skip to main content
10.1145/3468013.3468390acmotherconferencesArticle/Chapter ViewAbstractPublication PagesapcoriseConference Proceedingsconference-collections
research-article

Piping Circuit Development using K-Prototype Clustering

Published:27 November 2022Publication History

ABSTRACT

The oil and gas industry is one of Indonesia's vital industries, and it contributes the most to the country's foreign exchange. Piping is an important piece of equipment in the oil and gas production facilities; therefore, the piping inspection plan should be well prepared. An integral part of inspection plan development is a piping circuit; it allows an inspector to manage the necessary inspections, calculations, and better recordkeeping. A problem faced in piping circuit development is the need for relatively many working hours and variability results. Although this problem is often encountered, piping circuit development generated by manual work is still common in practice. To overcome the issues in the piping circuit development, therefore a k-prototype algorithm was introduced. A k-prototype algorithm was used to accommodate the shortcomings in grouping objects with features comprised of mixed categorical and numerical data. This study concludes that the k-prototype algorithm is a promising clustering technique that can reduce the time spent developing the piping circuit and eliminating the resulting variability.

Skip Supplemental Material Section

Supplemental Material

References

  1. Arum Sutrisni Putri, “Potensi Sumber Daya Alam Minyak Bumi,” kompas.com, May 2020.Google ScholarGoogle Scholar
  2. American Petroleum Institute, API 580 - Risk Based Inspection, 3rd ed., no. February. Washington D.C.: API, 2016.Google ScholarGoogle Scholar
  3. American Petroleum Institute, “API 574 - Inspection Practices for Piping System Components,” no. November, 2016.Google ScholarGoogle Scholar
  4. A. Rachman and R. M. C. Ratnayake, “Corrosion loop development of oil and gas piping system based on machine learning and group technology method,” J. Qual. Maint. Eng., vol. 26, no. 3, pp. 349–368, 2019, doi: 10.1108/JQME-07-2018-0058.Google ScholarGoogle ScholarCross RefCross Ref
  5. M. K. Chang, R. R. Chang, C. M. Shu, and K. N. Lin, “Application of risk based inspection in refinery and processing piping,” J. Loss Prev. Process Ind., vol. 18, no. 4–6, pp. 397–402, 2005, doi: 10.1016/j.jlp.2005.06.036.Google ScholarGoogle ScholarCross RefCross Ref
  6. N. N. Farahin and W. Pao, “Prototype of Piping Inspection and Maintenance Protocol,” 2012, doi: 10.13140/RG.2.2.27260.95366.Google ScholarGoogle Scholar
  7. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Pearson Addison Wesley, 2006.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. T. Larose and C. D., Data Mining and Predictive Analytics, Second Edi. John Wiley & Sons, Inc, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Z. Wang and S. Li, “Data-driven risk assessment on urban pipeline network based on a cluster model,” Reliab. Eng. Syst. Saf., vol. 196, no. January 2019, p. 106781, 2020, doi: 10.1016/j.ress.2019.106781.Google ScholarGoogle ScholarCross RefCross Ref
  10. P. E. Bhaskaran, M. Chennippan, and T. Subramaniam, “Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting,” J. Loss Prev. Process Ind., vol. 66, no. June, p. 104203, 2020, doi: 10.1016/j.jlp.2020.104203.Google ScholarGoogle ScholarCross RefCross Ref
  11. F. S. Hashemi-Nasab and H. Parastar, “Pattern recognition analysis of gas chromatographic and infrared spectroscopic fingerprints of crude oil for source identification,” Microchem. J., vol. 153, no. September 2019, p. 104326, 2020, doi: 10.1016/j.microc.2019.104326.Google ScholarGoogle ScholarCross RefCross Ref
  12. J. S. Al-Thuwaini, G. Zangl, and R. Phelps, “Innovative approach to assist history matching using artificial intelligence,” 2006 SPE Intell. Energy Conf. Exhib., vol. 2, pp. 405–411, 2006, doi: 10.2118/99882-ms.Google ScholarGoogle ScholarCross RefCross Ref
  13. S. Harous, M. Al Harmoodi, and H. Biri, “A Comparative Study of Clustering Algorithms for Mixed Datasets,” in Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 2019, pp. 484–488, doi: 10.1109/AICAI.2019.8701347.Google ScholarGoogle ScholarCross RefCross Ref
  14. Z. Huang, “Clustering Large Data Sets with Mixed Numeric and Categorical Values,” in The First Pacific Asia Knowledge Discovery and Data Mining Conference, 1997, pp. 21–34, doi: 10.1117/12.538864.Google ScholarGoogle Scholar
  15. Z. Huang, “Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values.,” Data Min. Knowl. Discov., vol. 2, no. 3, pp. 283–304, 1998.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. Ahmad and L. Dey, “A k-mean clustering algorithm for mixed numeric and categorical data,” Data Knowl. Eng., vol. 63, no. 2, pp. 503–527, 2007, doi: 10.1016/j.datak.2007.03.016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Esmaeilzadeh, A. Salehi, G. Hetz, F. Olalotiti-lawal, H. Darabi, and D. Castineira, “Multiscale modeling of compartmentalized reservoirs using a hybrid clustering-based non-local approach,” J. Pet. Sci. Eng., vol. 184, no. April 2019, p. 106485, 2020, doi: 10.1016/j.petrol.2019.106485.Google ScholarGoogle ScholarCross RefCross Ref
  18. M. Jannadi and K. Ben Driss, “Composed clustering of non-relational data with mixed types using K-modes and K-prototypes algorithms,” 2020, doi: 10.1145/3423603.3424050.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. Ji, W. Pang, C. Zhou, X. Han, and Z. Wang, “A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data,” Knowledge-Based Syst., vol. 30, pp. 129–135, 2012, doi: 10.1016/j.knosys.2012.01.006.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Li, X. Gao, and L. C. Jiao, “A GA-Based Clustering Algorithm for Large Data Sets With Mixed Numeric and Categorical Values,” in Proceedings - 5th International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003, 2003, pp. 102–107, doi: 10.1109/ICCIMA.2003.1238108.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Piping Circuit Development using K-Prototype Clustering

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      APCORISE '21: Proceedings of the 4th Asia Pacific Conference on Research in Industrial and Systems Engineering
      May 2021
      672 pages
      ISBN:9781450390385
      DOI:10.1145/3468013

      Copyright © 2021 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 November 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate68of110submissions,62%
    • Article Metrics

      • Downloads (Last 12 months)10
      • Downloads (Last 6 weeks)0

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format