicators and (ii) eleven subcategories of impact indicators:

(12)

3.1.3. Capacity Constraints. The total amount of CO2 sequestered Si,s,g in all regions cannot be less than T:

∑ ∑ ∑ Si ,s ,g ≥ T i

g

(13)

s

All facilities and transportation modes must be constrained by upper and lower boundaries. Therefore, the capture rate Ci,c,si,sp,g is bounded by the minimum capture capacity Ccapmini,c,si,sp,g and the maximum capture capacity Ccapmaxi,c,si,sp,g of all facilities established in a particular region: Ccapimin BCi , c ,si,sp , g , c ,si,sp , g ≤

n ∈ 5: = {HH, EQ, RD]

≤ Ci , c ,si,sp , g

Ccapimax BCi , c ,si,sp , g , c ,si,sp , g

∀ i , c , si, sp , g

x ∈ ? := {HHca , HH ro, HH ri , HHcc , HH ir , HHod , EQ tx , EQ ae, EQ lu, RDdr , REdf ]

(14) 14148



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Figure 3. Eco-indicator 99 procedure.

where HH = damage category of human health; EQ = damage category of ecosystem quality; RD = damage category of resource depletion; HHca= carcinogenic emission impact of human health damages; HHro = organic emission impact of human health respiratory damages; HHri = inorganic emission impact of human health respiratory damages; HHcc = climate change impact of human health damages; HHir = ionizing radiation impact of human health damages; HHod = ozone depletion impact of human health damages; EQtx = ecotoxic emission impact of ecosystem quality damages; EQae acidification and eutrophication impact of ecosystem quality damages; EQlu = land use impact of ecosystem quality damages; RDdr = the impact of resource depletion of raw materials; and REdf = the impact of resource depletion of fossil fuel.14 The major advantage of Ecoindicator 99 is that the 11 categorized impact indicators are aggregated into three main damages and finally a single score finally, and the single score which can support an objective environmental assessment (Figure 3). For the computation of the single Eco-indicator 99 score, the three steps of LCA procedure are followed as mentioned above. These steps are described in detail in the next subsections. Goal and Scope Definition. The goal and system boundaries of LCA are identified and the impact categories are chosen in this stage. In our case, the goal is the LCA analysis of the entire CCS system. The system boundary is restricted to the CO2 capture, transport, and sequestration infrastructure (Figure 4). Applied to a “cradle-to-grave” analysis, the system starts from the CO2 feed gas including other gases in emission sources and ends with the delivery of CO2 to sequestration regions. The system includes materials and energy used for establishing the CCS infrastructure

Figure 4. System boundary for LCA of CCS infrastructure.

as well as for the operating one. All damage and impact categories are also considered. Inventory Analysis. The inventory analysis step uses the list of Life Cycle Inventory (LCI) such as the inputs and outputs of materials and energy to calculate the environmental impact. If one considers the set of k ∈ 2 technologies such as capture and sequestration, each of which relates to a region g through their CO2 flows, the value of impact indicators of technology set k k, Ig,x,n , can be calculated as a general expression.15 14149



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Figure 5. System boundary and inventory for LCA of CCS infrastructure.

I gk, x , n =

∑ vb ,n,xωbkMgk

These impacts include the energy usage (i.e., steam and electricity) and direct emission of pollutants to air, water, and soil. In the capture and sequestration of CO2, the CO2 flow is one unit of mass captured/sequestrated. In the transportation, the CO2 flow is one unit of mass transported per one unit of distance. Similarly, the second one is a model for installing facilities which consist of some binary or integer decision variables (BCi,c,si,Xsp,g, NTPoni,l,g,g′,d, NTPoffi,l,g,g′,d, NSi,s,g).

