Characterization of basic properties for pure substances and petroleum fractions by neural network

A set of conventional feedforward multilayer neural networks have been proposed to predict basic properties (e.g., critical temperature ( T c), critical pressure ( P c), critical volume ( V c), acentric factor ( ω) and molecular weight (MW)) of pure compounds and petroleum fractions based on their n...

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Veröffentlicht in:	Fluid phase equilibria 2005-04, Vol.231 (2), p.188-196
Hauptverfasser:	Boozarjomehry, Ramin B., Abdolahi, Farzad, Moosavian, Mohammad A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Acentric factor Applied sciences Critical properties Crude oil, natural gas and petroleum products Energy Exact sciences and technology Fuels Molecular weight Neural network Petroleum fraction Prospecting and production of crude oil, natural gas, oil shales and tar sands
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