This paper is concerned with data science and analytics as applied to data from dynamic
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …

In this paper, the key performance indicator (KPI)-based multivariate statistical process
monitoring and fault diagnosis (PM-FD) methods for linear static processes are surveyed …

Partial least squares (PLS) regression is widely used to capture the latent relationship
between inputs and outputs in static system modeling. Several dynamic PLS algorithms …

In order to control product compositions in a multicomponent distillation column, the distillate
and bottom compositions are estimated from on-line measured process variables. In this …

In data-based monitoring field, the nonlinear iterative partial least squares procedure has
been a useful tool for process data modeling, which is also the foundation of projection to …

High‐dimensional and time‐dependent data pose significant challenges to Statistical
Process Monitoring. Most of the high‐dimensional methodologies to cope with these …

This article describes principal component analysis (PCA) of the power spectra of data from
chemical processes. Spectral PCA can be applied to the measurements from a whole unit or …

Low-density polyethylene (LDPE) and ethylene vinyl acetate (EVA) copolymers are
produced in free radical polymerization using reactors at extremely high pressure. The …

Canonical correlation analysis (CCA) and projection to latent structures (PLS) are popular
statistical approaches for process modeling and monitoring. CCA focuses on the correlation …

It is difficult to estimate the water quality of the wastewater treatment process, because the
operating conditions are frequently changed. This paper gives an effective adaptive …