CET-vol 105
DOI: 10.3303/CET23105030
Paper Received: 10 February 2023; Revised: 7 April 2023; Accepted: 15 July 2023
Please cite this article as: D'Amore F., Colbertaldo P., Romano M.C., 2023, A Mathematical Tool for Optimising Carbon Capture, Utilisation and
Sequestration Plants for e-MeOH Production, Chemical Engineering Transactions, 105, 175-180 DOI:10.3303/CET23105030
CHEMICAL ENGINEERING TRANSACTIONS
VOL. 105, 2023
A publication of
The Italian Association
of Chemical Engineering
Online at www.cetjournal.it
Guest Editors: David Bogle, Flavio Manenti, Piero Salatino
Copyright © 2023, AIDIC Servizi S.r.l.
ISBN 979-12-81206-04-5; ISSN 2283-9216
A Mathematical Tool for Optimising Carbon Capture,
Utilisation and Sequestration Plants for e-MeOH Production
Federico d’Amorea,b,*, Paolo Colbertaldob, Matteo C. Romanob
aCAPE-Lab – Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, University of Padova,
via Marzolo 9, 35131 Padova PD (Italy)
b GECoS – Group of Energy Conversion Systems, Politecnico di Milano, Department of Energy, via Lambruschini 4, IT-
20156 Milano (Italy)
federico.damore@unipd.it
Carbon capture, utilisation, and sequestration is key for the decarbonisation of hard-to-abate industries, as it
allows avoiding the direct release of CO2 to the atmosphere and generating carbon-based products. However,
for these products to be truly carbon-neutral, intermittent renewable electricity must be deployed at scale,
leading to the necessity of optimising flexible plants with potential for local buffer storages, geological
sequestration, and conversion units. The scope of this work is to provide a mathematical framework for the
economic optimisation of a carbon capture, utilisation, and sequestration system, to decarbonise a cement plant
located in the Puglia region (Italy), via CO2 geological confinement and/or power and CO2-to-methanol
conversion. The final aim is to determine the optimal sizing and cost of the process units of the plant, depending
on economic conditions such as the methanol sale price and different perspective costs scenarios. The main
outcome is an economic convenience of geological sequestration, as opposed to utilisation, while a long-term
scenario would allow for a cost-effective production of methanol when the sale price is above 550 €/t.
1. Introduction
CO2 capture and geological sequestration (CCS) is considered as an important set of technologies to
decarbonise industry, particularly hard-to-decarbonise sectors such as cement (Voldsund et al., 2019; d’Amore
et al., 2021), and to achieve future climate goals (IPCC, 2023). Alternatively, carbon capture and utilisation
(CCU) is an option to convert CO2 streams into useful products (Hepburn et al., 2019; d’Amore et al., 2023).
CCU may play an important role in future carbon management and circular carbon economy, as the CO2 can
be combined with green H2 (i.e., produced through electrolysis of H2O fed with renewable electricity) to produce
synthetic ‘e-chemicals’ or ‘e-Fuels’, such as e-Methanol (e-MeOH) (Pérez-Fortes et al., 2016). Designing flexible
value chains able to perform both CO2 permanent confinement and CO2 conversion and utilisation (carbon
capture, utilisation, and sequestration - CCUS) represents an opportunity to decrease the costs of a purely CCS-
driven chain, via revenues from the sale of climate neutral carbon-based e-Fuels (d’Amore and Bezzo, 2020).
This study proposes a mathematical tool for the cost-optimal design of a CCUS system producing e-MeOH. A
given stream of CO2, separated from the flue gases of a cement plant, is either sent to permanent geological
sequestration (i.e., CCS) or to chemical conversion alongside green H2 into e-MeOH (i.e., CCU). The green H2
is produced through electrolysers fed with renewable electricity (wind farm and/or photovoltaic plant), and the
intermittent load of renewables is tackled by considering the possibility to install buffer local storage capacities
for electric energy (i.e., batteries), H2, and/or CO2. The proposed CCUS modelling framework is tested on an
exemplificative geographic case study located in the Puglia region (Italy), which is optimised for a year-long
operation with a time resolution of 1 h. The ultimate objective is to assess the economic conditions (e.g., e-
MeOH sale price) that determine the exploitation of a CCS- and/or CCU-driven chain; hence, the optimal sizing
and cost of the process units of the CCUS plant.
