Most biotech and pharmaceutical companies are adopting Process Analytical Technology (PAT) in manufacturing to provide real time operational insights that allows better control and leads to higher yields, purity, and\or shorter cycle times.

However, to integrate PAT data into an existing data infrastructure has been challenging. Because each PAT – also a single measurement – is a spectrum consisting of a list or array of values (e.g. time, channel, value) that cannot be stored in a classical data historian.

This graph shows you several spectra forming a multi-dimensional time series:

integrate PAT data

Spectra are often stored in SQL type databases as plain tables, separate from the other manufacturing data stored in the historian. The main problem with this approach is the loss of equipment and batch context. This is problematic to any subsequent analysis.

So why are spectra stored separately? Because most industrial data historian store values as simple time series in different data types, typically bool, int, float and string. Each time series point is a tuple of a timestamp and a single value (scalar). For PAT and other use cases, it would be required to extend the existing data shapes to accommodate vectors, matrices, and tensors:

There are many use cases for these data structures:

In the OSIsoft Asset Framework, extending the Historian is accomplished by deploying a new source and linking it to a time series database that supports time-based vectors, matrices, and tensors:

The RAMAN spectra are attributes on the unit\vessel or located on the RAMAN equipment. Therefore, extending the existing OSIsoft AF data model allows the measurements to be analyzed in the present batch or event frame context:

Conclusion

Classical historians have been developed for scalar time series information. This has worked for most sensor data type, but they cannot accommodate higher dimensional time series information. The solution is to extend the existing historian with databases that allow a more flexible schema. This results in better utilization of existing equipment and batch context that enables context specific analysis.

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