Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine

2015 ◽  
Vol 37 (5) ◽  
pp. 486-494 ◽  
Author(s):  
Mohammad Ali Ahmadi ◽  
Alireza Bahadori
2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bin Zhang ◽  
Jinke Gong ◽  
Wenhua Yuan ◽  
Jun Fu ◽  
Yi Huang

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.


2013 ◽  
Vol 53 (2) ◽  
pp. 945-958 ◽  
Author(s):  
Amir Fayazi ◽  
Milad Arabloo ◽  
Amin Shokrollahi ◽  
Mohammad Hadi Zargari ◽  
Mohammad Hossein Ghazanfari

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