Development of a method to model the mixing energy of solutions using COSMO molecular descriptors linked with a semi-empirical model using a combined ANN-QSPR methodology

Sosa, Adriel; Ortega, Juan; Fernández, Luís; Palomar, José

doi:10.1016/j.ces.2020.115764

Cited by 12 publications

(5 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a comprise should be made between the complexity and the accuracy of the developed model. Several research groups in the literature utilized S σ-profiles in 6 regions, 66 8 regions, 67 and 10 regions. 68 In our previous work, we have tested several S σ-profiles discretizations in 4, 6, 8, 10, and 12 regions for the prediction of the pH of DESs using MLR and ANN approaches, and our results showed that an 8-level discretization of the S σ-profile was the best compromise between accuracy and the number of fitting parameters inputs.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

et al. 2022

View full text Add to dashboard Cite

Studies on deep eutectic solvents (DESs), a new class of “green” solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277–425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents ( S σ-profiles ). The training and testing results show that the best performing ANN architecture consisted of two hidden layers with 15 neurons each (9–15–15–1). The proposed ANN was excellent in predicting the surface tension of DESs, as R 2 values of 0.986 and 0.977 were obtained for training and testing, respectively, with an overall average absolute relative deviation of 2.20%. The proposed models represent an initiative to promote the development of robust models capable of predicting the properties of DESs based only on molecular parameters, leading to savings in investigation time and resources.

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Thus, the variances of L and R related to sx ji splitting (V L,sx ji and V R,sx ji ) can be obtained according to Eqs. (24)(25)(26)(27)(28). The total variance related to sx ji splitting (V sx ji ) is the sum of its left variance (V L,sx ji ) and right variance (V R,sx ji ).…”

Section: Svr Methods For Modelingmentioning

confidence: 99%

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Fang

Zhou

Wang

et al. 2021

Front. Chem. Sci. Eng.

View full text Add to dashboard Cite

Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

show abstract

“…To some extent, this also reflects the superiority of the COSMO molecular descriptors in taking into account the complex interactions between substance properties and solvent environment. 23 Compared with simple MLR, complex machine learning nonlinear regressions, including RFs and ANNs, show higher prediction levels in both the training set and external testing set. This result is further tested by the validation parameters of Q 2 F1 , Q 2 F2 , Q 2 F3 , and CCC, and a consistent conclusion is obtained.…”

Section: Comparison Of Developed Modelsmentioning

confidence: 99%

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms

Nie,

Liu,

Huang

et al. 2024

Analyst

View full text Add to dashboard Cite

show abstract

Development of a method to model the mixing energy of solutions using COSMO molecular descriptors linked with a semi-empirical model using a combined ANN-QSPR methodology

Cited by 12 publications

References 65 publications

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms

Contact Info

Product

Resources

About