Density and speed of sound prediction for binary mixtures of water and ammonium-based ionic liquids using feedforward and cascade forward neural networks

Zimmermann, Alexandre S.; Mattedi, Silvana

doi:10.1016/j.molliq.2020.113212

Cited by 13 publications

(7 citation statements)

References 39 publications

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“…For all solutions, density varies linearly with temperature. Both mixtures are less dense than most ILs, which generally have volume masses between 1.20 and 1.50 g cm –3 …”

Section: Resultsmentioning

confidence: 99%

“…Mixtures of ILs with protic molecular solvents such as water, alcohols, amides, and so forth ,, or aprotic solvents such as ACN, alkylcarbonates, DMSO, or DMF , improve the properties of ILs by lowering their viscosity and promoting ionic dissociations while retaining their thermal and electrochemical properties, unlike mineral salts, whose solubility in molecular solvents is often limited (close to 1–2 mol L –1 ) . ILs often composed of asymmetrical organic cations are much more soluble or miscible in all proportions. ,, The cross-interactions between ion liquid ions and molecular solvent molecules depend on several factors: the presence of a labile proton, the size of ions, the nature of the heteroatom (S, N, P) in the cation (sulfonium, ammonium, or phosphonium), , the length of the carbon chain of the cation or anion, , and also the properties of the molecular solvent chosen to make the mixture. , Among the solvents suitable for electrochemical applications, nitriles and more specifically dinitriles , have emerged as a base for electrolytes for batteries, , supercapacitors, and other energy storage applications. , Among dinitriles, glutaronitrile (GLN) is emerging as an electrochemically stable and safe solvent very suitable for various applications − …”

Section: Introductionmentioning

confidence: 99%

“…9 However, pure ILs are limited by their transport properties because of ionic associations, 10 ILs, and particularly those based on the sulfonium cation, have been used in various applications, batteries, supercapacitor, dyesensitized solar cells. 11−14 Mixtures of ILs with protic molecular solvents such as water, alcohols, amides, and so forth 1,10,15 or aprotic solvents such as ACN, alkylcarbonates, DMSO, or DMF 16,17 improve the properties of ILs by lowering their viscosity and promoting ionic dissociations while retaining their thermal and electrochemical properties, 18 unlike mineral salts, whose solubility in molecular solvents is often limited (close to 1−2 mol L −1 ). 19 ILs often composed of asymmetrical organic cations are much more soluble or miscible in all proportions.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Physical Properties and CO₂ Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

2020

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In this work, we present a comparative study of physical properties and CO2 solubility of two ionic liquids (ILs), protic trimethylammonium bis[(trifluoromethyl)sulfonyl]imide and aprotic trimethylsulfonium bis[(trifluoromethyl) sulfonyl]imide in mixture with glutaronitrile (GLN). The effect of temperature on density and viscosity of mixtures was studied and discussed in terms of intra- and intermolecular interactions. Applying Glasser’s theory, standard molar entropy, S 0, and the crystal energy, U POT, of the IL/GLN mixtures were estimated, showing a positive contribution of ILs to these quantities. This positive contribution to the entropy variation by the addition of an IL is because of the fact that it destroys GLN–GLN interactions, replacing them with new cation–GLN and anion–GLN interactions. The deconstructing effect of S111 is greater because there are no strong S111–GLN interactions, unlike the ammonium cation which can establish H-bonds with CN groups. The CO2 solubilities in both (IL/GLN) mixtures expressed by CO2 molar fractions, x CO2 , or the Henry constant K H/MPa showed that the [S111][TFSI]/GLN solubilizes more CO2 than the [HN111][TFSI]/GLN throughout the temperature range. The solubility of CO2 is sensitive to the cation nature and temperature. The sulfonium cation promotes CO2 solubility because it is less cluttered than the ammonium cation unlike HN111, which is the hydrogen-binding seat (NH–GLN) with GLN. Finally, the thermodynamic parameters such as the standard enthalpy Δdiss H 0, the free enthalpy Δdiss G 0, and the entropy Δdiss S 0 of CO2 dissolution were calculated. They showed that the dissolution of CO2 did not depend on the molecular organization (small variation in entropy) but that it is linked to the intermolecular interactions in the IL/GLN mixture, as shown by the difference in Δmix H.

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“…For all solutions, density varies linearly with temperature. Both mixtures are less dense than most ILs, which generally have volume masses between 1.20 and 1.50 g cm –3 …”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Physical Properties and CO₂ Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

2020

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“…The cascade feedforward neural network has a multi-hidden layer structure, which starts from a small network, automatically trains and adds hidden units, and finally forms a multilayer structure. The level in the network is cascaded with each other [15], and the information is propagated from the input layer to the final output step by step [16]. This neural network uses the back propagation method to solve and keep updating the weights and biases.…”

Section: Optimized Cascade Feedforward Neural Network a Characteristics Of Cascade Feedforward Neural Networkmentioning

confidence: 99%

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Yan

2021

IEEE Access

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Aiming at the large error of traditional reliability prediction method, and the defects of BP neural network prediction method, a new method of optimized cascade feedforward neural network was proposed based on Adam algorithm to predict the reliability of CNC machine tool spindles. A three-layer optimized cascade feedforward neural network model for reliability prediction was established based on the first n th reliability value and the mean time between failure corresponding to the (n+1) th reliability value as the input variables of the neural network. Besides, error comparison analysis was performed on the test set data using the existing relevant reliability data for simulation training. As per the research results, the absolute value of the maximum relative error of the reliability prediction value obtained by this method is 2.53% which is less than 3%, and the prediction method shows high accuracy. Compared with BP neural network, it has the advantages of faster learning speed and better nonlinear fitting ability. Therefore, this method is feasible to be utilized for the prediction of the reliability of CNC machine tool spindles, and provides a reference for improving the accuracy of CNC machine tool reliability prediction.INDEX TERMS Optimized cascade feedforward neural network, CNC machine tool spindle, predict, reliability.

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“…In this study, as the dataset is relatively small, we used WRE as feature descriptors and feedforward neural network (FNN) [13,14] as the classifier instead of the deep learning, which is widely used in biomedical image analysis. We believe that the order of WRE is of great significance for image feature detection.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

Wang¹,

Wu²,

Zhang³

et al. 2020

IJCIS

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Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.

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Density and speed of sound prediction for binary mixtures of water and ammonium-based ionic liquids using feedforward and cascade forward neural networks

Cited by 13 publications

References 39 publications

Comparative Study of Physical Properties and CO₂ Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

Comparative Study of Physical Properties and CO₂ Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

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Density and speed of sound prediction for binary mixtures of water and ammonium-based ionic liquids using feedforward and cascade forward neural networks

Cited by 13 publications

References 39 publications

Comparative Study of Physical Properties and CO2 Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

Comparative Study of Physical Properties and CO2 Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

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Product

Resources

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Comparative Study of Physical Properties and CO₂ Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile

Comparative Study of Physical Properties and CO₂ Solubility of Ammonium and Sulfonium Ionic Liquids in Mixture with Glutaronitrile