Abstract

Oil and gas industries have high carbon dioxide (CO2) emissions, which is a great environmental concern. Monoethanolamine (MEA) is widely used as a solvent in CO2 capture and storage (CCS) systems. The challenge is that MEA–CCS itself is an energy-intensive process that requires optimum configuration and operation, and numerous design parameters and heat demands must be considered. Thus, the current work evaluates the energy distributions and CO2 removal efficiency of a CCS installed in floating production storage and offloading units under different operating conditions of a power and heat generation hub. The optimization procedures are implemented using highly accurate surrogate models for the following responses: (1) overall power consumption of CCS, (2) CCS separation performance, and (3) CCS heating and cooling demands. The input variables considered in the present research include the following: (1) the exhaust gas compositions and mass flowrate, (2) the operating pressure and temperature parameters of CCS and the injection compression unit, (3) the structural parameters of absorber and stripper columns, and (4) MEA solution parameters. The optimum CCS configuration significantly reduces the total heating and cooling demands by 62.77% (7 × 106 kW) and the overall power consumption by 8.65% (1.8 MW), and it increases the CCS separation performance by 4.46% (97.46%) and mitigates the CO2 emissions of proper CCS by 1.02 t/h compared with conventional operating conditions.

References

3.
Statista
, “Oil Production Worldwide From 1998 to 2019,” https://www.statista.com/statistics/265203/global-oil-production-since-in-barrels-per-day/, Accessed April 20, 2020.
4.
IEA
, “Global CO2 Emissions in 2019,” https://www.iea.org/articles/global-co2-emissions-in-2019, Accessed April 20, 2020.
5.
Angelo
,
C.
, and
Rittl
,
C.
,
2019
, “Is Brazil on the Way to Meet Its Climate Targets? Explainer Note by the Climate Observatory,” https://www.oc.eco.br/wp-content/uploads/2019/09/Is-Brazil-on-the-way-to-meet-its-climate-targets_-1.pdf, Accessed April 20, 2020.
6.
Rackley
,
S. A.
,
2017
,
Carbon Capture and Storage
,
Butterworth-Heinemann
,
Amsterdam, The Netherlands
.
7.
Tola
,
V.
,
Cau
,
G.
,
Ferrara
,
F.
, and
Pettinau
,
A.
,
2016
, “
CO2 Emissions Reduction From Coal-Fired Power Generation: A Techno-Economic Comparison
,”
ASME J. Energy Resour. Technol.
,
138
(
6
), p. 061602.
8.
Pan
,
Z.
,
Yan
,
M.
,
Shang
,
L.
,
Li
,
P.
,
Zhang
,
L.
, and
Liu
,
J.
,
2020
, “
Thermoeconomic Analysis of a Combined Natural Gas Cogeneration System With a Supercritical CO2 Brayton Cycle and an Organic Rankine Cycle
,”
ASME J. Energy Resour. Technol.
,
142
(
10
), p. 102108.
9.
Falk-Pedersen
,
O.
, and
Dannström
,
H.
,
1997
, “
Separation of Carbon Dioxide From Offshore Gas Turbine Exhaust
,”
Energy Convers. Manage.
,
38
, pp.
S81
S86
.
10.
Li
,
K.
,
Leigh
,
W.
,
Feron
,
P.
,
Yu
,
H.
, and
Tade
,
M.
,
2016
, “
Systematic Study of Aqueous Monoethanolamine (MEA)-Based CO2 Capture Process: Techno-Economic Assessment of the MEA Process and Its Improvements
,”
Appl. Energy
,
165
, pp.
648
659
.
11.
Hetland
,
J.
,
Kvamsdal
,
H. M.
,
Haugen
,
G.
,
Major
,
F.
,
Kårstad
,
V.
, and
Tjellander
,
G.