175
2. Plant description
The CCUS plant (Figure 1) is based on a cement plant with an oxy-fuel combustion CO2 capture system (90%
capture rate) with air separation unit (ASU). Given the captured CO2 output from the cement plant [#q9,
Figure 1], the process units can be selected and sized accordingly, as a result of the optimisation; these units
can comprise: (i) geological sequestration [#q13] for CO2 permanent storage (i.e., CCS); (ii) a low-temperature
electrolysis system (ELs) (65% electricity-to-LHV efficiency) to produce green H2 [#q3] and a chemical plant
(i.e., CCU) to generate e-MeOH [#q15]; or (iii) a combination of CCS and CCU units (i.e., CCUS). Renewable
electricity can be provided by a photovoltaic plant (PV) and/or wind turbines (WTs), and their intermittency can
be tackled by installing local storage systems of electric energy (i.e., battery energy storage - BES), H2, and/or
CO2. The electric energy demand of ELs, ASU, CCU, and CO2 capture plant can be fulfilled also via backup grid
electricity [#P2grid, #P3grid, #P6grid, and #P7grid, respectively], while any excess renewable electricity can be
exported to the grid [#P4]. The O2 co-produced from the electrolysers [#q16] can be exploited to decrease the
ASU capacity, while any excess O2 [#q23] can be exported. The H2O requirement of the ELs [#q1] is provided
through make-up H2O [#q18] and recycled H2O from the CCU plant [#q17].
Figure 1. Simplified CCUS plant flowsheet.
3. Plant optimisation model
The mathematical problem is formulated as a mixed-integer linear programming (MILP) one, with the objective
of minimising the total annual cost TAC [€/year] of the CCUS plant, given by the contribution over process units
k of annualised expenditures CAPEXk [€/year], variable costs fOPEXk [€/year] and vOPEXk [€/year], and carbon
tax Ctax [€/year], decreased by the revenues REV [€/year] from products (such as e-MeOH):
{
𝑜𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 = min(𝑇𝐴𝐶)
𝑇𝐴𝐶 =∑(𝐶𝐴𝑃𝐸𝑋𝑘 + 𝑓𝑂𝑃𝐸𝑋𝑘 + 𝑣𝑂𝑃𝐸𝑋𝑘)
𝑘
+ 𝐶𝑡𝑎𝑥 − 𝑅𝐸𝑉
(1)
The process units k are the photovoltaic plant k={PV}, wind turbines k={WT}, electrolysers k={EL}, CO2 utilisation
plant k={CCU}, electricity storage in batteries k={BES}, local H2 storage k={𝐻2
𝑠𝑡𝑜}, local CO2 storage k={𝐶𝑂2
𝑠𝑡𝑜}
and geological CO2 sequestration k={𝐶𝑂2
𝑠𝑒𝑞
}. Investment costs CAPEXk of Eq.(1) are calculated as:
𝐶𝐴𝑃𝐸𝑋 =∑𝐶𝐴𝑃𝐸𝑋𝑘
𝑘
(2)
𝐶𝐴𝑃𝐸𝑋{𝑃𝑉,𝑊𝑇,𝐸𝐿,𝐵𝐸𝑆,𝐻2
𝑠𝑡𝑜} = 𝑁{𝑃𝑉,𝑊𝑇,𝐸𝐿,𝐵𝐸𝑆,𝐻2
𝑠𝑡𝑜} ∙ 𝑈{𝑃𝑉,𝑊𝑇,𝐸𝐿,𝐵𝐸𝑆,𝐻2
𝑠𝑡𝑜}
𝑐𝑜𝑠𝑡 ∙ 𝐴𝐹{𝑃𝑉,𝑊𝑇,𝐸𝐿,𝐵𝐸𝑆,𝐻2
𝑠𝑡𝑜} (3)
𝐶𝐴𝑃𝐸𝑋𝐶𝐶𝑈 =∑(𝑐𝑎𝑝𝐶𝐶𝑈,𝑟 ∙ 𝑈𝐶𝐶𝑈,𝑟
𝑐𝑜𝑠𝑡 ∙ 𝐴𝐹𝐶𝐶𝑈)
𝑟
(4)
where 𝑈𝑘
𝑐𝑜𝑠𝑡 [€/appropriate unit] and AFk [%/year] are the unitary cost and the annuity factor of process unit k,
respectively (Table 1), while N [appropriate unit] of Eq.(3) represents the relevant scaling variable. CAPEXk for
local and permanent CO2 storage are not evaluated, as this stages account only for operative costs. A
linearisation of the unitary cost curve for the e-MeOH reactor is included in Eq.(4) through set r (Figure 2).
