Prediction for the Riser’s Average Solids Concentration Based on the Least Square SVM Method
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.
Zhong Cao, Yinghe He, Lixian Sun and Xueqiang Cao
X. Y. Wang and 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