Recovery of uranium and plutonium from pyrochemical salt matrix using supercritical fluid extraction

Pitchaiah, K.C.; Sujatha, K.; Deepitha, J.; Ghosh, Suddhasattwa; Sivaraman, N.

doi:10.1016/j.supflu.2018.10.015

Cited by 18 publications

(6 citation statements)

References 56 publications

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“…Additionally, mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R 2 ) were also computed using the equations (3,4,5).…”

Section: Statistical Evaluation Of the Artificial Neural Network (Ffb-ann) Modelmentioning

confidence: 99%

“…Supercritical fluid extraction has been amassing prominence as an effective method to separate and purify a variety of substances from multi-component mixtures. Supercritical fluid extraction has been employed for the purification of a raft of different products ranging from petrochemicals [1] to phytochemicals [2] and from dyes [3] to radioactive elements [4]. Among the different solvents employed in the supercritical fluid extraction process, CO 2 has been extensively used for many of its fitting and desirable attributes [5].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Srinidhi¹

2021

Preprint

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The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

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“…Additionally, mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R 2 ) were also computed using the equations (3,4,5).…”

Section: Statistical Evaluation Of the Artificial Neural Network (Ffb-ann) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Srinidhi¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Mendez-Teja -0.00001000 -0.00001000 0.00050000 --- 4 Kumar-Johnston 0.00000100 0.02500000 -0.00100000 --- 5 Garlapati-Madras 0.00000000 0.00117374 0.41996382 0.00000000 0.31953855 - 6 Bian 0.00100000 -0.00125600 0.00000000 0.34070671 8.70164889 -7…”

Section: Sl Nomentioning

confidence: 99%

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Srinidhi¹,

Patel²,

A³

2020

Preprint

View full text Add to dashboard Cite

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation -artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

show abstract

“…Additionally, mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R 2 ) were also computed using the equations (3,4,5).…”

Section: Statistical Evaluation Of the Artificial Neural Network (Ffb-ann) Modelmentioning

confidence: 99%

“…Supercritical fluid extraction has been amassing prominence as an effective method to separate and purify a variety of substances from multi-component mixtures. Supercritical fluid extraction has been employed for the purification of a raft of different products ranging from petrochemicals [1] to phytochemicals [2] and from dyes [3] to radioactive elements [4]. Among the different solvents employed in the supercritical fluid extraction process, CO2 has been extensively used for many of its fitting and desirable attributes [5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network (FFBP-ANN) Based Grey’s Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Srinidhi

Patel²,

Vk³

2020

Preprint

View full text Add to dashboard Cite

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey’s relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

show abstract

Recovery of uranium and plutonium from pyrochemical salt matrix using supercritical fluid extraction

Cited by 18 publications

References 56 publications

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

Artificial Neural Network (FFBP-ANN) Based Grey’s Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

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