SpringerLink
Log in

Intelligent Monitoring Using Hazard Identification Technique and Multi-sensor Data Fusion for Crude Distillation Column

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1252))

Included in the following conference series:

Abstract

Hazard assessment techniques and multi-sensor fusion are used for intelligent systematic monitoring. Firstly, a hazard identification technique is considered using failure mode and effect analysis and advantages of using a combined hazard technique is discussed. Data sources are identified considering component failures and some sensors associated with potential failure. Possible consequences in a hazardous situation are identified using failure mode and effect analysis to choose suitable safety measures. Failure mode and effect analysis is systematically considers how sequences of events can lead to accidents by looking at components and faults recorded by sensors and anomalies. Data were presented based on their threat levels using a traffic light color code system. Refineries use sensors to observe the process of crude refining and the monitoring system uses real-time data to access information provided by sensors. Understanding hazard assessments, sensor multi-fusion and sensor pattern recognition in a distillation column could help to identify trends, flag major regions of growing malfunction, model risk threat of a crude distillation column and help to systematically make decisions. The decisions could improve design regulations, eliminate anomalies, improve monitoring and reduce threat levels.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 160.49
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 213.99
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bartok, J., Habala, O., Bednar, P., Gazak, M., Hluchý, L.: Data mining and integration for predicting significant meteorological phenomena. Procedia Comput. Sci. 1, 37–46 (2010)

    Article  Google Scholar 

  2. Chang, C.D., Wang, C.C., Jiang, B.C.: Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors. Expert Syst. Appl. 38(5), 5507–5513 (2011)

    Article  Google Scholar 

  3. Mabrouki, C., Bentaleb, F., Mousrij, A.: A decision support methodology for risk management within a port terminal. Saf. Sci. 63, 124–132 (2014)

    Article  Google Scholar 

  4. Slovic, P., Finucane, M.L., Peters, E., MacGregor, D.G.: Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality. Risk Anal. 24(2), 311–322 (2004)

    Article  Google Scholar 

  5. Deng, Y., Sadiq, R., Jiang, W., Tesfamariam, S.: Risk analysis in a linguistic environment: a fuzzy evidential reasoning-based approach. Expert Syst. Appl. 38(12), 15438–15446 (2011)

    Article  Google Scholar 

  6. Mokhtari, K., Ren, J., Roberts, C., Wang, J.: Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach. Expert Syst. Appl. 39(5), 5087–5103 (2012)

    Article  Google Scholar 

  7. Durrant-Whyte, H.: Sensor models and multisensor integration. Int. J. Robot. Res. 7(6), 97–113 (1988). https://doi.org/10.1177/027836498800700608

    Article  Google Scholar 

  8. Mohaghegh, S.D.: A new methodology for the identification of best practices in the oil and gas industry, using intelligent systems. J. Petrol. Sci. Eng. 49, 239–260 (2005)

    Article  Google Scholar 

  9. Hajizadeh, E., Davari, A.H., Shahrabi, J.: Application of data mining techniques in stock markets: a survey. J. Econ. Int. Finance 2(7), 109–118 (2010)

    Google Scholar 

  10. Khaleghi, B., Khamis, A., Karray, F., Razavi, S.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)

    Article  Google Scholar 

  11. Ikwan, F.: Reducing energy losses and alleviating risk in petroleum engineering using decision making and alarm systems. J. Comput. Syst. Eng. 422–429 (2018). ISSN 1472-9083

    Google Scholar 

  12. Mandal, S., Maiti, J.: Risk analysis using FMEA: fuzzy similarity value and possibility theory based approach. Expert Syst. Appl. 41(7), 3527–3537 (2014)

    Article  Google Scholar 

  13. Luo, R., Kay, M.: Multisensor integration and fusion in intelligent systems. IEEE Trans. Syst. Man Cybern. 19(5), 901–931 (1989). https://doi.org/10.1109/21.44007

    Article  Google Scholar 

  14. Riahi, R., Robertson, I., Bonsall, S., Jenkinson, I., Wang, J.: A proposed methodology for assessing the reduction of a seafarer’s performance with insufficient recuperative rest. J. Mar. Eng. Technol. 12(2), 11–28 (2013)

    Google Scholar 

  15. Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch, B.: Handling data uncertainties in event tree analysis. Process Saf. Environ. Prot. 87(5), 283–292 (2009)

    Article  Google Scholar 

  16. Lavasani, S.M.M.: Advanced quantitative risk assessment of offshore gas pipeline systems. Ph.D. thesis, School of Engineering and Maritime Operations, Liverpool John Moores University, UK (2010)

