Supercritical carbon dioxide. 2. .pi.* and the dielectric function for supercritical CO2 media at various densities

1986 ◽  
Vol 90 (22) ◽  
pp. 6063-6063 ◽  
Author(s):  
Michael E. Sigman ◽  
John E. Leffler
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Dielectric Function ◽  
Supercritical Co2 ◽  
Supercritical Carbon

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 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.

scholarly journals Inspection for supercritical CO2 dyeing of poly(m-phenylene isophthalamide) by kinetics and thermodynamics analysis

2019 ◽  
Vol 14 ◽  
pp. 155892501988640
Author(s):  
Fang Ye ◽  
Guohua Liu ◽  
Ibrahim Khalil ◽  
Laijiu Zheng ◽  
Huanda Zheng ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Diffusion Coefficients ◽  
Supercritical Co2 ◽  
Constant Pressure ◽  
Standard Entropy ◽  
Dye Uptake ◽  
Kinetics And Thermodynamics ◽  
Activation Energy For Diffusion ◽  
Supercritical Carbon

Eco-friendly dyeing by using supercritical carbon dioxide as a medium has already been investigated worldwide due to the advantages of dyeing without water and recyclability of dyes and carbon dioxide. In this article, dyeing mechanism of poly(m-phenylene isophthalamide) was investigated in supercritical carbon dioxide. The obtained results showed that the dye uptake of Disperse Red 60 increased moderately with the temperature raising at constant pressure and achieved dyeing equilibrium after 70 min. By adding the carrier, diffusion coefficients of Disperse Red 60 in the polymer increased significantly in supercritical carbon dioxide. The activation energy for diffusion of Disperse Red 60 with and without carrier was 1165.91 and 1050.66 kJ mol−1, respectively. Moreover, the distribution coefficient, the standard affinity, the standard enthalpy, and the standard entropy of dyeing were also determined in supercritical carbon dioxide. These fundamental data are of vital importance on the green dyeing production of poly(m-phenylene isophthalamide).

Research Progress on Supercritical CO2 Thickeners

Soft Matter ◽  
2021 ◽  
Author(s):  
Ming Zhou ◽  
Ruifeng Ni ◽  
Yaxiong Zhao ◽  
Jiangyu Huang ◽  
Xinyi Deng
Keyword(s):  
Molecular Structure ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Supercritical Co2 ◽  
Research Progress ◽  
Molecular Compounds ◽  
Supercritical Carbon

According to the thickening principle and molecular structure of thickeners, supercritical carbon dioxide (sc-CO2) thickeners were summarized and introduced by dividing into polymers, small molecular compounds, and surfactants. The properties...

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 Determination of extraction conditions of Ginkgo biloba L. leaves by supercritical CO2 using response surface methodology

2011 ◽  
Vol 65 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Svetlana Milosevic ◽  
Zika Lepojevic ◽  
Zoran Zekovic ◽  
Senka Vidovic
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Response Surface ◽  
Process Parameters ◽  
Ginkgo Biloba ◽  
Supercritical Co2 ◽  
Extraction Yield ◽  
Extraction Time ◽  
Order Polynomial ◽  
Supercritical Carbon

The effects of process parameters on the extraction of Ginkgo biloba L. leaves with supercritical carbon dioxide were investigated. The investigated parameters include particle size (mean particle diameter 0.19, 0.467 and 1.009 mm), solvent flow rate (1.5810-3, 3.2210-3 and 4.1610-3 kg CO2/min) and pressure (100-300 bar), which were obtained by the response surface methodology (RSM) under the following condition ranges: temperature 40-50-60?C, pressure 100-140-180 bar and extraction time of 2-3-4 h at the flow rate of 3.2210-3 kg/min. Based on the experimental results of kinetics of Ginkgo biloba leaves extraction with supercritical carbon dioxide, modeling of the extraction system of Ginkgo biloba-supercritical CO2 was done. Two mathematical models (Reverchon-Sesti Osseo and Sovov?) were applied to correlate the experimental data. RSM was applied to optimize the process parameters of supercritical carbon dioxide extraction of Ginkgo biloba L. leaves. A second-order polynomial response surface equation was developed indicating the effect of variables on Ginkgo biloba extraction yield. The statistical analysis of the experiment indicated that pressure (X1), extraction time (X3), the quadratic of temperature (X22), and the interaction between pressure and extraction time (X1X3), show significant effect on the extraction yield. The results showed that the data were adequately fitted into the second-order polynomial model. It was predicted that the optimum extraction process parameters within the experimental ranges would be the extraction temperature of 52.7?C, the pressure of 184.4 bar, and the extraction time of 3.86 h. Under these conditions, the predicted extraction yield is 2.39% (g/100 g drug).