∀ n, x , k , g (20)

b

where (i) b ∈ B is the set of the life cycle inventory; (ii) vb,n,x is the damage factor that life cycle inventory b contributes to impact category x of damage category n; (iii) ωkb is the entry of emissions inventory b per 1 unit of CO2 flow of CCS technology k; and (iv) Mkg is the amount of CO2 flow required for technology k by region g (such as ton of CO2 captured, load and distance of substances transported). The Ikg,x,n values of Human Health damage indicator are expressed as Disability Adjusted Life Years (DALY). On the other hand, the Ecosystem Quality damage indicator is the Potentially Disappeared Fraction per square meter per year (PDF·m−2 y−1) and MJ is used for Resources Depletion damage indicator to represent the surplus energy needed for future resource extraction. In the context of the CCS infrastructure system boundary, the generation of emission inventories depends on the amount of captured, transported, and sequestrated CO2. Moreover, the emission inventories are concerned with installing a facility of certain technology in a possible region (Figure 5). Thus, eq 20 is converted into two types as follows. The first type is an impact indicator model for operating a CCS system in which it is expressed as a function of some continuous decision variables, Ci,c,si,sp,g, Qpipelinei,l,g,g′,d, Si,s,g of the previous model. IOCa n,x ,g =

1

c

si

1

∑ ∑ ∑ ωobSq,sυn,x ,b Si ,s ,g 1

i

s

b1

1

∀ n, x , g

2

2

b2

g′

d

∀ n, x , g

∑ ∑ ∑ ωibSq,sυn,x ,b NSi ,s ,g

∀ n, x , g

2

s

2

(25)

(26)

b2

Equations 24−26 represent the score of impact indicators associated with installation of the capture, transport, and sequestration facilities. These impacts include the raw material uses (i.e., iron and concrete), land uses, and energy uses (i.e., diesel fuel and electricity). Impact Assessment. In this step, the individual indicators in the set of impacts categories x are aggregated into three indicators in the set of damage categories n. Using the normalization factor ηn and weighting factor ϑr,n, the single Eco-Indicator 99 score is obtained. Dg , n = ηn ∑ ∑ ∑ IOkn , x , g + IIkn , x , g ,

g′

Lonl , g , g ′ + Qpipelinei , l , g , g ′ , dLoffl , g , g ′)

(24)

l

i

1

b1

2

b2

sp

Lonl , g , g ′ + NTPoffi , l , g , g ′ , dLoffl , g , g ′)

IISq n,x ,g =

(21)

l

si

∑ ∑ ∑ ωibTr,l ,dυn,x ,b ∑ ∑ (NTPoni ,l ,g ,g ′ ,d i

∑ ∑ ∑ ωobTr,l ,dυn,x ,b ∑ (Qpipelinei ,l ,g ,g ′ ,d i

IOSq n,x ,g =

IIkn , x , g =

1

∀ n, x , g

2

c

∀ n, x , g

b1

sp

∑ ∑ ∑ ∑ ∑ ωibCa,cυn,x ,b BCi ,c ,si,sp ,g i

∑ ∑ ∑ ∑ ∑ ωobCa,cυn,x ,b Ci ,c ,si,sp ,g i

IOnTr, x , g =

IICa nx , g =

x

(22)

Eco99 =

∀ n, x , g

k

∀ n, g (27)

∑ ∑ ∑ ϑr ,nDg ,n g

(23)

v

r

n

(28)

Here, the normalization factor is to convert each damage value with a different unit to a dimensionless value considering the region. The weighting factor reflects the importance of each

Equations 21−23 represent the impact score associated with operating the capture, transport, and sequestration facilities. 14150



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4. CASE STUDY The case study proposed by Han and Lee4 is used to illustrate the applicability of our multiobjective modeling framework. Although the detailed design problem and input data are described in the original work, some minor details and changes must be discussed in the commented next paragraph. The case considers CO2 mitigation in Korea in 2020. The Korean government announced a plan to reduce CO2 emissions by 30% from the current levels. Moreover, we consider gas-fired and coal-fired power plants (Table 2) in Korea as major CO2

damage value. Both factors are determined from three different perspectives based upon the principles of Cultural Theory. For instance, the Hierarchist perspective weights the human health and ecosystem quality each 40% and the resource depletion 20%.14 Therefore, the optimal solutions for economic and environment concerns will be provided by two objective functions, TAC and Eco99. The detailed multiobjective optimization method will be described in the next section. 3.3. Multiobjective Optimization Method. The overall multiobjective formulation can be expressed as follows: ⎧ ⎫ ⎪ TAC(y , Y , Z ) = total annul cost ⎪ ⎬ min ⎨ ⎪ y , Y , Z⎪ ⎩ Eco99(y , Y , Z) = Eco‐Indicator 99 ⎭

