DOI: 10.3303/CET24114099 Paper Received: 20 June 2024; Revised: 28 September 2024; Accepted: 23 November 2024 Please cite this article as: Prousalis T., Papadopoulos A.I., Seferlis P., 2024, Optimal Design of CO2 Capture, Utilisation, Mineralisation, and Sequestration Networks within Industrial Clusters, Chemical Engineering Transactions, 114, 589-594 DOI:10.3303/CET24114099 CHEMICAL ENGINEERING TRANSACTIONS VOL. 114, 2024 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Min Zeng, Yee Van Fan, Xuechao Wang Copyright © 2024, AIDIC Servizi S.r.l. ISBN 979-12-81206-12-0; ISSN 2283-9216 Optimal Design of CO2 Capture, Utilisation, Mineralisation, and Sequestration Networks within Industrial Clusters Thomas Prousalisa, Athanasios I. Papadopoulosb, Panos Seferlisa,* aDepartment of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece bChemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, Thermi Thessaloniki 57001, Greece seferlis@auth.gr Holistic development of CO2 capture, utilisation, and storage (CCUS) networks is crucial for the cost- effectiveness and the widespread deployment of such technologies in the industry. This work proposes a novel framework for the design and optimisation of CCUS networks within industrial clusters. Models for advanced CO2 capture, utilisation, mineralisation, compression, transportation, and sequestration processes, coupled with economics, are developed and employed. Precipitated calcium carbonate (PCC) nanoparticles produced by a rotating-packed bed process are considered the sole product of CO2 utilisation and provide revenue in the CCUS network. The framework aims to minimise the total annual cost of the network by adapting the optimal designs of each subprocess and selecting the most suitable CO2 routes while ensuring a 90 % decrease in CO2 emissions. A mixed-integer linear programming (MILP) framework is used to solve the optimisation problem. The performed case studies involve 5 industrial emitters from different industrial sectors, 3 sequestration sites, and one mineral deposit site. The results showed that the traditional CO2 capture-transportation-sequestration chain is favourable when we assume no revenue from the utilisation process, and it offers a 7.2 % lower cost per ton of avoided CO2 than purchasing carbon permits with the current price. Considering revenue from PCC drives all available Ca(OH)2 into the utilisation process, reducing the network’s cost per ton of CO2 by 10.9 % and 3.9 % compared to the carbon permits and CCS network costs. 1. Introduction The increasing CO2 concentration in the atmosphere is identified as the main contributor to global warming. The Paris Agreement sets the goals for a 43 % decrease in greenhouse gas (GHG) emissions by 2030 and carbon neutrality by 2050. To achieve those goals, the systematic adaptation of different low-carbon technologies is imperative despite the current wide dependency on fossil fuel energy (Tapia et al., 2018). CO2 capture, utilisation, and sequestration (CCUS) are promising technologies to mitigate anthropogenic GHGs, as they can achieve high capture efficiencies and be integrated into existing industrial plants. As such, they can play an important role in the transition into low-carbon technologies and a fossil-free energy economy (Gibbins and Chalmers, 2008). Even so, the widespread deployment of CCUS technologies is slower than anticipated, mainly due to the high costs associated with them (Bui et al., 2018). CO2 utilisation can greatly facilitate capture cost compensation, as it creates a potential revenue stream for the CCUS network. The most common CO2 utilisation option in published literature is enhanced oil recovery (EOR). Yet, it is still under