Regression analysis constitutes an important tool for investigating the effect of explanatory
variables on response variables. When outliers and bias errors are present, the weighted …

Data that represent complex and multivariate processes are well known to be multiscale due
to the variety of changes that could occur in a process with different localizations in time and …

In this second edition of a bestseller, authors Paul H. Brunner and Helmut Rechberger guide
professional newcomers as well as experienced engineers and scientists towards mastering …

On-line optimization provides a means for maintaining a process near its optimum operating
conditions by providing set points to the process's distributed control system (DCS). To …

Gross‐error detection plays a vital role in parameter estimation and data reconciliation for
dynamic and steady‐state systems. Data errors due to miscalibrated or faulty sensors or just …

Data Rectification by univariate filtering is popular for processes lacking an accurate model.
Linear filters are most popular for online filtering; however, they are single‐scale best suited …

In a previous study, a nonlinear dynamic data reconciliation procedure (NDDR) based on
the particle swarm optimization (PSO) method was developed and validated in line and in …

Robust data reconciliation is an effective technique designed to minimize gross errors
drawbacks over estimated process variables. This work presents a review focusing on …

Principal component analysis (PCA) is a dimensionality reduction modeling technique that
transforms a set of process variables by rotating their axes of representation. Maximum …

Instrument Engineers' Handbook, Third Edition: Volume Three: Process Software and Digital
Networks provides an in-depth, state-of-the-art review of existing and evolving digital …