Table 2. Estimated CO2 emissions of each plant in 2020 region Busan Chungnam

s.t. capture facility capacity constraints ⎫ h(y , Y , Z) = 0 ⎪ overall mass balance constraints ⎬ g (y , Y , Z) ≥ 0 ⎪ ⎭ transportation constraints sequestration constraints

Gangwon

y ∈ , Y ∈ {0, 1}, Z ∈ 

where y represents the continuous variables of the problem (the amount of CO2 captured, transported, and sequestrated), Y denotes the binary variables (the installation of CO2 capture facilities), and Z is the integer variables representing the number of installation of sequestration facilities and transportation modes of each type selected. The multiobjective mixed integer linear programming (moMINP) problem can be solved with a set of Pareto optimal solutions to show trade-offs between the environmental and economic concerns in the analysis. The Pareto optimal solutions represent different CCS infrastructure configurations with capacity expansion plans and combinations of economic performance and environment damage. This type of problem is treated with two typical methods: the weighted-sum method and ε-constraint method.16 The ε-constraint method is proper for our case, which is rigorous for the nonconvex case. Therefore, the moMILP is expressed via the ε-constraint method, and the solutions are obtained for different values of the parameter ε.17

Gyeonggi

Gyeongnam

Incheon

Jeonbuk Jeonnam seoul Ulsan Busan

min TAC(y , Y , Z)

a

y,Y ,Z

emission source type

emission plant name

CO2 emissionsa (tCO2·y−1)

gas gas coal coal coal coal coal gas coal coal gas gas gas gas coal coal coal gas gas coal coal gas gas gas gas coal gas gas gas

KOSPO1 KOMIPO8 KOWEPO4 KOMIPO5 KEWESPO5 KOMIPO6 KOMIPO7 KOSPO4 KEWESPO4 KOSEP5 KOWEPO3 KOSEP3 KEWESPO3 KOSEP4 KOSPO5 KOSPO6 KOSEP7 KOWEPO1 KOSEP1 KOSPO2 KOSEP2 KOMIPO2 KOMIPO3 KOMIPO4 KOWEPO5 KEWESPO6 KOMIPO1 KEWESPO2 KOSPO1

8 597 058 6 207 077 33 570 239 2 520 465 30 558 157 28 999 240 840 155 3 742 870 2 645 610 1 199 135 667 705 2 746 008 2 584 264 1 512 27 083 384 9 027 795 28 022 995 7 975 978 13 132 559 9 418 250 13 132 559 333 019 2 635 129 2 663 707 3 633 927 4 224 707 750 254 3 221 690 8 597 058

Han and Lee3

s.t. capture facility capacity constraints h(y , Y , Z) = 0 ⎫ ⎪ overall mass balance constraints ⎬ g (y , Y , Z) ≥ 0 ⎪ ⎭ transportation constraints

emission sources because their CO2 emissions will be a considerable portion of the total CO2 emissions at time.18 Several capture, transport, and sequestration technologies were selected to test the proposed model (Table 3).

sequestration constraints

Table 3. Types of Emission Sources, Capture, Transport and Sequestration Technologies of the Case Study

Eco99(x , X , N ) ≤ ε

ε̲ ≤ ε ≤ ε ̅ y ∈ , Y ∈ {0, 1}, Z ∈ 

classification

type

emission source

gas-fired power plant coal-fired power plant the absorption using aqueous monoethanolamine (MEA) liquid CO2 via pipeline depleted gas reservoir (DGR) saline aquifer storage (SAS)

capture transport sequestration

The major advantage of this approach is that the decisionmaker can investigate trade-offs and select a particular CCS infrastructure plan that satisfies his/her purpose from the set of Pareto solutions. 14151



