Application of Artificial Intelligence-based predictive methods in Ionic liquid studies: A review

Falola, Yusuf; Olayiwola, Teslim; Afagwu, Clement

doi:10.1016/j.fluid.2020.112898

Cited by 54 publications

(21 citation statements)

References 157 publications

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“…They are also widely employed to predict thermal-physical properties of ionic liquids, such as density and viscosity [23]. The primary source of these values comes from experiments at the laboratory since ionic liquids do not present a universal description of their phase behavior.…”

Section: Thermodynamics and Transport Phenomenamentioning

confidence: 99%

Application of Artificial Neural Networks to Chemical and Process Engineering

Cavalcanti¹,

Kozonoe²,

Pacheco³

et al. 2021

Deep Learning Applications

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The accelerated use of Artificial Neural Networks (ANNs) in Chemical and Process Engineering has drawn the attention of scientific and industrial communities, mainly due to the Big Data boom related to the analysis and interpretation of large data volumes required by Industry 4.0. ANNs are well-known nonlinear regression algorithms in the Machine Learning field for classification and prediction and are based on the human brain behavior, which learns tasks from experience through interconnected neurons. This empirical method can widely replace traditional complex phenomenological models based on nonlinear conservation equations, leading to a smaller computational effort – a very peculiar feature for its use in process optimization and control. Thereby, this chapter aims to exhibit several ANN modeling applications to different Chemical and Process Engineering areas, such as thermodynamics, kinetics and catalysis, process analysis and optimization, process safety and control, among others. This review study shows the increasing use of ANNs in the area, helping to understand and to explore process data aspects for future research.

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Section: Thermodynamics and Transport Phenomenamentioning

confidence: 99%

Application of Artificial Neural Networks to Chemical and Process Engineering

Cavalcanti¹,

Kozonoe²,

Pacheco³

et al. 2021

Deep Learning Applications

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“…In both biological and material sciences, ML methods have been developed to predict thermodynamic or physical properties. [7][8][9][10][11][12][13][14] For example, several pieces of work were focused on the prediction of melting point. [7][8][9][10] Coley et al predicted octanol solubility, aqueous solubility, and toxicity in addition to the melting point.…”

Section: Introductionmentioning

confidence: 99%

“…13 Various related works of ionic liquids have been well summarized in a recent review. 14 Thermodynamic properties are sensitive to molecular structures. For instance, a small difference in the connectivity or composition of two molecules may lead to significant differences in their thermodynamic properties.…”

Section: Introductionmentioning

confidence: 99%

Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules

et al. 2021

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This work presents a Gaussian process regression (GPR) model on top of a novel graph representation of chemical molecules that predicts thermodynamic properties of pure substances in single, double, and triple phases. A transferable molecular graph representation is proposed as the input for a marginalized graph kernel, which is the major component of the covariance function in our GPR models. Radial basis function kernels of temperature and pressure are also incorporated into the covariance function when necessary. We predicted three types of representative properties of pure substances in single, double, and triple phases, i.e., critical temperature, vapor-liquid equilibrium (VLE) density, and pressure-temperature density. The data is collected from Knovel Data Analysis Beta: NIST ThermoDynamics Pure Compounds. The accuracy of the models is nearly identical to the precision of the experimental measurements. Moreover, the reliability of our predictions can be quantified on a per-sample basis using the posterior uncertainty of the GPR model. We compare our model against Morgan fingerprints and a graph neural network to further demonstrate the advantage of the proposed method.

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“…Indeed, several studies over the years have used artificial neural network ANN to model and predict ionic liquid properties such as density, [23] viscosity [24,25], melting point [26], toxicity [27], solubility of gases, such as CO 2 [28,29] and SO 2 [30] in ionic liquids, surface tension [31], investigating ionic liquid-solvent mixtures, [32,33,34,35], and prediction of rate constants in ionic liquid-organic mixtures [36]. Additional examples involving the application of ANN for various properties for ionic liquids can be found in a recent review article by Yusuf et al [37] ARecently, Beckner and Pfaendtner have demonstrated that it is possible to combine machine learning and genetic algorithm to develop new ionic liquids with high thermal conductivity. [38] Some advances have also occurred for correlating ionic conductivity, an extremely useful property for selecting electrolytes in electrochemical applications and the topic of the present article.…”

Section: Introductionmentioning

confidence: 99%

Developing Machine Learning Models for Ionic Conductivity of Imidazolium-Based Ionic Liquids

Dhakal¹,

Shah

2021

Preprint

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In this work, we have developed machine learning models based on support vector machine (SVM) and artificial neural network (ANN) to correlate ionic conductivity of imidazolium-based ionic liquids. The data, collected from the NIST ILThermo Database, spans six orders of magnitude and ranges from 275-475 K. Both models were found to exhibit very good performance. The ANN-model was then used to predict ionic conductivity for all the possible combinations of cations and anions contained in the original dataset, which led to the identification of an ionic liquid with 30% higher ionic conductivity than the highest conductivity reported in the database at 298 K. The model was further employed to predict ionic conductivity of binary ionic liquid mixtures. A large number of ionic liquid mixtures were found to possess non-ideal behavior in that an intermediate mole fraction for such ionic liquid mixtures resulted in either a maximum or minimum in the ionic conductivity.

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Application of Artificial Intelligence-based predictive methods in Ionic liquid studies: A review

Cited by 54 publications

References 157 publications

Application of Artificial Neural Networks to Chemical and Process Engineering

Application of Artificial Neural Networks to Chemical and Process Engineering

Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules

Developing Machine Learning Models for Ionic Conductivity of Imidazolium-Based Ionic Liquids

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