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공학석사학위논문

Integrated Simulation andOptimization for the Whole Chain

of CCS

CCS전공정통합시뮬레이션및최적화

2014년 8월

서울대학교대학원

화학생물공학부

박태균

Abstract

Integrated Simulation andOptimization for the Whole Chain

of CCS

Taekyoon Park

School of Chemical and Biological Engineering

The Graduate School

Seoul National University

Operation pressure of distillation column is one of the key variable for op-

timizing the required energy in a CCS process. It affects the steam drag

point in power plant, the regeneration energy in capture process and the

compression energy in liquefaction process. A new algorithm, which is less

dependent on simulation, for determining optimal stripper pressure for CCS

process using MEA as an absorbent is proposed based on the integrated sim-

ulation model. Total energy required is represented as a function of the pres-

sure based on the equivalent work. The results show that the compression

work can be reduced at high pressure while that for reboiler increases and

the total energy can be represented as a decreasing function with the stripper

pressure. The evaluated optimal pressure decreases as the terminal pressure

increases, showing the crucial condition for determining operation pressure

of stripper depends on the terminal pressure of liquefaction process. It is

i

also shown that a general analytical solution for optimal pressure including

both the capture and the liquefaction process cannot be made through dif-

ferentiation based on Abel Ruffini theorem. The total energy required in the

possible range of the pressure can be estimated directly using approximation

with given input variables.

Keywords: CCS, Simulation, Optimization, Integration, Algorithm

Student Number: 2012-23265

ii

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . 1

2. Carbon Capture and Sequestration/Storage(CCS) . . . 3

2.1 Concept of CCS . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Current Status of CCS . . . . . . . . . . . . . . . . . . . . 5

2.3 Necessity of the Integrated Simulation . . . . . . . . . . . . 6

3. Integrated Simulation Model . . . . . . . . . . . . . . . . 7

3.1 Power plant . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Capture process . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3 Compression and liquefaction process . . . . . . . . . . . . 12

3.4 Transmission and storage process . . . . . . . . . . . . . . . 13

4. Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 15

4.1 Input and output analysis . . . . . . . . . . . . . . . . . . . 16

4.2 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . 17

4.3 Selection of the key manipulated variable . . . . . . . . . . 18

5. Optimization Algorithm . . . . . . . . . . . . . . . . . . . 19

5.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . 20

5.2 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.2.1 Reboiler duty . . . . . . . . . . . . . . . . . . . . . 21

iii

5.2.2 Compression work . . . . . . . . . . . . . . . . . . 27

5.2.3 Regression analysis for temperature . . . . . . . . . 29

5.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.3.1 Case I. Ship transmission . . . . . . . . . . . . . . . 31

5.3.2 Case II. Pipeline transmission . . . . . . . . . . . . 34

5.4 Generallization . . . . . . . . . . . . . . . . . . . . . . . . 35

5.5 Analytical solution . . . . . . . . . . . . . . . . . . . . . . 38

6. Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

6.1 Comparison with other studies . . . . . . . . . . . . . . . . 42

6.2 Significance and limitations . . . . . . . . . . . . . . . . . . 43

7. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . 44

8. Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . 46

iv

List of Figures

Figure 1. Concept of CCS . . . . . . . . . . . . . . . . . . . . . 4

Figure 2. PFD of the power plant . . . . . . . . . . . . . . . . . 8

Figure 3. PFD of the capture process . . . . . . . . . . . . . . . 10

Figure 4. PFD of the compression and liquefaction process . . . 12

Figure 5. PFD of the transmission and the storage process . . . . 13

Figure 6. Integrated process model of CCS . . . . . . . . . . . . 14

Figure 7. Input and output analysis on CCS process . . . . . . . 16

Figure 8. Sensitivity analysis . . . . . . . . . . . . . . . . . . . 17

Figure 9. Heat of vaporization regressed with temperature . . . . 24

Figure 10.Evaluated value of energy . . . . . . . . . . . . . . . . 32

Figure 11.Evaluated value of energy . . . . . . . . . . . . . . . . 33

Figure 12.Evaluated value of energy . . . . . . . . . . . . . . . . 34

Figure 13.Comparison of different shape of the total energy be-

tween two cases . . . . . . . . . . . . . . . . . . . . . 36

Figure 14.Optimal stripper pressure and optimal energy evaluated 37

Figure 15.Comparison of the results . . . . . . . . . . . . . . . . 42

v

List of Tables

Table 1. Coal Composition For Power Plant Model . . . . . . . . 9

Table 2. Flue Gas Composition . . . . . . . . . . . . . . . . . . . 9

Table 3. The Composition of the Captured CO2 . . . . . . . . . . 11

Table 4. The Output Data for the Compression and Liquefaction

Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Table 5. The Output Data for Transmission and Storage Model . . 13

Table 6. The Value of Constants for Estimating the Heat of Reaction 23

vi

Chapter 1

INTRODUCTION

Carbon capture and storage/sequestration (CCS) technology has been

studied for reducing carbon dioxide (CO2) emission in order to manage

global warming issue. Generally, it is composed of 5 stages: power plant,

capture process, compression/liquefaction process, transmission process and

storage. According to the type of each stages, various combination can be

possible. Among these combinations, CCS chain with conventional coal

power plant and capture unit using aqueous monoethanolamine (MEA) is

typical and widely studied.

