Intelligent fitting of minimum spout‐fluidised velocity in spout‐fluidised bed |
| |
Authors: | Chun‐Hua Wang Zhao‐Ping Zhong Rui Li Jia‐Qiang E |
| |
Affiliation: | 1. School of Energy and Environment, Southeast University, Nanjing 211189, PR China;2. School of Mechanical and Automotive Engineering, Hunan University, Changsha 410082, PR China |
| |
Abstract: | The experiments were carried on to study the minimum spout‐fluidised velocity in the spout‐fluidised bed. It was found that the minimum spout‐fluidised velocity increased with the rise of static bed height, spout nozzle diameter, particle density, particle diameter, fluidised gas velocity but decreased with the rise of carrier gas density. Based on the experiments, least square support vector machine (LS‐SVM) was established to predict the minimum spout‐fluidised velocity, and adaptive genetic algorithm and cross‐validation algorithm were used to determine the parameters in LS‐SVM. The prediction performance of LS‐SVM is better than that of the empirical correlations and neural network. |
| |
Keywords: | spout‐fluidised bed minimum spout‐fluidised velocity support vector machine adaptive genetic algorithm cross‐validation |
|
|