debate, as it induces the production of more fossil fuels, and it does not satisfy the goals of a circular economy (Chauvy and De Weireld, 2020). Other utilisation options include agricultural products, synthetic fuels, and minerals (Leonzio et al., 2020). Studying each CCUS subprocess separately is not sufficient to understand the economy of such networks. In the selection of the most cost-effective technological options and the development of the highest-performing network structures, it is crucial to consider a holistic approach encompassing the entire CCUS chain (Leonzio et al., 2020). Mathematical programming enables the optimisation of such complex and scaled-up CCUS infrastructures and is a useful tool for decision-makers, especially in the early stages of the deployment of CCUS technologies (d’Amore and Bezzo, 2017). D’Amore and Bezzo (2017) developed a Mixed-Integer Linear Programming (MILP) framework for the strategic development of a European network for CO2 capture and 589 storage (CCS). The objective was to minimise the total cost of the network, while multiple CO2 capture and transportation options were included. Al-Mohannadi and Linke (2016) proposed a multi-step approach to the systematic design of CCUS networks for industrial parks. A continuous optimisation method was adopted in the final step, including a variety of utilisation options. Hasan et al. (2014) presented a multi-scale MILP framework for CCUS aiming to maximise the profits from EOR. Several material, process, and supply chain design options were integrated. The employed costing method was based on input-output models that were extracted from detailed Aspen Plus® models for each subprocess. Ostovari et al. (2023) proposed a supply chain network for CCUS by mineralisation, where all captured CO2 is converted into minerals that are either used as products or stored in abandoned mines. The objective was to minimise the total annual cost (TAC) of the network. Their results showed that the cost of CCUS by mineralisation could be comparable to traditional CCUS when implemented in a network. Although these works offer advancements in CCUS network design and optimisation, they lack detailed techno-economic analyses, as their cost assessment for each CCUS subprocess is mostly based on data from Intergovernmental Panel on Climate Change (IPCC) reports (Metz et al., 2005). Also, utilisation and mineralisation options are either limited within previously studied technological choices (e.g., EOR, agricultural products) or absent. The scope of this work is to develop a MILP framework that will provide an optimal design of a CCUS network within an industrial cluster. CO2 capture, mineralisation, utilisation, compression and pumping, transportation via pipeline, and sequestration subprocesses are incorporated into the framework. The economic evaluation of the CCUS subprocesses is based on data from precise techno-economic and process models that are optimised for different values within the operating range. The product from CO2 utilisation is in the form of precipitated calcium carbonate (PCC) nanoparticles and is responsible for the revenue of the network. Carbonate nanoparticles are gaining increasing attention, and they are a growing market as they find various emerging applications in many industrial sectors (Nessi et al., 2022). To the authors’ knowledge, this is the first time that CO2 utilisation through PCC nanoparticle production in a rotating packed bed (RPB) and CO2 mineralisation directly from flue gas for capture are integrated within a CCUS network design framework. The framework’s objective is to minimise the TAC of the whole network while decreasing the cluster’s emitted CO2 by 90 %. 