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Intelligent fitting of minimum spout‐fluidised velocity in spout‐fluidised bed
Author(s) -
Wang ChunHua,
Zhong ZhaoPing,
Li Rui,
E JiaQiang
Publication year - 2011
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20404
Subject(s) - nozzle , support vector machine , artificial neural network , particle density , particle (ecology) , materials science , mathematics , mechanics , computer science , engineering , artificial intelligence , thermodynamics , mechanical engineering , physics , geology , oceanography , volume (thermodynamics)
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.