Prediction Of Thermophysical Properties Research Articles

Measurement of Vapor-liquid Equilibria (VLE) for the Ternary, Propane + Dimethyl Ether + Butane, and the Three Constituent Binaries, and Predicted Values by the Benedict-Webb-Rubin (BWR) Equation of State: Measurement and Prediction of Thermophysical Properties for Dimethyl Ether with Liquefied Petroleum Gas Components (Part 2)

Measurement of Vapor-liquid Equilibria (VLE) for the Ternary, Propane + Dimethyl Ether + Butane, and the Three Constituent Binaries, and Predicted Values by the Benedict-Webb-Rubin (BWR) Equation of State: Measurement and Prediction of Thermophysical Properties for Dimethyl Ether with Liquefied Petroleum Gas Components (Part 2)

P-V-T Relationship/Saturated Vapor Pressure of Dimethyl Ether (DME) as an Alternative for Liquefied Petroleum Gas and Determination of 8 Constants in the Benedict-Webb-Rubin (BWR) Equation of State: Measurement and Prediction of Thermophysical Properties for Dimethyl Ether with Liquefied Petroleum Gas Contents (Part 1)

P-V-T Relationship/Saturated Vapor Pressure of Dimethyl Ether (DME) as an Alternative for Liquefied Petroleum Gas and Determination of 8 Constants in the Benedict-Webb-Rubin (BWR) Equation of State: Measurement and Prediction of Thermophysical Properties for Dimethyl Ether with Liquefied Petroleum Gas Contents (Part 1)

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network.

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

Measurement and prediction of thermophysical properties of binary mixtures of dicyclopentadiene with methylcyclohexane, toluene, and p-xylene

Flash point. density and viscosity of three binary mixtures containing dicyclopentadiene (DCPD) with methylcyclohexane, toluene and p-xylene were measured in the present work. The experimental data of flash points were compared with the values calculated by Le Chatelier modified equation. Activity coefficients were estimated by the UNIFAC, UNIFAC Dortmund and ASOG models. Also, excess molar volumes, viscosity deviation and excess Gibbs energy of activation of viscous flow were calculated from data of density and viscosity. Prigogine-Flory-Patterson theory was applied to predict VmE. Empirical models were used to predict the viscosity data. NRTL, UNIQUAC and Heil models were applied to calculate G*E. The findings have been employed to examine structural effects and molecular interactions which are dominant between compound molecules. The obtained experimental results showed weak molecular interactions and theory study demonstrated that UNIFAC, PFP, Eyring_NRTL, Lobe, and NRTL were the best models found to fit the flash point, excess molar volume, viscosity and excess free Gibbs energy.

Teorijsko predviđanje termofizičkih svojstava biodizela baziranog na goveđem loju

Teorijsko predviđanje termofizičkih svojstava biodizela baziranog na goveđem loju

A review on prediction of thermo physical properties of heat transfer nanofluids using intelligent techniques

Abstract Nanofluids are the novel fluids which obtained by dispersing the nano-sized particles into traditional fluids. Nanofluids are reported that they have vast potential in wide range of industrial applications. In particular, nanofluids are the best coolant for replacing the conventional heat transfer fluids. Determination of thermal conductivity and viscosity experimentally are time consuming, laborious and expensive which are the limitations of using nanofluids in real time applications. This review paper summarizes the latest research works on determination of thermo physical properties of different nanofluids by using soft computing or intelligent techniques and leads to the opportunities for future research and to reduce the effort of developing the experimental test runs and number of hypothesis.

The Theoretical Prediction of Thermophysical properties, HOMO, LUMO, QSAR and Biological Indics of Cannabinoids (CBD) and Tetrahhdrocannabinol (THC) by Computational Chemistry

Some thermophysical parameters of CBD and THC such as free energy, entropy, dipole moment, binding energy, nuclear energy, electronics energy, heat of formation, and chemical reactivity like HOMO (Occupied Molecular Orbital Highest) and LUMO (Lowest Unoccupied Molecular Orbital, HUMO-LUMO gap, ionization potential and electron affinity were calculated via semi-empirical and molecular mechanic method. For the characterization, the IR vibration spectroscopy, NMR in case of coupling and shielding constant were calculated. The Quantitative Structure Activity Relation (QSAR) properties of molecules like charge density, surface area grid, volume, LogP, polarizability, refractivity, molecular mass were determined using the HyperChem 8.0.10 program. Using the thermophysical and QSAR data, the IC50 and pHIC50 (–logIC50) was developed which is referred as biological activity parameter.