Although the necessity of CCS technology has been increased, the eco-

nomic feasibility of CCS is so low that it is difficult to apply the concept of

CCS to real industry resulting in the Unites States and China’s withdrawl

from the Kyoto protocol. To overcome this economic obstacle, various stud-

ies have been conducted. The majority of them focused on improving the ex-

ising design of capture process and compression/liquefaction process where

over 90% of the total energy are consumed[1][2][3][4][5][6]. Babatunde

suggested an alternative model for capture unit to reduce the required energy

in capture process[7]. It is reported that the adjustment of the compression

1

ratio can reduce the compression energy in transmission process by Ung[8].

However, the economic feasibility should be accomplished considering

the linkage of the entire process in CCS because there is no guarantee that

the optimal design for part of CCS is still optimal when the whole process

are integrated. Because it is difficult to make a simulation model which con-

tains the whole chain of CCS, only a small number of studies conducted a

global optimization by iterative simulation based on the commercial sim-

ulation software with extensive computation [9]. In this case, a significant

dependency on the specific simulation software exists and it is impossible

to observe various scenarios with different options because of its low con-

vergence and complex computations.

To overcome these obstacles, the integrated simulation model for the

whole chain of CCS is developed with a high convergence performance and

a fast calculation speed in this study. Also, a new algorithm for determin-

ing the optimal pressure of stripper is first suggested to minimize the total

energy in power plant, capture unit and compression/liquefaction process.

Because this algorithm is not dependent on the specific software, one can

easily estimate the total energy requirement and determine the optimal op-

erating pressure of stripper in various scenarios of CCS.

This paper is composed of three parts. In process and model description

section, the integrated simulation model and a detailed process to develop

the optimization algorithm are explained. With this algorithm, the total en-

ergy required and the optimal pressure is obtained with two different cases

in results section. Finally, the significance and the limitations of this study

is addressed in the conclusion.

2

Chapter 2

Carbon Capture andSequestration/Storage(CCS)

In this chapter, general description of the CCS is treated. The current

status of CCS technology is followed by the necessity of the integrated sim-

ulation model for CCS process.

3

2.1 Concept of CCS

Carbon capture and storage (CCS) is the process of capturing waste

CO2 from various sources, transporting it to a storage site. The purpose of

CCS is to prevent the release of CO2 into the atmosphere for mitigating the

global warming and ocean acidification issue.

Usually CCS is composed of 5 stages:power plant, capture process,

compression/liquefaction process, transmission process and storage process.

When the flue gas emitted from the power plant is sent to the capture pro-

cess, CO2 is separated by a high concentration by using absorbent or mem-

brane. The captured CO2 is compressed and liquefied for the transmission

using the pipeline or the ship. Finally, the liquefied CO2 is stored at CO2

reservoir normally an underground geological formation. The schematic di-

agram of possible CCS system is shown in Figure 1[10].

Figure 1: Concept of CCS

4

2.2 Current Status of CCS

Although the necessity of CCS technology has been increased, CCS

does not become widely commercialized due to its low economic feasibility.

It is possible to reduce CO2 emission from a conventional power plant to the

atmosphere by 80-90% when CCS is applied, however, the power generation

decreases by 30-40% at the same time[11]. It may increase the fuel needs of

a coal power plant by 25-40%[12]. For this reason, the United States has not

ratified the Kyoto Protocol since 2001 and Canada declared the withdrawal

from the Kyoto Protocol in 2011.

In addition to the economic feasibility, the safety issues still remain.

Storage of CO2 has a risk of leak into the atmosphere as well as the unknown

impact on the surrounding environment. To overcome these obstacles, there

have been many researches on optimizing the CCS process to minimize the

energy required. Also there has been an effort to ensure the operating data of

the CCS process which is equal to or greater than the pilot scale for practical

application and demonstration of CCS.

5

2.3 Necessity of the Integrated Simulation

To minimize the energy required for CCS process and find the optimal

design, it is inevitable to build a simulation model for CCS process. Since

CCS process has a high nonlinearity and many recycle streams, optimum

values for specific parts of the CCS process is usually not optimal in the

whole chain of CCS. It means that the integrated simulation model is needed

to evaluate the impact of certain variable in CCS process and to minimize

the total energy requirement. Because of complexity and nonlinearity of

CCS process, slow convergence and low computational speed remains as an

obstacle which leads to only a small number of the integrated models are

developed. The integrated simulation model must have these features:High

convergence, fast calculation speed, reasonable error and robustness.

6

Chapter 3

Integrated Simulation Model

Pro/II with Soave-Redlich-Kwong (SRK), Non-Random Two Liquid

(NRTL) and Benedict-Webb-Rubin-Starling (BWRS) model was employed

for simulation. The SRK equation is used for gaseous components while

NRTL and BWRS models are chosen for CO2 capture process and steam

cycle in power plant, respectively. The SRK equation is commonly used for

predicting the behavior of CO2 mixture at high pressure[8]. The simulated

power plant is a conventional coal power plant with 550MW power gen-

eration using Illinois No.6 bituminous coal. 30wt% of monoethanolamine

(MEA) is used as an absorbent in CO2 capture process to remove 90% of

CO2 in the flue gas emitted from the power plant. Compression and liq-

uefaction process is composed of a series of compressors to achieve the

given terminal pressure. During the liquefaction process, the water content

is maintained below 50 vppm and it is lower than 500 vppm reported by

Aspelund and Jordal[14]. In case of transmission process, only the trans-

mission using pipeline is simulated and the condition reported by Ung is

used. The location of reservoir for CO2 storage is assumed to be at 2000m

below the sea level.

