DOI: 10.3303/CET25120002 Paper Received: 05 June 2025; Revised: 9 September 2025; Accepted: 20 October 2025 Please cite this article as: Nanggala P.L.A., Renanto R., Juwari J., Anugraha R.P., Satriyo R.R., Kusuma P.F.A.I.G., Amarasuli S.F., 2025, A Comparative Study Between Pinch Design Method and Superstructure Applied on Carbon Capture and Storage, Chemical Engineering Transactions, 120, 7-12 DOI:10.3303/CET25120002 CHEMICAL ENGINEERING TRANSACTIONS VOL. 120, 2025 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Bing Shen How, Viknesh Andiappan, Denny K.S. Ng, Hon Loong Lam, Petar S. Varbanov Copyright © 2025, AIDIC Servizi S.r.l. ISBN 979-12-81206-21-2; ISSN 2283-9216 A Comparative Study Between Pinch Design Method and Superstructure Applied on Carbon Capture and Storage Pragusti Lintang Adhi Nanggala, Renanto*, Juwari, Rendra Panca Anugraha, Raden Ricko Satriyo, I G Putu Farrell Aditya Kusuma, Siti Fatimah Amarasuli Chemical Engineering Department, Faculty of Industrial Technology and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia renanto@chem-eng.its.ac.id Carbon Capture and Storage (CCS) has become trend topic among the researcher in Indonesia due to its urgency in reducing carbon emissions. Planning of CCS optimization is important in order to achieve maximum captured CO2. This study compares Pinch Analysis and Superstructure Modelling for CCS planning across multi-region sources and sinks in Indonesia. Focusing on three key parameters: alternative storage, captured CO2, and unutilized storage, both approaches will be evaluated. Grid diagram will be applied to both approach (Pinch Analysis and Superstructure Modelling) to show the reliability of the calculation results. For Pinch Analysis, cascade calculations will determine optimal parameters under temporal constraints while Superstructure Modelling will employ mixed-integer linear programming (MILP) to model multi-period CCS networks. Results indicate that Pinch Analysis achieved a 7 % higher captured CO2 compared to Superstructure Modelling, with a total of 8 pairings for Pinch Analysis and 5 pairings for Superstructure Modelling. These findings inform the comparison of hybrid frameworks between thermodynamic visualization (Pinch Analysis) and multi-objective optimization (Superstructure Modelling) to address Indonesia’s cross-regional CCS challenges. 1. Introduction Global CO2 emissions from the energy sector increased by 1.1 % and reached 37.4 B tons in 2023 (Ayyad et al., 2024). Several mitigation strategies, such as Carbon Capture and Storage (CCS), have been developed to overcome this issue. CCS is designed to capture CO2 from emissions sources, then transport and either store it safely or utilize it in industrial processes (Ooi et al., 2013). In short, the process begins with capturing CO2 from exhaust gases, transporting it through pipelines, and storing it in geological reservoirs (Tapia, 2023). In Indonesia, there are many potential CO2 sources and sinks, each with its own operational timeframe. However, selecting the appropriate method for matching sources and sinks is essential to effectively implement a CCS system and reduce the carbon footprint. These approaches are particularly valuable since CCS planning involves several complex challenges including the temporal and spatial mismatch between CO2 sources and sinks, infrastructure limitations, and capital cost (Handogo et al., 2020). Two commonly used approaches are Pinch Analysis and Superstructure Modeling. Pinch Analysis offers a heuristic method that simplifies the matching of sources and sinks based on time compatibility and capacity constraints, making it suitable for fast evaluation (Smith, 2005). Meanwhile, Superstructure Modeling provides more comprehensive