A length factor artificial neural network method for the numerical solution of the advection dispersion equation characterizing the mass balance of fluid flow in a chemical reactor

Neha Yadav, Kevin Stanley McFall, Manoj Kumar, Joong Hoon Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)


In this article, a length factor artificial neural network (ANN) method is proposed for the numerical solution of the advection dispersion equation (ADE) in steady state that is used extensively in fluid dynamics and in the mass balance of a chemical reactor. An approximate trial solution of the ADE is constructed in terms of ANN using the concept of the length factor in a way that automatically satisfies the desired boundary conditions, regardless of the ANN output. The mathematical model of ADE is presented adopting a first-order reaction, and the steady-state case for the same is examined by estimating the numerical solution using the ANN technique. Numerical simulations are performed by choosing the best ANN ensemble, based on a combination of numerous design parameters, random starting weights, and biases. The solution obtained using the ANN method is compared to the existing finite difference method (FDM) to test the reliability and effectiveness of the proposed approach. Three cases of ADE are considered in this study for different values of advection and dispersion. The numerical results show that the ANN method exhibits a higher accuracy than the FDM, even for the smaller number of training points in the domain, and eliminates the instability issues for the case where advection dominates dispersion.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalNeural Computing and Applications
Publication statusAccepted/In press - 2016 Nov 29



  • Advection
  • Artificial neural network
  • Dispersion
  • Finite difference method
  • Steady state

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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