Data-driven soft sensors are inferential models that use on-line available sensors (eg
temperature, pressure, flow rate, etc) to predict quality variables which cannot be …

Soft sensors are increasingly being used to provide important information about production
processes that is otherwise only available through off-line laboratory analysis. However …

In process industries, there is a great demand for additional process information such as the
product quality level or the exact process state estimation. At the same time, there is a large …

One of the challenges of utilizing soft sensors is that their prediction accuracy deteriorates
with time due to multiple factors, including changes in operating conditions. Once soft …

Increasing demands for on-line monitoring and control of industrial processes and their
associated variables, and the limitations of the available measuring systems have led to the …

With the advent of Industry 4.0, the automation and monitoring of manufacturing and process
industries became a necessary requirement. The estimation of the plant working process …

The lack of real-time measurement of certain critical product and process characteristics is a
major problem in the manufacturing industry, and it can lead to an out of specification …

It is often difficult or expensive to directly measure product properties or qualities in real time,
and soft sensors provide a way to infer critical process variables for process monitoring and …

As processes become more complex and the need to measure each and every variable
becomes more critical, the ability of physical sensors to always provide the sufficient …

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 …