Theoretical Analysis on Absorption of Carbon Dioxide (CO2) into Solutions of Phenyl Glycidyl Ether (PGE) Using Nonlinear Autoregressive Exogenous Neural Networks
TL;DR: In this article, the authors analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution.
Abstract: In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equations is subjected to Dirichlet boundary conditions and a mixed set of Neumann and Dirichlet boundary conditions. Further, to calculate the concentration of CO2, PGE, and the flux in terms of reaction rate constants, we adopt the supervised learning strategy of a nonlinear autoregressive exogenous (NARX) neural network model with two activation functions (Log-sigmoid and Hyperbolic tangent). The reference data set for the possible outcomes of different scenarios based on variations in normalized parameters (α1, α2, β1, β2, k) are obtained using the MATLAB solver “pdex4”. The dataset is further interpreted by the Levenberg–Marquardt (LM) backpropagation algorithm for validation, testing, and training. The results obtained by the NARX-LM algorithm are compared with the Adomian decomposition method and residual method. The rapid convergence of solutions, smooth implementation, computational complexity, absolute errors, and statistics of the mean square error further validate the design scheme’s worth and efficiency.
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TL;DR: In this article , an artificial neural network (NN)-based, backpropagated Levenberg-Marquardt (LM) algorithm is utilized to interpret a numerical solution for the roll angle (x(t), velocity (x′(t)), and acceleration (x') of the ship in random beam seas.
Abstract: In this paper, a mathematical model for the rolling motion of ships in random beam seas has been investigated. The ships’ steady-state rolling motion with a nonlinear restoring moment and damping effect is modeled by the nonlinear second-order differential equation. Furthermore, an artificial neural network (NN)-based, backpropagated Levenberg-Marquardt (LM) algorithm is utilized to interpret a numerical solution for the roll angle (x(t)), velocity (x′(t)), and acceleration (x′′(t)) of the ship in random beam seas. A reference data set based on numerical examples of the mathematical model for a rolling ship for the LM-NN algorithm is generated by the numerical solver Runge–Kutta method of order 4 (RK-4). The LM-NN algorithm further uses the created data set for the validation, testing, and training of approximate solutions. The outcomes of the design paradigm are compared with those of the homotopy perturbation method (HPM), optimal homotopy analysis method (OHAM), and RK-4. Statistical analyses of the mean square error (MSE), regression, error histograms, proportional performance, and computational complexity further validate the worth of the LM-NN algorithm.
12 citations
TL;DR: In this article , the authors investigated the steady two-phase flow of a nanofluid in a permeable duct with thermal radiation, a magnetic field, and external forces.
Abstract: This study investigated the steady two-phase flow of a nanofluid in a permeable duct with thermal radiation, a magnetic field, and external forces. The basic continuity and momentum equations were considered along with the Buongiorno model to formulate the governing mathematical model of the problem. Furthermore, the intelligent computational strength of artificial neural networks (ANNs) was utilized to construct the approximate solution for the problem. The unsupervised objective functions of the governing equations in terms of mean square error were optimized by hybridizing the global search ability of an arithmetic optimization algorithm (AOA) with the local search capability of an interior point algorithm (IPA). The proposed ANN-AOA-IPA technique was implemented to study the effect of variations in the thermophoretic parameter (Nt), Hartmann number (Ha), Brownian (Nb) and radiation (Rd) motion parameters, Eckert number (Ec), Reynolds number (Re) and Schmidt number (Sc) on the velocity profile, thermal profile, Nusselt number and skin friction coefficient of the nanofluid. The results obtained by the designed metaheuristic algorithm were compared with the numerical solutions obtained by the Runge–Kutta method of order 4 (RK-4) and machine learning algorithms based on a nonlinear autoregressive network with exogenous inputs (NARX) and backpropagated Levenberg–Marquardt algorithm. The mean percentage errors in approximate solutions obtained by ANN-AOA-IPA are around 10−6 to 10−7. The graphical analysis illustrates that the velocity, temperature, and concentration profiles of the nanofluid increase with an increase in the suction parameter, Eckert number and Schmidt number, respectively. Solutions and the results of performance indicators such as mean absolute deviation, Theil’s inequality coefficient and error in Nash–Sutcliffe efficiency further validate the proposed algorithm’s utility and efficiency.
