Available online at www.sciencedirect.com
ScienceDirect
Energy Procedia 63 (2014) 7429 – 7436
GHGT-12
Environmental Due Diligence of CO2 Capture and Utilization
Technologies – Framework and application
Bhawna Singha,*,Rick Reijersa, Mijndert W. van der Spekb, Wouter B. Schakelb,
Ragnhild Skagestadc, Hans Aksel Haugenc, Andrea Ramirezb, Nils Henrik Eldrupc, Edgar
G. Hertwicha, Anders Hammer Strømmana
a
Industrial Ecology Programme and Department of Energy and Process Engineering, Norwegian University of Science and Technology,
Trondheim-7491, Norway.
b
Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands.
c
Tel-Tek, Porsgrunn-3918, Norway.
Abstract
This article presents an overview of an environmental due diligence framework developed as part of the
EDDiCCUT project, and presents analysis and results from the first test case – MEA based CO2 capture process.
The framework draws upon well-established technical, economic and environmental assessment methods and
integrates technical performance, uncertainties, cost estimation and life cycle inventory data to ensure consistency
and enhance quality. Results show that for the modelled coal power plant of about 800 MW gross power output, the
CO2 capture system lowers the net efficiency by 10.4% efficiency points and results in a 68% increase in the cost of
electricity. Environmental performance evaluated on a broad range of 24 impacts and emission categories indicates a
68% reduction in climate change warming potential with 20-90% increase in other impacts. By comparing the
quality of the inventory data used for environmental assessment with the state-of-art data in available life cycle
assessment literature, it is found that the due diligence analysis brings significant improvement in the quality of data
for certain processes in the value chain.
©
Published
by Elsevier
Ltd. This
©2014
2013The
TheAuthors.
Authors.
Published
by Elsevier
Ltd. is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and peer-review under responsibility of GHGT.
Peer-review under responsibility of the Organizing Committee of GHGT-12
Keywords: environmental due diligence; CO2 capture and storage; post-combustion; monoethanolamine
* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 .
E-mail address: bhawna.singh@ntnu.no
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Organizing Committee of GHGT-12
doi:10.1016/j.egypro.2014.11.779
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Bhawna Singh et al. / Energy Procedia 63 (2014) 7429 – 7436
1. Introduction
Carbon dioxide Capture and Storage (CCS) can play a key role in decarbonizing the energy and industrial sector,
and can act as a bridging technology towards a renewable and sustainable energy future. Also, the use of captured
CO2 as feedstock (Carbon dioxide Capture and Utilization – CCU) for conversion to fuels and chemicals is
increasingly gaining attention. The current analysis and understanding of the potential environmental benefits and
trade-offs through the life cycle of CCS shows varying results. This variation exists both in the value as in the
direction of the environmental impacts (positive vs. negative). An additional aspect is that technical, economic and
environmental assessments are generally based on different assumptions and system boundaries, which contributes
to the large range of results reported in literature. It is therefore necessary that performance assessments are
available in a systematic and harmonized way, so that options that maximize environmental benefits along the chain
can be further developed, and potential adverse impacts on the environment are identified at an early stage. This is at
the core of the EDDiCCUT project.
The Environmental Due Diligence of novel CO2 Capture and Utilization Technologies (EDDiCCUT) [1] is a 4
year research project started in January 2013. The term Environmental Due Diligence refers to the thorough
assessment of the overall environmental performance of a technology. It is a means to manage risk, analogous to due
diligence studies preceding business acquisitions. The Environmental Due diligence (EDD) framework developed in
this project anchors on well-established technical, economic and environmental assessment methods that enable
consistent comparisons of CCS and CCU and integrate technical performance, uncertainties, cost estimation and life
cycle inventory data to ensure consistency and enhance quality. In addition to the systematic integrated evaluation,
the framework provides the novelty of monitoring the quality of the models and of the input and output data.
In this article we present the overview of the EDD framework and discuss the analysis and results from the first
case study of amine (MEA) based CO2 capture. The paper consists of four sections including this introduction.
Section 2 presents the developed framework and the case study analysis. Section 3 presents results and discussion
for technical, economic and environmental assessment with more details on environmental performance, and section
4 presents the outlook for future work.
2. Method
2.1. Environmental Due Diligence framework
Figure 1. Schematic approach for Environmental Due Diligence framework
The EDD framework provides a strategy for integrated technical, economic and environmental assessment with
an overarching management of the individual tasks where decisions are taken in conjunction with pre-defined goals
of a case i.e. assessment of a given technology. Figure 1 presents the main building blocks of the EDD framework.
