https://doi.org/10.1140/epjp/s13360-022-03226-0
Regular Article
Quantitative features analysis of a model for separation of dissolved substances from a fluid flow by using a hybrid heuristic
1
Department of Mathematics, Abdul Wali Khan University, 23200, Mardan, Pakistan
2
Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia
b
msulaiman@awkum.edu.pk
c
f.alshammari@psau.edu.sa
Received:
21
February
2022
Accepted:
18
August
2022
Published online:
17
September
2022
Removal of dyes from wastewater is a challenging task for scientists and environmentalists. This work has studied a mathematical model characterizing the typical staining process within sewage systems. Two widely used nanoparticles, ZnO, and 
, are used to remove dyes from wastewater. The BET (Brunauer, Emmett, and Teller) method determines the pore diameter d. The mathematical model of the phenomenon is modeled as a highly nonlinear partial differential equation (HNDE), detailed in a semi-infinite domain. In the present study, a hybridization of the Levenberg-Marquardt Backpropagation and Supervised Neural Network (LMB-SNN) is utilized to find the model’s surrogate solutions. The Runge-Kutta of the order four (RK4) technique is used to create reference solutions. We have analyzed our surrogate solution models by considering eight different scenarios. The stability and equilibrium of the mathematical model are checked by varying physical quantities like the ratio of final pressure to initial pressure. Our candidate solutions are divided into training, testing, and experimental categories to establish the reliability of our machine learning procedure. Comparative studies of statistical values based on mean squared error function (MSEF), effectiveness, regression plots, and failure histograms confirm the efficiency of the (LMB-SNN) scheme.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
