Paper Title:
Prediction for the Riser’s Average Solids Concentration Based on the Least Square SVM Method
  Abstract

The measurement for riser’s solids concentration is important but difficult. So, a high-efficiency and low-cost new predicted method based on the least square SVM method is proposed. The superficial operated gas velocity Ug and solids circulation rate εs are chosen as the effect factors of the solids concentration. Furthermore, the multiple linear regression model (Linear) and artificial neural net model (ANN) are also used to compare with the SVM model. By analyzing the training time and error, it is proved the SVM model has higher prediction accuracy and forecasting efficiency, which is a better choice for the riser’s solids concentration prediction.

  Info
Periodical
Advanced Materials Research (Volumes 236-238)
Edited by
Zhong Cao, Yinghe He, Lixian Sun and Xueqiang Cao
Pages
672-675
DOI
10.4028/www.scientific.net/AMR.236-238.672
Citation
X. Y. Wang, Y. H. Xiao, "Prediction for the Riser’s Average Solids Concentration Based on the Least Square SVM Method", Advanced Materials Research, Vols. 236-238, pp. 672-675, 2011
Online since
May 2011
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Price
$32.00
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