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Modeling of Supercritical Fluid Extraction by Neural Networks


Authors



Abstract

Modeling of the relationship between the pressure and yield of biomaterials is an essential issue in supercritical fluid extraction. In this paper, neural networks are proposed for modeling of supercritical fluid extraction. First a three-layer neural network with a fast learning algorithm is used, and its performance is compared to a conventional model of the Peng-Robinson equation of state. A novel hybrid model combining both a neural network and the Peng-Robinson equation is then proposed. With the learning capacity, the proposed models generally perform better than the conventional model that needs to select its parameters by trial and error. The effectiveness of the proposed approaches is demonstrated by simulation and comparison studies.


Keywords


Pages

Total Pages: 10
Pages: 3-12

DOI
10.1080/10798587.2000.10642837


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Published

Volume: 9
Issue: 1
Year: 2003

Cite this document


References

Clifford, T. Fundamentals of Supercritical Fluids . Oxford, 1998.

Karayiannis, NB. Artificial Neural Networks Learning Algorithms, Performance Evaluations and Applications . Kluwer, 1993.

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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