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PCA and neural networks-based soft sensing strategy with application in sodium aluminate solution

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Component concentration of sodium aluminate solution is an important quality index for alumina production. In this article, we propose a new on-line soft sensing strategy for measuring component concentration of sodium aluminate solution. With this method, on-line control can be realised in aluminate production plants. Several advance techniques are used, such as principal component analysis (PCA), neural modelling and the least square algorithm. Industry experiments are conducted in the alumina production process and the results show the effectiveness of this method.
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Keywords: neural modelling; principal component analysis; sodium aluminate solution; soft sensing

Document Type: Research Article

Affiliations: 1: Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, Liaoning Province, China 2: Departamento de Control Automatico, CINVESTAV-IPN, Mexico D.F., Mexico 3: Information Engineering School, Shenyang Institute Of Chemical Technology, Shenyang 110142, Liaoning Province, China

Publication date: March 1, 2011

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