scholarly journals A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

2019 ◽  
Vol 64 (1) ◽  
pp. 146-152 ◽  
Author(s):  
Hossein Rostamian ◽  
Mohammad Nader Lotfollahi
Keyword(s):  
Carbon Dioxide ◽  
Thermal Conductivity ◽  
Correlation Method ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Absolute Deviation ◽  
New Correlation ◽  
Absolute Residual ◽  
Data Points ◽  
Liquid Vapor

In this study, a new correlation for estimating thermal conductivity (TC) of carbon dioxide was developed based on 2319 data points. The data points were at the temperature ranging from 250 to 1100 K, pressure ranging from 1 to 3000 bar and density ranging from 0.3 to 1400 Kg.m-3 in different phases of liquid, vapor and supercritical. The statistical parameters including average absolute deviation (AAD%), average percent relative error (ARE%), sum of absolute residual (SAR) and the coefficient of determination (R2) have been calculated to evaluate the accuracy of present correlation. The obtained values of AAD%, ARE%, SAR and R2 were 1.98, −0.64, 3510.1 and 0.995, respectively. The predictions of proposed correlation were also compared with three widely used correlations. The results showed that the proposed correlation is able to accurately calculate thermal conductivity of carbon dioxide. In addition, the proposed model is superior to all the existing empirical models considered.

Improved General Correlation for Heat Transfer During Gas–Liquid Flow in Horizontal Tubes

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Mirza M. Shah
Keyword(s):  
Heat Transfer ◽  
Liquid Mixtures ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Earth Gravity ◽  
Horizontal Tubes ◽  
New Correlation ◽  
Wide Range ◽  
Data Points ◽  
Gas Liquid Flow

Heat transfer to two-component gas–liquid mixtures is needed in many industries but there is lack of a well-verified predictive method. A correlation is presented for heat transfer during flow of gas–liquid nonboiling mixtures in horizontal tubes. It has been verified with a wide range of data that includes tube diameters of 4.3–57 mm, pressures from 1 to 4.1 bar, temperatures from 12 to 62 °C, gravity <0.1% to 100% earth gravity, liquid Reynolds number from 9 to 1.2 × 105, and ratio of gas and liquid velocities from 0.24 to 9298. The 946 data points from 18 sources are predicted with mean absolute deviation (MAD) of 19.2%. The same data were compared to five other correlations; they had much larger deviations. Therefore, the new correlation is likely to be helpful in more accurate designs.

A Correlation for Heat Transfer to Gas–Solid Suspensions Flowing in Pipes

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Mirza M. Shah
Keyword(s):  
Heat Transfer ◽  
Chemical Processing ◽  
Absolute Deviation ◽  
Solids Loading ◽  
New Correlation ◽  
Accurate Design ◽  
Wide Range ◽  
Data Points ◽  
Loading Ratio ◽  
Vertical Up

Abstract Heat transfer to flowing gas–solid mixtures in pipes is required in many applications including chemical processing, pneumatic transport, and nuclear reactors but no well-verified method for predicting heat transfer is available. A new correlation is presented, which has been validated with a wide range of data that includes a variety of particles (minerals, metals) in several gases. Particle diameters range from 13 to 1130 µm, pipe diameters 5.1 to 77 mm, and the solids loading ratio of 0–520. Flow orientations include horizontal, vertical up, and vertical down. The new correlation has a mean absolute deviation (MAD) of 18.9% with 630 data points from 20 studies. The same data were also compared with six published correlations. Their MAD ranged from 35% to 57%. Hence, the new correlation is likely to help in more accurate design.

General Correlation for Heat Transfer to Gas–Liquid Flow in Vertical Channels

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
Mirza M. Shah
Keyword(s):  
Heat Transfer ◽  
Liquid Mixtures ◽  
Absolute Deviation ◽  
General Correlation ◽  
New Correlation ◽  
Rectangular Channels ◽  
Wide Range ◽  
Data Points ◽  
Gas Liquid Flow ◽  
Dry Out

Abstract A general correlation is presented for heat transfer during flow of gas–liquid mixtures flowing in vertical channels prior to dry out. It has been verified with a wide range of data that include upward and downward flow in heated and cooled tubes, annuli, and rectangular channels. The data are from 19 studies and include 14 gas–liquid mixtures with a wide range of properties. The parameters include pressure 1–6.9 bar, temperature 16–115 °C, liquid Reynolds number from 2 to 127,231, superficial gas and liquid velocities up to 87 and 13 m/s, respectively, and ratio of superficial gas and liquid velocities 0.03–1630. The 1022 data points are predicted by the new correlation with mean absolute deviation (MAD) of 18.1%. Several other correlations were also compared to the same data and had MAD of 28.6–45.5%.

