In the last two decades Soft Sensors established themselves as a valuable alternative to the
traditional means for the acquisition of critical process variables, process monitoring and …

Over the past twenty years, numerous research outcomes have been published, related to
the design and implementation of soft sensors. In modern industrial processes, various types …

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 …

This paper presents a systematic approach based on robust statistical techniques for
development of a data-driven soft sensor, which is an important component of the process …

Soft sensors are used widely to estimate a process variable which is difficult to measure
online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to …

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 …

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 …

With the predicted depletion of natural resources and alarming environmental issues,
sustainable development has become a popular as well as a much-needed concept in …

Most traditional soft sensors are built upon the labeled dataset that contains equal numbers
of input and output data samples. However, the output variables that correspond to quality …

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 …