Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Artificial intelligence (AI) based analysis for global warming mitigations of non-carbon emitted nuclear energy productions

  • Tae Ho Woo (Department of Mechanical and Control Engineering, The Cyber University of Korea)
  • Received : 2023.04.27
  • Accepted : 2023.08.03
  • Published : 2023.11.25
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Abstract

Nuclear energy is estimated by the machine learning method as the mathematical quantifications where neural networking is the major algorithm of the data propagations from input to output. As the aspect of nuclear energy, the other energy sources of the traditional carbon emission-characterized oil and coal are compared. The artificial intelligence (AI) oriented algorithm like the intelligence of a robot is applied to the modeling in which the mimicking of biological neurons is utilized in the mathematical calculations. There are graphs for nuclear priority weighted by climate factor and for carbon dioxide mitigation weighted by climate factor in which the carbon dioxide quantities are divided by the weighting that produces some results. Nuclear Priority and CO2 Mitigation values give the dimensionless values that are the comparative quantities with the normalization in 2010. The values are 1.0 in 2010 of the graphs which are changed to 24.318 and 0.0657 in 2040, respectively. So, the carbon dioxide emissions could be reduced in this study.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (NRF-2022M2B5A1080696).

References

  1. S.D. Patil, Y. Gu, F.S. A Dias, M. Stieglitz, G. Turk, Predicting the spectral information of future land cover using machine learning, Int. J. Rem. Sens. 38 (20) (2017) 5592-5607.
  2. I. Tirkel, G. Rabinowitz, D. Price, D. Sutherland, Wafer fabrication yield learning and cost analysis based on in-line inspection, Int. J. Prod. Res. 54 (12) (2016) 3578-3590.
  3. S.M.A. Zaidi, V. Chandola, M.R. Allen, J. Sanyal, R.N. Stewart, B.L. Bhaduri, R. A. McManamay, Machine learning for energy-water nexus:challenges and opportunities, Big Earth Data 2 (3) (2018) 228-267.
  4. IEA, IEA World Energy Outlook 2018, International Energy Agency (IEA), Paris, 2019. France, https://www.iea.org/weo/.
  5. T.H. Woo, Social selection analysis for a role of nuclear power generation by evolutionary game theory (EGT) in the aspect of global warming assessment, Int. J. Glob. Warming 20 (1) (2020) 25-36.
  6. S. Ali, J. Jiang, S.T. Hassan, A.A. Shah, Revolution of nuclear energy efficiency, economic complexity, air transportation and industrial improvement on environmental footprint cost, Nucl. Eng. Technol. 54 (10) (2022) 3682-3694.
  7. I. Khan Bilal, D. Tan, W. Azam, S.T. Hassan, Alternate energy sources and environmental quality: the impact of inflation dynamics, Gondwana Res. 106 (2022) 51-63.
  8. S.T. Hassan, P. Wang, I. Khan, B. Zhu, The impact of economic complexity, technology advancements, and nuclear energy consumption on the ecological footprint of the USA: towards circular economy initiatives, Gondwana Res. 113 (2023) 237-246.
  9. S.T. Hassan, B. Batool, P. Wang, B. Zhu, M. Sadiq, Impact of economic complexity index, globalization, and nuclear energy consumption on ecological footprint: first insights in OECD context, Energy 263 (2023), 125628. Part A.
  10. M. Sadiq, R. Shinwari, F. Wen, M. Usman, S.T. Hassan, F. Taghizadeh-Hesary, Do globalization and nuclear energy intensify the environmental costs in top nuclear energy-consuming countries? Prog. Nucl. Energy 156 (2023), 104533.
  11. M.T. Kartal, U.K. Pata, S.K. Depren, O. Depren, Effects of possible changes in natural gas, nuclear, and coal energy consumption on CO2 emissions: evidence from France under Russia's gas supply cuts by dynamic ARDL simulations approach, Appl. Energy 339 (2023), 120983.
  12. U.K. Pata, A. Samour, Do renewable and nuclear energy enhance environmental quality in France? A new EKC approach with the load capacity factor, Prog. Nucl. Energy 149 (2022), 104249.
  13. U.K. Pata, M.T. Kartal, Impact of nuclear and renewable energy sources on environment quality: testing the EKC and LCC hypotheses for South Korea, Nucl. Eng. Technol. 55 (2) (2023) 587-594.
  14. U.K. Pata, M.T. Kartal, S. Erdogan, S.A. Sarkodie, The role of renewable and nuclear energy R&D expenditures and income on environmental quality in Germany: scrutinizing the EKC and LCC hypotheses with smooth structural changes, Appl. Energy 342 (2023), 121138.
  15. X. Jin, Z. Ahmed, U.K. Pata, M.T. Kartal, S. Erdogan, Do investments in green energy, energy efficiency, and nuclear energy R&D improve the load capacity factor? An augmented ARDL approach, Geosci. Front. (2023), 101646 (In Press).
  16. M. Amozegar, K. Khorasani, An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines, Neural Network. 76 (2016) 106-121.
  17. B. Wilsom, The Machine Learning Dictionary, 2012. http://www.cse.unsw.edu.au/~billw/mldict.html#activnfn.
  18. Wikipedia, Artificial Neural Network, 2015. https://en.wikipedia.org/wiki/Artificial_neural_network#cite_note-30.
  19. F.C. Jelen, J.H. Black, Cost and Optimization Engineering, third ed., McGraw-Hill Book Company, New York, USA, 1983.
  20. T.H. Yeh, S. Deng, Application of machine learning methods to cost estimation of product life cycle, Int. J. Comput. Integrated Manuf. 25 (4-5) (2012) 340-352.
  21. Ventana, Vensim Software, 2015. Harvard, USA, https://vensim.com.
  22. T.H. Woo, Global warming analysis for greenhouse gases impacts comparable to carbon-free nuclear energy using neuro-fuzzy algorithm, Int. J. Glob. Warming 17 (2) (2019) 219-233.