Data‐driven soft sensing approaches have been a hot research field for decades and are
increasingly used in industrial processes due to their advantages of easy implementation …

This article proposes a new data-driven model devised to integrate process knowledge into
its structure to increase the human–machine synergy in the process industry. The proposed …

Virtual sensors have established themselves as effective tools in process industries for
online estimating variables that are crucial but difficult to measure. However, multimode …

Modeling high‐dimensional dynamic processes is a challenging task. In this regard,
probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft …

The cement calcination process suffers serious pollutant emission problems, especially
nitrogen oxide (NO x) emission. Traditional methods mainly used physical modeling to …

Batch processes are important manufacturing approaches widely used in modern industry.
During the manufacturing process, quality prediction is essential. Data-driven-based soft …

In modern industrial process control, just-in-time learning (JITL)-based soft sensors have
been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor …

This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive
modeling in multimode-multiphase industrial processes. For this purpose, different …

The mechanical properties of hot rolled strip are the key index of product quality, and the soft
sensing of them is an important decision basis for the control and optimization of hot rolling …

Kernel partial least squares (KPLS) is widely employed to address the issue of nonlinearity
inherent in complex industrial processes. However, KPLS can only extract shallow features …