Estimation Of Thermodynamic Properties Research Articles

Joint incremental learning network for flexible modeling of carbon dioxide solubility in aqueous mixtures of amines

Vapor-liquid equilibrium (VLE) modeling is one of the most essential tasks for rigorous estimations of thermodynamic properties in chemical process design and analysis. To realize any commercially feasible carbon dioxide (CO2) capture scheme using a carefully chosen aqueous amine solution, the accurate model of CO2 solubility in this water-amine system plays a vital role. However, the experimental data of various mixtures of amine solvents are insufficient and expensive to acquire. In view of this problem, a novel joint incremental learning network (JILN) structure is proposed for modeling equilibrium solubility of CO2 in various bi-solvent aqueous amine mixtures. With the proposed method, the knowledge embedded in the well-trained mono-solvent models can be extracted and effectively transferred to their related multi-solvent models. By adopting the proposed modeling method, the numerical experimental results showed that the mean absolute percentage errors (MAPEs) for various bi-solvent models were 2.1–4.9%, which indicates a maximum of 68% reduction in prediction blunders if compared with their ANN counterparts.

Critical review on graph theory: Estimation of thermodynamic properties of liquid mixtures

Critical review on graph theory: Estimation of thermodynamic properties of liquid mixtures

Effect of temperature on N-A-S-(H) and zeolite composition, solubility, and structure

This paper investigates the effect of temperature on the composition, solubility, and structure of sodium aluminosilicate hydrate (N-A-S-(H)) and zeolite synthesized using sodium silicate and sodium aluminate solutions across a range of bulk aqueous Si/Al ratios at different temperatures. It was found that temperature had minimal effect on the composition for both N-A-S-(H) and zeolite species. A decrease in N-A-S-(H) and zeolite solubility with increasing temperature was observed and attributed to the temperature-dependent pH. As expected, the solubility of N-A-S-(H) was higher than that of its crystalline counterpart. Estimation of thermodynamic properties also revealed that for both the N-A-S-(H) and zeolite phases, the Van't Hoff expression yielded similar enthalpy and Gibbs free energy of formation data as a three-parameter Gibbs free energy model that includes a heat capacity term. An additional benefit of the Van't Hoff approach is that it obviates the need for estimating the heat capacity from reference reactions.

Thermodynamic Assessment of CrCl2 with NaCl–KCl–MgCl2–UCl3–UCl4 for Molten Chloride Reactor Corrosion Modeling

The formation of corrosion species in molten salt reactor systems is driven by the salt redox condition, which can be indicated by the uranium oxidation state ratio (U4+/U3+). In chloride salts, chromium is well known to have the highest tendency to deplete from alloy surfaces; however, no available thermodynamic database suitably represents the effect of trivalent and tetravalent uranium chloride on the corrosion potential. In this work, we extend the Molten Salt Thermal Properties Database–Thermochemical with Gibbs energy models suitable for application to alloy corrosion in chloride molten salt-fueled nuclear reactors. The work required the development of fully constrained thermodynamic models, utilizing the modified quasi-chemical model in the quadruplet approximation for the melt and a single sublattice model for the solid solution phases allowing accurate estimations of thermodynamic properties even in systems for which few thermodynamic data are available. The effort included differential scanning calorimetry (DSC) measurements to find the previously unreported phase equilibria of the CrCl2–UCl3 system and to confirm the equilibria of the MgCl2–UCl3 system. Elemental period correlations allowed the estimation of thermodynamic values for compounds in the NaCl–CrCl2 and KCl–CrCl2 systems allowing a well-informed description of CrCl2 behavior in the compositions of technical interest for the Na–K–Mg–U3+–U4+ chloride salt.

Estimation of thermodynamic properties of environmentally friendly new-generation R515B and R450A as an alternative to R134a

In the analysis of vapor compression refrigeration systems, simple and effective mathematical formulas are required to determine the thermodynamic properties of refrigerants. This study aims to determine the thermodynamic properties such as enthalpy, entropy, and specific volume of environmentally friendly new-generation refrigerants (R515B and R450A) with low global warming potential using gene expression programming (GEP). The thermodynamic properties calculated using the formulations obtained from the GEP model and the actual thermodynamic properties obtained from the REFROP software were compared. Performance evaluation criteria such as R2, root mean square error, and mean absolute percent error are in the range of 0.86 to 0.999, 0.0000285-6.489, and 0.0009-0.35, respectively, and these values are acceptable. This study offers simple and efficient formulations to calculate the thermodynamic properties of new-generation refrigerants without the need for any software. So, the simulation of cooling and heat pump systems will be greatly facilitated.