2. Methodology 2.1 Description of the framework This work proposes a design framework for cost-optimal CCUS industrial cluster networks while using cost data from optimised, rigorous models for each network subprocess. The framework tests two novel processes, the RPB-based PCC nanoparticle production as a CO2 utilisation option and the slurry-based CO2 mineralisation for capture using directly the plants’ flue gas. Figure 1 shows the flowsheet of the CCUS network to be designed. Figure 1: CCUS network flowsheet The framework takes as input the flue gas characteristics and the location of each industrial emitter, as well as the location of the sequestration and mineral deposit sites. It is assumed that CO2 capture, utilisation, compression and pumping, mineralisation, and PCC filtering and drying can take place only in any of the emitter plant sites. The decision framework determines for each emitter the selected technology for the treatment of the flue gas. The two choices are calcium hydroxide-Ca(OH)2, slurry-based, CO2 mineralisation and amine-based post-combustion CO2 capture. The first technology employs an RPB reactor to intensify the reaction of CO2 contained in the flue gas with Ca(OH)2 slurry. The reaction produces calcium carbonate-CaCO3, which is stable and non-hazardous for the environment. After this stage, the CaCO3 aqueous solution is left to dry, and then it can be transported via trucks for underground deposit. Common sites for the deposit are depleted mines. The 590 second technology uses packed-bed (PB) absorption/desorption and an aqueous MEA solution for the separation of CO2 from the flue gas. After the separation, the exit stream contains almost pure CO2. There are three choices at this stage: CO2 conversion to products, transportation to a nearby emitter’s plant site, and compression and pumping. CO2 conversion takes place using the same technology as that used in the CO2 mineralisation case but in a controlled mode to target the desired particle size distribution; however, it uses the pure CO2 stream instead of the flue gas. The product of this process is high-quality PCC nanoparticles free from impurities, which are a value-added product with various industrial applications, and usually, it is commercially valued in the range of 1,000 to 2,000 €/t. Filtering and drying stages are required for the removal of moisture from the PCC crystals. CO2 transportation via a low-pressure pipeline grid pertains to the interconnection between the industrial emitters to either transport CO2 for utilisation in another emitter’s site or to inject CO2 into the high-pressure pipeline grid. The transportation is performed with the CO2 in the gaseous phase, as it is not cost-effective to liquefy it for short distances. Finally, compression and pumping pertain to the increase of the pressure of the pure CO2 stream up to a critical point where it turns into liquid and is then pumped to further increase its pressure. Transportation via the high-pressure pipeline grid is favourable for long distances, as the density of CO2 is higher compared to its gaseous phase. Finally, when the CO2 stream reaches the sequestration site, it is injected through wells into the underground cavity, which may be a saline aquifer or a depleted oil or natural gas reservoir, for permanent storage. 