frameworks by using mathematical programming to determine the optimal configuration (Ayyad et al., 2024). Therefore, using these two approaches allows more holistic comparison of system performance. Putra et al. (2018) reported that this approach was applied to compare the effectiveness of simultaneous and sequential approaches in CO2 capture. The results indicated that the sequential approach achieved a capture efficiency of up to 93.86 % under a zero time-difference scenario whereas the simultaneous approach achieved 83.94 %. Furthermore, this approach could reduce the system’s total annual cost by up to US$ 159 M under optimal configuration. On the other hand, the Superstructure Modeling allows for the simultaneous exploration of multiple system configurations. Dwiputro et al. (2021) developed a multi-region CCS scenario for Kalimantan, Sulawesi, and East Java, demonstrating that the approach achieved 93.36 % CO2 capture efficiency with full sink utilization 7 and no unutilized storage, despite incurring higher costs compared to the Pinch Analysis. The Superstructure Modelling minimized the Total Annual Cost (TAC) to approximately US$ 2.6 B. Although previous studies have applied either Pinch Analysis or Superstructure Modelling in CCS, a direct comparison between the two under identical assumptions has not yet been explored. Prior works, such as Putra et al. (2018) and Dwiputro et al. (2021), have utilized these methods in isolation, often under differing assumptions and system constraints. However, a direct, side-by-side evaluation using consistent system boundaries and data inputs remains largely unaddressed in existing literature. To address this gap, both approaches are evaluated side by side using consistent input data, timeframes, and capacity constraints so it will become quantitative comparison that highlights the trade-offs between Pinch Analysis and Superstructure Modelling. The outcomes are expected to contribute to the development of efficient CCS systems and support emissions reduction policies in the transition towards a low-carbon economy. 2. Method The following paragraph outlines the basis data related to CO2 source and existing potential sinks in Indonesia, detailing the procedures for conducting Pinch Analysis and Superstructure Modelling. 2.1 Basis Data Table 1 and Table 2 show data on the sources and sinks that will be utilized in this study. This data was adopted from Handogo et al. (2020), which includes four sources and two sinks. These specific facilities were selected due to their high emissions profiles and proximity to identified storage sites, as well as the availability of complete temporal and emissions data. In this study, the term ‘alternative storage’ will be used to indicate CO2 from sources that cannot be captured by sinks and can be utilized as chemical products. Conversely, ‘unutilized storage’ will be used to indicate inactive sinks since they do not receive any CO2 from sources. Table 1: Source Data No. Source Start of Operation (y) End of Operation (y) Average CO2 Emissions Rate (Mt/y) Total CO2 Emitted (Mt) 1. Pupuk Kaltim 5 30 2.403 60.075 2. Badak LNG 5 20 5.514 82.71 3. Semen Bosowa 7 27 1.575 31.5 4. Semen Tonasa 8 28 3.791 75.82 Total CO2 Emitted 250.105 Table 2: Sink Data No. Sink Start of Operation (y) End of Operation (y) Average CO2 Injectivity (Mt/y) Total CO2 Capacity (Mt) 1. Kutai-Tarakan Basin 5 35 4.65 139.5 2. North East Java 5 30 3.76 94 Total CO2 Capacity 233.5 2.2 Pinch Analysis Referring to Handogo et al. (2020), the cascade table for Pinch Analysis is constructed by initially listing unique operational time points (t1 to tn), followed by recording CO2 flow rates for sources and sinks during their operational time. This are illustrated