Soft Sensor For Estimation Research Articles

Soft sensor based on a lumped parameter model of solids drying in a spouted bed

The aim of this study was to develop a soft sensor for estimation of the solids temperature and moisture content during the drying of porous alumina in a spouted bed. Mechanistic-empirical and fundamental models were employed to develop the soft sensors, which were evaluated for continuous and intermittent drying. Soft sensors provided consistent results and good agreement with experimental measurements. The robustness of the soft sensor also was confirmed, since it was able to predict the solids moisture content during intermittent drying based on a calibration from the continuous process. Therefore, this work describes the successful development of a reliable, robust, and low computing cost mathematical model soft sensor.

Soft-Sensor for Estimation of Lead Slices Thickness in Continuous Casting Process

Continuous casting process is a traditional and widely-used technique in producing the cathode of electric lead. In this paer, soft-sensors based on a support vector regression （SVR, in short）model and an artificial neural networks （ANNs, in short）model respectively, were presented for the estimation of the lead slices thickness in the process.Experiments had been performed on the continuous casting machine to obtain the data used for training and testing of the soft-sensors. For the continuous casting process, the soft-sensors proposed here represents a viable and inexpensive on-line sensors.The study results indicate that a good prediction accuracy of the slice thickess can be provided by the soft-sensors, and even a better performance can be achieved by using pre-processing procedures to the input data, it also shows that the SVR model is an attractive alternative to ANNs model for the soft-sensors, when the number of samples is relatively small.