An Alternative Method for Correlation and Prediction of Thermo Physical Properties of Fluids-Critical Pressures

Based on the linear free energy relationships (LFERs) theory and thermodynamics formulas, an alternative method predicting the critical pressure (Pc) of pure fluids is proposed for the first time. According to the regression result of 15 homologues, 516 substances, correlation equations between Pc and molecular descriptors were obtained. The mean relative deviations (MD) of the 15 equations are from 1.68% to 3.76%. In addition, the squared correlation coefficients (R2) of the most of equations are larger than 0.91. The results reveal that the equations exhibit better effects with a simple form of the equation, high prediction accuracy, definition theory meaning, and wide applicability. This study successfully combines the macroscopic physical properties with the characteristics of molecules and breaks through the experimental or theoretical application scope, while perfecting the calculation of Pc for pure fluids.

Uncertainties in the predictions of thermo-physical properties of thermoplastic polymers via molecular dynamics

We quantify the effect of various sources of uncertainties in the prediction of thermo-physical properties of polymers using molecular dynamics simulations. We quantify how the choice of polymer builder, force field, molecular weight and data analysis affect predicted values of the glass transition temperature (Tg), room temperature density and coefficient of thermal expansion of poly(methyl-methacrylate) (PMMA) and polystyrene (PS). Interestingly, we find that the data analysis introduces significant uncertainties in Tg (approximately 5%) while the other properties are insensitive to it. The force field is the only variable that significantly affects the predictions of density. Polymer-consistent force field (PCFF) resulted in a higher density for PMMA than Dreiding and the opposite trend was observed in PS; in all cases the difference in density was less than 2%. Strongly correlated with density, we find that PCFF leads to a higher Tg than Dreiding for PMMA and both force fields predict similar Tg values for PS. The trends in Tg can be explained by differences in segmental mobility of the melts predicted by the two force fields. We find that the choice of amorphous polymer builder results in uncertainties in predictions comparable to those associated with the force field due to subtle, but persistent, differences in molecular structure. The results presented here provide insight into the physics behind molecular simulations of polymers and quantitative levels of uncertainties associated with individual sources that can help practitioners of molecular simulations interested in using their results in engineering applications.

The New Method for Correlation and Prediction of Thermophysical Properties of Fluids. Critical Temperature

On the basis of the linear free energy relationships theory and thermodynamics formulas, a new method predicting critical temperature (Tc) of pure fluids is proposed for the first time. Sixteen homologues of 616 substances have been regressed and correlation equations between Tc and molecular descriptors are obtained. The mean relative deviations of the 16 equations are from 0.01% to 2.73%, and most of them are under 2%. In addition, the squared correlation coefficients are from 0.90 to 0.98. Moreover, the equations are tested through cross-validation by the leave-one-out procedure and most of the squared correlation coefficients are greater than 0.90. The results reveal that the equations exhibit better effect with simple form of equation, high prediction accuracy, and definitude theory meaning. This study successfully combines macroscopic physical properties of fluids with their molecular microstructure and breaks through the experimental or theoretical application scope, perfecting calculation of criti...

Thermophysical properties of polymer composite materials

Subject of Study. The paper deals withthermophysical properties of a new polydimethylsiloxane potting compound. We study the principles of thermal conductivity and heat capacity changing depending on composition of materials in the temperature range of 25-175 ° C. The search of optimal composition of the compound is carried out providing thermal conductivity not less than 0.5 W/(м·К) and data set for the change prediction of thermo-physical properties depending on a compound composition. Methods. All the samples were produced by FSUE “Institute of Synthetic Rubber” on the basis of polymers used in serial production. All the compounds are two- or three-component materials based on a low molecular weight (liquid) polydimethylsiloxane caoutchouc made by a cold solidification method. Polyethylsiloxane PES-5, aluminum hydroxide, TiN, boron, zinc oxide are used as additives. The research of thermal conductivity and heat capacity are carried out by meters λ-400 and С-400by dynamic method of monotonic warming. Main Results. We have obtained new experimental data about the thermal conductivity and heat capacity of the polysiloxane compound, depending on the temperature and the concentrations of various fillers. With increasing of the filler concentration the thermal conductivity of the samples increases as well and the heat capacity decreases. It is shown that with increasing of temperature the thermal conductivity of compounds is falling by about 15%, and the heat capacity increases by about 60-70%. Practical Relevance. The retrieved data give the possibility to find the optimal composition of compound material that guarantees the following operational properties: thermal conductivity not less than 0.5 W/(м·К), long thermostability in the temperature range of -60 - +200 °C, the density of hydrogen nucleus not less than 1∙1014 nucleus/cm2 and the summary absorbed gamma radiation dose up to 300 Gy. The developed compound is being tested and can be applicable in the development of neutron shielding for the transportation of the used nuclear fuel in the nuclear fuel containers.