8 citations
TL;DR: In this article, a hybrid neurocomputing algorithm called ANN-SCA-SQP algorithm was proposed to analyze the boundary flow of the Falkner-Skan (FS) model.
Abstract: In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.
8 citations
TL;DR: In this paper , the authors investigated the incompressible mixed convection flow of electrically conductive micropolar fluid with a thermal non-equilibrium condition that passes through the vertical circular (pipe) porous medium.
Abstract: This paper investigates the incompressible mixed convection flow of electrically conductive micropolar fluid with a thermal non-equilibrium condition that passes through the vertical circular (pipe) porous medium. The extension of the non-Darcy–Brinkman–Forchheimer model is considered to formulate the governing non-linear system of differential equations for the problem. Furthermore, the rigorous impact of different parameters such as thermal conductivity ratio (γ), inter-phase heat transfer coefficient (H), Darcy number (Da), Grashof number (Gr), Eringen micro-polar parameter (Er), Hartmann number (Ha), solid-heat generation (β), and fluid heat generation parameter (α) on velocity profile (f∗), micro-rotational (angular velocity) (g∗), temperature of solid (Θf∗) and fluid (Θs∗) has been investigated by using the computational strength of artificial intelligence based Elman neural networks (ENN) and Levenberg–Marquardt algorithm (LMA). We have compared the solutions calculated by the designed ENN-LM algorithm with the Cuckoo Search Algorithm (CSA), Chebyshev spectral collocation method (CSCM), particle swarm optimization (PSO) algorithm, and Runge–Kutta method. The convergence rate and stability of the ENN-LM technique show that it can be applied to solving complex models involving partial and fractional differential equations.
7 citations
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2,187 citations
TL;DR: It is shown that the original architecture of the NARX network can be easily and efficiently applied to long-term (multi-step-ahead) prediction of univariate time series and consistently outperforms standard neural network based predictors, such as the TDNN and Elman architectures.
381 citations
TL;DR: In this article, the cycloaddition reactions of CO2 with various epoxides to form five-membered cyclic carbonates catalyzed by chitosan functionalized 1-ethyl-3-methyl imidazolium halides (CS-EMImX, X = Cl, Br) without additional solvent and metal co-catalyst were achieved in high yield and selectivity.
302 citations
TL;DR: It is concluded that, in wild-type strains, the NarQ protein communicates the presence of nitrite to both the NarP and NarL proteins and that the NarX protein inhibits this communication with the NarL protein.
Abstract: Two sensor proteins, NarX and NarQ, mediate nitrate regulation of anaerobic respiratory gene expression. Either of these sensors is sufficient to signal the presence of nitrate to the response regulator protein, NarL, a transcriptional activator and repressor. Two observations suggested the existence of a second response regulator that is also involved in nitrate regulation. First, narL null mutants retain residual nitrate induction of fdnG operon expression; this residual induction is absent in narX narQ double-null strains. Second, nitrate induction of aeg-46.5 operon expression is substantially enhanced in narL null strains (M.H. Choe and W.S. Reznikoff, J. Bacteriol. 173:6139-6146, 1991). We found that this nitrate induction requires either the NarX or the NarQ protein, consistent with the existence of a second response regulator. We designate this second regulator NarP. We isolated insertion mutants that are defective in aeg-46.5 operon expression. These insertions are in the narP gene, which encodes a response regulator that is 44% identical to the NarL protein. Null alleles of narP abolished aeg-46.5 induction and also eliminated the residual NarL-independent nitrate induction of fdnG operon expression. Both the NarX and NarQ proteins communicate with both the NarP and NarL proteins. We found that the primary signal for NarP-dependent aeg-46.5 operon induction is nitrite rather than nitrate. By contrast, nitrite is a relatively weak signal for NarL-dependent induction. In narX null strains, nitrate was an efficient signal for NarL-dependent induction, and this induction required the NarQ protein. We conclude that, in wild-type strains, the NarQ protein communicates the presence of nitrite to both the NarP and NarL proteins and that the NarX protein inhibits this communication with the NarL protein.
239 citations
TL;DR: In this paper, a Nonlinear Autoregressive Exogenous (NARX) neural network was used to predict the solar radiation on a horizontal surface of a race sailboat.
Abstract: The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.
238 citations