7

3.1 Power plant

Figure 2: PFD of the power plant

Conventional coal power plant was designed with 550 MW power ca-

pacity. Illinois No.6 coal was simulated as a solid material and it was mixed

with process water. The particle size of the coal is set to be normally dis-

tributed and its composition is shown in Table 1. Basic input variables are

obtained from [15].

8

Table 1: Coal Composition For Power Plant Model

Boiler, steam turbine chain, feed water heater, and condenser were in-

cluded. Steam turbines are composed of 2 high pressure(HP), 2 intermediate

pressure(IP) and 5 low pressure(LP) steam turbines while showing 46.4% of

net efficiency without capture process. Flue gas composition is shown in Ta-

ble 2.

Table 2: Flue Gas Composition

9

3.2 Capture process

Figure 3: PFD of the capture process

There are three types of capturing CO2:wet process, dry process and

membrane process. Since capture process using dry absorbent or membrane

are not commercialized to cover the large scale of power plant, wet process

is selected in this integrated model and CO2 in the flue gas is captured by us-

ing 30 wt% MEA. The initial pressure of absorption column(absorber) and

regeneration column(stripper) were 1 bar and 1.5 bar respectively. With the

amine package, the specification for CO2 capture performance is set to be

90%.Energy for regenerating MEA was calculated as 3.9 GJ/tonCO2. The

composition of the captured CO2 is shown in Table 3.

10

Table 3: The Composition of the Captured CO2

11

3.3 Compression and liquefaction process

Figure 4: PFD of the compression and liquefaction process

Multistage compression was tentatively designed with 4 compressors.

Triethylene glycol (TEG) was used to control the water concentration be-

low 50 vppm for preventing hydrate formation[16]. The initial pressure and

temperature condition of product from liquefaction process were 10 bar and

198K respectively. Table 4 shows the ] and liquefaction model correspond-

ing with the result from [17].

Table 4: The Output Data for the Compression and Liquefaction Process

12

3.4 Transmission and storage process

Figure 5: PFD of the transmission and the storage process

After the liquefaction process, CO2 rich gas is compressed to 100 bar

for the pipeline transmission. It is assumed that CO2 reservoir is located at

2000m below sea level. The output data for transmission and storage model

is shown in Table 5.

Table 5: The Output Data for Transmission and Storage Model

13

Figure 6: Integrated process model of CCS

14

Chapter 4

Simulation Results

Simulations were performed based on the developed integrated model

prior to optimization. First, the important variables that interacts with each

other in CCS process are identified through input and output analysis. Sec-

ond, the most sensitive variable to changes in the given input value is deter-

mined. Finally, the key variable to be optimized for minimizing the energy

required is selected based on theses results.

15

4.1 Input and output analysis

The whole chain of CCS is composed of 5 stages:power plant, capture

process, compression/liquefaction process, transmission process and storage

process. Figure 7 shows the major input and output variables in the chain of

CCS. The figure number refers to the sequential flow. When the coal type

and power capacity is given, CO2 concentration and the amount of emission

can be obtained from the flue gas. This flue gas is an input variable for the

capture process and if we set the absorbent, the capture ratio, the operating

pressure and the lean amine loading(LAL), now the pressure and the tem-

perature of captured CO2 can be estimated. After setting the transmission

method, the liquefied CO2 is obtained and the total energy required and the

life span of system can be measured if the location and capacity of the reser-

voir is fixed. In the purple box, you can see the reverse influencing variables

and the results.

Figure 7: Input and output analysis on CCS process

16

4.2 Sensitivity analysis

The power generation varies with the power demand. For example,

There is a lot of demand for power generation in midsummer and midwinter

while relatively small demand occurs in spring and autumn. Assuming this

scenario, the amount of coal was varied by 5%. The resulting regeneration

energy, CO2 emission, compression energy and capture ratio are presented

in Figure 8. The regeneration energy showed the most significant changes

followed by CO2 emission, capture ratio and compression energy. Sudden

reduction of feed gas could lead to higher regeneration energy. Meanwhile,

the changes of compression energy and capture ratio were not significantly

affected by the feed change. Based on these results, the most sensitive part

of the process was concluded to be the capture process.

Figure 8: Sensitivity analysis

17

4.3 Selection of the key manipulated variable

It is important to select the key manipulated variable in optimization.

The selection of the correct variable can lead to maximum effect with min-

imal effort so that the variable should be selected carefully. Based on the

input and output analysis followed by the sensitivity analysis, the operat-

ing pressure of stripper in capture process is selected as the key variable.

Approximately about 90% of total energy is consumed in capture process

to regenerate the absorbent, MEA. To cover the energy of the reboiler, the

steam is dragged from the power plant which leads to the loss in the power

generation. If we increase the stripper pressure, the reboiler energy can be

reduced because the desorption reaction is the endothermic reaction and it

is advantageous to reduce the compression energy in the compression and

liquefaction process. However, the high temperature condition in the strip-

per causes the heat degradation and requires the high quality steam that

increases the cost for make-up MEA and the loss in power generation re-

spectively. Here a method for determining the optimal operating pressure of

the stripper is needed.