Composition-Based Prediction of Temperature-Dependent Thermophysical Food Properties: Reevaluating Component Groups and Prediction Models.

Prediction of temperature-dependent thermophysical properties (thermal conductivity, density, specific heat, and thermal diffusivity) is an important component of process design for food manufacturing. Current models for prediction of thermophysical properties of foods are based on the composition, specifically, fat, carbohydrate, protein, fiber, water, and ash contents, all of which change with temperature. The objectives of this investigation were to reevaluate and improve the prediction expressions for thermophysical properties. Previously published data were analyzed over the temperature range from 10 to 150 °C. These data were analyzed to create a series of relationships between the thermophysical properties and temperature for each food component, as well as to identify the dependence of the thermophysical properties on more specific structural properties of the fats, carbohydrates, and proteins. Results from this investigation revealed that the relationships between the thermophysical properties of the major constituents of foods and temperature can be statistically described by linear expressions, in contrast to the current polynomial models. Links between variability in thermophysical properties and structural properties were observed. Relationships for several thermophysical properties based on more specific constituents have been identified. Distinctions between simple sugars (fructose, glucose, and lactose) and complex carbohydrates (starch, pectin, and cellulose) have been proposed. The relationships between the thermophysical properties and proteins revealed a potential correlation with the molecular weight of the protein. The significance of relating variability in constituent thermophysical properties with structural properties--such as molecular mass--could significantly improve composition-based prediction models and, consequently, the effectiveness of process design.

Methods for the prediction of thermophysical properties of polyurethane raw material mixtures

According to the world wide safety standards, a classification for transport and handling is required for every single mixture. To reduce the experimental effort to determine physical properties for the classification of multicomponent mixtures of polyurethane raw materials, existing predictive methods for the initial normal boiling point and for the flash point have been extended based on group contribution methods like UNIFAC and modified UNIFAC (Dortmund). A new predictive method for the auto ignition temperature has been developed. The calculation results were evaluated based on experimental data for more than 1500 mixtures. It could be shown that these approaches can be reliably applied for the classification of multicomponent mixtures of polyurethane raw materials.

Application of SAFT‐VR Equation of State for Prediction of Thermophysical Properties of Sugar Solutions

AbstractThe thermodynamic properties of sugar solutions, which are important solutions in food industries, have been calculated. Because of the large molecules and hydrogen bonding in the water/sugar solutions, the statistical association fluid theory of variable ranged (SAFT‐VR) equation of state was used to calculate the thermodynamic properties such as water activity, solubility, vapor pressure and boiling temperature of the aqueous sugar solutions, including glucose, sucrose and fructose. The SAFT‐VR parameters were calculated using vapor pressure and density data for water and water activity data for sugars. The total average absolute deviation percentage (AAD%) for all the calculated properties was 1.93%. The results showed that the model can accurately predict the properties of the sugar solution.Practical ApplicationsSugar solutions have important roles and uses in different industries such as food production, food preservation industries, chemical feed stock, pharmaceutical industries, metabolic reactions, industrial operations and chemical processes, such as crystallization, evaporation and selective separation. For example, water activity in a solution can be used to stop deteriorative reactions. The density of the solution is important in separation processes and mixing equipment. Solubility of sugar in water is another important property in food preservation and processes such as gasification and separation. The boiling temperature of the solution is an important thermodynamic property in different industrial processes such as purification and separation processes.

Prediction of thermophysical properties of novel ionic liquids based on serine [Cnmim][Ser] (n = 3, 4) using semiempirical methods

Two novel ionic liquids based on serine [Cnmim][Ser] (n=3,4) were prepared by the neutralization method and their structures were confirmed by 1H NMR spectroscopy and differential scanning calorimetry (DSC). The density, surface tension, and refractive index of the two ILs were measured from T=(298.15 to 338.15)K. Since these ILs [Cnmim][Ser] (n=3,4) could form strong hydrogen bonds with water, small amount of water in the ILs is difficult to removed by common methods. In order to eliminate the effect of trace of water, the standard addition method (SAM) was applied to these measurements. On the basis of the experimental data, the speed of sound (μ), thermal expansion coefficient (α), molecular volume (Vm), standard entropy (S0298), entropy of surface (Sa), energy of surface (Ea), parachor (P), molar polarization (Rm), and polarization coefficient (αp) were calculated, and the relationship between each of these properties of [Cnmim][Ser] (n=3,4) and temperatures was discussed. According to the additivity, the average value of anionic parachor, P−(ave), was 180.81 for [Ser]−. At the same time, the surface tension of these serine ionic liquids could be estimated from their parachor and refractive index. The estimated values of the surface tension and the corresponding experimental data were almost identical.