The assessment process includes five distinct tasks or phases:
Bhawna Singh et al. / Energy Procedia 63 (2014) 7429 – 7436
1.
2.
3.
4.
5.
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Information compilation aimed at collecting, arranging, evaluating and harmonizing the available data in the
context of technical-economic-environmental modeling,
Modeling the technical-economic-environmental performance, including uncertainties
Generate assessment indicators on each aspect,
Due diligence assessment (individual): review the assessment indicators for the case,
Due diligence assessment (comparative): review the assessment indicators for the case in comparison with
other case studies.
2.2. Case study analysis
A coal based power plant with CO2 capture unit using Monoethanolamine (MEA) as solvent is selected as the
first case study to test the EDD framework.
2.2.1. Scoping and design basis
Scope of the case study is defined in two phases – initial scoping and detailed scoping. In the initial scoping
decisions are made on purpose of the assessment, technology to be assessed, plant type, location of the plant,
temporal aspect and system boundary. The detailed scoping deals with the more comprehensive decisions on
specific plant area of interest for detailed modeling, desired model complexity, plant scale/capacity/lifetime/power
specification, air/water emission limits, fuel origin, local conditions, economic factors consideration, functional unit
for analysis etc. The design basis for the case is developed in accordance with the CESAR [2] project and constitutes
defining design criteria for the power plant, CO2 capture unit and CO2 compression unit. The expected outcomes
from the EDD analysis, as technical, economic and environmental performance indicators are decided. Selected
parameters for scope settings and design basis are presented in Table 1.
Figure 2. System area layout for the MEA case
A system area layout (figure 2) was developed to facilitate the analysis and represents the system boundary for
the assessment. The whole system is divided into 10 inter-linked system areas. While the environmental analysis
considers all 10 system areas, the technical analysis is limited to plant fence i.e. power generation, flue gas
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Bhawna Singh et al. / Energy Procedia 63 (2014) 7429 – 7436
treatment, CO2 capture and CO2 compression (system area 5 to 8) and the economic analysis considers CO2
transport and storage (system area 9 and 10) in addition to the system areas considered in technical analysis.
Table 1. Selected parameters for the case: scope and design basis
Parameter
Unit
Value
Scope
Plant output (gross)
Plant capacity
Plant lifetime
Load hours
Parameter
Unit
Value
Power plant - design basis
MW
§800
%
80
year
25
h/year
7000
Emission limits
CESAR [2]
Functional unit
per kWh
Boiler efficiency
%
95
Mechanical efficiency
%
99.6
Generator efficiency
%
98.5
CO2 capture rate
%
90
Solvent mass fraction
%
30
Absorber pressure
bar
1.148
0.048
CO2 capture unit - design basis
Absorber pressure drop
bar
Lean solvent temperature
o
C
30
Reboiler steam pressure
bar
3.1
Compressor mechanical efficiency
%
99.6
Pump efficiency
%
75
CO2 outlet pressure
bar
110
Compression unit – design basis
2.2.2. Phase 1- Information compilation
In this phase, technical, economic and environmental data, information available in literature for the MEA based
CCS case and generic information on different system areas is collected and organized. Pedigree matrices [3] for
each research area (technical/economic/environmental) are developed and used as a tool to assess quality of
data/information and quality of the knowledge base of different system areas is assessed. Based on the information,
a decision is taken in the models that are to be used for the assessment in each research area.
2.2.3. Phase 2- Modeling
In this phase technical, economic and environmental modeling of the system is undertaken. Technical modeling
covers simulation of system areas 5 to 8 i.e. at the plant site, while the economic and environmental modeling
simulates all 10 system areas at different levels of complexity. Models are selected for each system area simulation
depending on the data/information available and, for the present case studied, can be characterized as mid to high
level complexity, except for the models used flue gas treatment and MEA degradation in the CO2 capture unit which
have low quality. To assess the cost indicators economic modeling utilizes information on equipment sizing and
other operation requirements as produced in the technical modeling. For environmental assessment, process based
life cycle inventory is modeled using process stream data from technical modeling for system area 5 to 8 and best
available estimates based on the scope and literature for other system areas, with Ecoinvent v2.2 database as
background. The functional unit for the LCA is chosen as 1 kWh of the net electricity produced. Quality of the
results is evaluated using the developed pedigree matrices.