18

Chapter 5

Optimization Algorithm

In this chapter, new algorithm for determining the optimal operating

pressure of the stripper is explained. After the problem is defined, formula-

tions is conducted using the concept of the equivalent work. The total energy

required is represented as a function of the stripper pressure and the optimal

pressure to estimate the optimal pressure which gives the minimal energy.

19

5.1 Problem definition

To find the optimal stripper pressure, three objectives are set.

(1) Derive the total energy requirement in terms of the stripper

pressure

Here the total energy requirement is a sum of the net power generation

considering the regeneration energy for capture process and the compres-

sion energy in the compressiona and liquefaction process.

(2) Find the optimal pressure with which the total energy can be

minimized

After representing the total energy as a function of the pressure, the

optimal pressure can be determined using the direct calculation according

to the different value of the pressure.

(3) Find the general analytical solution for the optimal pressure

The general analytical solution for the optimal pressure may be derived

if we differentiate the objective function, the total energy, with respect to the

pressure.

20

5.2 Formulations

Our previous work[13] suggested that the most sensitive part in the

MEA based CCS chain was the capture process. In capture process, the

pressure of stripping column is the key variable which highly affects the

total energy consumption of CCS chain. It interacts with the energy loss in

power plant for making up the reboiler duty in capture process as well as the

energy requirement for compression and liquefaction process. To determine

optimal pressure of stripper analytically, it is necessary to express required

energy in terms of pressure. It has been reported that the comcept of equiv-

alent work is useful in estimating the energy loss in power plant due to the

heat energy for reboiler in capture process and it is adopted in this study[7].

In this study, three general basic assumptions were used. First, both liquid

and vapor phases are well mixed in stripping column. Second, the reboiler

is in vapor/liquid equilibrium. Finally, the amount of vaporized amine is

negligible.

5.2.1 Reboiler duty

The reboiler duty required for stripping is composed of three terms: the

heat required to desorb CO2 (Qdesorption), the heat required to vaporize the

water(QH2Ovaporization) and the sensible heat requirement(Qsensibleheat).

Qreboiler = Qdesorption +QH2Ovaporization +Qsensibleheat (5.1)

21

5.2.1.1 The heat of desorption (Qdesorption)

The heat of desorption can be calculated by differentiating the equation

of VLE expression for MEA with respect to 1T if we assume the desorption

reaction as an equilibrium reaction rather than the rate based reaction. Al-

though the electrolyte-NRTL model is the most appropriate thermodymic

method for estimating the temperature profile in absorber and stripper, it

requires complex computations resulting in the low convergence. Since the

exact temperature profile is not the focus of this study, the MEA desorption

reaction is treated as an equilibrium reaction to build a fast optimization al-

gorithm for optimal stripper pressure where the heat of desorption is more

critical than the exact temperature profile in stripper. If we represent the par-

tial pressure of CO2 in terms of temperature, it is easy to estimate the heat

of desorption.

lnPCO2 = a+bγ+cT+d

γ2

T2 + eγ

T2 + fγ

T(5.2)

− ∆HR

= c+2dγ2

T+2e

γ

T+ f γ (5.3)

∆ H is the heat of desorption, R is the universal gas constant, T is the

temperature in K, γ is the rich amine loading and c, d, e, f are constants

which can be obtained from regression using the simulation results and the

experimental data. The value of constants are shown in Table 6[11].

22

Table 6: The Value of Constants for Estimating the Heat of Reaction

23

5.2.1.2 The heat of vaporization (QH2Ovaporization)

The heat of vaporization for H2O varies with the temperature of the

reboiler. To prevent MEA degradation issue, operation in a small range of

temperature, 373.15K to 408.15K, is permitted and it is acceptable to use a

regressed function. Five values of the heat of vaporization are used to regress

∆H with temperature whose values are between 373.15 K and 408.15 K. The

heat of vaporization for H2O is decreasing as the temperature rises and the

result is shown in Figure 9.

Figure 9: Heat of vaporization regressed with temperature

24

5.2.1.3 The sensible heat (Qsensibleheat)

The sensible heat is the energy for heating the liquid mixture to the

reboiler temperature and it is necessary to consider this term because the

inlet temperature is usually not same as that of the reboiler. The sensible

heat can be expressed as follows:

Qsensibleheat =LCp∆T

nCO2

(5.4)

∆T is assumed to have a constant value of 10 K and the value of the

heat capacity, Cp, is obtained from the simulation results which almost un-

changed in the temperature and loading value change. Here, L is the flowrate

of the inlet to the stripper and nCO2 is the number of moles of captured CO2.

It is possible to expect that the value of the sensible heat will be a constant

if L and nCO2 are fixed since we set the value of ∆T to be 10 K.

25

5.2.1.4 Equivalent work

In order to calculate the reboiler duty, part of which can be supplied

by the steam dragged from power plant, it is necessary to adopt the concept

of equivalent work. It is advantegeous to estimate the loss in power plant

quickly without iterative simulation using commercial simulation software.

The heat required for the stripper can be converted to the equivalent work

using following equation.

Weq,reboiler = 0.75×Qreboiler ×(Treboiler +10−313.15)

(Treboiler +10)(5.5)

Here, the value of 0.75 means the turbine efficiency and 10 is the re-

boiler approach temperature. Sink temperature is assumed to be 313.15 K.

