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Modeling of gas–liquid separation through stacked neural network
Author(s) -
Qazi Nadeem,
Yeung Hoi
Publication year - 2014
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.1777
Subject(s) - separator (oil production) , artificial neural network , nonlinear system , separation (statistics) , computer science , separation process , artificial intelligence , engineering , machine learning , physics , quantum mechanics , chemical engineering , thermodynamics
ABSTRACT In recent years, few mechanistic models have been proposed for design optimization and predicting the performance of reserve flow cyclonic separators. However, there is not much literature available on design and performance evaluation of axial flow compact separator. The work presented in this paper demonstrates the use of stacked neural network to model the nonlinear process of gas–liquid separation through a novel designed axial flow compact separator (called as I‐SEP in this study). We trained several standard back propagation training algorithms to predict the separation efficiency of the I‐SEP; these base models are then combined to form a single composite model. Principle component regression techniques are used to estimate the weight of the participant trained neural networks in the composite model. It has shown in this work that combination of trained neural networks improves prediction accuracy of gas–liquid separation as compared with that of participant individual neural network models. The stacked neural network model produced satisfactory prediction on unseen experimental data and may be helpful to the operators in effectively controlling the separator by predicting the separation efficiency at the changing operating inlet condition. © 2014 Curtin University of Technology and John Wiley & Sons, Ltd.