In many industrial plants, development and implementation of advanced monitoring and
control techniques require real-time measurement of process quality variables. However, on …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

Process monitoring systems are necessary for ensuring the long-term reliability of the
operation of industrial systems. This article provides some perspectives on progress in the …

Data-driven soft sensors have been widely utilized in industrial processes to estimate the
critical quality variables which are intractable to directly measure online through physical …

Dimensionality reduction is important for the high-dimensional nature of data in the process
industry, which has made latent variable modeling methods popular in recent years. By …

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of
nonlinear processes. However, traditional JITL approaches mainly focus on equal sample …

The design and application of Soft Sensors (SSs) in the process industry is a growing
research field, which needs to mediate problems of model accuracy with data availability …

Discrete and delayed laboratory analyses of product quality restrict the operational
optimization of industrial processes. However, it is challenging to build an accurate online …

Latent variable (LV) models provide explicit representations of underlying driving forces of
process variations and retain the dominant information of process data. In this study, slow …

Developing accurate and robust sensors for nonlinear and highly varying systems is a
challenge. Deep learning, an advanced technique to learn deep architectures, has become …