26

5.2.2 Compression work

Energy required for compression process varies with the possible pro-

cess configurations. To simplify the optimization algorithm, we assumed a

series of N compressors with a constant flow rate in which the liquid loss

that occurs after the captured CO2 is neglected. In this case, compression

work per a mole of CO2 can be obtained as follows:

Wcomp =µkRTin

nCO2(k−1)η(

n∑i=1

pi+1

pi−n) (5.6)

Here, µ is the inlet flowrate to the compression process, which has the

same value as the outlet flowrate from the top of the stripper. k is defined as

a heat capacity ratio of the captured gas mixture. R is the gas constant and

Tin is the temperature of the inlet flow to the compression process while η is

the compression efficiency of each compressors which is assumed to have a

same values.

5.2.2.1 Number of compressors

In order to evaluate the compression work, we need to specify the ter-

minal pressure, maximal compression ratio and efficiency. Two cases are

considered; for ship transportation, 10 bar is set as a terminal pressure while

100 bar is chosen for pipeline transmission. Maximal compression ratio and

efficiency are set as 2.5 and 85%, respectively. To determine the number of

compressors, n, we use the following method with the floor function, [x].

27

(1) Determine x as a real number

Since the maximum value of compression ratio is set to 2.5, it is pos-

sible to find an approximated value of the number of compressors when the

stripper pressure and the terminal pressure are given.

p× (2.5)x = pterminal (5.7)

Real number x is obtained and it is necessary to correct that value be-

cause the number of compressors must be a natural number.

(2) Determine n as an integer

if x is not a integer, n = [x]+1 = [log2.5pterminal

p]+1]

if x is a integer, n = x = log2.5pterminal

p(5.8)

Here, [x] is a floor function. If we define n as a sum of [x] and 1 when

the x is not an integer, it is easy to find an integer n which automatically

satisfies two conditions:the compression ratio has a value of 2.5 or less and

the number of compressors is a natural number. If the value of x is already

an integer, further addition of 1 is not needed. Because it is an extremely

rare case, the second case will be ignored in the subsequent discussion.

(3) Find the compression ratio θ

After determining n, the number of compressors, only the simple cal-

culation is remained.

28

p×θn = pterminal (5.9)

θ = lognpterminal

p(5.10)

5.2.2.2 Compresion work

As a result, compression work can be expressed by p and pterminal as.

µkRTin

nCO2(k−1)η(

n∑i=1

pi+1

pi−n)=

µkRTin

nCO2(k−1)η([log2.5

pterminal

p]+1)(logn

pterminal

p−1)

(5.11)

5.2.3 Regression analysis for temperature

Total energy can be expressed as a sum of the equivalent work for re-

boiler and compression work.

Etot =Weq,reboiler +Wcomp (5.12)

If we expand the individual terms, the total energy is obtained as follows:

Etot = 0.75× [−R(c+2dγ2

T+2e

γ

T+ f γ)+

nH2O

nCO2

∆H(T )+LCp∆T

nCO2

]×

(T +10−313.15)(T +10)

+µkRTin

nCO2(k−1)η([log2.5

pterminal

p]+1)(logn

pterminal

p−1)

(5.13)

In this equation, the total energy is represented as a function of two key

variables:the pressure and the temperature of the stripper. It is necessary

to eliminate the temperature term in order to make the total energy as a

29

function of the pressure. To find optimal pressure that minimizesl Etot , it is

necessary to map T as a function of P, the pressure of the reboiler. Since it is

impossible to have analytical expression for T as a function of P, empirical

relationship is constructed using operational or simulation data. This study

uses simulation data. Given the values of pressure ranged from 1 bar to

2.5 bar and rich amine loading ranging from 0.45 to 0.49, and obtains the

temperature as a function of pressure. Process flow diagram is shown in

Figure 2. Since temperature dependence onthe rich amine loading (RAL)

is sufficiently small compared to that on the pressure, temperature can be

represented only by pressure term and neglecting RAL.

T (p,γ)≈−4.54p2 +34.3p+73.7(0.45 ≤ γ ≤ 0.49) (5.14)

At this point, we can have a significant result which can reduce the degree of

freedom and complexity due to the combination of the different conditions

of the flue gas and MEA solution. In other words, it is possible to repre-

sent the infinitely many cases as a value of RAL based on the fact that the

value of RAL is in the range between 0.45 and 0.52 when MEA is used to

capture CO2. Also the regressed relationship between the stripper tempera-

ture and pressure shows that the effec of RAL on the temperature of stripper

can be neglected, guaranteeing the versatility of the suggested optimization

algorithm.

30

5.3 Case study

To test the suggested algorithm, two cases are considered depending

on the method of transmission of captured CO2. Thje first case is the ship

transmission where the terminal pressure of liquefaction process is set to be

10 bar in order to fulfill the appropriate condition for shipping. Transmission

using the pipeline is selected as the second case and the terminal pressure is

assumed to be 100 bar.

5.3.1 Case I. Ship transmission

In case of using ship to transfer the captured CO2, the transmission

pressure is usually in the range between 7 bar and 20 bar to prevent boil off

gas and 10 bar is chosen in this study[8]. Based on the Eq. 5.13, the reboiler

duty, the equivalent work for reboiler, the compression energy and the total

energy required are evaluated with the different values of stripper pressure

and are shown in Figure 10. As the stripper pressure rises, the reboiler duty

increases while the equivalent work for reboiler and the compression en-

ergy show reverse trends. The total energy required, which is the sum of the

Weq,reboiler and Wcompression, is obtained as a monotonically decreasing func-

tion without any relative minimum value in given range. It means that high

pressure is favorable in saving the total energy.

