TY - GEN
T1 - Determination of optimal process parameters to prepare licorice extract micro-particles using artificial neural network based particle swarm optimization
AU - Zhang, Honghao
AU - Tian, Guangdong
AU - Zhou, Mengchu
AU - Zhang, Chaoyong
N1 - Funding Information:
This work is supported in part by National Natural Science Foundation of China under Grant No. 51405075, and Exchange of the National Natural Science Foundation of China under Grant No. 51561125002
Publisher Copyright:
� 2016 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2016/9/27
Y1 - 2016/9/27
N2 - The ways to obtain high-quality licorice extract (LE) micro-particles have a large impact on their aqueous solubility and bioavailability. Researchers have addressed their preparation and property modification problems. A new issue arises when decision-makers want to determine automatically the best process parameters to prepare them to minimize mean particle size. To do so, this work presents an intelligent decision-making method to obtain the optimal process parameters to prepare them. By using already obtained experimental data, we apply a hybrid algorithm integrating artificial neural network and particle swarm optimization to obtain the best experimental conditions for preparing LE micro-particles. The results indicate that this method is feasible and effective to determine the best process parameters.
AB - The ways to obtain high-quality licorice extract (LE) micro-particles have a large impact on their aqueous solubility and bioavailability. Researchers have addressed their preparation and property modification problems. A new issue arises when decision-makers want to determine automatically the best process parameters to prepare them to minimize mean particle size. To do so, this work presents an intelligent decision-making method to obtain the optimal process parameters to prepare them. By using already obtained experimental data, we apply a hybrid algorithm integrating artificial neural network and particle swarm optimization to obtain the best experimental conditions for preparing LE micro-particles. The results indicate that this method is feasible and effective to determine the best process parameters.
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U2 - 10.1109/WCICA.2016.7578579
DO - 10.1109/WCICA.2016.7578579
M3 - Conference contribution
AN - SCOPUS:84991650721
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 787
EP - 791
BT - Proceedings of the 2016 12th World Congress on Intelligent Control and Automation, WCICA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th World Congress on Intelligent Control and Automation, WCICA 2016
Y2 - 12 June 2016 through 15 June 2016
ER -