176
Table 1: Economic parameters for unit operations k, for short-term (ST) and long-term (LT) perspective.
Uk
cost
AFk C1k
k ST LT Unit [%] [%/year] Reference
WT 1075 920 [€/kWel] 8.7% 2.0% Ram et al. (2020)
PV 570 320 [€/kWel] 8.7% 3.0% Ram et al. (2020)
EL 800 400 [€/kWel] 13.6% 2.5% Ram et al. (2020)
CCU 451 451 [€/t e-MeOH/year] 7.8% 3.0% Pérez-Fortes et al. (2016)
BES 300 100 [€/kWhel] 6.7% 2.0% Ram et al. (2020)
H2
sto
9600 3500 [€/t H2] 7.8% 1.0% Ram et al. (2020)
CO2
sto
10 10 [€/t CO2] - - Ram et al. (2020)
CO2
seq
50 30 [€/t CO2] - - d’Amore et al. (2021)
(a) (b)
Figure 2. e-MeOH reactor cost curve discretisation derived from Pérez-Fortes et al. (2016): (a) total cost [M€];
and (b) unitary cost [€/(t e-MeOH/y)].
Fixed operating costs fOPEXk of Eq.(1) are evaluated as a fixed percentage C1k [%/year] (Table 1) from total
(i.e., not annualised) CAPEXk of Eq.(2) for each unit k:
𝑓𝑂𝑃𝐸𝑋𝑘 ∙ 𝐴𝐹𝑘 = 𝐶1𝑘 ∙ 𝐶𝐴𝑃𝐸𝑋𝑘 ∀𝑘 (5)
Variable operating costs vOPEXk of Eq.(1) are evaluated for process units k as:
𝑣𝑂𝑃𝐸𝑋 =∑𝑣𝑂𝑃𝐸𝑋𝑘
𝑘
+∑[(𝑃3𝑡
𝑔𝑟𝑖𝑑
+ 𝑃7𝑡
𝑔𝑟𝑖𝑑
) ∙ 𝑝𝑡
𝐸𝐸]
𝑡
(6)
𝑣𝑂𝑃𝐸𝑋𝐸𝐿 =∑(𝑞18𝑡 ∙ 𝑝
𝐻2𝑂 + 𝑃2𝑡
𝑔𝑟𝑖𝑑
∙ 𝑝𝑡
𝐸𝐸)
𝑡
(7)
𝑣𝑂𝑃𝐸𝑋𝐶𝐶𝑈 =∑(𝑃6𝑡
𝑔𝑟𝑖𝑑
∙ 𝑝𝑡
𝐸𝐸)
𝑡
(8)
𝑣𝑂𝑃𝐸𝑋𝐻2
𝑠𝑡𝑜 =∑(𝑞4𝑡 ∙ 4/3600 ∙ 𝑝𝑡
𝐸𝐸)
𝑡
(9)
𝑣𝑂𝑃𝐸𝑋𝐶𝑂2𝑠𝑡𝑜 =∑(𝑞10𝑡 ∙ 𝑈𝐶𝑂2𝑠𝑡𝑜
𝑐𝑜𝑠𝑡 )
𝑡
(10)
𝑣𝑂𝑃𝐸𝑋𝐶𝑂2
𝑠𝑒𝑞 =∑(𝑞13𝑡 ∙ 𝑈𝐶𝑂2
𝑠𝑒𝑞
𝑐𝑜𝑠𝑡 )
𝑡
(11)
In particular, Eqs.(6-8) take into account the H2O (i.e., q18t) and grid electricity consumptions of the ELs, ASU,
CCU, and cement plant, and respective unitary costs (i.e., pH2O [€/t H2O] for H2O and p
t
EE [€/MWh] for grid
electricity). The electric energy consumption of H2 storage is evaluated through Eq.(9) depending on the inlet
H2 flow rate q4t, while CO2 temporary storage and permanent sequestration costs of Eqs.(10,11) are evaluated
according to their inlet CO2 flowrates (i.e., q10t and q13t, respectively) and unitary costs (i.e., U
CO2
sto
cost
and U
CO2
seq
cost
,
respectively) reported in Table 1. The costs associated to the application of a carbon tax (i.e., ctax [100 €/t CO2])
177
on the indirect CO2 emissions associated to grid electricity, plus those associated to the direct release of CO2
to the atmosphere from the cement plant (i.e., q8t), are evaluated as:
𝐶𝑡𝑎𝑥 = 𝑐𝑡𝑎𝑥 ∙ {∑[(𝑃2𝑡
𝑔𝑟𝑖𝑑
+ 𝑃3𝑡
𝑔𝑟𝑖𝑑
+ 𝑃6𝑡
𝑔𝑟𝑖𝑑
+ 𝑃7𝑡
𝑔𝑟𝑖𝑑
) ∙ 𝐸𝑖𝑛𝑑,𝑡 + 𝑞8𝑡]
𝑡
} ∀𝑘 (12)
where Eind,t [t CO2/MWh] is the carbon intensity of the electric grid. Revenues REV of Eq.(1) derive from the sale
of e-MeOH (i.e., REVMeOH [€/year]) and excess H2 (i.e., REV
H2 [€/year]), and from the savings determined by
