Skip to main content
SpringerLink
Log in

We Want AI to Help Us

An Explorative Study Regarding How AI Could Assist Operators in the Main Control Room of Nuclear Power Plants

  • Conference paper
  • First Online:
Artificial Intelligence in HCI (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14051))

Included in the following conference series:

  • 975 Accesses

Abstract

Artificial intelligence is regarded as a very promising tool in nuclear power plants. While recent research has made progress in utilizing AI to improve digital systems’ performances in nuclear power plants, less research focuses on connecting AI and reactor operators and understanding how operators want AI to assist them in their work. Such understanding is important because it is the prerequisite for good usability and positive user experience. We address this research gap by conducting an exploratory interview and letting reactor operators’ opinions be voiced out. Through thematic analysis, we derived insights on operators’ understanding of AI, the pain points of their work, and how they expect AI to assist them in their work. We identified three assistive roles that operators want AI to be in nuclear power plants to mitigate their work pressure and discussed the reasons behind their choices. Finally, we discussed the limitations of our research and provide possible future research directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hamer, R., Waterson, P., Jun, G.T.: Human factors and nuclear safety since 1970 – A critical review of the past, present and future. Saf. Sci. 133, 105021 (2021). https://doi.org/10.1016/j.ssci.2020.105021

    Article  Google Scholar 

  2. Moray, N.P., Huey, B.M.: Human factors research and nuclear safety. National Academies Press (1988)

    Google Scholar 

  3. Swaton, E., Neboyan, V., Lederman, L.: Human factors in the operation of nuclear power plants. IAEA Bull. 29, 27–30 (1987)

    Google Scholar 

  4. Zhang, L., Shi, J., Chen, W.: Revolution of nuclear power plant design through digital technology.  In: International Atomic Energy Agency (IAEA) (2015)

    Google Scholar 

  5. McCarthy, J.: What is artificial intelligence? (2007)

    Google Scholar 

  6. Lu, C., et al.: Nuclear power plants with artificial intelligence in industry 4.0 era: Top-level design and current applications—A systemic review. IEEE Access 8, 194315–194332 (2020). https://doi.org/10.1109/ACCESS.2020.3032529

  7. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access. 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052

    Article  Google Scholar 

  8. Goodwin, N.C.: Functionality and usability. Commun. ACM 30, 229–233 (1987). https://doi.org/10.1145/214748.214758

    Article  Google Scholar 

  9. Norman, D.: User centered system design. New Perspect. Hum.-Comput. Interact. (1986)

    Google Scholar 

  10. Shneiderman, B.: Human-centered AI. Oxford University Press (2022)

    Google Scholar 

  11. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa

    Article  Google Scholar 

  12. Medema, H., Savchenko, K., Boring, R., Ulrich, T.: Defining mutual awareness: Results of reactor operator surveys on the emergence of digital technology in main control rooms. In: Advances in Human Error, Reliability, Resilience, and Performance: Proceedings of the AHFE 2018 International Conference on Human Error, Reliability, Resilience, and Performance, 21–25 July 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA, vol. 9, pp. 58–67. Springer (2019). https://doi.org/10.1007/978-3-319-94391-6_6

  13. Gertman, D.I., Haney, L.N., Jenkins, J.P., Blackman, H.S.: Operational decisionmaking and action selection under psychological stress in nuclear power plants. EG and G Idaho Inc, Idaho Falls (USA) (1985)

    Google Scholar 

  14. Kontogiannis, T.: Stress and operator decision making in coping with emergencies. Int. J. Hum.-Comput. Stud. 45, 75–104 (1996). https://doi.org/10.1006/ijhc.1996.0043

  15. Park, J., Kim, J., Jung, W.: Comparing the complexity of procedural steps with the operators’ performance observed under stressful conditions. Reliab. Eng. Syst. Saf. 83, 79–91 (2004). https://doi.org/10.1016/j.ress.2003.09.001

    Article  Google Scholar 

  16. Chuang, C.F., Chou, H.P.: Investigation on the design of human-system interface for advanced nuclear plant control room (2006)

    Google Scholar 

  17. Lin, C.J., Hsieh, T.-L., Tsai, P.-J., Yang, C.-W., Yenn, T.-C.: Development of a team workload assessment technique for the main control room of advanced nuclear power plants. Hum. Factors Ergon. Manuf. Serv. Ind. 21, 397–411 (2011). https://doi.org/10.1002/hfm.20247

    Article  Google Scholar 

  18. Preischl, W., Hellmich, M.: Human error probabilities from operational experience of German nuclear power plants. Reliab. Eng. Syst. Saf. 109, 150–159 (2013). https://doi.org/10.1016/j.ress.2012.08.004

    Article  Google Scholar 

  19. Sheridan, T.B.: Human error in nuclear power plants. Technol. Rev. 82, 22–33 (1980)

    Google Scholar 

  20. O’hara, J.M., Higgins, J.C., Persensky, J.J., Lewis, P.M., Bongarra, J.P.: Human factors engineering program review model. Brookhaven National Lab Upton NY (2004)

    Google Scholar 

  21. Plott, C., Engh, T., Barnes, V.E.: Technical basis for regulatory guidance for assessing exemption requests from the nuclear power plant licensed operator staffing requirements specified in 10 CFR 50.54 (m). Division of Systems Analysis and Regulatory Effectiveness, Office of Nuclear (2004)