Overview of Common Thermophysical Property Modelling Approaches for Cryogenic Fluid Simulations at Supercritical Conditions

Computational Fluid Dynamics (CFD) frameworks of supercritical cryogenic fluids need to employ Real Fluid models such as cubic Equations of State (EoS) to account for thermal and inertial driven mechanisms of fluid evolution and disintegration. Accurate estimation of the non-linear variation in density, thermodynamic and transport properties is required to computationally replicate the relevant thermo and fluid dynamics involved. This article reviews the availability, performance and the implementation of common Real Fluid EoS and data-based models in CFD studies of supercritical cryogenic fluids. A systematic analysis of supercritical cryogenic fluid (N2, O2 and CH4) thermophysical property predictions by cubic (PR and SRK) and non-cubic (SBWR) Real Fluid EoS, along with Chung’s model, reveal that: (a) SRK EoS is much more accurate than PR at low temperatures of liquid phase, whereas PR is more accurate at the pseudoboiling region and (b) SBWR EoS is more accurate than PR and SRK despite requiring the same input parameters; however, it is limited by the complexity in thermodynamic property estimation. Alternative data-based models, such as tabulation and polynomial methods, have also been shown to be reliably employed in CFD. At the end, a brief discussion on the thermophysical modelling of cryogenic fluids affected by quantum effects is included, in which the unsuitability of the common real fluid EoS models for the liquid phase of such fluids is presented.

Systematically derived thermodynamic properties for alkane oxidation

Key combustion properties, such as ignition delay time, show strong sensitivity to the thermochemistry of the main species in the standard radical oxidation pathway at low temperatures (600–1000 K). Significant uncertainties persist in current estimates of thermodynamic properties, particularly for the larger species of relevance to common practical fuels. In this article, we use advanced computational schemes to evaluate thermodynamic properties for the fuel, fuel radical, peroxy, hydroperoxy-alkyl radical, and hydroperoxide species for a set of 17 fuels containing up to 9 carbon atoms, and with various degrees of branching in the alkane backbone. The procedure, termed STAR-1D, combines conformer sampling to find the minimum geometry, B2PLYP-D3/cc-pVTZ harmonic frequency evaluations, and ωB97X-D/cc-pVTZ one-dimensional torsional mapping. It includes two physically-based scaling routines: a frequency-dependent scaling to B2PLYP-D3/cc-pVTZ anharmonic frequencies and a scaling of one-dimensional ωB97X-D/cc-pVTZ torsional profiles to reproduce the product of the B2PLYP-D3/cc-pVTZ frequencies in the harmonic limit. Substantive comparisons with existing experimental databases, together with careful examinations of key theoretical assumptions, are used to explore the accuracy of the predictions. These computationally intensive explorations of 195 species were facilitated by automated thermochemistry software. High accuracy 0 K heats of formation from a separate study are used with the STAR-1D computations to generate NASA polynomial representations. In addition to their intrinsic value, the present results also provide a reliable database for the optimization of group additivity or machine learning schemes for scaling to larger combustion systems. Towards this end a complementary extensive conformational analysis is carried out for the medium sized species and the thermodynamic properties of the lowest energy hydrogen-bonded and non-hydrogen bonded conformers are contrasted for larger hydroperoxy-alkyl radical species.

SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation.

Generation of representative conformations for small molecules is a fundamental task in cheminformatics and computer-aided drug discovery, but capturing the complex distribution of conformations that contains multiple low energy minima is still a great challenge. Deep generative modeling, aiming to learn complex data distributions, is a promising approach to tackle the conformation generation problem. Here, inspired by stochastic dynamics and recent advances in generative modeling, we developed SDEGen, a novel conformation generation model based on stochastic differential equations. Compared with existing conformation generation methods, it enjoys the following advantages: (1) high model capacity to capture multimodal conformation distribution, thereby searching for multiple low-energy conformations of a molecule quickly, (2) higher conformation generation efficiency, almost ten times faster than the state-of-the-art score-based model, ConfGF, and (3) a clear physical interpretation to learn how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls in low energy minima. Extensive experiments demonstrate that SDEGen has surpassed existing methods in different tasks for conformation generation, interatomic distance distribution prediction, and thermodynamic property estimation, showing great potential for real-world applications.