the use of the O2 produced by the ELs in (partial) substitution of that generated in the ASU (i.e., 𝑅𝐸𝑉𝑂2 [€/year]),
being pMeOH [€/t e-MeOH], pH2 [€/t H2], and pO2 [€/t O2] the sale prices of e-MeOH, H2, and O2, respectively:
𝑅𝐸𝑉 = 𝑅𝐸𝑉𝑀𝑒𝑂𝐻 + 𝑅𝐸𝑉𝐻2 + 𝑅𝐸𝑉𝑂2 (13)
𝑅𝐸𝑉𝑀𝑒𝑂𝐻 =∑(𝑞15𝑡 ∙ 𝑝
𝑀𝑒𝑂𝐻)
𝑡
(14)
𝑅𝐸𝑉𝐻2 =∑(𝑞2𝑡 ∙ 𝑝
𝐻2)
𝑡
(15)
𝑅𝐸𝑉𝑂2 =∑(𝑞23𝑡 ∙ 𝑝
𝑂2)
𝑡
(16)
4. Assumptions, constraints, and case studies
This study optimises the design and operation of a CCUS plant under the following assumptions: (i) the entire
amount of H2 (if any) produced by the ELs is sent to CCU, i.e., H2 cannot be exported to other users (q2t=0).
This constraint prevents the results being affected by the choice in the H2 price; (ii) any excess of renewable
electricity can be exported to the grid (P4t≥0), but it is assumed that a strictly positive export would not produce
any revenues, assuming that the grid is highly penetrated with renewables and the electricity price in high-wind
and high-sun periods (i.e., when excess electricity is produced in the assessed plant) is near-zero. A maximum
10% threshold is set on the contribution of grid electricity imported in the operation of the CCUS process units;
(iii) any O2 export (q23t) is monetised at a fixed price of 50 €/t of O2; and (iv) the model is optimised for a baseline
e-MeOH selling price of 450 €/t of e-MeOH (i.e., representative of the current market price increased by the
contribution of a carbon tax of 100 €/t of CO2). Cases hindering the installation of the CCU route are tested upon
variations (i.e., increases) of this price beyond 450 €/t of e-MeOH, to assess the CCS vs. CCU competitiveness.
The model is tested on the following case studies: (i) the optimisation of the CCUS plant is based on an
exemplifying geographic location, namely Puglia (Italy), and the mathematical model is optimised by utilising
geographic-specific data (with hourly resolution) in terms of solar (JRC, 2022) and wind (Renewables Ninja,
2023) relative generation, electricity price (ENTSO-E, 2023), and indirect CO2 emissions from the grid based on
hourly electric mix (ISPRA, 2022); (ii) unitary material and installation costs are based on two scenarios: a short
term perspective (ST) and a long-term one (LT) (Table 2). As electricity prices and indirect emissions are
characterised by a high degree of uncertainty, this study assumes the same values of these for ST and LT
cases; and (iii) the model is optimised by considering a limit of 30% on the minimum load of the CCU plant.