31

Figure 10: Evaluated value of energy

To analyze these trends, it is necessary to observe each part of the total

energy. According to Eq. 5.13, total energy is composed of 4 parts: CO2

desorption energy, H2O vaporization energy, the sensible heat and the com-

pression work. Since the rate of CO2 desorption reaction increases as the

temperature rises, high pressure of the stripper is favorable in lowering des-

orption energy. Also as the temperature of stripper rises, the energy for H2O

vaporization decreases since the heat of vaporization decreases. Because we

set ∆T =10, sensible heat is independent of the stripper pressure showing

constant value of 31.42 kJ/gmol of CO2. The equivalent work for reboiler,

which is the function of the reboiler duty and the stripper temperature, in-

creases because the increase in temperature term ( (T+10−313.15)(T+10) ) dominates

the decrease in the reboiler duty (Qreboiler). In evaluating the compression

energy, n and θ play an important role. The value of the integer n changes

from 4 to 3 at the pressure larger than 1 bar resulting in a small value of theta

32

and the compression ratio leads to a smaller abount of energy for compress-

ing the captured CO2. Because the amount of reduced compression work

is larger than that of equivalent work for reboiler, the total energy which is

the sum of these two values decreases as the pressure rises. These different

trends of each part are shown in Figure 11.

Figure 11: Evaluated value of energy

33

5.3.2 Case II. Pipeline transmission

In the case II, the value of pterminal is set as 100 bar. Same trends are

observed except the total energy. The shape of the total energy has a con-

vex form which is different from that for case I. As the terminal pressure

increases, the compression work rises about 220% compared to case I. Be-

cause of this increase, the total energy shows the minimum value of 64.00

kJ/gmoles of CO2 at 2.4 bar. Figure 12 shows the plots of each energy term.

Figure 12: Evaluated value of energy

34

5.4 Generallization

Since there is no relative minimum, it is impossible to obtain the value

of optimal pressure in case I. The total energy is a form of decreasing func-

tion in pressure and it can be minimized at pressure as high as possible.

However, the optimal value of operation pressure is near 2.4 bar in case II,

where the terminal pressure is 100 bar and the total energy has a convex

form. This means the total energy changes according to the value of the ter-

minal pressure. As the terminal pressure rises, the energy required to com-

press captured CO2 increases contrary to that for the reboiler which remains

in constant value. As a result, the decrease in compressing work cannot sur-

pass the increase in the equivalent work for reboiler as the stripper pressure

rises, changing the form of the total energy from a monotonically decreas-

ing function to a convex function in pressure. It is easy to find the difference

between two cases and the different shape of total energy is shown in Fig-

ure 13. The value of optimal pressure decreases as the terminal pressure

increases as shown in Figure 14. Also the optimal energy tends to increase

because the increase in the value of the terminal pressure cause the amount

of compression energy to increase significantly. Based on this trends, one

can expect that the optimal pressure will vary with respect to the value of

the terminal pressure.

35

Figure 13: Comparison of different shape of the total energy between twocases

36

Figure 14: Optimal stripper pressure and optimal energy evaluated

37

5.5 Analytical solution

Equation (13) can be modified as follows after switching [log2.5pterminal

p ]

to (log2.5pterminal

p ) for differentiation. The primary reasons for observing dEtotdP

instead of handling dEtot are small error and simplicity in calculation. Since

we have some approximated term, the absolute value of Etotal might have

low reliability and we need to focus on dEtotdP rather than Etotal itself. Also if

we make a simple form of poptimal as a function of input data, it is easy to

find a reasonable initial value when we design CCS process.

Replacing T (p) with (−4.54p2 +34.3p+73.7) in equation (19) gives

fully expanded equation for total energy in terms of pressure. Setting dEtotdP =

0, poptimal can be obtained, however, because of its complexity and nonlin-

earity, it is impossible to get explicit solution.

f (p) = 0.75× [R(2dγ2 +2eγ)

1T 2

dTd p

+d∆H(T )

dTdTd p

nH2O

nCO2

+LCp

nCO2

dTd p

]×

(Treboiler +10−313.15)(Treboiler +10)

+0.75×Qreboiler ×d[ (Treboiler+10−313.15)

(Treboiler+10) ]

dTdTd p

+µkRTin

nCO2(k−1)η[

−1pln2.5

(lognptotal

p−1)+(log2.5

ptotal

p+1)×

(−ln(n)

p+ ln

ptotal

p1

npln(2.5))

1(ln(n))2 ] (5.15)

38

For evaluating analytic solution of poptimal , it is necessary to convert

logarithm term to polynomial form using the Taylor series.

ln(1+x)≈ x (5.16)

Here we only consider the 1st term of the Talor series for reducing

complexity. As a result, f (p) can be represented as a fractional function

with polynomial form.

f (p) = 0.75× [R(2dγ2 +2eγ)

1T 2

dTd p

+d∆H(T )

dTdTd p

nH2O

nCO2

+LCp

nCO2

dTd p

]×

(Treboiler +10−313.15)(Treboiler +10)

+0.75×Qreboiler ×d[ (Treboiler+10−313.15)

(Treboiler+10) ]

dTdTd p

+µkRTin

nCO2(k−1)η[

−1pln2.5

(ln2.5−1)+(ln(ptotal)− (p−1)

ln2.5+1)(

−ln(ptotal)− (p−1)pln2.5

+ ln(ptotal)−(p−1))(1

( ln(ptotal)−(p−1)ln2.5 +1)pln(2.5)

)(ln2.5

(ln(ptotal)− (p−1)))2]

(5.17)

39

Since T (p) and ∆H(T ) are 2nd order polynomial in a single variable p,

the degree of polynomial equation g(p) = 0, which is the equivalent trans-

form of f (p) = 0 is more than 5 and general solution cannot be obtained

explicitly according to Abel-Ruffini theorem. This shows that it is impossi-

ble to make an analytic general solution for poptimal including all possible

cases through differentiation while we can calculate the expected value of

total energy required using Eq.5.13 with given input variables.

