Challenge:
- Large number of process variables makes it hard to identify the KPIs which are responsible for any deviations or excursions in the process.
- Delay in realization of any excursion from the point in time when it happened.
- Monitoring becomes difficult for the operations which involve large number of variables
- Lack of knowledge about the correlated parameters
Dataset:
- Real-time process data, Batch and continuous operations
Modeling approach:
- Principal component Analysis to perform the root cause analysis, and identifying the critical parameters
- Extending the PCA, using t-score plots to realize any anomaly in the operations
- Time domain analysis to identify any deviations using the concept of Hotelling’s T2 plot
Output:
- Hotelling’s T2 plot enables the operator to take corrective actions against the identified deviations in near-real time
- The dimensionality reduction technique helps the operator to focus on the important process variables that contributes maximum to the variability of the operations
Written by,
ParthPrasoon Sinha
Sr. Data Scientist
Tridiagonal Solutions
