Statistical Process Control Charts Applied to Steelmaking Quality Improvement

Abstract The complex nature for steelmaking processes makes the classical Statistical Process Control (SPC) methodologies non-optimal when used to monitor and control steam boiler generation used to supply the required steam for vacuum degassing process. These processes include a large number of variables that need to be monitored and controlled, while classical SPC requires a control chart for each variable. Thus the effect of one variable can be confounded with effects of other correlated variables. Such a situation can lead to false alarm signals. Univariate control charts are also difficult to manage and analyze because of the large numbers of control charts of each process. An alternative approach is to construct a single multivariate control T2 chart that minimizes the occurrence of false process alarms, monitors the relationship between the variables, and identifies real process changes not detectable using univariate charts. It is necessary to simultaneously monitor and control these variables to achieve optimal vacuum degassing process performance to remove harmful gases from the molten steel before casting. This application represents the main focus of the presented paper. This paper also studies the application of univariate and multivariate control charts in the field of steel industry. Performance analysis for each charting method is studied using the Average Run Length (ARL). A comparison of the univariate out-of-control signals with the multivariate out-of-control signals is also made to illustrate the efficiency of the Hotelling’s T2 statistic.