40

Chapter 6

Verification

This chapter describes the validation of the proposed algorithm in this

study. Three different cases will be considered and the significance and the

limitations of the suggested algorithm will be covered.

41

6.1 Comparison with other studies

To verify the algorithm, three different cases are selected. Data for case

I is obtained from Babatunde and that for case II is from P.Galindo[7][9].

And the new data is generated by using the integrated simulation model in

this study for case III. Figure 15 shows the difference between the data from

other study and and the result from the suggested algorithm and the error is

less than 5%.

Figure 15: Comparison of the results

42

6.2 Significance and limitations

The suggested algorithm is independent of the simulation software. It

means that users are not need to do build a complex integrated model for

the whole chain of CCS. The total energy and the optimal pressure can be

easily verified in a short period of time when the 7 input variables are given.

There are also several limitations. This algorithm can be used only for

CCS process using 30 wt% MEA solution as the absorbent. Also the value

of RAL should be in the range of 0.45 to 0.49. Finally, the energy for the

transmission and the storage process is not considered in the calculated re-

sult.

43

Chapter 7

Concluding Remarks

This article proposes the algorithm for evaluating the total energy re-

quired and determining optimal operation pressure of stripper with given

input values in CO2 capture and liquefaction process. The regression of the

temperature as a function of the pressure enables to represent the total en-

ergy requirement in terms of the stripper pressure, which is the key variable

in whole CCS chain, resulting in determining tendency of total energy with

the change of operation pressure. As the pressure of the stripper increases,

the desorption energy, the vaporization energy and sensible heat decreases

and the reboiler duty, which is the sum of them, decreases likewise. How-

ever, the equivalent work for compensating the reboiler duty increases be-

cause the rise of the operation pressure leads to high temperature in reboiler,

requiring high pressure steam dragged from power plant. In mathematical

point of view, the equivalent work is composed of two different parts: the re-

boiler duty (Qreboiler) and the fractional function of the reboiler temperature

( (Treboiler+10−313.15)(Treboiler+10) ). Since the increase in the fractional function of the tem-

perature overwhelms the decrease in the reboiler duty, the equivalent work

rises as the operation pressure increases. Meanwhile, the compression work

44

is reduced as the pressure rises and the value of the total energy required, the

sum of the equivalent work for reboiler and compression work, is obtained

as a decreasing function which does not have relative minimum value under

the condition of 1 bar ≤ p ≤ 2.5 bar, showing that it is favorable in sav-

ing energy in high pressure. However, as the value of the terminal pressure

(pterminal) increases, the optimal pressure where the relative minimum of the

total energy occurs decreases. It is shown that the value of optimal pressure

is 2.4 bar when that of terminal pressure is 100 bar. Using the Talor series,

the total energy required is transformed to the polynomial function of p and

it is revealed that the general solution of optimal pressure cannot be obtained

explicitly through differentiation according to Abel-Ruffini theorem while

it is possible to determine the expected value of total energy required and

the optimal pressure through direct calculation using our suggested equation

less dependent on simulation. Our equation model shows the error less than

5% to the result from another researches.

45

Chapter 8

Nomenclature

∆T=temperature approach in cross exchange [K]

γ = CO2 loading [mol CO2 /(mol MEA+)]

η=efficiency of compressor

µ=inlet flowrate to the compression process [gmoles/s]

θ=compression ratio

Cp = heat capacity of liquid [kJ/mol-K]

Etot=total energy required [kJ/gmol of CO2]

∆H=heat of absorption/desorption [kJ/gmol CO2]

k=heat capacity ratio of the outlet flow from the condensor

n=number of compressors required

nCO2=mole of CO2 [molde]

nH2O=mole of H2O [moles]

PCO2=equilibrium partial pressure of CO2 [bar]

pi=pressure of the inlet flow to ith compressor [bar]

pterminal=terminal pressure [bar]

Qreboiler=reboiler duty [kJ/gmol CO2]

Qdesorption=heat of desorption of CO2 [kJ/gmol of CO2]

46

QH2Ovaporization=heat of H2O vaporization [kJ/gmol of CO2]

Qsensibleheat=sensible heat required to heat rich solution to reboiler tem-

perature [kJ/gmol of CO2]

R=universal gas constant [J/K-mol]

T=temperature [K]

Tin=temperature of the inlet flow to the compression process [K]

Treboiler=temperature of reboiler

Weq,reboiler=equivalent work for reboiler [kJ/gmol of CO2]

Wcomp=isentropic work of compression [kJ/mol of CO2]

47

참고문헌

[1] Polasek JC; Bullin JA; Donnelly ST, ”Alternative flow schemes to re-

duce capital and operating costs of amine sweetening units”, energy

processing. Canada. 1982; 74(5): 45–50.