2.2 Optimisation problem formulation The decision variable vector (𝑋) of the MILP optimisation problem consists of 𝑁 variables with the name 𝐶𝐶 which denote the amine-based CO2 capture plants, 𝑁 variables with the name 𝑀𝐼𝑁 which denote the mineralisation-based capture plants, 𝑁 variables with the name 𝐶𝑃 which refer to the CO2 compression and pumping, 𝑁 × (𝑁 − 1) + 𝑆 × 𝑁 variables with the name 𝑃𝐼𝑃𝐸 and 𝑃𝐼𝑃𝐸𝑠𝑒𝑞 which denote the CO2 transportation via pipeline, 𝑁 × 𝐺 variables with the name 𝑇𝑅𝑈𝐶𝐾 which denote the mineral transportation via truck from each of the 𝑁 industrial emitter sites to each of the 𝐺 mineral deposit sites, 𝐺 variables with the name 𝐺𝐸𝑂 which refer to the mineral underground deposits, 𝑆 + 𝑙 variables with the name 𝑆𝐸𝑄 which refer to the CO2 sequestration, 𝑁 variables with the name 𝐶𝑈 which refer to the CO2 utilisation, 𝑁 variables with the name 𝐹𝐷 which refer to the PCC filtering and drying, 𝑁 variables with the name 𝑃𝐶𝐶 which refer to the PCC produced by each one of the 𝑁 CO2 utilisation subprocesses, and 𝑆 + 𝑙 binary variables (𝑖𝑆𝐸𝑄) related to the CO2 sequestration. It must be mentioned that 𝑁 is the number of industrial emitters, 𝐺 is the number of mineral underground deposit sites, 𝑆 is the number of sequestration sites and 𝑙 is the number of extra sequestration variables needed to capture the process behaviour. For the transportation of CO2 via pipeline, 𝑁 × (𝑁 − 1) variables, named 𝑃𝐼𝑃𝐸, refer to the interconnection between the industrial emitter sites (low-pressure pipeline grid), and 𝑆 × 𝑁 variables, named 𝑃𝐼𝑃𝐸𝑠𝑒𝑞 refer to the connection of each industrial emitter site with each sequestration site (high-pressure pipeline grid). For the sequestration subprocess, the total number of variables is 2 × (𝑆 + 𝑙). This is due to the nonlinear behaviour of the process, which is approximated by piece-wise linear models. All variables of vector 𝑋 express the mass flow rate of CO2 that enters the corresponding network subprocess (t/d), except 𝑇𝑅𝑈𝐶𝐾, 𝐺𝐸𝑂, 𝐹𝐷, and 𝑃𝐶𝐶 that express the mass flow rate of PCC (t/h), and 𝑖𝑆𝐸𝑄 that are binary. The objective function to be minimised (𝑂𝐹) is expressed as the sum of all the cluster subprocesses’ TAC minus the revenue generated by the produced PCC (Eq(1)). The constraints of the problem are presented in set of Eq(2) and express the mass balances of CO2, minerals, and PCC for each subprocess and node of the network. min 𝑋 𝑂𝐹 = ∑ 𝑇𝐴𝐶𝐶𝐶𝑖 𝑁 𝑖=1 + ∑ 𝑇𝐴𝐶𝑀𝐼𝑁𝑖 𝑁 𝑖=1 + ∑ 𝑇𝐴𝐶𝐶𝑃𝑖 𝑁 𝑖=1 + ∑ ∑ 𝑇𝐴𝐶𝑃𝐼𝑃𝐸𝑖,𝑗 𝑁−1 𝑗=1,𝑖≠𝑗 𝑁 𝑖=1 + ∑ ∑ 𝑇𝐴𝐶𝑃𝐼𝑃𝐸𝑠𝑒𝑞,𝑖,𝑗 𝑆 𝑗=1, 𝑁 𝑖=1 + ∑ ∑ 𝑇𝐴𝐶𝑇𝑅𝑈𝐶𝐾𝑖,𝑗 𝐺 𝑗=1 𝑁 𝑖=1 + ∑ 𝑇𝐴𝐶𝐺𝐸𝑂𝑖 𝐺 𝑖=1 + ∑ ∑ 𝑇𝐴𝐶𝑆𝐸𝑄𝑖,𝑗 4 𝑗=1,𝑓𝑜𝑟 𝑖≠3 𝑆 𝑖=1 + ∑ ∑ 𝑇𝐴𝐶𝑖𝑆𝐸𝑄𝑘,𝑗 4 𝑗=1,𝑓𝑜𝑟 𝑖≠3 𝑆 𝑘=1 + ∑ 𝑇𝐴𝐶𝐶𝑈𝑖 𝑁 𝑖=1 + ∑ 𝑇𝐴𝐶𝐹𝐷𝑖 𝑁 𝑖=1 − ∑ 𝑅𝐸𝑉𝑃𝐶𝐶𝑖 𝑁 𝑖=1 (1) 𝐶𝐶𝑖 + 𝑀𝐼𝑁𝑖 = 𝐶𝑂𝑀𝑖 , 𝑖 ∈ (1, 𝑁) 𝑓𝑃𝐶𝐶(𝑀𝐼𝑁𝑖) − ∑ 𝑇𝑅𝑈𝐶𝐾𝑖,𝑗 𝑗 = 0 , 𝑖 ∈ (1, 𝑁) 𝑎𝑛𝑑 𝑗 ∈ (1, 𝐺) ∑ 𝑇𝑅𝑈𝐶𝐾𝑖,𝑗 𝑖 − 𝐺𝐸𝑂𝑗 = 0 , 𝑖 ∈ (1, 𝑁) 𝑎𝑛𝑑 𝑗 ∈ (1, 𝐺) 𝑃𝐼𝑃𝐸𝑖,𝑗 + 𝑃𝐼𝑃𝐸𝑗,𝑖 = 0 , 𝑖 ∈ (1, 𝑁), 𝑗 ∈ (1, 𝑁), 𝑖 ≠ 𝑗 𝑓𝑃𝐶𝐶(𝐶𝑈𝑖) − 𝐹𝐷𝑖 = 0 , 𝑖 ∈ (1, 𝑁) (2) 591 ∑ 𝑆𝐸𝑄𝑖,𝑗 𝑗 − ∑ 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,𝑘,𝑖 𝑘 = 0 , 𝑖 ∈ (1, 𝑆), 𝑗 < 𝑙, 𝑎𝑛𝑑 𝑘 ∈ (1, 𝑁) 𝐶𝑃𝑖 − ∑ 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,𝑖,𝑗 𝑗 = 0 , 𝑖 ∈ (1, 𝑁) 𝑎𝑛𝑑 𝑗 ∈ (1, 𝑆) 𝐶𝐶𝑖 ⋅ 𝑓𝐶𝐴𝑃𝑇 − 𝐶𝑈𝑖 − 𝐶𝑃𝑖 + ∑ 𝑃𝐼𝑃𝐸𝑗,𝑖 𝑗 = 0 , 𝑖 ∈ (1, 𝑁), 𝑗 ∈ (1, 𝑁), 𝑖 ≠ 𝑗 ∑ 𝐶𝑈𝑖 𝑖 − 𝑓𝐿𝐼𝑀𝐸(𝐶𝐴𝑃𝐶𝑎(𝑂𝐻)2 ) ≤ 0 , 𝑖 ∈ (1, 𝑁) 𝑃𝐶𝐶𝑖 − 𝐹𝐷𝑖 = 0 , 𝑖 ∈ (1, 𝑁) 0 ≤ ∑ 𝑖𝑆𝐸𝑄𝑖,𝑗 𝑗 ≤ 1 , 𝑖 ∈ (1, 𝑆) 𝑎𝑛𝑑 𝑗 < 𝑙 0 ≤ 𝑆𝐸𝑄𝑖,𝑗 , 𝑖 ∈ (1, 𝑆), 𝑗 < 𝑙 𝑆𝐸𝑄𝑘,𝑗 − 𝑀ℎ ⋅ 𝑖𝑆𝐸𝑄𝑘,𝑗 ≤ 0 , 𝑘 ∈ (1, 𝑆), 𝑗 < 𝑙, 𝑎𝑛𝑑 ℎ ∈ (1, 𝑆 + 𝑙) where 𝑓𝑃𝐶𝐶 is the function that correlates the amount of CO2 that enters in either of the mineralisation or utilisation subprocesses (t/d) with the amount of minerals or PCC that will be produced (t/h), 𝑓𝐶𝐴𝑃𝑇 is the CO2 capture efficiency goal, 𝑓𝐿𝐼𝑀𝐸 is the function that correlates CO2 emissions with the available Ca(OH)2 from the quicklime plant, 𝐶𝐴𝑃𝐶𝑎(𝑂𝐻)2 is the maximum available Ca(OH)2 to be used for PCC production, and 𝑀 constants are the “big-M” multipliers. The value of 𝑀 in each constraint is chosen so that it represents the upper limit in the respective independent variable range it refers to. 2.3 Process models All CCUS network