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Table 4. Environment Impact Data of CCS Operation technology capture

type coal−MEA

gas−MEA

transport sequestration

pipe (16 in) pipe (21.6 in) DGR SAS

damage

impact

value

unit

human health ecosystem quality resource depletion human health ecosystem quality resource depletion resource depletion resource depletion resource depletion resource depletion

respiratorya acidificationa fossil fuelsa respiratoryb acidificationb fossil fuelsc fossil fuelsa fossil fuelsa fossil fuelsd fossil fuelsd

3.582 × 10−5 4.241 34.36 4.349 × 10−5 2.801 216.0 0.02929 0.03954 0.8844 0.2066

DALYs·tCO2−1 PDF·m2·yr·tCO2−1 MJ·tCO2−1 DALYs·tCO2−1 PDF·m2·yr·tCO2−1 MJ·tCO2−1 MJ·tCO2−1 MJ·tCO2−1 MJ·tCO2−1 MJ·tCO2−1

a

Estimated based on Koornneef and Keulen et al.9 bEstimated based on Odeh and Cockerill.19 cEstimated based on IPCC.1 dEstimated based on Wildbolz.8

Table 5. Environment Impact Data of Capture Facility Installation

a

emission source

power plant

region

damage

impact

valuea

unit

gas−MEA gas−MEA coal−MEA coal−MEA coal−MEA gas−MEA gas−MEA gas−MEA gas−MEA coal−MEA coal−MEA coal−MEA gas−MEA coal−MEA coal−MEA gas−MEA gas−MEA coal−MEA coal−MEA gas−MEA gas−MEA gas−MEA coal−MEA coal−MEA gas−MEA gas−MEA coal−MEA gas−MEA

KEWESPO2 KEWESPO3 KEWESPO4 KEWESPO5 KEWESPO6 KOMIPO1 KOMIPO2 KOMIPO3 KOMIPO4 KOMIPO5 KOMIPO6 KOMIPO7 KOMIPO8 KOSEP1 KOSEP2 KOSEP3 KOSEP4 KOSEP5 KOSEP7 KOSPO1 KOSPO2 KOSPO4 KOSPO5 KOSPO6 KOWEPO1 KOWEPO3 KOWEPO4 KOWEPO5

Ulsan Gyeonggi Gangwon Chungnam Jeonnam seoul Incheon Incheon Incheon Chungnam Chungnam Chungnam Chungnam Incheon Incheon Gyeonggi Gyeonggi Gangwon Gyeongnam Busan Incheon Gangwon Gyeongnam Gyeongnam Incheon Gyeonggi Chungnam Jeonbuk

Resources depletion Resources depletion Resources depletion Resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion resources depletion

Minerals Minerals Minerals Minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals minerals

9553.8697 7663.5932 7845.5139 90619.7217 12528.3005 2224.8661 987.5625 7814.4325 7899.18015 7474.3983 85996.7785 2491.4661 18406.9867 38944.3919 38944.3919 8143.2424 4.4838 3556.0155 83101.7397 25494.4368 27929.6685 11099.4206 80315.3384 26771.7805 23652.6341 1980.0684 99552.0023 10776.3519

MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility MJ per a capture facility

Estimated based on Koornneef and Keulen et al.9

On the other hand, we estimated the data of the environment inventories associated with the installation and operation of the CCS infrastructure from several sources in the literature which performed LCA of CCS systems.1,7−9,19 Moreover, the impact factor of each environmental burden was found in the Ecoindicator 99 method,14 assuming the weighting and normalizing set of the Hierarchist perspective. The input data for the case study of the LCA analysis are summarized as the following four assumptions (Tables 4−7): (i) A capture facility of a certain power plant in a certain region utilizes heat energy supplied from that power system only. (ii) The operation of transportation and sequestration considers only the electric energy consumption. (iii) For a certain capture facility, the amount of installation material changes linearly with its capacity. (iv) Installation of

