Evaluation of Thermal Conductivity of Gases at Atmospheric Pressure through a Corresponding States Method

Calculation of evaporation requires accurate thermophysical properties of the liquid. Such data are well-known for conventional fossil fuels. In contrast, e.g., thermal conductivity or dynamic viscosity of the fuel vapor are rarely available for modern liquid fuels. To overcome this problem, molecular models can be used. Currently, the measurement-based properties of n-heptane and diesel oil are compared with estimated values, using the state-of-the-art molecular models to derive the temperature-dependent material properties. Then their effect on droplet evaporation was evaluated. The critical parameters were liquid density, latent heat of vaporization, boiling temperature, and vapor thermal conductivity where the estimation affected the evaporation time notably. Besides a general sensitivity analysis, evaporation modeling in a practical burner ended up with similar results. By calculating droplet motion, the evaporation number, the evaporation-to-residence time ratio can be derived. An empirical cumulative distribution function is used for the spray of the analyzed burner to evaluate evaporation in the mixing tube. Evaporation number did not exceed 0.4, meaning a full evaporation prior to reaching the burner lip in all cases. As droplet inertia depends upon its size, the residence time has a minimum value due to the phenomenon of overshooting.

show abstract

“…For a general overview of the molecular-based thermophysical model development, see reference [4]. Baum [41] ρ v (kg/m 3 ) ideal gas…”

Section: Thermophysical Data Estimationmentioning

confidence: 99%

Fuel Evaporation in an Atmospheric Premixed Burner: Sensitivity Analysis and Spray Vaporization

Csemány

Józsa

2017

Processes

View full text Add to dashboard Cite

show abstract

“…It also includes dealing with Hat matrix being made of experimental and predicted data. The equation below is used for calculating the Hat indices [ 14 – 16 ]:

X ( n × x ) is a matrix including n data and k parameters of the model, and t denotes the transpose matrix. The diagonal values of the matrix ( H ) are called H values.…”

Section: Identification Of Outlying Experimental Datamentioning

confidence: 99%

On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD‐2 Using Machine Learning Techniques

Chen¹,

Huang²,

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications include healthy products, oil recycling, and biological refining. In this research, to calculate the curves of rhamnolipid adsorption compared to Amberlite XAD-2, the least-squares vector machine algorithm has been used. Then, the obtained model is formed by 204 adsorption data points. Various graphical and statistical approaches are applied to ensure the correctness of the model output. The findings of this study are compared with studies that have used artificial neural network (ANN) and data group management method (GMDH) models. The model used in this study has a lower percentage of absolute mean deviation than ANN and GMDH models, which is estimated to be 1.71%.The least-squares support vector machine (LSSVM) is very valuable for investigating the breakthrough curve of rhamnolipid, and it can also be used to help chemists working on biosurfactants. Moreover, our graphical interface program can assist everyone to determine easily the curves of rhamnolipid adsorption on Amberlite XAD-2.

show abstract

“…In this technique, the residual is the difference between the experimental MMP and the predictions of the proposed model. The leverage or hat indices are calculated as follows:

H = X {(X^{t} X)}^{- 1} X^{t}

where X is a two‐dimensional ( n×k ) matrix consisting of n data points (rows) and k parameters of the model (columns), and t denotes the transpose of matrix X. The diagonal elements of the H matrix are the hat values of the data in the feasible region of the problem.…”

Section: Model Evaluationmentioning

confidence: 99%

“…In this technique, the residual is the difference between the experimental MMP and the predictions of the proposed model. The leverage or hat indices are calculated as follows: [63][64][65][66] H…”

Section: Applicability Domain Of the Model And Outlier Detectionmentioning

confidence: 99%

Accurate determination of the CO₂‐crude oil minimum miscibility pressure of pure and impure CO₂ streams: A robust modelling approach

Hemmati-Sarapardeh¹,

Ghazanfari²,

Ayatollahi³

et al. 2016

Can J Chem Eng

View full text Add to dashboard Cite

Gas flooding processes have emerged as attractive enhanced oil recovery (EOR) methods over the last few decades. Among different gas flooding processes, CO2 flooding is recognized as being most efficient for displacing oil through miscible displacement. Minimum miscibility pressure (MMP) is a crucial parameter for successfully designing CO2 flooding, which is traditionally measured through time‐consuming, expensive, and cumbersome experiments. In the present study, a new reliable model based on feed‐forward artificial neural networks was presented to predict both pure and impure CO2‐crude oil MMP. Among various properties and parameters, reservoir temperature, reservoir oil composition, and injected gas composition were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with existing models, both statistical and graphical error analyses were simultaneously employed. The results showed that the proposed model is more reliable and accurate compared to existing models in a wide range of thermodynamic and process conditions. Furthermore, by employing the relevancy factor, it was found that the reservoir temperature has the most significant impact on the MMP. Finally, in order to identify probable outliers and the applicability domain of the proposed model, the leverage approach was performed. The results illustrated that only two experimental MMP data points were located outside of the applicability domain of the proposed model. As a result, the developed model is statistically reliable for predicting crude oil MMP.

show abstract

Evaluation of Thermal Conductivity of Gases at Atmospheric Pressure through a Corresponding States Method

Cited by 40 publications

References 33 publications

Fuel Evaporation in an Atmospheric Premixed Burner: Sensitivity Analysis and Spray Vaporization

Fuel Evaporation in an Atmospheric Premixed Burner: Sensitivity Analysis and Spray Vaporization

On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD‐2 Using Machine Learning Techniques

Accurate determination of the CO₂‐crude oil minimum miscibility pressure of pure and impure CO₂ streams: A robust modelling approach

Contact Info

Product

Resources

About

Evaluation of Thermal Conductivity of Gases at Atmospheric Pressure through a Corresponding States Method

Cited by 40 publications

References 33 publications

Fuel Evaporation in an Atmospheric Premixed Burner: Sensitivity Analysis and Spray Vaporization

Fuel Evaporation in an Atmospheric Premixed Burner: Sensitivity Analysis and Spray Vaporization

On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD‐2 Using Machine Learning Techniques

Accurate determination of the CO2‐crude oil minimum miscibility pressure of pure and impure CO2 streams: A robust modelling approach

Contact Info

Product

Resources

About

Accurate determination of the CO₂‐crude oil minimum miscibility pressure of pure and impure CO₂ streams: A robust modelling approach