Soft sensors are mathematical models employed to estimate hard-to-measure variables from
available easy-to-measure variables. These sensors are typically developed using either …

With recent advances in digitisation and big data analytics, more pharmaceutical firms are
adopting digital tools to achieve modernisation. The biological phenomena within …

Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …

Just-in-time learning (JITL) is a widely used method for online soft sensing. The limitation of
available data and the increase of sample dimensions will make the historical dataset …

The principal component regression (PCR) based soft sensor modeling technique has been
widely used for process quality prediction in the last decades. While most industrial …

This paper proposes an adaptive soft sensing method based on selective ensemble of local
partial least squares models, referring to as the SELPLS, for quality prediction of nonlinear …

For complex industrial plants with multiphase/multimode data characteristic, Gaussian
mixture model (GMM) has been used for soft sensor modeling. However, almost all GMM …

The goal of this paper is to design and implementation of a new data-driven soft sensor that
uses state dependent parameter (SDP) models to improve product quality monitoring. The …

Most applications of soft sensors in process industries require learning from a stream of
data, which may exhibit nonstationary dynamics, or concept drift. In this study, we develop a …

In semiconductor manufacturing processes, virtual metrology (VM) has been investigated as
a promising tool to predict important characteristics of products. Although partial least …