Table 6. Environment Impact Data of Transport Facility Installation type liquid CO2 via pipeline

diameter (in) 16

21.6

a

14152

damage

impact

valuea

unit

ecosystem quality resource depletion ecosystem quality resource depletion

land use

68941.3

minerals

2977.5

PDF·m2· yr·km−1 MJ·km−1

land use

93070.8

minerals

4019.7

PDF·m2· yr·km−1 MJ·km−1

Estimated based on Wildbolz.8



Article

Table 7. Environment Impact Data of Sequestration Facility Installation

a

type

damage

impact

valuea

unit

DGR SAS

ecosystem quality ecosystem quality

land use land use

18876 18876

PDF·m2·yr PDF·m2·yr

Estimated based on Wildbolz.8.

capture facilities does not use land because they are established within existing power plants. In fact, there is a limit to the system boundary of the LCA we can consider because the case study in our previous work4 was adopted as a benchmark. For example, this model cannot be compared to other cases with no CCS system or a system which uses CO2 for tertiary oil recovery. Moreover, the LCA of extraction and transport of coal and gas, power generation and transmission and power plant construction is not included. These limitations will be supplemented in future works.

5. RESULTS AND DISCUSSION The proposed multiobjective model is solved by the ε-constraint method for optimal planning of the CCS infrastructure of Korea in 2020 with minimizing total cost and Eco-indicator 99 score. The model was implemented in GAMS and solved using the CPLEX 9.0 solver on an Intel 2.80 GHz machine. All solutions were obtained quickly with low optimality gaps. In all case Table 8. Capital, Operating Costs, and Eco-indicator 99 Damage Score of CO2 Infrastructure Planning for Two Extreme Cases

Figure 6. Breakdown of cost for the extreme Pareto solutions.

CO2 reduction target: 1.5 × 107 tCO2· y−1) (million $/y)

minimize cost

Capital Cost (million $/y) capture facilities 609.7 sequestration facilities 15.34 transportation modes 67.46 total capital cost 692.1 Operating Cost (million $/y) capture facilities 345.08 sequestration facilities 28.36 transportation modes 36.3 total operating cost 409.75 total cost 1102 Eco-indicator 99 Impact (Points) human health, capture human health, transport human health, sequestration total human health

minimize Eco99 1457.53 15.34 56.02 1529 138.37 28.36 27.96 194.7 1723

16 953 200

13 964 800

16 953 200

13 964 800

eco quality capture (million points) eco quality transport eco quality sequestration total ecosystem quality

3 277 600 5 179 200 2 800 8 459 600

4 963 200 3 927 600 2 800 8 893 600

resources capture (million points) resources transport resources sequestration total resources

77 095 000 5 560 000 315 600 82 970 600

12 266 800 3 760 000 315 600 16 342 400

108 384 600

39 202 100

total environment impact, Eco99

Figure 7. Breakdown of Eco99 score for the extreme Pareto solutions.

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Figure 8. Minimize cost solution.

studies, the number of constraints, integer variables, and continuous variables are 5621, 838, and 15261 with zero optimality gap. Each solving time is less than one second. First, the total cost and Eco-indicator 99 score results of two extreme case studies were provided (Table 8). In the case of minimization of total cost, the total cost is $ 1,102 million and the Eco99 score is 1.083 × 108. On the other hand, the total cost is $ 1,723 million and the Eco99 score is 3.92 × 107 in the case of minimization of Eco99 score. These results imply that a trade-off exists between total cost and Eco99 score. The detailed Pareto solution set will be presented later. Moreover, Figure 6 shows that the largest portion of total cost is the capital cost of CCS capture facilities in both cases, and the operating cost of capture facilities is the second largest portion. Similarly, Figure 7 shows that the largest damages are also caused by capture facilities in

both cases. These results show that the overall CCS infrastructure planning is sensitive to the economic and environmental level of CO2 capture technologies. Figures 8 and 9 illustrate the optimized CCS configurations of these cases. The configurations show the number and type of capture and sequestration facilities installed in each region along with the selected transportation modes between them. Note that the former case mainly uses aqueous monoethanolamine (MEA) capture facilities in gas power plants, whereas they are installed in coal power plants only in the latter case. This implies that the gas−MEA facility is better than coal−MEA facility economically. This is because the plant size and CO2 emission of a coal power plant are larger than those of a gas power plant. The larger plant needs a larger capture facility, which causes the total capital cost to be more expensive. On the other hand, the coal−MEA facility 14154



