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Neural Computing and Applications

, Volume 31, Issue 4, pp 1165–1172 | Cite as

Application of artificial neural networks and genetic programming in vapor–liquid equilibrium of C1 to C7 alkane binary mixtures

  • Aliakbar RoostaEmail author
  • Javad Hekayati
  • Jafar Javanmardi
Original Article
  • 99 Downloads

Abstract

In this study, the capacity of artificial neural networks (ANNs) and genetic programming (GP) in making possible, fast and reliable predictions of equilibrium compositions of alkane binary mixtures is investigated. A data set comprising 847 data points was gathered and used in both training the proposed ANN and generating the closed-form expressions of the GP procedure. The results obtained demonstrate the relative precision of the proposed ANN, while, on the other hand, exhibit that the GP model, although less precise, affords high CPU time efficiency and simplicity. Concisely, the proposed models can serve the purpose of being close first estimates for more thermodynamically rigorous vapor–liquid equilibrium calculation procedures and do obviate the necessity for the availability of a large set of experimental binary interaction coefficients. Mean absolute errors of 0.0100 and 0.0404 for liquid compositions and of 0.0054 and 0.0254 for vapor-phase mole fractions, for the proposed ANN and GP models, respectively, are a testament to the reliability of the proposed models.

Keywords

Hydrocarbon Vapor–liquid equilibria Artificial neural network Genetic programming Equation of state 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

521_2017_3150_MOESM1_ESM.xlsx (86 kb)
Supplementary material 1 (XLSX 86 kb)

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Chemical, Petroleum and Gas EngineeringShiraz University of TechnologyShirazIran
  2. 2.Young Researchers and Elite Club, Shiraz BranchIslamic Azad UniversityShirazIran

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