[2] Aroonwilas A. Evaluation of Split-Flow Scheme for CO2 Absorption

Process Using Mechanistic Mass-Transfer and Hydrodynamic Model.

In: 7th International Conference on Greenhouse Gas Control Tech-

nologies. Rubin ES, Keith DW, Gilboy CF. Vol 1: Peer-Reviewed

Papers and Plenary Presentations. IEA Greenhouse Gas Programme.

Cheltenham, UK; 2004.

[3] Oyenekan BA,Rochelle GT. Energy performance of stripper configu-

rations for CO2 capture by aqueous amines. Ind Eng Chem Res. 2006;

45(8): 2457–2464.

[4] Jassim MS,Rochelle GT. Innovative absorber/stripper configurations

for CO2 capture by aqueous monoethanolamine. Ind & Eng Chem Res.

2006; 45(8): 2465–2472.

[5] Seeyub Yang;Ung Lee;Youngsub Lim;Yeong Su Jeong;Jeongnam

Kim;Chiseob Lee;Chonghun Han, ”Process Design and Cost Estima-

tion of Carbon Dioxide Compression and Liquefaction for Transporta-

tion”, Korean Chem. Eng. REs, 2012, 50(7), 988-993

48

[6] Jaeheum Jung;Youngsub Lim;;Yeong Su Jeong;Ung Lee;Seeyub

Yang;Chonghun Han, ”CO2 Capture Process using Aqueous Mo-

noethanolamine (MEA): Reduction of Solvent Regeneration Energy

by Flue gas Splitting”, Korean Chem. Eng. REs, 2011, 49(6), 764-768

[7] Babatunde A. Oyenekan; Gary T.Rochelle, ”Alternative Stripper Con-

figurations for CO2 Capture by Aqueous Amines”, AIChE J, 2007, 53:

3144–3154,

[8] Lee, U.; Yang, S.; Jeong, Y.; Lim, Y.; Lee, C.; Han, C. ”Carbon Diox-

ide Liquefaction Process for Ship Transportation”, Ind. Eng. Chem.

Res. 2012, No.51, 15122-15131.

[9] P. Galindo Cifrea; K. Brechtela; S. Hochb; H. Garcı́ac; N. Asprionc; H.

Hasseb; G. Scheffknechta, ”Integration of a chemical process model in

a power plant modelling tool for the simulation of an amine based CO2

scrubber”, Fuel, 2009, 88, 2481–2488

[10] ”Overview”, Retrieved on May 03, 2014,

http://www.co2crc.com.au/aboutccs

[11] Freguia S. Modeling of CO2 Removal from Flue Gas with Mo-

noethanolamine. University of Texas, Austin. 2002. M.S. Thesis

[12] IPCC special report on Carbon Dioxide Capture and Storage. Pre-

pared by working group III of the Intergovernmental Panel on Climate

Change. Metz, B., O. Davidson, H. C. de Coninck, M. Loos, and L.A.

Meyer (eds.). Cambridge University Press, Cambridge, United King-

dom and New York, NY, USA, 442 pp

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[13] Park, T.; Lee, S.; Kim, S.; Lee, U.; Lee, J.; Han, C. ”In-

tegrated Simulation and Optimization for Carbon Capture and

Storage System”, International Science Index Vol:7, No:10, 2013

waset.org/Publication/17201

[14] Aspelund, A.; Moelnvik, M. J.; De Koeijer, G., “Ship Transport of

CO2:Technical Solutions and Analysis of Costs, Energy Utilization,

Exergy Efficiency and CO2 Emissions”, Chem. Eng. Res. Des. 2006,

84(9), 847-855.

[15] JieXiong; Haibo Zhao; Meng Chen and Chuguang Zheng, “Simulation

Study of an 800MW Oxy-combustion Pulverized-Coal-Fired Power

Plant”, Energy & Fuels, 2011, 2405-2415.

[16] H. Li; J. Jakobsen; J. Stang, Hydrate formation during CO2 trans-

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trol, 2010, 4, 5, 857-864.

50

초록

이산화탄소포집공정에서의분리탑운전압력은 CCS공정에소

요되는전체에너지를최적화하기위한핵심변수이다.운전압력은발

전소에서의증기추출지점,포집공정에서의재생에너지그리고액화

과정에서의압축에너지에영향을미친다.본연구에서는이산화탄소

흡수제로 MEA를사용하는 CCS공정에서의분리탑압력의최적값을

결정하기위한새로운알고리즘이독자적인 CCS전공정통합모델을

바탕으로제안되었다. CCS공정에서소요되는전체에너지는적절한

가정과근사를통하여최초로운전압력의함수로표현되었고,그개형

은최종압력값에따라단조감소함수의형태를보이기도하고볼록한

형태로나타날수있음을밝혔다.또한최적압력의해석적인해는아

벨 Ruffini 이론에 따라 분리된 형태로 구해질 수 없다는 것을 보였다.

본 연구에서 제안된 알고리즘을 활용하여, 운전 가능한 압력 범위에

서 CCS공정에서소요되는총에너지와최적압력은상용시뮬레이션

소프트웨어를거치지않고빠르고쉽게추정될수있다.

주요어 : 이산화탄소포집및저장,시뮬레이션,최적화,통합모델링

학번 : 2012-23265

51