subprocess design and costing is performed using data through optimised rigorous process models. For CO2 capture, the model using MEA and the conventional PB configuration is attained from Damartzis et al. (2014). For CO2 mineralisation and utilisation, the model tested in Prousalis et al. (2023) is used. For CO2 compression and pumping, transportation via pipeline, and sequestration, a detailed analysis by McCollum and Ogden (2006) is adopted. Data for sequestration sites in Greece are attained by Koukouzas et al. (2011). Finally, for the PCC filtering and drying, the cost estimation is performed using the techniques in Walas (1988). All cost values for electricity and equipment purchases are updated to recent values. Figure 2 shows the linear and piece-wise linear models derived for the utilisation and sequestration processes. (a) (b) Figure 2: a) Model data and linear function for CU process, b) Model data for CO2 sequestration in site 2 The linear equations are cost-related in most cases, except for functions 𝑓𝑃𝐶𝐶 and 𝑓𝐿𝐼𝑀𝐸. The equations are extracted through linear regression on model data that are optimised for different CO2 or flue gas compositions and flow rates or different PCC or minerals flow rates, covering the entire operating range for each CCUS subprocess. In most cases, the linear equations present great agreement with the model data (Figure 2a), leading to the assumption that there is no need for a more complex optimisation method. In the cases where the trade-offs are nonlinear, the behaviour is approximated by piece-wise linear functions (Figure 2b). 2.4 Implementation For the case studies, 5 industrial emitters (𝑁), one mineral deposit site (𝐺), 3 sequestration sites (𝑆), and 6 extra sequestration process variables (𝑙) are considered, leading to a total of 89 decision variables in the vector 𝑋. The industrial emitters’ flue gas characteristics are presented in Table 1. The cluster consists of a quicklime plant, a cement plant, a pulp and paper plant, a natural gas power plant, and a refinery. The total treated CO2 in the cluster is 5.5 Mt/y. Maximum Ca(OH)2 availability is restricted to 20 % of the annual capacity of the cluster’s quicklime plant, while the capacity is estimated assuming that 1.2 tons of CO2 are emitted for the production of a ton of Ca(OH)2 (Simoni et al., 2022). Finally, it must be mentioned that both CO2 capture, utilization, and mineralization processes are designed to achieve 90 % CO2 conversion efficiency. 592 Table 1: Industrial emitter’s flue gas composition and flow rates Type Total flow rate (mol/s) CO2 (vol. %) H2O (vol. %) N2 (vol. %) CO2 flow rate (Mt/y) Source Quicklime 320 12.3 12.5 75.5 0.055 Kazepidis et al. (2021) Cement 9,922 16.5 13.2 70.3 2.272 Gerbelová et al. (2017) Pulp & Paper 4,600 13.3 19.0 67.7 0.849 Gardarsdottir et al. (2014) NG Power 17,675 4.1 12.5 83.0 1.106 Kazepidis et al. (2021) Refinery 11,218 7.9 14.9 77.2 1.230 Nazerifard et al. (2023) 3. Results and discussion 3.1 Case study A: Without revenue from PCC In case study A, the framework is tested without considering any revenue. The algorithm terminated successfully after finding the optimal solution. The cost-optimal CCS network presents the total cost per ton of avoided CO2 as equal to 76.9 €/t. Comparing this to the current carbon permit cost under EU ETS of 82.9 €/t (end of 2023), the CCS network offers a 7.2 % lower cost per ton of CO2. The decision variable results are presented in Table 2. The results show that the CO2 from each industrial emitter is captured on site, then compressed, and finally transported by each one of them separately to sequestration site 2, which is the nearest site to the cluster. The transportation of