,
2009
, “
Integrating a Full Carbon Capture Scheme Onto a 450 MWe NGCC Electric Power Generation Hub for Offshore Operations: Presenting the Sevan GTW Concept
,”
Appl. Energy
,
86
(
11
), pp.
2298
2307
.
12.
Carranza-Sánchez
,
Y. A.
, and
de Oliveira
,
S.
, Jr
,
2015
, “
Exergy Analysis of Offshore Primary Petroleum Processing Plant With CO2 Capture
,”
Energy
,
88
, pp.
46
56
.
13.
Liu
,
Y.
,
Fan
,
W.
,
Wang
,
K.
, and
Wang
,
J.
,
2016
, “
Studies of CO2 Absorption/Regeneration Performances of Novel Aqueous Monothanlamine (MEA)-Based Solutions
,”
J. Cleaner Prod.
,
112
, pp.
4012
4021
.
14.
Pashaei
,
H.
,
Ghzaemi
,
A.
,
Nasiri
,
M.
, and
Karami
,
B.
,
2020
, “
Experimental Modeling and Optimization of CO2 Absorption Into Piperazine Solutions Using RSM-CCD Methodology
,”
ACS Omega
,
5
(
15
), pp.
8432
8448
.
15.
Choi
,
J.
,
Cho
,
H.
,
Yun
,
S.
,
Jang
,
M.-G.
,
Oh
,
S.-Y.
,
Binns
,
M.
, and
Kim
,
J.-K.
,
2019
, “
Process Design and Optimization of MEA-Based CO2 Capture Processes for Non-Power Industries
,”
Energy
,
185
, pp.
971
980
.
16.
Xin
,
K.
,
Gallucci
,
F.
, and
van Sint Annaland
,
M.
,
2020
, “
Optimization of Solvent Properties for Post-Combustion CO2 Capture Using Process Simulation
,”
Int. J. Greenhouse Gas Control
,
99
, p.
103080
.
17.
Allahyarzadeh-Bidgoli
,
A.
,
Salviano
,
L. O.
,
Dezan
,
D. J.
,
de Oliveira Junior
,
S.
, and
Yanagihara
,
J. I.
,
2018
, “
Energy Optimization of an FPSO Operating in the Brazilian Pre-Salt Region
,”
Energy
,
164
, pp.
390
399
.
18.
Allahyarzadeh-Bidgoli
,
A.
,
Hamidishad
,
N.
, and
Yanagihara
,
J. I.
, “
Thermodynamic and Sensitivity Analyses of an FPSO With CCS for Variation of GOR and CO2 Content in Crude Oil Compositions
,”
Proceeding of the 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact Of Energy Systems
, ECOS 2020,
Osaka, Japan
,
June 29–July 3
.
19.
Song
,
C.
,
Kitamura
,
Y.
, and
Li
,
S.
,
2014
, “
Optimization of a Novel Cryogenic CO2 Capture Process by Response Surface Methodology (RSM)
,”
J. Taiwan Inst. Chem. Eng.
,
45
(
4
), pp.
1666
1676
.
20.
Carranza-Sánchez
,
Y. A.
,
2017
, “
Exergy and Environmental Assessment of FPSO Offshore Platforms With CO2 Capture and Storage
,”
Doctoral dissertation
,
São Paulo, Brazil
.
21.
Thermoflow, Thermoflex n.d.
,
2020
, Florida.
22.
Allahyarzadeh-Bidgoli
,
A.
,
Dezan
,
D. J.
,
Salviano
,
L. O.
,
de Oliveira Junior
,
S.
, and
Yanagihara
,
J. I.
,
2019
, “
FPSO Fuel Consumption and Hydrocarbon Liquids Recovery Optimization Over the Lifetime of a Deepwater Oil Field
,”
Energy
,
181
, pp.
927
942
.
23.
Aspen Technology Inc.
,
2017
, Aspen hysys V10.1, Aspen Tech, Bedford, MA.
24.
Peng
,
D.-Y.
