INVERSE-EMULSION STABILITY: QUANTIFICATION WITH AN ARTIFICIAL NEURAL NETWOR
作者:
Huafang Ni,
David Hunkeler,
期刊:
Journal of Dispersion Science and Technology
(Taylor Available online 1998)
卷期:
Volume 19,
issue 5
页码: 551-569
ISSN:0193-2691
年代: 1998
DOI:10.1080/01932699808913199
出版商: Taylor & Francis Group
关键词: inverse-emulsion;backpropagation (13P) network;stability;acrylamide;cationic monomer
数据来源: Taylor
摘要:
Due to the multiplicity of parameters governing emulsion stability, existing theories generally cannot quantitatively predict the phase separation in oil/water systems. In this work, an artificial neural network, which is known to have a strong nonlinear mapping ability, was used to “learif” the correlation between the factors influencing emulsion stability (phase ratio, surfactant concentration and comonomer concentrations) and the magnitude of phase separation. This has been applied to the water-in-oil copolymerizations of acrylamide with quaternary ammonium cationic monomers. It was found that the ANN can accurately predict the subset of the stability state (stable, metastable, unstable), along with the extent of oil separation for metastable systems.
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