CO2 between the industrial emitters or any other sequestration site was avoided. Also, the use of the mineralisation for the capture subprocess and its after-treatment chain was avoided. Table 2: Optimal network routes for case study A Decision variable Value (t/d) Decision variable Value (t/d) Decision variable Value (t/d) Decision variable Value (t/d) 𝐶𝐶1 150.68 𝐶𝑃1 135.62 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,1,2 135.62 𝑆𝐸𝑄2,4 13,591.23 𝐶𝐶2 6,224.65 𝐶𝑃2 5,602.19 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,2,2 5,602.19 𝑖𝑆𝐸𝑄2,4 1 𝐶𝐶3 2,326.02 𝐶𝑃3 2,093.42 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,3,2 2,093.42 - - 𝐶𝐶4 3,030.13 𝐶𝑃4 2,727.12 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,4,2 2,727.12 - - 𝐶𝐶5 3,369.86 𝐶𝑃5 3,032.88 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,5,2 3,032.88 - - 3.2 Case study B: With revenue from PCC In case study B, a price of 1,000 €/t of PCC is considered. The algorithm terminated successfully after finding the optimal solution once again. The total cost per ton of avoided CO2 for the designed CCUS network is 73.9 €/t. In this case, the cost per ton of CO2 is 10.9 % lower than the carbon permit price and 3.9 % compared to the CCS network configuration. The decision variables' values are presented in Table 3. The results indicate that the revenue from PCC is so strong, as all available Ca(OH)2 is exploited in the utilisation process. Once again, no use of the low-pressure interconnection grid or the mineralisation for the capture process takes place. Table 3: Optimal network routes for case study B Decision variable Value (t/d) Decision variable Value (t/d) Decision variable Value (t/d) Decision variable Value (t/d) Decision variable Value (t/h) 𝐶𝐶1 150.68 𝐶𝑃1 135.62 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,1,2 135.62 𝑆𝐸𝑄2,4 13,575.61 𝐹𝐷2 1.33 𝐶𝐶2 6,224.65 𝐶𝑃2 5,586.58 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,2,2 5,586.58 𝑖𝑆𝐸𝑄2,4 1 𝑃𝐶𝐶2 1.33 𝐶𝐶3 2,326.02 𝐶𝑃3 2,093.42 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,3,2 2,093.42 𝐶𝑈2 15.62 - - 𝐶𝐶4 3,030.13 𝐶𝑃4 2,727.12 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,4,2 2,727.12 - - - - 𝐶𝐶5 3,369.86 𝐶𝑃5 3,032.88 𝑃𝐼𝑃𝐸𝑠𝑒𝑞,5,2 3,032.88 - - - - 4. Conclusions A framework for the optimal design of CCUS networks within industrial clusters was developed. Advanced process and techno-economic models for CO2 capture, utilisation, mineralisation, compression and pumping, transportation, and sequestration were integrated into the framework through regressed linear and piece-wise linear functions. The results showed that the traditional CO2 capture-compression-transportation-sequestration chain is more cost-effective than CO2 mineralisation for capture. In the case studies, the optimal CCS and CCUS networks achieved a total cost per ton of avoided CO2 equal to 76.9 €/t and 73.9 €/t. These options are favourable compared to purchasing carbon permits with the current price, as they offer reduced costs by 7.2 % and 10.9 %. Future work will involve the integration of different CCUS subprocess technologies, as well as analysis with constraints in raw materials availability and product demand, including uncertainty. 593 Acknowledgments Funded by the European Union. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101075727. