Investigating vapor–liquid equilibria of binary mixtures containing supercritical or near-critical carbon dioxide and a cyclic compound using cascade neural network-Reference-Cited by-同舟云学术

Investigating vapor–liquid equilibria of binary mixtures containing supercritical or near-critical carbon dioxide and a cyclic compound using cascade neural network

Published:2013-04 Issue: Volume:343 Page:24-29
ISSN:0378-3812
Container-title:Fluid Phase Equilibria
language:en
Short-container-title:Fluid Phase Equilibria

Author:

Lashkarbolooki Mostafa,Vaferi Behzad,Shariati Alireza,Zeinolabedini Hezave Ali

Publisher

Elsevier BV

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy,General Chemical Engineering

Reference58 articles.

1. Estimation of vapour liquid equilibria for the system carbon dioxide–difluoromethane using artificial neural networks

2. Global Optimization in Parameter Estimation of Nonlinear Algebraic Models via the Error-in-Variables Approach

3. Applied Parameter Estimation for Chemical Engineers;Englezos,2001

4. Evaluation of stochastic global optimization methods for modeling vapor–liquid equilibrium data

5. Radical polymerization in supercritical carbon dioxide—use of supercritical carbon dioxide as a mixing assistant

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