Predicting the Shear Viscosity of Carbonated Aqueous Amine Solutions and Their Blends by Using an Artificial Neural Network Model

Abstract

In the present work, a neural network (NN) model based on quantitative structure−viscosity relationship was implemented for predicting the shear viscosity of CO 2 -loaded and CO 2 -free aqueous amine solutions and their blends. A total of 1682 amine + CO 2 + water viscosity data sets for primary, secondary, and tertiary amines and 220 data points for further accuracy examinations were used. Molecular mechanic methods with CHARMM + CFF force fields were utilized in order to optimize, simulate, and extract the required molecular structure properties. Then, weighted nearest neighbor feature selection algorithm was used for selecting the most influencing descriptors, while cascade-forward NN (CFNN) model was applied for prediction purposes. For generality examinations, CO 2 -loaded aqueous systems of 3DMA1P(1) + EAE(2), MEA(1) + PZ(2), DMA2P(1) + MEA(2), DEAE(1) + PZ(2), and CO 2 + pure water were used to find the solution viscosities, and comparisons were made against experimentations, which showed the quite robustness of the proposed model for the systemsm which were completely unaware of the trained model. Comparison between the values of average relative deviation of the NN model and the most important semiempirical viscosity models showed that CFNN model outperforms the other alternatives.

[Retracted] Compensated Fuzzy Neural Network‐Based Music Teaching Ability Assessment Model

“…Talasila et al [ 18 ] proposed a tiered teaching method based on music pedagogy theory in order to improve the teaching efficiency and overall stability of higher education music teaching classroom by studying and analysing different students in various aspects. Aminian and Zarenezhad [ 19 ] validated it through teaching practice and the final results showed that the teaching method was effective in improving. Forrester [ 4 ] proposed a new teaching method for grouped music teaching in colleges and universities based on hyperchaotic mapping, which uses the deformed chaotic sequences to positionally displace the original college music teaching method to achieve an optimal teaching plan for the college music teaching classroom teaching process.…”