as directional arrows inside the second and third column. Time intervals (Δt) between adjacent time points are then determined, and the net CO2 flow rate for each interval is calculated as the difference between total sink and source flow rates. Next, the net CO2 load for each interval is computed, followed by the cumulative CO2 load starting from zero (infeasible cascade). Finally, the feasible CO2 cascade is established, using the absolute value of the most negative infeasible cascade to identify the pinch point and alternative storage, with the final row indicating unutilized storage. 2.3 Superstructure Modelling Development of the Superstructure Modelling consist of three general steps: determination of the objective function, formulation of the mathematical model, and optimization approach (Yang et al., 2022). Matlab R2019b software will be used to facilitate the optimization requirements. 8 2.3.1 Objective Function In this case, maximization of CO2 transfer between source to sink will be chosen as objective function using the following formula: 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 ∑ ∑ 𝑄(𝑖,𝑗) 𝑗𝑖 (1) With Q(i,j) is the total CO2 transfer from source-i to sink-j (Mt). 2.3.2 Overall Material Balance Total CO2 transferred from source to sink must satisfy the material balance rule according to the following equation: CO2 emissions rate 𝑠𝑜𝑢𝑟𝑐𝑒−𝑖 ∗ (tend 𝑠𝑜𝑢𝑟𝑐𝑒 i − tstart 𝑠𝑜𝑢𝑟𝑐𝑒 i) = ∑ Q(i, j) + Alternative Storage j (2) CO2 injectivity 𝑠𝑖𝑛𝑘−j ∗ (tend 𝑠𝑖𝑛𝑘 j − tstart 𝑠𝑖𝑛𝑘 j) = ∑ Q(i, j) + Unutilized Storage i (3) where tstart and tend refer to start and end time of each source and sink (y). Additionally, the total CO2 transferred from source to sink must not exceed the minimum capacity of either the source or the sink, as follows: Q(i, j) ≤ min[CO2 emitted by source − i, CO2 capacity of sink − j] (4) Furthermore, there is a rule that the amount of alternative storage and unutilized storage must not exceed the total CO2 available at the source or the total capacity of the sink, as follows: Alternative storage ≤ CO2 emitted by source − i (5) Unutilized storage ≤ CO2 capacity of sink − j (6) 2.3.3 Constraint for Stream Splitting To demonstrate that one source can transfer CO2 to multiple sinks, or vice versa, constraint for stream splitting shall be defined. This condition is represented by the following equation: 𝑓ℎ𝑖𝑗 ∗ CO2 emissions rate 𝑠𝑜𝑢𝑟𝑐𝑒−i ∗ (tend 𝑠𝑜𝑢𝑟𝑐𝑒 i − tstart 𝑠𝑜𝑢𝑟𝑐𝑒 i) ≥ ∑ Q(i, j) j (7) 𝑓𝑐𝑖𝑗 ∗ CO2 injectivity𝑠𝑖𝑛𝑘−j ∗ (tend 𝑠𝑖𝑛𝑘 j − tstart 𝑠𝑖𝑛𝑘 j) ≥ ∑ Q(i, j) i (8) Where fhij and fcij represent the split fractions for the source and sink, respectively, with values ranging from 0 to 1. If no pairing occurs between source-i and sink-j in stage-k, the split fraction must be zero. 2.3.4 Time Configuration In configuring source and sink, it is necessary to consider the compatibility of operational timing between the two. The time point at each stage must also be adjusted. Source and sink can be matched each other if there is time overlap between each other. This time configuration can be observed in the following equation: t𝑠𝑡𝑎𝑟𝑡 𝑜𝑣𝑒𝑟𝑙𝑎𝑝 = 𝑚𝑎𝑥 (𝑡𝑠𝑡𝑎𝑟𝑡 𝑠𝑜𝑢𝑟𝑐𝑒−𝑖 − 𝑡𝑠𝑡𝑎𝑟𝑡 𝑠𝑖𝑛𝑘 𝑗) (9) t𝑒𝑛𝑑 𝑜𝑣𝑒𝑟𝑙𝑎𝑝 = 𝑚𝑖𝑛 (𝑡𝑒𝑛𝑑 𝑠𝑜𝑢𝑟𝑐𝑒−𝑖 − 𝑡𝑒𝑛𝑑 𝑠𝑖𝑛𝑘 𝑗) (10) Eq (9) and Eq (10) indicate that CO2 transfer can occur when there is a time overlap between the source and sink. If there is a difference in the start times of source and sink, the overlap start time is determined by the maximum value between those start time. Conversely, if there is a difference in the end times of the source and sink, the overlap end time is determined by the minimum value between those end time. This configuration determines the duration of CO2 transfer between the source and sink. 2.3.5 Optimization The optimization process is conducted using Matlab 2019b software. The optimization aims to obtain the total amount of CO2 transferred for each pairing and stream splitting fraction at the optimum condition based on the mentioned objective function. 