Monitoring the variability of petrochemical ZA fertilizer production process based on subgroup observations
The development in technology and requirement to control the process quality characteristics simultaneously have leads to the use of multivariate control chart. This chart considered the correlations between quality characteristics, hence improve the performance of that statistical chart. This study...
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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/47959/24/KhasmanizanMohamadYusoffMFS2011.pdf |
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| Summary: | The development in technology and requirement to control the process quality characteristics simultaneously have leads to the use of multivariate control chart. This chart considered the correlations between quality characteristics, hence improve the performance of that statistical chart. This study deals with the Shewhart-type control chart of multivariate process of ZA fertilizer production in petrochemical industry. The main objective of this study is to monitor the process variability. In order to perform the monitoring process, Phase I and Phase II need to be done. In Phase I, the historical data set (HDS) with m = 36 observations were used to construct the control limits in order to detect any outlier observations, and then removed them to calculate the new control limits with the remaining observations. This step was repeated eight times before the incontrol process was obtained together with the estimated parameter, which is the average of sample covariance matrices. Thus, a clean HDS with m = 20 observations were used to calculate the control limits for monitoring multivariate process variability of new observation in Phase II operation. Here, the study concluded whether the process is stable or not. In purpose of the study, generalized variance (GV) chart is presented as well as vector variance (VV) chart to perform the operations in both phases. Based on GV chart, no outlier is detected and the process is in-control. However, VV chart had detected outliers and concluded that the process is out-of-control. This illustrated that the VV chart is more effective in detecting process variability rather than GV chart. |
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