Abstract
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.








Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Himmelblau DM (1967) Basic principles and calculations in chemical engineering, 2nd edn. Prentice-Hall, Englewood Cliffs
Freijera JI, Veling EJM, Hassanizadeh SM (1998) Analytical solutions of the convection–dispersion equation applied to transport of pesticides in soil columns. Environ Model Softw 13(2):139–149
O’Loughlin EM, Bowner KH (1975) Dilution and decay of aquatic herbicides in flowing channels. J Hydrol 26(34):217–235
Hossain MA, Yonge DR (1999) On Galerkin models for transport in ground water. Appl Math Comput 100(2–3):249–263
Kumar N (1983) Unsteady flow against dispersion in finite porous media. J Hydrol 63(3–4):345–358
Guvanasen V, Volker R (1983) Numerical solutions for solute transport in unconfined aquifers. Int J Numer Methods Fluids 3(2):103–123
van Genuchten MT, Alves WJ (1982) Analytical solutions of the one dimensional convective dispersive solute transport equations. US Dep Agric Tech Bull 1661:151
Ataie-Ashtiani B, Hosseini SA (2005) Numerical errors of explicit finite difference approximation for two-dimensional solute transport equation with linear sorption. Environ Model Softw 20(7):817–826
Ataie-Ashtiani B, Hosseini SA (2005) Error analysis of finite difference methods for two-dimensional advection dispersion reaction equation. Adv Water Resour 28(8):793–806
Sheu TWH, Chen YH (2002) Finite element analysis of contaminant transport in groundwater. Appl Math Comput 127(1):23–43
Zheng C, Bennett GD (2002) Applied contaminant transport modelling. Wiley, New York
Kojouharov HV, Chen BM (1999) Nonstandard methods for the convective–dispersive transport equation with nonlinear reactions. Numer Methods Part Differ Equ 15(6):617–624
Kadalbajoo MK, Yadaw AS (2008) B-Spline collocation method for a two-parameter singularly perturbed convection–diffusion boundary value problems. Appl Math Comput 201(1–2):504–513
Thongmoon M, McKibbin R (2006) A comparison of some numerical methods for the advection diffusion equation. Res Lett Inf Math Sci 10:49–62
Lagaris IE, Likas A, Fotiadis DI (1998) Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans Neural Netw 9(5):987–1000
Yadav N, Yadav A, Kumar M (2015) An introduction to neural network methods for differential equations, Springer briefs in applied sciences and technology. Springer, Netherlands
Raja MAZ, Samar R (2013) Neural network optimized with evolutionary computing technique for solving the 2-dimensional Bratu problem. Neural Comput Appl 23(7):2199–2210
Malek A, Beidokhti RS (2006) Numerical simulation for high order differential equations using a hybrid neural network-optimization method. Appl Math Comput 183(1):260–271
Smaoui N, Al-Enezi S (2004) Modelling the dynamics of nonlinear partial differential equation using neural networks. J Comput Appl Math 170(1):27–58
Shirvany Y, Hayati M, Moradian R (2009) Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of partial differential equation. Appl Soft Comput 9(1):20–29
McFall KS (2013) Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities. J Frankl Inst 350(2):300–317
Yadav N, Yadav A, Kumar M, Kim JH (2015) An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem. Neural Comput Appl. doi:10.1007/s00521-015-2046-1
McFall KS, Mahan JR (2009) Artificial neural network method for solution of boundary value problems with exact satisfaction of arbitrary boundary conditions. IEEE Trans Neural Netw 20(8):1221–1233
Lagaris IE, Likas A, Papageorgiou DG (2000) Neural network methods for boundary value problems with irregular boundaries. IEEE Trans Neural Netw 11(5):1041–1049
Fogler HS (1999) Elements of chemical reaction engineering, 3rd edn. Prentice-Hall, Englewood Cliffs
Rawlings JB, Ekerdt JG (2002) Chemical reactor analysis and design fundamentals. Nob Hill Publishing, New Jersey
Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231–238
Acknowledgements
This work was supported by National Research Foundation of Korea (NRF) Grant funded by the Korean government (MSIP) (NRF-2013R1A2A1A01013886) and the Brain Korea 21 (BK-21) fellowship from the Ministry of Education of Korea.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yadav, N., McFall, K.S., Kumar, M. et al. 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. Neural Comput & Applic 30, 917–924 (2018). https://doi.org/10.1007/s00521-016-2722-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-016-2722-9