A Correlation for Heat Transfer to Two-Component Gas-Liquid Flowing in Vertical Channels

2018 ◽  
Author(s):  
Mirza M. Shah
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Liquid Mixtures ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
New Correlation ◽  
Rectangular Channels ◽  
Wide Range ◽  
Two Component ◽  
Data Points

A general correlation is presented for heat transfer during flow of gas-liquid mixtures flowing in vertical channels prior to dryout. It has been verified with a wide range of data that include upwards and downwards flow in heated and cooled tubes, annuli, and rectangular channels. The data are from 19 studies and include 14 gas-liquid mixtures with a very wide range of properties. The parameters include pressure 1 to 6.9 bars, temperature 16 to 115 oC, liquid Reynolds number from 2 to 127231, superficial gas and liquid velocities up to 87 and 13 m/s respectively, and ratio of superficial gas and liquid velocities 0.03 to 1630. The 1022 data points are predicted by the new correlation with mean absolute deviation (MAD) of 18.1 %. Several other correlations were also compared to the same data and had much larger deviations.

scholarly journals Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids

2019 ◽  
Vol 10 (1) ◽  
pp. 304 ◽  
Author(s):  
Hocine Ouaer ◽  
Amir Hossein Hosseini ◽  
Menad Nait Amar ◽  
Mohamed El Amine Ben Seghier ◽  
Mohammed Abdelfetah Ghriga ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Ionic Liquids ◽  
Equations Of State ◽  
Gene Expression Programming ◽  
Coefficient Of Determination ◽  
Full Dataset ◽  
Data Points ◽  
Points Of View ◽  
State Models ◽  
Carbon Dioxide Co2

Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

An electrical analyzer for carbon dioxide in respiratory gases

1962 ◽  
Vol 17 (1) ◽  
pp. 126-130
Author(s):  
Leon Bernstein ◽  
Chiyoshi Yoshimoto
Keyword(s):  
Carbon Dioxide ◽  
Thermal Conductivity ◽  
Medical Students ◽  
Venous Blood ◽  
Mixed Venous Blood ◽  
The Subject ◽  
Respiratory Gases ◽  
Rebreathing Method ◽  
Mixed Venous

The analyzer described was de signed for measuring the concentration of carbon dioxide in the bag of gas from which the subject rebreathes in the “rebreathing method” for estimating the tension of carbon dioxide in mixed venous blood. Its merits are that it is cheap, robust, simple to construct and to service, easy to operate, and accurate when used by untrained operators. (Medical students, unacquainted with the instrument, and working with written instructions only, obtained at their first attempt results accurate to within ±0.36% [sd] of carbon dioxide.) The instrument is suitable for use by nurse or physician at the bedside, and also for classes in experimental physiology. Some discussion is presented of the theoretical principles underlying the design of analyzers employing thermal conductivity cells. Submitted on July 13, 1961

Liquid-vapor equilibria of the hydrogen-carbon dioxide system

1968 ◽  
Vol 13 (2) ◽  
pp. 168-171 ◽  
Author(s):  
J. O. Spano ◽  
C. K. Heck ◽  
P. L. Barrick
Keyword(s):  
Carbon Dioxide ◽  
Liquid Vapor

scholarly journals ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

2012 ◽  
Vol 38 (2) ◽  
pp. 57-69 ◽  
Author(s):  
Abdulghani Hasan ◽  
Petter Pilesjö ◽  
Andreas Persson
Keyword(s):  
Large Scale ◽  
Drainage Area ◽  
Data Sets ◽  
Topographic Wetness Index ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Elevation Data ◽  
Scale Modelling ◽  
Data Points ◽  
Emission Modelling

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

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