Title :
Distributed SVMs based soft sensor and its application for high pressure dissolving
Author :
Li, Yonggang ; Gui, Weihua ; Yang, Chunhua ; Chen, Zhisheng
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha
Abstract :
High pressure dissolving (HPD) is a very important process for alumina production. During HPD process, alumina caustic ratio (ACR) of the dissolved slurry is a very important economic technical indicator. In practice, there are many factors influencing ACR and there are different noise levels for different HPD conditions. So, it is very difficult to predict ACR with single model accurately. In this paper, an improved rival penalized competitive learning clustering algorithm is used to cluster the learning samples. Then a distributed support vector machine based soft sensor is proposed to predict ACR on-line. The simulation and practical application results showed its effectiveness.
Keywords :
alumina; dissolving; learning (artificial intelligence); manufacturing processes; pattern clustering; sensor fusion; slurries; support vector machines; ACR online; alumina caustic ratio; alumina production; dissolved slurry; distributed SVM; distributed support vector machine; economic technical indicator; high pressure dissolving; rival penalized competitive learning clustering algorithm; soft sensor; Analytical models; Clustering algorithms; Economic forecasting; Heating; Intelligent control; Noise level; Silicon; Slurries; Support vector machine classification; Support vector machines; Alumina caustic ratio; Distributed SVM; High pressure dissolving; Soft sensor;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593843