    Google Scholar 

  22. Kuang, C., Fabricant, R.: User friendly: How the hidden rules of design are changing the way we live, work & play. Random House (2019)

    Google Scholar 

  23. Rouse, W.B., Rouse, S.H.: Human information seeking and design of information systems. Inf. Process. Manag. 20, 129–138 (1984). https://doi.org/10.1016/0306-4573(84)90044-X

    Article  Google Scholar 

  24. Carvalho, P.V., Gomes, J.O., Borges, M.R.: Human centered design for nuclear power plant control room modernization. In: CEUR Proceedings 4th Workshop HCP Human Centered Processes, pp. 10–11 (2011)

    Google Scholar 

  25. Abras, C., Maloney-Krichmar, D., Preece, J., Bainbridge, W.: Encyclopedia of human-computer interaction. Thousand Oaks Sage Publ. 37, 445–456 (2004)

    Google Scholar 

  26. Buchanan, B.G.: A (very) brief history of artificial intelligence. Ai Mag. 26, 53 (2005)

    Google Scholar 

  27. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: A brief review. Comput. Intell. Neurosci. 2018 (2018). https://doi.org/10.1155/2018/7068349

  28. Gaikwad, S.K., Gawali, B.W., Yannawar, P.: A review on speech recognition technique. Int. J. Comput. Appl. 10, 16–24 (2010). https://doi.org/10.5120/1462-1976

  29. Nadkarni, P.M., Ohno-Machado, L., Chapman, W.W.: Natural language processing: an introduction. J. Am. Med. Inform. Assoc. 18, 544–551 (2011). https://doi.org/10.1136/amiajnl-2011-000464

    Article  Google Scholar 

  30. Harika, J., Baleeshwar, P., Navya, K., Shanmugasundaram, H.: A review on artificial intelligence with deep human reasoning. In: 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 81–84. IEEE (2022)

    Google Scholar 

  31. Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. Int. J. Inf. Manag. 48, 63–71 (2019). https://doi.org/10.1016/j.ijinfomgt.2019.01.021

    Article  Google Scholar 

  32. Rajan, K., Saffiotti, A.: Towards a science of integrated AI and Robotics. Elsevier (2017)

    Google Scholar 

  33. Herzig, A., Lorini, E., Pearce, D.: Social intelligence. Springer (2019)

    Google Scholar 

  34. Alamaniotis, M., Tsoukalas, L.H.: Neuro-SVM anticipatory system for online monitoring of radiation and abrupt change detection. Int. J. Monit. Surveill. Technol. Res. IJMSTR. 1, 40–53 (2013). https://doi.org/10.4018/ijmstr.2013040103

  35. McCoy, K., Alamaniotis, M., Jevremovic, T.: A conceptual model for integrative monitoring of nuclear power plants operational activities based on historical nuclear incidents and accidents. Int. J. Monit. Surveill. Technol. Res. IJMSTR. 1, 69–81 (2013). https://doi.org/10.4018/ijmstr.2013010105

    Article  Google Scholar 

  36. Lee, D., Kim, J.: Autonomous algorithm for safety systems of the nuclear power plant by using the deep learning. In: Advances in Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries: Proceedings of the AHFE 2017 International Conference on Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries, July 17–21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA, vol. 8, pp. 72–82. Springer (2018). https://doi.org/10.1007/978-3-319-60204-2_8

  37. Saeed, H.A., Wang, H., Peng, M., Hussain, A., Nawaz, A.: Online fault monitoring based on deep neural network & sliding window technique. Prog. Nucl. Energy. 121, 103236 (2020). https://doi.org/10.1016/j.pnucene.2019.103236

    Article  Google Scholar 

  38. Quan, Y., Yang, X.: A method for alarming water level of boiler drum on nuclear power plant based on BP neural network. In: 2014 10th International Conference on Natural Computation (ICNC), pp. 83–87. IEEE (2014)

    Google Scholar 

  39. Hanna, B., Son, T.C., Dinh, N.: An artificial intelligence-guided decision support system for the nuclear power plant management. In: Proceedings of the 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH 2019) (2019)

    Google Scholar 

  40. Lazar, J., Feng, J.H., Hochheiser, H.: Research methods in human-computer interaction. Morgan Kaufmann (2017)

    Google Scholar 

  41. Saldaña, J.: The coding manual for qualitative researchers. Coding Man. Qual. Res. 1–440 (2021)

    Google Scholar 

  42. Bhattacherjee, A.: Social science research: Principles, methods, and practices (2012)

    Google Scholar 

Download references

Acknowledgement

This study is supported by Science and Technology Commission of Shanghai Municipality (No. 20QB1403100).

Author information

Authors and Affiliations

  1. Shanghai Nuclear Engineering Research and Design Institute, Shanghai, China

    Ruochen Wang, Fei Song, Jun Ma & Shuhui Zhang

Authors
  1. Ruochen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuhui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruochen Wang .

Editor information

Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, R., Song, F., Ma, J., Zhang, S. (2023). We Want AI to Help Us. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14051. Springer, Cham. https://doi.org/10.1007/978-3-031-35894-4_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35894-4_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35893-7

  • Online ISBN: 978-3-031-35894-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation