Modeling the Thermal Conductivity of Ionic Liquids and Ionanofluids Based on a Group Method of Data Handling and Modified Maxwell Model

Atashrouz, Saeid; Mozaffarian, M.; Pazuki, Gholamreza

doi:10.1021/acs.iecr.5b00932

Cited by 57 publications

(30 citation statements)

References 43 publications

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“…However, to examine the viability of whether IL based-HTFs can be used in a specific application, their thermophysical properties, especially thermal conductivity, heat capacity, density and viscosity, must be accurately determined to allow a better understanding of their structure-properties relationships. In the case of ILs, due to the large number of possible ion combinations and with a goal of reducing the compositional space to be studied experimentally, predictive models have been developed and tested against experimentally determined data [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Many models, based on group contribution methods, artificial neural networks, or genetic algorithm, have been proposed for the predictions of the ILs thermal conductivity, to date [10,11,13,15,16,18,20,25,31]. It is worth noting that the terms 'correlative' and 'predictive' are often wrongly used in a interchangeably manner.…”

Section: Introductionmentioning

confidence: 99%

“…The third group, evaluative methodologies, are usually assessed with group contribution methods [32]. As well as temperature and compositional effects, a number of studies have included pressure as an input parameter, however, the availability of those data is extremely limited so the errors are potentially large in predicting the IL properties [10,15,16,18].…”

Section: Introductionmentioning

confidence: 99%

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Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Oster

Jacquemin

Hardacre

et al. 2018

The Journal of Chemical Thermodynamics

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Efficient, fast and accurate heat transfer units design is currently a 'hot topic' due to the demand for more approachable and high-performance-ability materials. This is usually performed by the prediction of physical properties coupled with sufficient structure searching (for example with genetic algorithm). Ionic liquids have been found to be prospective replacement materials in this case; however, the predictive capabilities of existing models still remain poor which affects their practical application significantly. It has also been observed that for some quaternary-phosphonium based carboxylate ILs, the models fail, particularly, for thermal conductivity and heat capacity predictions. The impact of electronic structure on the heat capacity was confirmed by DFT calculations, and this was also included in further refinement. The aim of this work is to assess the predictive capabilities of existing models for thermal conductivity and heat capacity, with further improvements based on more accurate investigated structure characterization (DFT) and reparameterization (group contribution methodology). The ILs chosen for the initial study are trihexyl(tetradecyl)phosphonium-based carboxylate derivatives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Oster

Jacquemin

Hardacre

et al. 2018

The Journal of Chemical Thermodynamics

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show abstract

“…As a matter of fact, a hybrid GMDH, which allows more interaction among the independent variables, is stronger and more complex to model complicated systems. [86][87][88][89] The final format of the hybrid GMDH model is as follows:…”

Section: Hybrid Gmdh-type Neural Networkmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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“…In this regard, a predictive approach like artificial intelligence (AI) can be helpful. Over recent years, AI approaches like artificial neural networks (ANN), group method of data handling type neural network systems (GMDH-NN) and least square support vector machines (LSSVM) have widely been used by researchers to model various chemical engineering systems [19][20][21][22][23][24][25]. Each one of this approaches benefited by its own advantages.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

et al. 2020

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Recent developments in multisite catalysts based on metallocenes and post-metallocenes attracted the attention of researchers and industrial petrochemical companies due to the production of high-performance polymeric materials which generally are not achievable based on Ziegler-Natta catalysts. In this study, with the aim of predicting the average molecular weight of produced polyethylene and activity of ethylene polymerization using multisite catalysts, robust precise models based on artificial neural networks are developed. The average error for the prediction of the average molecular weight and activity are 3.76% and 5.89%, respectively. The Leverage method was used to check the reliability of the proposed model and the quality of experimental data which have been used for model development. The results showed that just a few data points are outside of the applicability domain of the developed models, confirming that both developed models and their predictions are statistically correct. Comparison of the artificial neural network models with other artificial intelligence approaches including support vector machine and group method of data handling type neural networks illustrates the better performance and robustness of the proposed models. The results of this study promise that neural networks can be used as reliable models with reasonable accuracy to estimate the performance of ethylene polymerization over this type of new metallocene/post-metallocene multisite catalysts.

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Modeling the Thermal Conductivity of Ionic Liquids and Ionanofluids Based on a Group Method of Data Handling and Modified Maxwell Model

Cited by 57 publications

References 43 publications

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Further development of the predictive models for physical properties of pure ionic liquids: Thermal conductivity and heat capacity

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

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