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Article

Use of Statistical Process Control for Coking Time Monitoring

by
Marta Benková
,
Dagmar Bednárová
,
Gabriela Bogdanovská
* and
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
*
Author to whom correspondence should be addressed.
Mathematics 2023, 11(16), 3444; https://doi.org/10.3390/math11163444
Submission received: 30 June 2023 / Revised: 31 July 2023 / Accepted: 4 August 2023 / Published: 8 August 2023
(This article belongs to the Special Issue Statistical Process Control and Application)

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.
Keywords: statistical process control; control chart; autocorrelated process; ARIMA model; statistical test; coking time statistical process control; control chart; autocorrelated process; ARIMA model; statistical test; coking time

Share and Cite

MDPI and ACS Style

Benková, M.; Bednárová, D.; Bogdanovská, G.; Pavlíčková, M. Use of Statistical Process Control for Coking Time Monitoring. Mathematics 2023, 11, 3444. https://doi.org/10.3390/math11163444

AMA Style

Benková M, Bednárová D, Bogdanovská G, Pavlíčková M. Use of Statistical Process Control for Coking Time Monitoring. Mathematics. 2023; 11(16):3444. https://doi.org/10.3390/math11163444

Chicago/Turabian Style

Benková, Marta, Dagmar Bednárová, Gabriela Bogdanovská, and Marcela Pavlíčková. 2023. "Use of Statistical Process Control for Coking Time Monitoring" Mathematics 11, no. 16: 3444. https://doi.org/10.3390/math11163444

APA Style

Benková, M., Bednárová, D., Bogdanovská, G., & Pavlíčková, M. (2023). Use of Statistical Process Control for Coking Time Monitoring. Mathematics, 11(16), 3444. https://doi.org/10.3390/math11163444

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