2.2.3. Phase 3- Performance indicators
Technical and economic performance indicators identified in the scoping phase are evaluated and analyzed. For
environmental indicator evaluation, ReCiPe v1.08 [4] characterization method is applied to the life cycle inventory
modeled in phase 2. Additional characterization factors for MEA degradation emissions related human toxicity are
obtained from USETox [5] and Veltman et al. 2010 [6].
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3. Results and discussion
Table 2. Performance indicators
Indicator
Unit
w CCS
w/o CCS
% change
Technical
Gross power output
MWe
719
833
-14
Parasitic load
MWe
118
57
107
Net power output
MWe
601
776
-23
Gross efficiency LHV
%
42.7
49.5
-14
Net efficiency LHV
%
35.7
46.1
-23
kg/MWh
95
734
-87
Regeneration heat
GJ/tCO2
3.9
-
-
SPECCA
GJ/tCO2
3.6
-
-
Specific cooling water use
kg/MWe
60
34
76
CO2 intensity
Economic
CAPEX
kEUR
2191348
1622910
35
capex
EUR/kW
3646
2091
74
OPEX
annual kEUR
187203
150557
24
EUR/MWh
44.5
27.8
60
opex
CoE
EUR/MWh
96.2
57.4
68
Capture cost
EUR/tonne CO2
48.1
-
-
Avoided cost
EUR/tonne CO2
65.5
-
-68
Environmental
Climate change
climate change
kg CO2 eq
2.7E-01
8.6E-01
Typical non-CC
terrestrial acidification
kg SO2 eq
2.2E-03
1.8E-03
21
particulate matter formation
kg PM10 eq
4.9E-01
3.8E-01
29
photo oxidant formation
kg NMVOC
1.9E-03
1.5E-03
29
freshwater eutrophication
kg P eq
5.4E-04
4.1E-04
30
marine eutrophication
kg N eq
2.1E-04
1.5E-04
38
Toxicity
Resource depletion
Land use change
Others
human toxicity
kg 1,4-DB eq
3.4E-01
2.6E-01
31
terrestrial eco-toxicity
kg 1,4-DB eq
1.9E-05
1.0E-05
90
35
freshwater eco-toxicity
kg 1,4-DB eq
8.9E-03
6.6E-03
marine eco-toxicity
kg 1,4-DB eq
8.5E-03
6.5E-03
32
fossil depletion
kg oil eq
2.4E-01
1.8E-01
32
metal depletion
kg Fe eq
6.3E-03
4.1E-03
53
water depletion
m3
5.7E-01
3.1E-01
83
land transformation
m2
7.3E-05
5.0E-05
46
Agricultural land occupation
m2a
1.2E-02
9.5E-03
31
urban land occupation
m2a
6.8E-03
5.2E-03
30
kg CFC-11 eq
1.1E-08
8.0E-09
41
kg U235 eq
3.1E-02
2.2E-02
40
CO2
kg
2.2E-01
8.2E-01
-73
SO2
kg
9.5E-04
9.4E-04
0
NOx
kg
1.7E-03
1.3E-03
30
ozone depletion
ionizing radiation
Emissions
PM < 2.5μm
kg
4.4E-01
3.4E-01
29
PM > 10μm
kg
1.2E+0
9.5E-01
29
COD
kg
4.3E-04
2.7E-04
60
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Table 2 presents results for the technical, economic and environmental performance indicators for the studied
case. Table 3 presents the quality analysis for LCA as part of the environmental due diligence assessment and Figure
3 presents the contribution analysis for climate change potential (CCP) together with the quality aspect, in a
presentation format developed in the project.
3.1. Overall performance indicators
Technical performance indicators shown in table 2 point out a drop of 175 MW in net power output (with CO2
capture system) resulting in a reduction of 10.4% points in net efficiency of the plant. Analysis of the power losses
show that the major power loss is in the MEA reboiler unit (114 MW), while the parasitic load from CO2
compressors and feed water pumps adds another 72.5 MW loss. Specific Primary Energy Consumption for CO2
Avoided SPECCA for the power plant with MEA system is calculated to be 3.6 GJ/tCO2 and the cooling water use
is found to increase by 76% in the CCS case as compared to without CCS. These results are in range with those
reported in the literature.
