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

2020 ◽  
Author(s):  
Srinidhi . ◽  
Deepak Patel ◽  
Vasantha Kumara S A
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Supercritical Carbon

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.

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

2021 ◽  
Author(s):  
FNU SRINIDHI
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Grey Relational ◽  
Supercritical Carbon

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.

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

2020 ◽  
Author(s):  
. Srinidhi ◽  
Deepak Patel ◽  
Vasantha Kumara S A
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Grey Relational ◽  
Supercritical Carbon

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.

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

2020 ◽  
Author(s):  
. Srinidhi ◽  
Deepak Patel ◽  
Vasantha Kumara S A
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Grey Relational ◽  
Supercritical Carbon

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.

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

2020 ◽  
Author(s):  
. Srinidhi ◽  
Deepak Patel ◽  
Vasantha Kumara S A
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Grey Relational ◽  
Supercritical Carbon

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.

Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models

2020 ◽  
Vol 30 (4) ◽  
pp. 249-257
Author(s):  
Reid J. Reale ◽  
Timothy J. Roberts ◽  
Khalil A. Lee ◽  
Justina L. Bonsignore ◽  
Melissa L. Anderson
Keyword(s):  
Root Mean Square Error ◽  
Metabolic Rate ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Compartment Model ◽  
Absolute Error ◽  
Mean Square ◽  
Adolescent Athletes ◽  
Two Compartment Model

We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models’ performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) − (340 male or 537 female).

scholarly journals Effect of CO2 Preservation Treatments on the Sensory Quality of Pomegranate Juice

Molecules ◽  
2020 ◽  
Vol 25 (23) ◽  
pp. 5598
Author(s):  
Ana Carolina Mosca ◽  
Leonardo Menghi ◽  
Eugenio Aprea ◽  
Maria Mazzucotelli ◽  
Jose Benedito ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Supercritical Co2 ◽  
Sensory Quality ◽  
Pomegranate Juice ◽  
Process Conditions ◽  
Volatile Profile ◽  
Projective Mapping ◽  
Supercritical Carbon

Due to the interest in identifying cost-effective techniques that can guarantee the microbiological, nutritional, and sensorial aspects of food products, this study investigates the effect of CO2 preservation treatment on the sensory quality of pomegranate juice at t0 and after a conservation period of four weeks at 4 °C (t28). The same initial batch of freshly squeezed non-treated (NT) juice was subjected to non-thermal preservation treatments with supercritical carbon dioxide (CO2), and with a combination of supercritical carbon dioxide and ultrasound (CO2-US). As control samples, two other juices were produced from the same NT batch: A juice stabilized with high pressure treatment (HPP) and a juice pasteurized at high temperature (HT), which represent an already established non-thermal preservation technique and the conventional thermal treatment. Projective mapping and check-all-that-apply methodologies were performed to determine the sensory qualitative differences between the juices. The volatile profile of the juices was characterized by gas chromatography-mass spectrometry. The results showed that juices treated with supercritical CO2 could be differentiated from NT, mainly by the perceived odor and volatile compound concentration, with a depletion of alcohols, esters, ketones, and terpenes and an increase in aldehydes. For example, in relation to the NT juice, limonene decreased by 95% and 90%, 1-hexanol decreased by 9% and 17%, and camphene decreased by 94% and 85% in the CO2 and CO2-US treated juices, respectively. Regarding perceived flavor, the CO2-treated juice was not clearly differentiated from NT. Changes in the volatile profile induced by storage at 4 °C led to perceivable differences in the odor quality of all juices, especially the juice treated with CO2-US, which underwent a significant depletion of all major volatile compounds during storage. The results suggest that the supercritical CO2 process conditions need to be optimized to minimize impacts on sensory quality and the volatile profile.

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