Inferential or soft sensing techniques have been gaining momentum recently as viable
alternatives to hardware sensors in various situations, eg continuous emission monitoring …

Inferential sensing, or soft sensing, gained popularity in recent years as an alternative to
continuous emission monitoring systems because of its simplicity, reliability, and cost …

For combustion processes, it is important to monitor gases such as NO, in exhaust streams.
Traditional approaches for such emission monitoring use analytical instruments, which are …

This paper analyzes a number of strategies that are devoted to improving the generalization
capabilities of neural-network-based soft sensors when only small data sets are available …

Soft sensors have been extensively used to predict the difficult-to-measure key quality
variables. The robust soft sensors should be able to sufficiently extract the local dynamic and …

Soft sensing or inferential estimation has long been considered a potent tool to deal with the
conflict between small control interval and large sampling interval existing in a wide variety …

Soft sensing of quality/key variables is critical to the control and optimization of industrial
processes. One of the main drawbacks of data-driven soft sensors is to deal with the …

The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion
efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor …

The development of accurate data-driven models is of great significance for online
monitoring and control of industrial processes. In this study, a nonlinear dynamic soft …

In this work, we proposed a data-driven soft sensor based on deep learning techniques,
namely the convolutional neural network (CNN). In the proposed soft sensor, instead of only …