    Google Scholar 

  17. Selim, G., Seker, S.: Signal based approach for data mining in fault detection of induction motor. Sci. Res. Essays 6(22), 4720–4731 (2011)

    Google Scholar 

  18. Saybani, M.R., Wal, T.Y.: Applied data mining approach in ubiquitous world of air transportation. In: 4th International Conference on Computer Science Convergence Information Technology, ICCIT 2009, pp. 1218–1222 (2009)

    Google Scholar 

  19. White, F.E.: Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3, Data Fusion Sub-Panel. Naval Ocean Systems Center, San Diego (1991)

    Google Scholar 

  20. Klein, L.A.: Sensor and Data Fusion Concepts and Applications, 2nd edn. Society of Photo-Optical Instrumentation Engineers (SPIE), Bellingham (1999)

    Google Scholar 

  21. Smets, P.: Analyzing the combination of conflicting belief functions. Inf. Fusion 8(4), 387–412 (2007)

    Article  Google Scholar 

  22. Omoarebun, P.O.: Disaster risk reduction in petroleum engineering. J. Comput. Syst. Eng. (2018). ISSN 1472-9083

    Google Scholar 

  23. Sanders, D., Gegov, A., Ndzi, D.: Knowledge-based expert system using a set of rules to assist a tele-operated mobile robot. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) Studies in Computational Intelligence, vol. 751, pp. 371–392. Springer (2018)

    Google Scholar 

  24. Sanders, D., Okonor, O.M., Langner, M., Hassan Sayed, M., Khaustov, S.A., Omoarebun, P.: Using a simple expert system to assist a powered wheelchair user. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1037, pp. 662–379. Springer

    Google Scholar 

  25. Gegov, A., Gobalakrishnan, N., Sanders, D.A.: Rule base compression in fuzzy systems by filtration of non-monotonic rules. J. Intell. Fuzzy Syst. 27(4), 2029–2043 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  26. Sanders, D., Gegov, A., Haddad, M., Ikwan, F., Wiltshire, D., Tan, Y.C.: A rule-based expert system to decide on direction and speed of a powered wheelchair. In: IEEE Proceedings of the SAI Conference on Intelligent Systems, London, U.K., pp. 426–433 (2018)

    Google Scholar 

  27. Sanders, D., Gegov, A., Haddad, M., Ikwan, F., Wiltshire, D., Tan, Y.C.: A rule-based expert system to decide on direction and speed of a powered wheelchair. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 822–838. Springer

    Google Scholar 

  28. Sanders, D.: Using self-reliance factors to decide how to share control between human powered wheelchair drivers and ultrasonic sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 25(8), 1221–1229 (2017)

    Article  Google Scholar 

  29. Haddad, M., Sanders, D., Bausch, N., Tewkesbury, G., Gegov, A., Hassan Sayed, M.: Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 1303–1316. Springer

    Google Scholar 

  30. Haddad, M.J.M., Sanders, D., Tewkesbury, G., Gegov, A., Hassan Sayed, M., Ikwan, F.: Initial results from using Preference Ranking Organization METHods for Enrichment of Evaluations to help steer a powered wheelchair. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1037, pp. 648–661. Springer

    Google Scholar 

  31. Sanders, D., Robinson, D.C., Hassan Sayed, M., Haddad, M.J.M., Gegov, A., Ahmed, N.: Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence. In: Arai, K., Kapoor, S., Bhati, R. (eds.) Advances in Intelligent Systems and Computing, vol. 869, pp. 1229–1236. Springer

    Google Scholar 

  32. Sanders, D., Robinson, D.C., Hassan Sayed, M., Haddad, M.J.M., Gegov, A., Ahmed, N.: Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 869, pp. 1229–1236. Springer

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical and Design Engineering, Portsmouth University, Portsmouth, UK

    Peter Omoarebun, David Sanders, Favour Ikwan & Malik Haddad

  2. School of Chemical Engineering, Southampton University, Southampton, UK

    Mohamed Hassan

  3. Energy and Electronic Engineering, Portsmouth University, Portsmouth, UK

    Mohamad Thabet & Amjad Shah

  4. InTandem Systems Ltd., Watton Lane, Southampton, UK

    Jake Piner

Authors
  1. Peter Omoarebun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Sanders
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Favour Ikwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Malik Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamad Thabet
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jake Piner
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Amjad Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malik Haddad .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Omoarebun, P. et al. (2021). Intelligent Monitoring Using Hazard Identification Technique and Multi-sensor Data Fusion for Crude Distillation Column. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_61

Download citation

Publish with us

Policies and ethics

Search

Navigation