Predictions of equilibrium solubility and mass transfer coefficient for CO2 absorption into aqueous solutions of 4-diethylamino-2-butanol using artificial neural networks

Meesattham, Sutida; Charoensiritanasin, Pornmanas; Ongwattanakul, Songpol; Liang, Zhiwu; Tontiwachwuthikul, Paitoon; Sema, Teerawat

doi:10.1016/j.petlm.2018.09.005

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Thus, the present work studied various training algorithms of Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG). In comparison with the conventional algorithms (e.g., standard back propagation (BP), radial basis function (RBF), gradient decent, conjugate gradient, and quasi-Newton algorithms), the recent robust LM, BR, and SCG algorithms show a much better prediction performance in terms of accuracy, speed, and overfitting issue. − Thus, the three algorithms of LM, BR, and SCG were selected to construct the neural networks for predicting solubility of H 2 S.…”

Section: Model Developmentmentioning

confidence: 99%

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

et al. 2021

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Solubility of hydrogen sulfide (H2S) in 46 single and blended physical absorbents, amines, ionic liquids, and hybrid absorbents of amines + ionic liquids and amines + physical absorbents was successfully predicted based on artificial neural networks (ANNs). Three neural network algorithms of Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) were applied for architecting the ANN models. The results showed that both the number of hidden neurons and the prediction algorithm affected the prediction of H2S solubility. Based on the mean square error (MSE) and determination coefficient (R 2), the most attractive model was the LM-ANN model with 17 hidden neurons. As a result, very satisfactory prediction performance (for the testing data set) with an MSE of 0.0014 and an R 2 of 0.9817 was obtained from the developed LM-ANN model. Additionally, a parity chart confirmed that the predicted solubility of H2S well aligned with the experimental data. To effectively absorb H2S and maintain high solubility of H2S, the absorbent should be well complied with the operating pressure. For a low-pressure range of less than 100 kPa, amines are very attractive. As the pressure elevated to 100–1000 kPa, amines and hybrid amine + physical absorbents are suggested. Lastly, at a high pressure over 1000 kPa, physical absorbents and ionic liquids are recommended.

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Section: Model Developmentmentioning

confidence: 99%

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

et al. 2021

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“…In addition to providing accurate predictions, by determination of the connection weights, the developed ANN was able to provide a quantitative assessment of the relative importance of various physicochemical properties that are required for a good hydrotrope (36). The reported use of ANNs in the prediction of solubility enhancements for drug substances and their successful use in other solubility applications in various research areas (66,67) are encouraging for further exploration of their potential uses in more pharmaceutical preformulation research.…”

Section: Machine Learning In Pharmaceutical Preformulationmentioning

confidence: 99%

Digital Pharmaceutical Sciences

Damiati

2020

AAPS PharmSciTech

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Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-today pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

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“…They optimized the network with three hidden layers and 15, 25, and 25 neurons in each layer to predict the performance with R squared of 0.9896 and 0.9877 on the training test and test set, respectively. Meesattham et al [ 15 ] used the Levenberg–Marquardt algorithm to train the data with one hidden layer in a model of predicting the mass transfer coefficient of CO 2 absorption into aqueous solutions. This model showed an outstanding performance over the predictive proposed correlations in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach

et al. 2022

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Water transport in a hollow-fiber membrane depends on mass convection around the tube, mass convection inside the tube, and water diffusion through the membrane tube. The performance of water transport is then explained by the overall mass transfer coefficient in hollow-fiber membranes. This study presents the prediction of overall mass transfer coefficients of water transport in a hollow-fiber membrane module by an artificial neural network (ANN) that is used for a humidifier of a vehicular fuel cell system. The input variables of ANN are collected from water transport experiments of the hollow-fiber membrane module that is composed of inlet flow rates, inlet relative humidity, system pressures, and operating temperatures. The experimental mass transfer coefficients are the targets of the training model, which are determined via the effectiveness analysis. When unknown data are applied to the ANN model, the correlation of the overall mass transfer coefficient predicts precise results with R = 0.99 (correlation coefficient). The ANN model shows good prediction capability of water transport in membrane humidifiers.

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Predictions of equilibrium solubility and mass transfer coefficient for CO2 absorption into aqueous solutions of 4-diethylamino-2-butanol using artificial neural networks

Cited by 8 publications

References 30 publications

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Digital Pharmaceutical Sciences

A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach

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