5. Results
The MILP model was optimised with GAMS software via CPLEX solver (350k continuous variables and 7
discrete ones). The results highlight that CCS is the best solution in a ST perspective for the entire range of
investigated e-MeOH prices with TAC of 62.2 M€/y, dominated by geological sequestration costs (38.3 M€/y,
i.e. 62%). CCU becomes cost-effective in a LT scenario for an e-MeOH price higher than 550 €/t (Figure 3a). In
particular, the CCU plant costs 326.0 M€/y, with significant contributions of PV (77.3 M€/y, i.e. 23.7%), WT
(113.7 M€/y, i.e. 34.9%), and ELs (81.7 M€/y, i.e. 25.1%), being TAC compensated by revenues from e-MeOH
sale. As for the levelised cost of renewable electricity (LCOE) and that including BES (LCOE’), in ST these result
equal to 45.9 and 82.9 €/MWhel, respectively, while in LT they decrease to 29.1 and 51.9 €/MWhel (-37%) in the
CCS plant, and to 31.5 and 36.6 €/MWhel (-31% and -56%) in the CCU one (Table 2). The optimal e-MeOH
plant obtained under a LT cost perspective exhibits a levelised cost of H2 (LCOH) of 2.6 €/kg (it was verified that
this plant is not affected by the choice in the maximum use of grid electricity). In ST, the optimal CCS plant
involves the installation of 54.3 MWel of PV, 62.1 MWel of WT, and 66.8 MWhel of BES, against 2.2 GWel of PV,
1.1 GWel of WT, and 1.3 GWhel of BES in the LT CCU plant; this plant has 1.2 GWel of ELs installed (with a
capacity factor of 49%), a 80 t/h e-MeOH reactor (with a capacity factor of 80%), and local H2 and CO2 storages
178
of 5 kt and 6 kt, respectively (Table 2). ST scenarios involve a significant export of renewable electricity to the
grid (31% export), against a higher internal use of renewables in the LT CCU plant (only 5% export).
Figure 3b shows a comparison in terms of CO2 balance among a base reference system consisting in the
cement plant without CO2 capture and a conventional MeOH plant (computed with specific emission of 470 kg
CO2/t MeOH and producing the same amount of product as in the e-MeOH CCU plant) (Ingham, 2017), the
CCS plant resulting from this study, and the CCU one. In the base case, direct and indirect emissions from
conventional cement and MeOH plants without CO2 capture are 1.15 Mt/y, which is higher than the total CO2
generated in the CCS and CCU cases (862.7 Mt/y, i.e. -25.1%). Moreover, in the CCS ad CCU cases, the great
majority of this CO2 (766.2 Mt/y) is sent to permanent geological storage and to e-MeOH production,
respectively. As a result, the CCS and CCU cases allow for a reduction 91.3% in the total direct and indirect
CO2 emissions with respect to the conventional base case.
(a) (b)
Figure 3. (a) Economic results: yearly cost and revenues and TAC [M€/y]. (b) CO2 balance of optimal CCS and
CCU plants, compared with reference cement plant and MeOH plant (i.e., Base) (Pérez-Fortes et al., 2016).
Table 2: Technical results: plant design, mass and electric balance, and performance indicators.
