Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions

Chen, Guang‐Ying; Luo, Xiao; Zhang, Haiyan; Fu, Kaiyun; Liang, Zhiwu; Rongwong, Wichitpan; Tontiwachwuthikul, Paitoon; Idem, Raphael

doi:10.1016/j.ijggc.2015.05.005

Cited by 44 publications

(25 citation statements)

References 60 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: Applications Of Machine Learning In Pharmaceutical Sciencesmentioning

confidence: 99%

Digital Pharmaceutical Sciences

Damiati

2020

AAPS PharmSciTech

View full text Add to dashboard Cite

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.

show abstract

Section: Applications Of Machine Learning In Pharmaceutical Sciencesmentioning

confidence: 99%

Digital Pharmaceutical Sciences

Damiati

2020

AAPS PharmSciTech

View full text Add to dashboard Cite

show abstract

“…In another work, Chen et al . presented ANN models for estimation of amine solutions' CO 2 loading capacity. Ghiasi et al .…”

Section: Introductionmentioning

confidence: 99%

Decision tree‐based modeling of CO₂ equilibrium absorption in different aqueous solutions of absorbents

Yarveicy

Saghafi²,

Ghiasi

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

In absorption‐based CO2 removal processes, one of the most important parameters that must be determined is the equilibrium solubility of CO2 in solvents. This study is the first time to employ the extremely randomized trees, Extra Trees, methodology to develop an Extra Trees model for the CO2 loading capacity of various absorbents. The ability of the proposed decision tree‐based model in estimation of CO2 solubility values was compared to that of the previously presented models on the basis of the adaptive neuro‐fuzzy inference system (ANFIS), least squares version of the support vector machine (LSSVM), and artificial neural network (ANN), using error analysis. According to the obtained results, the presented Extra Trees model is able to estimate the CO2 loading capacity of solvents with an absolute relative deviation in percent (AARD%) equal to 0.15. The calculated values of AARD% for the literature models, i.e., LSSVM, radial basis function‐artificial neural network (RBF‐ANN), multilayer perceptron‐ANN (MLP‐ANN), and ANFIS, are 2.00, 10.03, 6.18, and 14.15, respectively. Hence, the developed Extra Trees model provides much higher robustness and accuracy in estimating/representing the solubility of CO2 in different aqueous solutions of solvents. © 2019 American Institute of Chemical Engineers Environ Prog, 38: S441–S448, 2019

show abstract

“…For example, Chen et al . developed artificial neural network (ANN) models for estimation of CO 2 equilibrium solubility in several amine solutions. Li and Rochelle presented a semi‐empirical correlation to estimate the CO 2 loading capacity of amine solutions.…”

Section: Introductionmentioning

confidence: 99%

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Ghiasi

Abedi‐Farizhendi

Mohammadi

2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

Precise calculation of carbon dioxide equilibrium solubility in aqueous amine solutions is decisive in the success of establishment or maintenance of amine‐based absorptive carbon dioxide capture processes. To implement the AdaBoost algorithm in conjunction with the classification and regression tree (AdaBoost‐CART) aimed at developing models to accurately estimate the equilibrium absorption of carbon dioxide in ethanolamine solutions, experimental data for monoethanolamine (MEA), diethanolamine (DEA), and triethanolamine (TEA) systems were gathered from the literature. Furthermore, neural‐based models were developed using the collected databank as the basis of comparison. The results of the presented models were compared to the results of the available models in the literature. It was found that the proposed AdaBoost‐CART models for the investigated amine systems present more precise and reliable outputs compared to the results of the neural‐based and literature models. In a respective order, the introduced AdaBoost‐CART models for MEA, DEA, and TEA solutions show average absolute relative deviation percent of 0.51, 2.76, and 1.41 which indicate their reliability and superiority over other models. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

show abstract

Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions

Cited by 44 publications

References 60 publications

Digital Pharmaceutical Sciences

Digital Pharmaceutical Sciences

Decision tree‐based modeling of CO₂ equilibrium absorption in different aqueous solutions of absorbents

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches

Contact Info

Product

Resources

About

Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions

Cited by 44 publications

References 60 publications

Digital Pharmaceutical Sciences

Digital Pharmaceutical Sciences

Decision tree‐based modeling of CO2 equilibrium absorption in different aqueous solutions of absorbents

Modeling Equilibrium Systems of Amine‐Based CO2 Capture by Implementing Machine Learning Approaches

Contact Info

Product

Resources

About

Decision tree‐based modeling of CO₂ equilibrium absorption in different aqueous solutions of absorbents

Modeling Equilibrium Systems of Amine‐Based CO₂ Capture by Implementing Machine Learning Approaches