Development of a softsensor for particle size monitoring

Abstract Some key control variables of industrial processes, associated with product quality, often cannot be measured directly or frequently enough to establish adequate control. In such cases, it is possible to use available measurements to provide a prediction for these process variables and use them in a control strategy, thereby giving rise to what is now commonly called a softsensor. In some industrial grinding circuits, the on-line particle size analyzer is shared between various sampling points. Therefore, for a given location, the actual measurement is only available every 10 to 20 minutes, a delay which is unacceptable for automatic control purposes. To alleviate this problem, a softsensor based on an artificial neural network has been investigated. First, the structure of the neural network and different schemes for the training process are analyzed. Then, the performance of the neural network softsensor is compared with other inferential methods such as ARMA models and Kalman filters.

[1]  B. A. Wills,et al.  Mineral processing technology , 1979 .

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  Peter C. Young,et al.  Recursive Estimation and Time-Series Analysis: An Introduction , 1984 .

[4]  Brian Roffel,et al.  Computer control in the process industries , 1987 .

[5]  J Thibault,et al.  On‐line prediction of fermentation variables using neural networks , 1990, Biotechnology and bioengineering.

[6]  Daniel Hodouin,et al.  Computer-aided dynamic analysis and control design for grinding circuits , 1987 .

[7]  G. S. Samdani.  Software takes on air monitoring , 1994 .

[8]  Ming Rao,et al.  Intelligent operation support system for batch chemical pulping process , 1993 .

[9]  A. J. Lynch,et al.  Mineral Crushing and Grinding Circuits , 1977 .

[10]  Aldo Cipriano,et al.  On-line Dynamic Particle Size Estimator for Grinding Plant Control , 1988 .

[11]  Xuemin Shen,et al.  Artificial Neural Network Approach to Inferential Control of Volatility in Refinery Plants , 1992 .

[12]  Jean-Luc Collins,et al.  Computer applications in the mineral industry , 1988 .

[13]  T. C. Rao.  The characteristics of hydrocyclones : and their application as control units in comminution circuits , 1966 .

[14]  Jules Thibault,et al.  Adaptive neural models for on‐line prediction in fermentation , 1991 .

[15]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[16]  Charles W. Butler,et al.  Naturally intelligent systems , 1990 .