Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process
industries, thus allowing for less fault occurrence and better control performance. However …

Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-
time estimation of hard-to-measure variables as a function of available data obtained from …

Abstract Soft Sensors (SSs) are inferential models which are widely used in industry. They
are generally built through data-driven approaches that exploit industry historical databases …

The use of large number of on-line sensors in control and automation for optimized
operation of WWTPs is increasing popular, which makes manual expert-based evaluation …

Data-driven soft sensors are usually used to predict quality-related but hard-to-measure
variables in industrial systems. The acceptable prediction performance, however, mainly …

Deep learning has been growing in popularity for soft sensor modeling of nonlinear
industrial processes, infeuality-related variables. However, applications may be highly …

Data-driven soft sensors have been widely used in process systems for delivering online
estimations of hard-to-measure yet important quality-related variables. However, in many …

The predictive ability of soft sensors, which estimate values of an objective variable y online,
decreases due to process changes in chemical plants. To reduce the decrease of predictive …

Soft sensors are the most commonly used tools to predict hard-to-measure variables in
industrial processes. However, the presence of a large number of hard-to-measure variables …

Soft sensors are the most commonly used tools to estimate the hard-to-measure variables in
the chemical processes and other industries, mainly due to unknown mechanism, significant …