THERMODYNAMIC ASPECTS OF OXIDES REDUCING BY ALUMINUM AND TITANIUM AT THE THERMIT WELDING OF RAILS

During aluminothermic welding of rails, formation of aluminum oxides can take place, which become centers of residual stresses forming resulting in cracks formation followed by fracture and destruction of the welding joint. It was proposed to add titanium scrap in the form of preliminary made powders to the welding thermites composition. At the replacement of aluminum by titanium, titanium oxides formation takes place providing higher resistance against cracks formation. Comparative estimation of thermodynamic properties [∆rH°(T), ∆rG°(T)] of thermite reactions with aluminum and titanium in the systems metal oxide – aluminum and metal oxide – titanium under standard conditions presented. As reaction components for calculation of thermodynamic properties, oxides of iron, manganese, silicon and chrome were used. Thermodynamic analysis of 6 reactions with aluminum and 30 reactions with titanium were carried out. It was established that all the reactions under consideration are thermodynamically probable. However, reactions of both aluminum and titanium with iron oxides had the highest probability and correspondently the highest heat generation. Reducing of silicon oxide had the lowest probability and correspondently the lowest heat generation. The reaction with oxides of manganese and chrome had an intermediate position. By the reducing ability titanium is not worse than aluminum and in reactions with iron oxide even exceeds it, particularly in the temperature range over 2000 K. Enthalpies of reducing reactions of iron oxide by titanium is in the same area of values as the enthalpies of reaction of reducing by aluminum. A thermodynamic analysis showed that application of titanium as a reducing agent at the thermit welding is quite acceptable along with aluminum.

Neural net implementation of steam properties on FPGA

Real time applications like model predictive control, monitoring and data reconciliation of power plants and industrial processes employ nonlinear mathematical models and require thermodynamic properties and their derivatives of working fluids. Applications like super heater temperature control based on energy balance and real time data reconciliation, require an efficient and a compact method for simultaneous estimation of thermodynamic properties, and their partial derivatives suitable for implementation in field-programmable gate array (FPGA). However, the complex mathematical formulations of these properties prohibit direct implementations in FPGAs. Single artificial neural network (ANN) architecture is used to replace the entire code in higher level languages, running into a few thousand lines. FPGA implementation of a compact neural network for the entire range of thermodynamic properties is presented. Large arguments in sigmoid function are factored into a product of integer and a fractional part which is represented using series approximation with five terms only and the integers are represented in look up table (LUT). This ensures optimum storage and computational burden for the above applications. The ANN is implemented in IEEE 754 floating point with synthesis in Xilinx ISE design suite using Verilog HDL. The results are presented for a typical pressure versus saturation temperature.

Thermodynamic calculations using reverse Monte Carlo.

We introduce the theoretical background needed to perform thermodynamic calculations using reverse Monte Carlo (RMC). The theory is developed for binary A_{x}B_{1-x} lattice systems. The main assumption is that the arrangement of A and B atoms can be described using short-ranged order (SRO) parameters. The detailed balance equation, which is expressed in terms of SRO parameters, is solved to obtain the equilibrium SRO parameter value for the given material interactions, temperature, and composition. Thermodynamic properties, such as the chemical potential, are evaluated using the equilibrium SRO parameter value. RMC enables the calculation of the probability distribution of the local atomic environments, which is needed in the detailed balance equation. We illustrate the application of our method to bulk lattice materials with different first nearest neighbor pair interactions. The main advantage of our approach is that the probability distribution from RMC can be stored in form of look-up tables, and used with a variety of interaction strengths and temperature for rapid estimation of thermodynamic properties. In all examples, the chemical potential is accurately evaluated in the matter of a few seconds on a desktop computer.