A never-ending search for the truth: Thermodynamics in the uncertain era of the internet

This article is based on the 20th Rossini Lecture delivered on 28 July 2014 at the opening of the 23rd International Conference on Chemical Thermodynamics in Durban, South Africa.In the last several decades, enormous progress in material and computer sciences has led, in many scientific disciplines, to fundamental improvements in experimental measurement technologies. That, in combination with new communication technologies and gradually increasing societal commitment to support public scientific research, has resulted in an unprecedented growth in the “production” of the reported experimental results. These trends together with dramatically growing demand for thermophysical and thermochemical property data related to new chemical processes and products necessitated development of dynamic methods of critical data evaluation within a framework of the concept of Global Information Systems in Science which was developed and implemented for the field of thermodynamics at the Thermodynamics Research Center (TRC) of the US National Institute of Standards and Technology (NIST). Principal advantages of this approach in comparison with the traditional static methods of critical data evaluation are illustrated.Major components of the developed system and its impact on such areas of human activities as information delivery, data publication, chemical process design, chemical product design, experiment planning, and education are discussed. The systems and software tools designed for global validation of experimental values are outlined. A variety of experimental data-driven technologies for thermophysical property prediction developed with the use of the elements of the Global Information System in Thermodynamics, including those based on surrogate mixture models, group contributions, QSPR, UNIFAC, and Monte Carlo molecular simulation, are described.

Prediction of Thermophysical Properties of Helium Using Linear Prediction and Artificial Neural Networks

Thermophysical properties of helium are significant in practical applications. However, the values of properties vary under different circumstances, which may have bad impacts on practical productions and applications. In our study, computational models like Linear Prediction and Artificial Neural Networks (ANNs) are applied to predict the thermophysical properties of the chemical substances. By analyzing 50 data groups using Linear Prediction, General Regression Neural Network (GRNN) and Multilayer Feedforward Neural Network (MLFN) methods, 9 models were successfully established to predict the thermophysical properties of helium, including density, energy, enthalpy, entropy, isochoric heat capacity, isobaric heat capacity, viscosity, thermal conductivity and dielectric constant. Within permissible error range (30% tolerance), our models were proved to be robust and accurate which indicates that ANN models can be used to predict the thermophysical properties of helium.

Prediction of thermophysical properties of saturated steam and wellbore heat losses in concentric dual-tubing steam injection wells

Concentric dual-tubing steam injection is important in the process of thermal recovery for heavy oils. This paper firstly presented a mathematical model to predict thermophysical properties of saturated steam (i.e. steam pressure, temperature and quality) and wellbore heat losses in CDTSIW (concentric dual-tubing steam injection wells). More importantly, a semi-analytical model for estimating pressure gradient for steam/water flow in annuli was developed. Then the mathematical model is solved using an iterative technique. Predicted results were compared with measured field data to verify the accuracy of the model. The results indicate that the direction of heat transfer between fluids in the integral joint tubing and in the annulus depends not only on wellhead injection conditions but on temperature drop in each tubing. In addition, the steam qualities in CDTSIW are significantly influenced by heat exchange between fluids in dual tubing, which can cause steam boiling or condensation. Moreover, the paper shows that to effectively reduce the wellbore heat losses and to ensure high bottomhole steam qualities in Well Xing 67 of Liaohe Oilfield, the thermal conductivity of insulation materials should be less than 0.7 W/(m K).

Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N222(n)]Tf2N) by Means of Artificial Intelligence Techniques

In this work, thermophysical properties of quaternary ammonium-based ionic liquids (ILs) including density, surface tension, and viscosity are produced by two powerful artificial intelligence techniques: genetic function approximation (GFA) and artificial neural network (ANN). In proposed GFA and ANN models, the critical temperature and water content of studied ILs ([N222(n)]Tf2N with n = 5, 6, 8, 10, and 12) as well as operation temperature were given as the input parameters and the density, surface tension, and viscosity were predicted as the output results. The obtained results reveal that the selected input parameters are appropriate for prediction of thermophysical properties of quaternary ammonium-based ILs. In addition, the high statistical quality represented by various criteria and the low prediction errors of the presented models indicate that they can accurately predict the density, surface tension, and viscosity of new ILs without recourse to experimental data.

Measurement and Prediction of Thermo Physical Properties to study Intermolecular Interactions of Binary Liquid Mixtures at Various Temperatures by Using McAllister Model

The important of fundamental data for the design and optimization of chemical process are densities and viscosities. The study conducted in this research are viscosities, n, and densities p, of 1,4 Dioxane with two hydrocarbons Benzene and Chloro Benzene have been measured over the entire range of composition at (303, 15, 308,15) K.