Theoretical Analysis on Absorption of Carbon Dioxide (CO2) into Solutions of Phenyl Glycidyl Ether (PGE) Using Nonlinear Autoregressive Exogenous Neural Networks

• Published in: Molecules (Basel, Switzerland) Switzerland), 2021-10, Vol.26 (19), p.6041
• Main Authors: Khan, Naveed Ahmad, Sulaiman, Muhammad, Tavera Romero, Carlos Andrés, Alarfaj, Fawaz Khaled
• Format: Article
• Language: English
• Subjects: Absorption; Algorithms; Artificial intelligence; Back propagation; Back propagation networks; Boundary conditions; Carbon dioxide; Chemical reactions; chemical reactivity concentration of CO2 and PGE; Civil engineering; Coal-fired power plants; Computer applications; Decomposition; Differential equations; Dirichlet problem; Dynamical systems; Mathematical models; Neural networks; Nonlinear differential equations; phenyl glycidyl ether; Reaction kinetics; reaction kinetics and diffusion; reaction mechanisms; Statistical analysis; Theoretical analysis; Time series
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules26196041

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.