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them. Funding from the UKRI under the Horizon Europe Guarantee is gratefully acknowledged (Ref 10042326, 10042487, 10050119, 10058640). References Al-Mohannadi D.M., Linke P., 2016, On the systematic carbon integration of industrial parks for climate footprint reduction. Journal of Cleaner Production, 112, 4053-4064. Bui M., Adjiman C.S., Bardow A. et al., 2018, Carbon capture and storage (CCS): The way forward. Energy & Environmental Science, 11, 1062-1176. Chauvy R., De Weireld G., 2020, CO2 Utilization Technologies in Europe: A Short Review. Energy Technology, 8, 2000627. Damartzis T., Papadopoulos A.I., Seferlis P., 2014, Optimum synthesis of solvent-based post-combustion CO2 capture flowsheets through a generalized modeling framework. Clean Technologies Environmental Policy, 16, 1363-1380. D’Amore F., Bezzo F., 2017, Economic optimization of European supply chains for CO2 capture, transport and sequestration. International Journal of Greenhouse Gas Control, 65, 99-116. Gardarsdottir S.O., Normann F., Andersson K., Johnsson F., 2014, Process evaluation of CO2 capture in three industrial case studies. Energy Procedia, 63, 6565-6575. Gerbelová H., Van der Spek M., Schakel W., 2017, Feasibility assessment of CO2 capture retrofitted to an existing cement plant: post-combustion vs. oxy-fuel combustion technology. Energy Procedia, 114, 6141- 6149. Gibbins J., Chalmers H., 2008, Carbon Capture and Storage. Energy Policy, 36, 4317-4322. Hasan M.M.F., Boukouvala F., First E.L., Floudas C.A., 2014, Nationwide, Regional, and Statewide CO2 Capture, Utilization, and Sequestration Supply Chain Network Optimization. Industrial & Engineering Chemistry Research, 53, 7489-7506. Kazepidis P., Papadopoulos A.I., Tzirakis F., Seferlis P., 2021, Optimum design of industrial post-combustion CO2 capture processes using phase-change solvents. Chemical Engineering Research and Design, 175, 209-222. Koukouzas N., Ziogou F., Gemeni V., 2011, Cost of pipeline-based CO2 transport and geological storage in saline aquifers in Greece. Energy Procedia, 4, 2978-2983. Leonzio G., Foscolo P.U., Zondervan E., Bogle I.D.L, 2020, Scenario Analysis of Carbon Capture, Utilization (Particularly Producing Methane and Methanol), and Storage (CCUS) Systems. Industrial & Engineering Chemistry Research, 59, 6961-6976. Metz B., Davidson O., De Coninck H.C., Loos M., Meyer L.A. (Eds.), IPCC Special Report on Carbon Dioxide Capture and Storage. Cambridge University Press, UK and USA. McCollum D.L., Ogden J.M., 2006, Techno-Economic Models for Carbon Dioxide Compression, Transport, and Storage. University of California, Davis, USA. Nazerifard R., Mohammadpourfard M., Heris S.Z., 2023, Design, thermodynamic and economic evaluation, and optimization of gasoline production from refinery furnaces flue gas. Energy Conversion and Management, 293, 117492. Nessi E., Dimoliani M., Papadopoulos A.I. Seferlis P., 2022, Experimental Testing for Calcium Carbonate Nanoparticles Production in a Rotating Packed Bed. Chemical Engineering Transactions, 94, 727-732. Ostovari H., Kuhrmann L., Mayer F. Minten H., Bardow A., 2023, Towards a European supply chain for CO2 capture, utilization, and storage by mineralization: Insights from cost-optimal design. Journal of CO2 Utilization, 72, 102496. Prousalis T., Gkizas G. Papadopoulos A.I., Seferlis P., 2023, Design of CO2 Capture and Mineralization Systems: Integrated Process Optimization and Controllability Assessment in Parallel Infrastructures. Computer Aided Chemical Engineering, 52, 2815-2820. Simoni M., Wilkes M.D., Brown S., Provis J.L., Kinoshita H., Hanein T., 2022, Decarbonising the lime industry: State-of-the-art. Renewable and Sustainable Energy Reviews, 168, 112765. Tapia J.F.D., Lee J.-Y., Ooi R.E.H., Foo D.C.Y., Tan R.R., 2018, A review of optimization and decision-making models for the planning of CO2 capture, utilization and storage (CCUS) systems. Sustainable Production and Consumption, 13, 1-15. Walas S.M.,1988, Chemical Process Equipment: Selection and Design. Butterworth-Heinemann, USA. 594