Multivariate Analysis (MVA) is a well-established technique to analyse highly correlated process variables. It is well known in batch, but also successfully applied in discrete or continuous processing. In comparison to single variable applications, for example statistical process control, MVA has shown to be superior in the detection of process drifts and upsets. In practice, the implementation of MVA requires two different data structures or models:
Event Frames are usually autogenerated from the batch execution system (BES) and reflect the logical\automation sequences for recipe execution. Both AF Elements and Event Frames are being used to create MVA models and calculate statistics. Below is an example of a multivariate model that combines the autogenerated Event Frame “Unit Procedure” and process variables in the Element: “Bio Reactor 0”:
This type of analysis is typically used for batch-to-batch comparison (T2 and speX statistics) and batch evolution monitoring in the pharmaceutical, biotech and chemical Industry.
One of the shortcomings of using automation phases is that they seldom line up with time frames that are critical for the underlying process evolution (process phases). Often there is a mismatch in the granularity, process phases are either longer or shorter in duration compared to the automation phases. Also start and end might be based on specific process conditions, for example temperature, batch maturity, online measurements and others. The mismatch between automation and process phases causes misalignment in the MVA model and a broadening of the process control envelopes. . The resulting models are often not optimal.
SEEQ has developed a platform that excels in creating time series segments as well as time series data cleansing and conditioning. The platform provides several different approaches to define very precise start and end condition. The following show the definition of a new capsule based on a profile search that solely focuses on the process peak temperature:
These capsules can be utilized in other applications through an API and blended with other PI data models to create very precise multivariate models:
Multivariate Analysis is a powerful method to analyze highly correlated process data. It depends on equipment\process models and time series segments. OSIsoft PI provides data models for both. And typically time segments are automatically populated from a BES or MES systems. SEEQ provides new capabilities to create highly precise time segments called capsules, that refines the MVA analysis and creates meaningful process envelops. The integration is seamless since both systems provide powerful API’s to their time series data and models. The resulting MVA models target specific process phases that can be used to create improved process control limits or regression analysis.
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