9 2.4 Grid Diagram Generation In the implementation of Carbon Capture and Storage (CCS), the use of a grid diagram aims to design potential connections between available emissions sources (sources) and storage locations (sinks) (Tapia et al., 2018). This diagram provides information regarding the amount of CO2 that can be transferred as well as the estimated time required for the process (Kim et al., 2024). 3. Result and Discussion 3.1 Pinch Analysis Table 3 shows the cascade table for the Pinch Analysis, with the pinch point identified at year 20. In CCS, the pinch point indicates the critical time point where the cumulative CO2 storage capacity of the sinks matches the cumulative CO2 emissions from the sources, representing the most constrained point in the system where no further CO2 transfer is feasible without additional storage capacity or alternative measures (Thengane et al., 2019). Furthermore, Table 3 reveals that the alternative storage amounts to 58.572 Mt, and the unutilized storage is 41.967 Mt. Based on this configuration, the total captured CO2 is determined to be 190.54 Mt. Errore. L'origine riferimento non è stata trovata. illustrates the grid diagram which was derived from the results of Table 3. Eight pairings are foreseen to represent the captured CO2. Stream splitting is applied to Badak LNG, Semen Bosowa, and North East Java to meet the criteria for the grid diagram below the pinch. Conversely, for the above-pinch region, the stream splitting rule is applied to Semen Bosowa, Kutai Tarakan Basin, and North East Java due to the number of sinks being fewer than the number of sources, necessitating the splitting of sinks. Alternative storage is observed at Semen Bosowa and Semen Tonasa, all located below the pinch, as an impact of differences in the start times of these sources relative to the available sinks and the limited capacity of sinks to accommodate CO2. The amount of alternative storage derived from the grid diagram is consistent with the results from Table 3. On the other hand, unutilized storage is identified at Kutai Tarakan Basin start from years 20 to 35. In this duration, at years 27 and 28, Kutai Tarakan Basin was no longer received CO2 from Semen Bosowa and Semen Tonasa. For North East Java, unutilized storage is foreseen at years 5 to 7 and years 27 to 30. This occurs because, during these periods, all CO2 from all sources has been successfully captured (Kemp, 2007). The amount of unutilized storage obtained also aligns with the results from Table 3. Table 3: Cascade Table for Pinch Analysis Time (y) Source Sink ∆t (y) CO2 Flowrate (Mt/y) CO2 Load (Mt) Infeasible Cascade (Mt) Feasible Cascade (Mt) Pupuk Kaltim Badak LNG Semen Bosowa Semen Tonasa Kutai Tarakan Basin North East Java 5 2.403 5.514 4.65 3.76 0 58.572 ↓ ↓ ↓ ↓ 2 0.493 0.986 7 ↓ ↓ 1.575 ↓ ↓ 0.986 59.558 ↓ ↓ ↓ ↓ ↓ 1 -1.082 -1.082 8 ↓ ↓ ↓ 3.791 ↓ ↓ -0.096 58.476 ↓ ↓ ↓ ↓ ↓ ↓ 12 -4.873 -58.476 20 ↓ ↓ ↓ ↓ ↓ ↓ -58.572 0 ↓ ↓ ↓ ↓ ↓ 7 0.641 4.487 (Pinch) 27 ↓ ↓ ↓ ↓ ↓ -54.085 4.487 ↓ ↓ ↓ ↓ 1 2.216 2.216 28 ↓ ↓ ↓ ↓ -51.869 6.703 ↓ ↓ ↓ 2 6.007 12.014 30 ↓ ↓ ↓ -39.855 18.717 ↓ 5 4.65 23.25 35 ↓ -16.605 41.967 3.2 Superstructure Modeling Superstructure Modeling use iterative process to achieve optimal result and it does not rely on cascade table (Tan et al., 2012). Based on optimization results, derived from the applied mathematical equations, it indicate that alternative storage, captured CO2, and unutilized storage amount to 73.095 Mt, 177.01 Mt, and 56.49 Mt, respectively. The value of captured CO2 