Optimization of CCUS Supply Chains for Some European Countries under the Uncertainty
Abstract
:1. Introduction
2. Materials and Methods
2.1. Problem Statement
2.2. CCUS Supply Chain Model
2.2.1. Sets
2.2.2. Parameters
2.2.3. Variables
2.2.4. Constraints
2.2.5. Equations
2.2.6. Objective Function
2.3. Case Studies
2.3.1. CCUS Supply Chain of Italy
2.3.2. CCUS Supply Chain of the UK
2.3.3. CCUS Supply Chain of Germany
2.4. Monte Carlo Method
3. Results and Discussion
3.1. Results for the CCUS Supply Chain of Italy
3.2. Results for the CCUS Supply Chain of the UK
3.3. Results for the CCUS Supply Chain of Germany
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
Indices | |
c | concrete production site |
cc | calcium carbonate production site |
cr | concrete by red mud production site |
d | calcium carbonate production site |
g | methane production site |
i | carbon dioxide sources |
j | carbon dioxide capture technologies |
k | carbon dioxide storage and overall utilization sites |
l | lignin production site |
m | methanol/methane production site |
p | polyurethane production site |
s | scenario |
t | tomatoe growing site |
u | urea production site |
w | wheat production site |
Abbreviation | |
AIMMS | Advanced Interactive Multidimensional Modeling |
CC | Capture Costs (€/year) |
CCUS | Carbon Capture Utilization and Storage |
CCU | Carbon Capture Utilization |
CDC | Flue gas dehydration costs (€/year) |
CIC | Capture Investment Costs (€/year) |
COC | Capture Operative Costs (€/year) |
CO2-EOR | CO2-enhanced oil recovery |
CS | Storage Costs (€/year) |
F | Flue gas flow rate (mol/s) |
FCO2 | amount of carbon dioxide that is transported (ton/year) |
IC | Injection Capacity per well |
MEA | Monoethanolamine |
MILP | Mixed Integer Linear Programming |
Nbuildwell | Number of well |
PSA | Pressure Swing Adsorption |
PZ | Piperazine |
SIC | Storage Investment Costs (€) |
SOC | Storage Operative Costs (€/year) |
TC | Transportation Costs (€/year) |
TH | Time Horizon |
TIC | Transportation Investment Costs (€) |
TOC | Transportation Operative Costs (€/year) |
VSA | Vacuum Swing Adsorption |
Parameters | |
b | parameter in SIC (M€) |
Calcium Carbonatedem | National calcium carbonate demand (ton/year) |
Concretedem | National concrete demand (ton/year) |
Concrete by red muddem | National concrete by red mud demand (ton/year) |
Ckmax | Maximum storage capacity at the storage site k (ton) |
CRmin | Minimum target for carbon dioxide reduction (ton/year) |
CSi | Total carbon dioxide emission from each source i (ton/year) |
D | distance (km) |
dwell | depth of well |
Fi | Total feed flue gas flow rate from each source i (mol/s) |
Ft | Terrestrial factor |
Lignindem | National lignin demand (ton/year) |
m | parameter in SIC (M€/km), CIC and COC |
MeOHdem | National methanol demand (ton/year) |
n | parameter in CIC and COC |
Polyurethanedem | National polyurethane demand (ton/year) |
xCO2 | Carbon dioxide molar fraction |
XSi | Carbon dioxide composition in the flue gas emission from source i (mol%) |
XLi | Lowest carbon dioxide composition processing limit for the capture plant j (mol%) |
XHi | Highest carbon dioxide composition processing limit for the capture plant j (mol%) |
Ureadem | National urea demand (ton/year) |
Wheatdem | National wheat demand (ton/year) |
Variables | |
Binary | |
Xi,j,k,s 1 if carbon dioxide is captured from source i with technology j and sent to storage site k in the scenario s, otherwise 0 | |
Yi,j,k,s 1 if carbon dioxide is capture from source i with technology j and sent to storage/utilization site k in the scenario s, otherwise 0 | |
Continuous | |
Calcium carbonatei,j,cc,s fraction of captured carbon dioxide from source i with technology j sent to calcium carbonate production site cc in the scenario s | |
Calcium Carbonatei,j,d,s fraction of capture carbon dioxide from source i with technology j sent to calcium carbonate production site d in the scenario s | |
Concrete by red mudi,j,cr,s fraction of captured carbon dioxide from source i with technology j sent to concrete production site by red mud cr in the scenario s | |
Concretei,j,c,s fraction of captured carbon dioxide from source i with technology j sent to concrete production site c in the scenario s | |
FRi,j,k,s fraction of captured carbon dioxide from source i with technology j sent to storage site k in the scenario s | |
Lignin,i,j,l,s fraction of captured carbon dioxide from source i with technology j sent to lignin production site l in the scenario s | |
Methanei,j,g,s fraction of captured carbon dioxide from source i with technology j sent to methane production site g in the scenario s | |
Methanoli,j,m,s fraction of captured carbon dioxide from source i with technology j sent to methanol production site m in the scenario s | |
MRi,j,k,s fraction of captured carbon dioxide from source i with technology j sent to methane production site k in the scenario s | |
Polyurethanei,j,p,s fraction of captured carbon dioxide from source i with technology j sent to polyurethane production site p in the scenario s | |
Tomatoi,j,t,s fraction of captured carbon dioxide from source i with technology j sent to tomato growing t in the scenario s | |
Ureai,j,u,s fraction of captured carbon dioxide from source i with technology j sent to urea production site u in the scenario s | |
Utilizationi,j,k,s fraction of captured carbon dioxide from source i with technology j sent to overall utilization site k in the scenario s | |
Wheati,j,w,s fraction of captured carbon dioxide from source i with technology j sent to wheat production site w in the scenario s | |
Greek Letters | |
α | parameter in CIC and COC |
αt | parameter in TIC |
β | parameter in CIC and COC |
βt | parameter in TIC |
γ | parameter in CIC and COC |
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CO2 Source | CO2 Capture Technology | CO2 Amount (Mton/Year) |
---|---|---|
To storage | ||
Lombardy | MEA absorption | 33 |
To utilization | ||
Emilia Romagna | MEA absorption | 20 |
Piedmont | MEA absorption | 18 |
Veneto | Membrane | 7 |
CO2 Source | CO2 Capture Technology | CO2 Amount (Mton/Year) |
---|---|---|
To storage | ||
Leeds | VSAWEI | 0.4 |
To utilization | ||
Leeds | PZ absorption | 6 |
CO2 Source | CO2 Technology | CO2 Amount (Mton/Year) |
---|---|---|
To storage | ||
Madgeburg | PZ absorption | 20 |
To utilization | ||
Munich | PZ absorption | 1 |
Hannover | PZ absorption | 52 |
Dresda | PZ absorption | 44 |
Wiesbaden | PZ absorption | 39 |
Madgeburg | PZ absorption | 4 |
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Leonzio, G.; Foscolo, P.U.; Zondervan, E. Optimization of CCUS Supply Chains for Some European Countries under the Uncertainty. Processes 2020, 8, 960. https://doi.org/10.3390/pr8080960
Leonzio G, Foscolo PU, Zondervan E. Optimization of CCUS Supply Chains for Some European Countries under the Uncertainty. Processes. 2020; 8(8):960. https://doi.org/10.3390/pr8080960
Chicago/Turabian StyleLeonzio, Grazia, Pier Ugo Foscolo, and Edwin Zondervan. 2020. "Optimization of CCUS Supply Chains for Some European Countries under the Uncertainty" Processes 8, no. 8: 960. https://doi.org/10.3390/pr8080960