In process industries, use of the data-driven soft sensors for the purpose of process control
and monitoring has gained much popularity. Data-driven soft sensors infer the process …

Soft sensor technique has become a promising solution to enable real-time estimations of
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …

Soft sensors have been widely used in industrial processes to predict uneasily measured
important process variables. The core of data-driven soft sensors is to construct a soft sensor …

Data-driven soft sensors have been widely used in both academic research and industrial
applications for predicting hard-to-measure variables or replacing physical sensors to …

Soft sensors estimate values of difficult-to-measure process variables (y) from values of easy-
to-measure process variables (X). Although adaptive soft sensors have been developed to …

Soft sensors have been widely used in the industrial process control to improve the quality of
the product and assure safety in the production. The core of a soft sensor is to construct a …

A soft Sensor is a model which is used to estimate the unmeasurable output of an industrial
process. Designing a soft sensor is usually difficult because its modeling is often based on …

The main challenge in developing soft sensors in process industry is the existence of
irregularity of data, such as measurement noises, outliers, and missing data. This paper is …

Soft sensors have been widely used in chemical plants to estimate process variables that
are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive …

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 …