is lower compared to the result provided by Pinch Analysis. Pairing configuration between sources and sinks in the superstructure approach also differs from Pinch Analysis, as 10 illustrated by the grid diagram in Figure 2. It is evident that the number of pairings between sources and sinks using the superstructure Modelling is fewer, reaching 5 pairings. Alternative storage is foreseen in Badak LNG from years 5 to 20, while unutilized storage is foreseen in the Kutai-Tarakan Basin and North East Java at varying time intervals. In the Kutai-Tarakan Basin, unutilized storage emerges in years 20, 27, 28, and 30, continuing until the basin's operational end in year 35. Similarly, in North East Java, unutilized storage appears from year 28 to 30, as all CO2 from the sources has been successfully captured during this period. Figure 1: Grid Diagram for Pinch Analysis Figure 2: Grid Diagram for Superstructure Modelling 3.3 Final Pairing Table 4: Final Result Parameters Pinch Analysis Superstructure Modelling Alternative storage (Mt) 58.572 73.095 Captured CO2 (Mt) 190.54 177.01 Unutilized Storage (Mt) 41.967 56.49 Table 5: Final Pairing Results (P: Pinch, S: Superstructure) Source/Sink Pupuk Kaltim Badak LNG Semen Bosowa Semen Tonasa Kutai-Tarakan Basin - 60.075 (S) 69.75 Mt (P) 9.615 Mt (S) 1.526 Mt (P) 31.5 Mt (S) 30.328 Mt (P) 0.62 Mt (S) North East Java 60.075 (P) - 12.96 Mt (P) - 9.499 Mt (P) - - 75.2 Mt (S) Table 4 summarizes alternative storage, captured CO2, and unutilized storage for Pinch Analysis and Superstructure Modelling, while Table 5 presents their source-sink pairings. Pinch Analysis captures up to 7 % 11 more CO2 than Superstructure Modelling. This is due to its ability to identify optimal pairings with fewer computational constraints, requiring less time (Diamante et al., 2014) and resources (Foo and Tan, 2020). While both methods yield similar pairings, Pinch Analysis uniquely pairs North East Java with Pupuk Kaltim, Badak LNG, and Semen Bosowa, whereas Superstructure Modeling pairs Pupuk Kaltim with Kutai-Tarakan Basin and Semen Tonasa with North East Java. Some pairings are infeasible in both methods due to mismatched operational time ranges, marked by “-“ in Table 5. 4. Conclusion Strategic planning in CCS development was successfully conducted using both Pinch Analysis and Superstructure Modelling. Pinch Analysis captured approximately 7 % more CO2, due to its fast, heuristic-based source-sink matching without complex constraints, making it suitable for preliminary evaluations. Meanwhile, Superstructure Modelling offers broader optimization capabilities by evaluating multiple configurations simultaneously, which is beneficial when dealing with large-scale systems involving numerous variables and trade-offs. The variation in pairing results between the two methods further demonstrates the importance of method selection based on system complexity and data availability. Additionally, the alignment of source–sink operational periods remains a critical determinant in ensuring feasible CO2 transfer in both approaches. Acknowledgements Authors greatly appreciate the support from Chemical Engineering Department funding scheme 2025 at Institut Teknologi Sepuluh Nopember. References Ayyad F.M., Shehata W.M., Bhran A.A., Gadallah A.G., Shoaib A.M., 2024, Optimum Planning of Carbon Capture and Storage Network using Goal Programming. Processes, 12, 2463. 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Yang Z., Zhang N., Smith R., 2022, Enhanced Superstructure Optimization for Heat Exchanger Network Synthesis using Deterministic Approach, Frontiers in Sustainability, 3, 976717. 12 0012.pdf A Comparative Study Between Pinch Design Method and Superstructure Applied on Carbon Capture and Storage