  Data reconciliation (DR) and Principal Component Analysis (PCA) are two
popular data analysis techniques in process industries. Data reconciliation is
used to obtain accurate and consistent estimates of variables and parameters
from erroneous measurements. PCA is primarily used as a method for reducing the
dimensionality of high dimensional data and as a preprocessing technique for
denoising measurements. These techniques have been developed and deployed
independently of each other. The primary purpose of this article is to
elucidate the close relationship between these two seemingly disparate
techniques. This leads to a unified framework for applying PCA and DR. Further,
we show how the two techniques can be deployed together in a collaborative and
consistent manner to process data. The framework has been extended to deal with
partially measured systems and to incorporate partial knowledge available about
the process model.