Optimal conditions determination for hydrodeoxygenation of free fatty acids to obtain green diesel

AbstractIn this work, the production of green diesel involving the hydrodeoxygenation reaction of vegetable oil, such as oleic acid, producing CO or CO2 as byproducts, was studied. This reaction takes place under hydrogen saturation to produce stearic acid; subsequently, heptadecane is produced by the decarbonylation and decarboxylation pathways, while octadecane is obtained by the subsequent hydrodeoxygenation of octadecanal. In the formation of octadecane, water is obtained as a byproduct, while the production of heptadecane leads to CO and CO2 as byproducts of the decarbonylation and decarboxylation reactions. When considering the products and reaction routes in the hydrotreatment of triglycerides and free acids, a mathematical model was found to be reliable in determining the minimization of the Gibbs free energy of reaction over a range of reaction temperatures and pressures. This model followed the methods of Marrero‐Gani, Joback‐Reid, and Chen‐Dinivahi‐Jeng for the estimation of thermodynamic properties of the pure components. The fugacity was calculated by considering the partial fugacity coefficients, and the Peng‐Robinson equation of state and the van der Waals mixing rule were used. The mole fractions at equilibrium were estimated by least squares analysis, using MATLAB, in order to account for non‐idealities in reaction thermodynamic properties and to find the equilibrium composition that minimized the total Gibbs free energy. Likewise, the reaction coordinates of the equilibrium reaction were estimated together with the known values of the composition in order to know the effect of pressure and temperature on the aforementioned reactions.

Study of specific heat capacities and speed of sound for uranium and uranium fluoride gases

A new approach is proposed for accurate estimation of thermodynamic properties of nuclear fuels. The method is based on the use of virial coefficients with Lennard-Jones (12-6) potential. The model is applicable to the calculations of thermodynamic quantities of nuclear gases. In this work, we derived new formulae for the calculation of the specific heat capacities and speed of sound of nuclear gases by using the second virial coefficient with Lennard-Jones (12-6) potential. The final results are expressed through the gamma and parabolic cylinder functions that is a better approach to the accurate evaluation of the second virial coefficient. The new approach has been applied to the accurate evaluation of the specific heat capacities and speed of sound for uranium and uranium fluoride gases of $$ {\text{U}} $$ , $$ {\text{UF}} $$ , $$ {\text{UF}}_{2} $$ , $$ {\text{UF}}_{3} $$ , $$ {\text{UF}}_{4} $$ , $$ {\text{UF}}_{5} $$ and $$ {\text{UF}}_{6} $$ whose importance in nuclear technology is well known. All the results for nuclear gases $$ {\text{U}} $$ , $$ {\text{UF}} $$ , $$ {\text{UF}}_{2} $$ , $$ {\text{UF}}_{3} $$ , $$ {\text{UF}}_{4} $$ , $$ {\text{UF}}_{5} $$ and $$ {\text{UF}}_{6} $$ are controlled by the calculations in the literature, and their accuracy is confirmed.

Estimation of Thermodynamic Properties and Phase Equilibria in Systems of Deep Eutectic Solvents by PC-SAFT EoS

This study examines the capabilities and shortcomings of perturbed-chain-statistical associating fluid theory equations of state (PC-SAFT EoS) applied for predicting properties and phase equilibria in systems of deep eutectic solvents. Treating deep eutectic solvents as pseudo-pure compounds and describing them as mixtures of two independent constituents are explored. Examples of choline chloride as the hydrogen bond acceptor (HBA) and levulinic acid, ethylene glycol, and phenol as hydrogen bond donors (HBDs) in a 1:2 molar ratio are considered. Excess properties of deep eutectic solvents with n-alkanols and water along with solubilities of carbon dioxide and ammonia are also considered. Besides that, liquid–liquid ternary systems of deep eutectic solvents with n-alkanes and 1-alkanols are analyzed. It has been found that although the pseudo-pure compounds approach with 2B associative scheme correctly estimates excess volumes, it exhibits poor accuracy in predicting excess enthalpies, solubility data, and liquid–liquid equilibria in ternary systems. When a temperature-dependent binary interaction between HBA and HBDs is applied, the individual approach predicts these properties in a more accurate manner.

