Deconstructing principal component analysis using a data reconciliation perspective

• The close connection between principal component analysis (PCA) and data reconciliation (DR) is established.
• A unified framework for applying PCA and DR to process data is proposed.
• Technique for incorporating prior knowledge of process constraints in PCA is proposed.

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.