Economic performance indicators (table 2) show a 74% increase in CAPEX per kW output (compared to a power
plant without CCS), and an increase of 60% in operating cost for per unit net product. Thus with a MEA based CCS
system the cost of electricity increases by 68% to 0.057€/kWh. Further analysis for the cost of electricity shows that
for a system without CCS, the main cost drivers are the coal supply and power generation unit; while for a system
with CCS, in addition to the coal supply and power generation unit, the flue gas treatment and capture unit also
contribute significantly to the cost (26% of the CoE).
Environmental performance indicators (table 2) show that although there is a significant decrease in CCP and
CO2 emissions, the CCS system has increased scores on all other impacts and emissions. With CCS the CCP
decreases by 68% while non-CCP impacts increase by 21%-38% with the highest increase in marine eutrophication.
Toxicity impacts increase by 31-90% with the highest increase in terrestrial eco-toxicity and 31% increase in human
toxicity. Resource depletion impacts increase by 32-83% with the highest increase in water depletion and the land
use impacts increase by 30-46%. The degree of trade-offs in non-CCP impacts require further investigation to
understand the relevance of these increases for overall comparison and to obtain clear indicators for decision
making. Among the considered emissions, CO2 emission is reduced by 73% in CCS case, and although the
additional fuel combustion releases additional SO2 emissions, the co-capture of SO2 in the MEA absorption process
results in a no net increase.
3.2. Quality analysis for environmental assessment
Table 3. Life cycle inventory data quality for the MEA based CCS case
Table 3 shows the quality of the inventory data used in the LCA. The table provides quality scores on the
attributes of source reliability, representativeness, robustness and completeness. The ‘strength’ of the quality scores,
based on the attributes, increases from 0 to 4 and is also given in the table as colored codes. Since the MEA case has
being quite extensively analyzed in the open LCA literature, the quality of inventory data for the basic system areas
of power generation and capture unit is of good quality. However the quality of inventory data for other system
Bhawna Singh et al. / Energy Procedia 63 (2014) 7429 – 7436
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areas is assessed from poor to low. The procedure applied to the case analysis within the EDD framework allowed to
identify those aspects of the inventory data that needed to be improved to fulfil the scope goals. Table 2 shows that
there is a general improvement in the LCI as obtained by EDD process with noticeable improvement in capture and
transport unit and significant improvement in coal processing and utilities. The quality of power generation unit is
unaltered due to already good understanding of this area in the existing literature.
3.3. Contribution analysis – Climate Change Potential
Figure 3. Process contribution analysis with quality indicators for climate change potential
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Bhawna Singh et al. / Energy Procedia 63 (2014) 7429 – 7436
Figure 3 presents the process contribution and quality analysis of CC indicator for electricity generation technology
with CCS. The core circle in the two graphs gives the absolute CC score, which is 68% lower in the case of CCS
option (with a net CC potential of 271 g CO2eq/kWh). The second ring (from centre) in the graphs represents the
contribution of different system areas as defined in the detailed scoping phase. It shows that power generation
(system area 5) and feedstock production chain (system area 1) appear as the main contributors to the impact in both
the cases. The third ring in the graphs represents contribution of processes within a system e.g., for system area 1
(feedstock chain) consisting of coal mine, mining, transport and other processes. In the case without CCS (figure not
presented) 86% of the total CCP is from coal combustion (system area 5 – power generation) and 12% from coal
mining and related processes (system area 1 – feedstock). In the CCS case, 48% of the CCP score is from coal
mining and related process, while 39% is from coal combustion and CO2 capture (to discount for CO2 capture
process). The outer ring in the graphs represents the quality scores for specific system areas. Quality scores for all
system areas is evaluated as good to high and is presented in light to dark green (refer to quality colour codes in
table 3).
4. Conclusion
This article presented the outline of the developed framework and the first case study results, which will be used as
benchmark to compare results with other CO2 capture case studies. The main novelties of the developed framework
are in the area of integrated approach for technical, economic and environmental modelling, data quality analysis
using developed pedigree matrices, model selection and result presentation. Experience gained in this case study is
being used to improve the framework, strengthening the areas of information flows in different areas (technical,
economic and environmental), refined pedigree matrices at different phases and strategy for trade-offs analysis in
environmental assessment.
Acknowledgements
The authors thank the EDDiCCUT project funding consortium for their financial support, and Karsten Riedl and
Ödön Jonas Majoros at E.ON Technologies GmbH for their valuable comments and expert reviews throughout the
first case study. Authors also thank Andrea Diaz and Geoffrey Guest for their help in developing graphics.
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