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In order to fight climate change, the EU has set a goal of achieving net-zero greenhouse gas emissions by 2050. To achieve carbon neutrality, greenhouse emissions from human activities should be at least 85% lower than in 1990. The remaining 15% can be achieved through additional measures such as increasing carbon capture and storage (CCS) and reducing emissions. CCS will facilitate the decarbonization of heavy industry, contribute to the emergence of a clean hydrogen economy, and aid in achieving net-zero emissions. As an emerging technology in the Industry 4.0, Digital Twin (DT) is gaining attention due to the possibilities arising from its application, such as precise process optimization in the design phase, quality control, monitoring, decision-making, and through comprehensive modeling of the physical world as a group of connected digital models. The introduction of digital technologies into the CCS sector has the potential to revolutionize the way CO2 capture, transportation, and storage processes are carried out. This article aims to present the fundamental value of different modeling techniques, technologies enabling the creation of DT’s uncertainty quantification methods commonly used in Digital Twins, as well as the application of Digital Twin in CCS technology and the potential benefits it can bring, including increases efficiency and cost minimization. Additionally, the possibilities of using DS’s in improving process monitoring and forecasting were discussed which can contribute to better emission control and increases system effectiveness. Current research and projects utilizing this technology were also presented, including real-time modeling of fluid flow, CO2 transport network optimization, and storage process improvement.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
5--10
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
Bibliografia
- [1] Yokogava Co-innovation Tomorrow: Digital-Twin. https://www.yokogawa.com/pl/solutions/featured-topics/digital-transformation/digital-twin/ [27.04.2023].
- [2] Wołejko M.: Czym jest cyfrowy bliźniak i jakie możliwości otwiera ta technologia? cz. 1. Biuletyn Projektu Chemia 4.0, no. 2, 2021, pp. 19–22. https://www.pipc.org.pl/files/2088134616/file/Biuletyn_Chemia_4_0_Wrzesien2021.pdf [27.04.2023].
- [3] Grochowski L.: Cyfrowa Transformacja w przemyśle – skokowa zmiana w zakresie efektywności i zrównoważonego rozwoju. Biuletyn Projektu Chemia 4.0, no. 1, 2021, pp. 20–21. https://www.pipc.org.pl/files/1473620269/file/Biuletyn_Chemia4_0_Maj_2021.pdf [27.04.2023].
- [4] Lee J., Lapira E., Bagheri H.: Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, vol. 1, 2013, pp. 38–41.
- [5] Verdouw C., Tekinerdogan B., Beulens A., Wolfert S.: Digital twins in smart farming. Agricultural Systems, vol. 189, 2021, art. no. 103046.
- [6] Nagy S., Barbacki J.: Zastosowanie sztucznych sieci neuronowych do modelowania eksploatacji gazu ze złóż łupkowych. Prace Naukowe Instytutu Nafty i Gazu, vol. 209, 2016, pp. 811–815.
- [7] Thelen A., Zhang X., Fink O., Lu Y., Ghosh S., Youn B.D., Hu Z.: A comprehensive review of digital twin – part 1: modeling and twinning enabling technologies. Structural and Multidisciplinary Optimization, vol. 65(12), 2020, art. no. 354.
- [8] Roberts D.: Siemens goes all-in on Digital Twin. https://cambashi.com/siemens-goes-all-in-on-digital-twin/ [27.04.2023].
- [9] Ayello F., Yang Y., Li L., Liu G., Zhang Y., Zhang S.: Probabilistic Digital Twins for Transmission Pipelines. Proceedings of the 2020 13th International Pipeline Conference, vol. 2, 2020, art. no. 16780, DOI: 10.1115/IPC2020-9240.
- [10] European Comission: Carbon capture, storage and utilisation. https://energy.ec.europa.eu/topics/oil-gas-andcoal/carbon-capture-storage-and-utilisation_en [27.04.2023].
- [11] Nvidia Developer: Using Carbon Capture and Storage Digital Twins for Net Zero Strategies. https://developer.nvidia.com/blog/using-carbon-capture-and-storage-digital-twins-for-net-zero-strategies/ [27.04.2023].
- [12] Wen G., Li Z., Long Q., Azizzadenesheli K., Anandkumar A., Benson S.M.: Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators. Energy & Environmental Science, vol. 16(4), 2023, pp. 1732–1741. https://arxiv.org/pdf/2210.17051.pdf [27.04.2023].
- [13] Wen G., Li Z., Azizzadenesheli K., Anandkumar A., Benson S.: M.U-FNO – An enhanced Fourier neural operator-based deep-learning model for multiphase flow. Advances in Water Resources, vol. 163, 2022, art. no. 104180. https://arxiv.org/pdf/2109.03697.pdf [27.04.2023].
- [14] SI – Sztuczna Inteligencja: Sztuczne sieci neuronowe. https://www.sztucznainteligencja.org.pl/kurs/sztuczna-inteligencja-dla-poczatkujacych/sztuczne-sieci-neuronowe [27.04.2023].
- [15] IBM Neural Networks. https://www.ibm.com/topics/neural-networks [27.04.2023].
- [16] Mirza N., Kearns D.: State of the Art: CCS Technologies 2022. Global CCS Institute: Melbourne, Australia, 2022.
- [17] Duong C., Bower C., Hume K., Rock L., Tessarolo S.: Quest carbon capture and storage offset project: Findings and learnings from 1st reporting period. International Journal of Greenhouse Gas Control, vol. 89, 2019,pp. 65–75.
- [18] Sask Power: Boundary Dam Carbon Capture Project. https://www.saskpower.com/Our-Power-Future/Infrastructure-Projects/Carbon-Capture-and-Storage/Boundary-Dam-Carbon-Capture-Project [27.04.2023].
- [19] Software SLB: Digital CCS customer success stories. https://www.software.slb.com/energy-transition/ccs/digital-ccs-customer-success-stories [27.04.2023].
- [20] MIT Carbon Capture & Sequestration Technologies: Decatur Fact Sheet: Carbon Dioxide Capture and Storage Project. https://sequestration.mit.edu/tools/projects/decatur.html [27.04.2023].
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0041dd99-3864-422a-a650-2d04620cee67