, and
Robinson
,
D. B.
,
1976
, “
A New Two-Constant Equation of State
,”
Ind. Eng. Chem. Fundam.
,
15
(
1
), pp.
59
64
.
25.
Kunz
,
O.
, and
Wagner
,
W.
,
2012
, “
The GERG-2008 Wide-Range Equation of State for Natural Gases and Other Mixtures: An Expansion of GERG-2004
,”
J. Chem. Eng. Data
,
57
(
11
), pp.
3032
3091
.
26.
Austgen
,
D. M.
,
Rochelle
,
G. T.
,
Peng
,
X.
, and
Chen
,
C. C.
,
1989
, “
Model of Vapor-Liquid Equilibria for Aqueous Acid Gas-Alkanolamine Systems Using the Electrolyte-NRTL Equation
,”
Ind. Eng. Chem. Res.
,
28
(
7
), pp.
1060
1073
.
27.
Bidgoli
,
A. A.
,
2018
, “
Simulation and Optimization of Primary Oil and Gas Processing Plant of FPSO Operating in Pre-Salt Oil Field
,”
Doctoral dissertation
,
São Paulo, Brazil
.
28.
Kotas
,
T. J.
,
2013
,
The Exergy Method of Thermal Plant Analysis
,
Elsevier
,
New York
.
29.
Buhmann
,
M. D.
,
2003
,
Radial Basis Functions: Theory and Implementations
, Vol.
12
,
Cambridge University Press
,
Cambridge, UK
.
30.
Irie
,
B.
, and
Miyake
,
S.
,
1988
,
Capabilities of Three-Layered Perceptrons
,
ICNN
,
San Diego, CA
, pp.
641
648
.
31.
Haykin
,
S.
, and
Network
,
N.
,
2004
, “
A Comprehensive Foundation
,”
Neural Networks
,
2
(
2004
), p.
41
.
32.
Koza
,
J. R.
, and
Koza
,
J. R.
,
1992
,
Genetic Programming: On the Programming of Computers by Means of Natural Selection
, Vol.
1
,
MIT Press
,
Cambridge, MA
.
33.
Fillon
,
C.
,
2008
, “
New Strategies for Efficient and Practical Genetic Programming
,”
Doctoral dissertation
,
Trieste, Italy
.
34.
Matheron
,
G.
,
1965
,
Les Variables Régionalisées et Leur Estimation: une Application de la Théorie des Fonctions Aléatoires aux Sciences de la Nature
,
Masson, Paris, France
.
35.
Rasmussen
,
C. E.
,
2003
, “Gaussian Processes in Machine Learning,”
Summer School on Machine Learning
,
Springer
,
Berlin/Heidelberg
, pp.
63
71
.
36.
Corless
,
R. M.
,
Gianni
,
P. M.
,
Trager
,
B. M.
, and
Watt
,
S. M.
,
1995
, “
The Singular Value Decomposition for Polynomial Systems
,”
Proceedings of the 1995 International Symposium on Symbolic and Algebraic Computation
,
Quebec, Canada
,
July 10–12
, pp.
195
207
.
37.
Alfeld
,
P.
,
1989
, “Scattered Data Interpolation in Three or More Variables,”
Mathematical Methods in Computer-Aided Geometric Design
,
Academic Press
,
Cambridge, MA
, pp.
1
33
.
38.
Vrieze
,
S. I.
,
2012
, “
Model Selection and Psychological Theory: A Discussion of the Differences Between the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)
,”
Psychol. Methods
,
17
(
2
), pp.
228
243
.
39.
ESTECO, ModeFRONTIER V6.4, Trieste,
2019
, Trieste, Italy.
40.
Intergovernmental Panel on Climate Change (IPCC)
,
2018
, Mitigation of Climate Change—ENERGY SYSTEMS—2018, https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter7.pdf, Accessed April 20, 2020.
You do not currently have access to this content.