IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i16p3444-d1212778.html
   My bibliography  Save this article

Use of Statistical Process Control for Coking Time Monitoring

Author

Listed:
  • Marta Benková

    (Institute of Control and Informatization of Production Processes, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Němcovej 3, 042 00 Košice, Slovakia)

  • Dagmar Bednárová

    (Institute of Control and Informatization of Production Processes, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Němcovej 3, 042 00 Košice, Slovakia)

  • Gabriela Bogdanovská

    (Institute of Control and Informatization of Production Processes, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Němcovej 3, 042 00 Košice, Slovakia)

  • Marcela Pavlíčková

    (Institute of Control and Informatization of Production Processes, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Němcovej 3, 042 00 Košice, Slovakia)

Abstract

Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart’s, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time.

Suggested Citation

  • Marta Benková & Dagmar Bednárová & Gabriela Bogdanovská & Marcela Pavlíčková, 2023. "Use of Statistical Process Control for Coking Time Monitoring," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3444-:d:1212778
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/16/3444/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/16/3444/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ethel García & Rita Peñabaena-Niebles & Maria Jubiz-Diaz & Angie Perez-Tafur, 2022. "Concurrent Control Chart Pattern Recognition: A Systematic Review," Mathematics, MDPI, vol. 10(6), pages 1-31, March.
    2. Don G. Wardell & Herbert Moskowitz & Robert D. Plante, 1992. "Control Charts in the Presence of Data Correlation," Management Science, INFORMS, vol. 38(8), pages 1084-1105, August.
    3. Bruno Chaves Franco & Philippe Castagliola & Giovanni Celano & Antonio Fernando Branco Costa, 2014. "A new sampling strategy to reduce the effect of autocorrelation on a control chart," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1408-1421, July.
    4. Yaping Li & Haiyan Li & Zhen Chen & Ying Zhu, 2022. "An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations," Energies, MDPI, vol. 15(5), pages 1-13, February.
    5. Alwan, Layth C & Roberts, Harry V, 1988. "Time-Series Modeling for Statistical Process Control," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 87-95, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ord, J. Keith & Koehler, Anne B. & Snyder, Ralph D. & Hyndman, Rob J., 2009. "Monitoring processes with changing variances," International Journal of Forecasting, Elsevier, vol. 25(3), pages 518-525, July.
    2. Ridley, D. & Duke, D., 2007. "Moving -window spectral model based statistical process control," International Journal of Production Economics, Elsevier, vol. 105(2), pages 492-509, February.
    3. Gulser Koksal & Burcu Kantar & Taylan Ali Ula & Murat Caner Testik, 2008. "The effect of Phase I sample size on the run length performance of control charts for autocorrelated data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(1), pages 67-87.
    4. Schmid Wolfgang & Okhrin Yarema, 2003. "Tail behaviour of a general family of control charts," Statistics & Risk Modeling, De Gruyter, vol. 21(1), pages 79-92, January.
    5. Samari, Goleen & Catalano, Ralph & Alcalá, Héctor E. & Gemmill, Alison, 2020. "The Muslim Ban and preterm birth: Analysis of U.S. vital statistics data from 2009 to 2018," Social Science & Medicine, Elsevier, vol. 265(C).
    6. Amira Dridi & Mohamed El Ghourabi & Mohamed Limam, 2012. "On monitoring financial stress index with extreme value theory," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 329-339, March.
    7. Cang Wu & Huijuan Hou & Chunli Lei & Pan Zhang & Yongjun Du, 2023. "A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    8. Weihs, Claus & Theis, Winfried & Messaoud, Amor & Hering, Franz, 2004. "Monitoring of the BTA Deep Hole Drilling Process Using Residual Control Charts," Technical Reports 2004,60, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. Johannes Freiesleben & Nicolas Gu'erin, 2015. "Homogenization and Clustering as a Non-Statistical Methodology to Assess Multi-Parametrical Chain Problems," Papers 1505.03874, arXiv.org, revised Dec 2017.
    10. Miguel Flores & Salvador Naya & Rubén Fernández-Casal & Sonia Zaragoza & Paula Raña & Javier Tarrío-Saavedra, 2020. "Constructing a Control Chart Using Functional Data," Mathematics, MDPI, vol. 8(1), pages 1-26, January.
    11. Timothy M. Young & Ampalavanar Nanthakumar & Hari Nanthakumar, 2021. "On the Use of Copula for Quality Control Based on an AR(1) Model," Mathematics, MDPI, vol. 9(18), pages 1-13, September.
    12. Thaga K. & Kgosi P. M. & Gabaitiri L., 2007. "Max-Chart for Autocorrelated Processes," Stochastics and Quality Control, De Gruyter, vol. 22(1), pages 87-105, January.
    13. Ramjee, Radhika & Crato, Nuno & Ray, Bonnie K., 2002. "A note on moving average forecasts of long memory processes with an application to quality control," International Journal of Forecasting, Elsevier, vol. 18(2), pages 291-297.
    14. P. Vellaisamy & S. Sankar & M. Taniguchi, 2003. "Estimation and Design of Sampling Plans for Monitoring Dependent Production Processes," Methodology and Computing in Applied Probability, Springer, vol. 5(1), pages 85-108, March.
    15. A. Snoussi, 2011. "SPC for short-run multivariate autocorrelated processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2303-2312.
    16. Messaoud, Amor & Weihs, Claus & Hering, Franz, 2008. "Detection of chatter vibration in a drilling process using multivariate control charts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3208-3219, February.
    17. Žmuk Berislav, 2016. "Capabilities of Statistical Residual-Based Control Charts in Short- and Long-Term Stock Trading," Naše gospodarstvo/Our economy, Sciendo, vol. 62(1), pages 12-26, March.
    18. Mohamed El Ghourabi & Amira Dridi & Mohamed Limam, 2015. "A new financial stress index model based on support vector regression and control chart," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 775-788, April.
    19. Hwarng, H. Brian, 2001. "Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures," Omega, Elsevier, vol. 29(3), pages 273-289, June.
    20. Pan, Xia & Jarrett, Jeffrey, 2007. "Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation," International Journal of Production Economics, Elsevier, vol. 106(1), pages 204-216, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3444-:d:1212778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.