Selecting the Optimum Supercritical CO2 Cycle for Indirect-Fired Applications

2019 ◽  
Author(s):  
Brittany Tom ◽  
January Smith ◽  
Aaron M. McClung
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Supercritical Co2 ◽  
Operating Conditions ◽  
Working Fluid ◽  
Performance Goals ◽  
Brayton Cycle ◽  
Power Cycle ◽  
And Performance ◽  
Supercritical Carbon

Abstract Existing research has demonstrated the viability of supercritical carbon dioxide as an efficient working fluid with numerous advantages over steam in power cycle applications. Selecting the appropriate power cycle configuration for a given application depends on expected operating conditions and performance goals. This paper presents a comparison for three indirect fired sCO2 cycles: recompression closed Brayton cycle, dual loop cascaded cycle, and partial condensation cycle. Each cycle was modeled in NPSS with an air side heater, given the same baseline assumptions and optimized over a range of conditions. Additionally, limitations on the heater system are discussed.

scholarly journals Microencapsulation and Nanoencapsulation Using Supercritical Fluid (SCF) Techniques

Pharmaceutics ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 21 ◽  
Author(s):  
Soon Hong Soh ◽  
Lai Yeng Lee
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Supercritical Fluid ◽  
Supercritical Co2 ◽  
Drug Loading ◽  
Particle Morphology ◽  
Operating Conditions ◽  
Rapid Expansion ◽  
Comprehensive Overview ◽  
Supercritical Carbon

The unique properties of supercritical fluids, in particular supercritical carbon dioxide (CO2), provide numerous opportunities for the development of processes for pharmaceutical applications. One of the potential applications for pharmaceuticals includes microencapsulation and nanoencapsulation for drug delivery purposes. Supercritical CO2 processes allow the design and control of particle size, as well as drug loading by utilizing the tunable properties of supercritical CO2 at different operating conditions (flow ratio, temperature, pressures, etc.). This review aims to provide a comprehensive overview of the processes and techniques using supercritical fluid processing based on the supercritical properties, the role of supercritical carbon dioxide during the process, and the mechanism of formulation production for each process discussed. The considerations for equipment configurations to achieve the various processes described and the mechanisms behind the representative processes such as RESS (rapid expansion of supercritical solutions), SAS (supercritical antisolvent), SFEE (supercritical fluid extraction of emulsions), PGSS (particles from gas-saturated solutions), drying, and polymer foaming will be explained via schematic representation. More recent developments such as fluidized bed coating using supercritical CO2 as the fluidizing and drying medium, the supercritical CO2 spray drying of aqueous solutions, as well as the production of microporous drug releasing devices via foaming, will be highlighted in this review. Development and strategies to control and optimize the particle morphology, drug loading, and yield from the major processes will also be discussed.

Supercritical CO2-assisted amorphization of WO2.72 and its high-efficiency photothermal conversion

2020 ◽  
Vol 56 (56) ◽  
pp. 7805-7808
Author(s):  
Youzeng Li ◽  
Pengfei Yan ◽  
Cang Guo ◽  
Qun Xu
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Conversion Efficiency ◽  
Supercritical Co2 ◽  
High Efficiency ◽  
Photothermal Conversion ◽  
Optical Absorbance ◽  
Treated Sample ◽  
Supercritical Carbon

Amorphization of WO2.72 was successfully achieved with the assistance of supercritical carbon dioxide (SC CO2). Amorphous SC CO2-treated sample has strong optical absorbance and excellent photothermal conversion efficiency of 52.5% indicates they can be a promising photothermal agent.

Developing Heat-Transfer Correlations for Supercritical CO2 Flowing in Vertical Bare Tubes

2012 ◽  
Author(s):  
Sahil Gupta ◽  
Donald McGillivray ◽  
Prabu Surendran ◽  
Liliana Trevani ◽  
Igor Pioro
Keyword(s):  
Heat Transfer ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Supercritical Fluid ◽  
Supercritical Water ◽  
Fast Reactor ◽  
Supercritical Co2 ◽  
Critical Parameters ◽  
Heat Transfer Correlations ◽  
Supercritical Carbon

This paper presents an analysis of three new heat-transfer correlations developed for supercritical carbon dioxide (CO2) flowing in vertical bare tubes. A large set of experimental data was obtained at Chalk River Laboratories (CRL) AECL. Heat-transfer tests were performed in upward flow of CO2 inside 8-mm ID vertical Inconel-600 tube with a 2.208-m heated length. Data points were collected at outlet pressures ranging from 7.4 to 8.8 MPa, mass fluxes from 900 to 3000 kg/m2s, inlet fluid temperatures from 20 to 40°C, and heat fluxes from 15 to 615 kW/m2; and for several combinations of wall and bulk-fluid temperatures that were below, at, or above the pseudocritical temperature. The objective of the present experimental research is to obtain reference dataset on heat transfer in supercritical CO2 and improve our fundamental knowledge of the heat-transfer processes and handling of supercritical fluids. In general, heat-transfer process to a supercritical fluid is difficult to model, especially, when a fluid passes through the pseudocritical region, as there are very rapid variations in thermophysical properties of the fluid. Thus, it is important to investigate supercritical-fluid behaviour within these conditions. In general, supercritical carbon dioxide was and is used as a modelling fluid instead of supercritical water due to its lower critical parameters compared to those of water. Also, supercritical carbon dioxide is proposed to be used as a working fluid in the Brayton gas-turbine cycle as a secondary power cycle for some of the Generation-IV nuclear-reactor concepts such as a Sodium-cooled Fast Reactor (SFR), Lead-cooled Fast Reactor (LFR) and Molten-Salt-cooled Reactor (MSR). In addition, supercritical carbon dioxide was proposed to be used in advanced air-conditioning and geothermal systems. Previous studies have shown that existing correlations deviate significantly from experimental Heat Transfer Coefficient (HTC) values, especially, within the pseudocritical range. Moreover, the majority of correlations were mainly developed for supercritical water, and our latest results indicate that they cannot be directly applied to supercritical CO2 with the same accuracy as for water. Therefore, new empirical correlations to predict HTC values were developed based on the supercritical CO2 dataset. These correlations calculate HTC values with an accuracy of ±30% (wall temperatures with accuracy of ±20%) for the analyzed dataset.

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