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Figure 9. Minimize Eco99 score solution.

mainly for their capacity. This is because the cost and Ecoindicator 99 score of transportation modes and sequestration facilities are regarded as less important factors than those of capture facilities, as mentioned before. Applying the multiobjective optimization approach to the case study results in the set of trade-off solutions presented in Figure 10. This figure clearly shows that the trade-off exists between total annual cost and environment impact score. Specifically, the solutions are classified into four regions: A, minimum cost solution, has CO2 captured from the gas−MEA facilities only and uses the 21.6 in. pipeline and DGR as the major means of transportation and sequestration; B uses coal−MEA and gas− MEA facilities to capture increase similar amounts of CO2. The 21.6 in. pipelines and DGR are also mainly used; C-1 to C-3 increase CO2 captured in coal−MEA facilities to decrease

is more eco-friendly than the gas−MEA facility. This result makes sense because (i) the energy consumption, specifically heat energy, is the main contributor of Eco-indicator 99 scores and (ii) the damage factor of energy uses from gas-resources for operating the MEA facility is 17.5 times higher than that of coalresources.14 On the other hand, the optimal solution for sequestration regions and transportation modes has no significant differences between these cases. Both cases prefer the 21.6 in. pipeline as the means of delivery to transport large quantities of CO2 and the 16 in. pipeline to transport moderate amount of CO2. Similarly, the depleted gas reservoir (DGR) sequestration region in Korea, which has more available sequestration capacities than the saline aquifer storage (SAS) region4 is mainly selected. This implies the optimal transportation and sequestration means are selected 14155



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Figure 10. Pareto set.

The capabilities of the proposed model were demonstrated through a case study based on the real scenario of Korea in 2020. First, simulation results show that improving the capture technology economically and environmentally is more important than others. Specifically, the CO2 capture in coal-fired power plants is more preferred than in the gas-fired power plant since the coal−MEA capture facility is a more eco-friendly solution. This is because energy consumption, specifically heat energy, for CO2 capture processes is the main contributor of Eco-indicator 99, and energy uses in a gas−MEA capture facility are more significant. Furthermore, the Pareto solutions which show trade-offs between cost and environmental impact suggest meaningful insights into the planning problem that may lead to improvements of costs and environmental impacts. These decision strategies are recommended to adopt more sustainable alternatives for the CCS infrastructure.

environment impact and use 16 in. pipelines more and more; and D, the minimum Eco99 solution, has CO2 captured in coal− MEA facilities only and transports CO2 through both 16 in. and 21.6 in. pipelines and sequestrates it in DGR regions. These results show that the type of emission source dominates the CCS infrastructure planning. Moreover, whereas the solution curve is smooth in the A to B region, the C to D region has a significant slope. These results suggest that MEA facilities in coal power plants should be operated rather than gas plant facilities to reduce the environment impact (planning solution from B to D). On the other hand, replacing more than 50% of coal−MEA plants with gas− MEA plants seems to be a bad choice since this solution increases the total environment impact to a large extent without reducing the total cost. For example, although the total cost of solution A is only 3% lower than that of B, its environment impact score is 150% that of B.

■

6. CONCLUSION This paper addressed the economically and environmentally considered CCS infrastructure planning model. The model supports the decisions of selecting optimal CO2 capture, transport, and sequestration technologies, allocating these selected technologies to potential regions and determining their operating capacity to satisfy the CO2 reduction target. The planning task was formulated as a multiobjective mixed-integer linear programming problem that finds minimized cost and environmental impact. The environmental impact was measured by applying the Eco-indicator 99 method, which is a LCA measure method. The ε-constraints method was applied to confirm the trade-off between the two objective functions.

AUTHOR INFORMATION

Corresponding Author

*Tel.: +82-54-279-5967. Fax: +82-54-279-5528. E-mail: jhhan@ postech.ac.kr. Notes

The authors declare no competing financial interest.

■

REFERENCES

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