Soft sensing method for magnetic tube recovery ratio via fuzzy systems and neural networks

Authors:
Fenghua Wu;Tianyou Chai
Affiliations:
Key Laboratory of Process Industry Automation, Northeastern University, Shenyang 110006, China;Key Laboratory of Process Industry Automation, Northeastern University, Shenyang 110006, China
Venue:
Neurocomputing
Year:
2010

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Abstract

A magnetic tube recovery ratio (MTRR) is an important index in mineral processing, though it cannot be measured online. Real-time control for this product index is impossible. In this paper a new soft sensing method is proposed, which uses fuzzy system and neural network techniques. The contributions of our soft sensing method are that a fuzzy mechanism model for MTRR is used, which is obtained from data clustering. We do not update the fuzzy model, but use a neural compensator to improve the modeling accuracy, where the training algorithm for the neural network stable. This soft sensing method has been successfully applied in a metal company in China.