Description Unit ST-450 ST-550 LT-450 LT-550
Scenario ST/LT ST ST LT LT
MeOH price [€/t] 450 550 450 550
P
la
n
t
d
e
s
ig
n
Capacity PV [MWel] 54.3 54.3 99.6 2254.1
Capacity WT [MWel] 62.1 62.1 25.3 1054.1
Capacity EL [MWel] 0.0 0.0 0.0 1245.0
Capacity CCU [t MeOH/h] 0.0 0.0 0.0 80.0
Capacity BES [MWhel] 66.8 66.8 243.3 1284.3
Capacity H2 storage [t H2] 0.0 0.0 0.0 5347.2
Capacity CO2 storage [t CO2] 0.0 0.0 0.0 6239.8
M
a
s
s
b
a
la
n
c
e
CO2 captured (Q9) [t CO2/y] 766249 766249 766249 766249
CO2 avoided [t CO2/y] 754915 754915 757658 757573
CO2 to CCS (Q13) [%] 100.0% 100.0% 100% 0.0%
CO2 to CCU (Q14) [%] 0.0% 0.0% 0.0% 100.0%
Direct CO2 emission (Q8) [t CO2/y] 85139 85139 85139 85139
Indirect CO2 emission [t CO2/y] 11334 11334 8591 8676
H2 produced (Q3) [t H2/y] 0 0 0 104394
MeOH export (Q15) [t MeOH/y] 0 0 0 557462
O2 to cement plant (Q24) [t O2/y] 0 0 0 232060
O2 export (Q23) [t O2/y] 0 0 0 603089
E
le
c
tr
ic
b
a
la
n
c
e
Total renewable electricity [MWhel/y] 242980 242980 211879 6065901
Total grid electricity imported [MWhel/y] 26998 26998 23542 38142
Grid/tot electricity ratio [%] 10.0% 10.0% 10.0% 0.6%
PV el. pow. out (P1PV) [MWhel/y] 79266 79266 145217 3287542
WT el. pow. out (P1WT) [MWhel/y] 163714 163714 66661 2778359
Renewable el. export (P4) [%] 30.8% 30.8% 14.3% 5.1%
P
e
rf
o
rm
a
n
c
e
i
n
d
ic
a
to
rs
Carbon capture rate [%] 90.0% 90.0% 90.0% 90.0%
Carbon avoidance rate [%] 88.7% 88.7% 89.0% 89.0%
Capacity factor renewables [%] 23.8% 23.8% 19.4% 20.9%
Capacity factor rene. net [%] 16.5% 16.5% 16.6% 19.9%
Capacity factor PV [%] 16.6% 16.6% 16.6% 16.6%
Capacity factor WT [%] 30.1% 30.1% 30.1% 30.1%
Capacity factor EL [%] 0.0% 0.0% 0.0% 49.1%
Capacity factor CCU [%] 0.0% 0.0% 0.0% 79.5%
Equivalent cycles BES [cycles/y] 192 192 94 176
LCOE (levelised, PV, WT) [€/MWhel] 45.9 45.9 29.0 31.5
LCOE' (levelised, PV, WT, BES) [€/MWhel] 82.9 82.9 51.9 36.6
LCOH (levelised cost of H2) [€/kg H2] - - - 2.6
179
6. Conclusions
This article proposed an optimisation framework for the design of a carbon capture, utilisation, and storage
(CCUS) plant for the production of e-Methanol (e-MeOH) from green H2 (derived from low-temperature
electrolysis fed with renewables) and CO2 (derived from carbon capture at a cement plant). The chosen
geographic setting was located in the Puglia region (Italy), to represent a case study for the installation and
operation of renewable electricity plants, namely a photovoltaic plant and/or a wind farm, and of local storage
systems for electricity (in the form of batteries), hydrogen, and carbon dioxide.
When the costs of process units were accounted on a short-term perspective, the main outcome was a general
economic convenience of geological sequestration (i.e., CCS), as opposed to utilisation (CCU), while a long-
term (i.e., lower investment costs) scenario would allow for a cost-effective production of e-MeOH when the sale
price was above 550 €/t, which corresponded to a levelised production cost of green H2 (as an intermediate
product) of 2.6 €/kg. The resulting optimal CCS and CCU plants were shown to ensure a good performance in
terms of carbon avoidance with respect to benchmark MeOH production, even though a more thorough analysis
(e.g., based on life cycle assessment criteria) may lead to different outcomes, especially when considering the
significant deployment of renewables and batteries in the CCU plant.
The proposed modelling framework is of general validity and future work will involve testing it on different
geographic locations, to assess the competitiveness of CCS, CCU, and CCUS pathways under different
renewable energy profiles and market prices.
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A Mathematical Tool for Optimising Carbon Capture, Utilisation and Sequestration Plants for e-MeOH Production