R513A Soğutucu Akışkanın Termodinamik Özelliklerini Tahmin Etmek İçin GEP Tabanlı Model Yaklaşımı

The changes in the politic and environmentally are highly effective in refrigerant choices. Nowadays it is critical using environmentally friendly and energy-efficient refrigerants for a sustainable future. The R513A refrigerant provides both GWP reduction and increases energy efficiency. Accurate estimates of thermodynamic properties of refrigerant are critical when considering the changing of temperature in a small interval. Many data of thermodynamic properties of refrigerants are hard-to-reach data from literature and tables. In the present study, thermodynamic properties such as entropy, enthalpy and density are modelled using gene expression programming (GEP). In this context, for R513A new refrigerant determined mathematical equations through gene expression programming. Developed mathematical models for saturation and superheated R513A refrigerant under different temperature and pressure condition compared experimental data obtained from the literature. The results have shown that the GEP model could be considered as an efficient modelling technique for the estimate data of predicting thermodynamic properties of refrigerants.

Reliable and Novel Approach Based on Thermodynamic Property Estimation of Low to High Salinity Aqueous Sodium Chloride Solutions for Water-Energy Nexus Applications

There is a significant need for reliable and accurate thermodynamic property data of hypersaline solutions to understand the minimum heat and work of separation required and it is quite useful for ...

Extraction of naphthenic acid from low-grade crude oil using diol compounds

The extraction of naphthenic acid from low-grade crudes offers economic and environmental advantages over chemical conversion or other separation processes. The properties of the solvent play critical roles for the successful implementation of the extraction process. Solvent screening techniques based on thermodynamic prediction can facilitate the selection of candidate solvents. This study first performed solvent screening based on thermodynamic property estimation using the COSMO-RS method and subsequently verified the solvent performances experimentally. The formation of liquid–liquid equilibrium (LLE), mutual solubility, and ternary LLE were predicted for n-octanol + n-octanoic acid + solvent mixtures. The predicted results were analyzed for the selection of the best candidates to remove acidic components from crude oil. From the theoretical calculations, 1,6-hexanediol was selected as the best candidate solvent. This result was experimentally verified by extracting reagent-grade naphthenic acid from diesel oil using the candidate solvents. The measured results were consistent with the trend observed in the theoretical calculations. Among the candidate solvents, 1,6-hexanediol reduced the acidic component composition corresponding to 5 mg KOH/g down to 1 mg KOH/g with a solvent fraction of 0.6.

Speed-up of Monte Carlo simulations by preparing starting off-lattice structures that are close to equilibrium.

The Monte Carlo (MC) technique is an important tool for studying equilibrium properties of materials. When the starting configuration provided as an input to a MC calculation is far from equilibrium, an inordinate amount of computational effort may be required to bring the system closer to equilibrium in the pre-equilibration step of the MC calculation. In order to alleviate this cost, a new computational strategy is presented with the aim of rapidly generating starting off-lattice atomic structures that are already close to equilibrium. The method involves preparing a collection of on-lattice configurations using fast reverse MC calculations. Each configuration corresponds to a different value of short-range order parameter(s). Next, by performing short MC calculations with each starting structure, one measures the extent to which the distribution of local atomic arrangements has changed. The optimal configuration exhibits the smallest change in the distribution. While the optimal configuration can serve as an input to longer MC calculations, in many situations, the resulting structure may be directly used for the estimation of thermodynamic properties. Application of our approach to several off-lattice binary and ternary metal alloy systems with phase separation, good mixing, ordering, and surface segregation is demonstrated. A speed-up of >100-1000 times over the standard MC approach is achieved even with small systems containing a few thousand particles, and close-to-equilibrium structures containing million atoms can be rapidly prepared using our method within a day on a standard desktop computer.

A Python Multiscale Thermochemistry Toolbox (pMuTT) for thermochemical and kinetic parameter estimation

Estimating the thermochemical properties of systems is important in many fields such as material science and catalysis. The Python multiscale thermochemistry toolbox (pMuTT) is a Python software library developed to streamline the conversion of ab-initio data to thermochemical properties using statistical mechanics, to perform thermodynamic analysis, and to create input files for kinetic modeling software. Its open-source implementation in Python leverages existing scientific codes, encourages users to write scripts for their needs, and allows the code to be expanded easily. The core classes developed include a statistical mechanical model in which energy modes can be included or excluded to suit the application, empirical models for rapid thermodynamic property estimation, and a reaction model to calculate kinetic parameters or changes in thermodynamic properties. In addition, pMuTT supports other features, such as Brønsted–Evans–Polanyi (BEP) relationships, coverage effects, and ab-initio phase diagrams. Program summaryProgram title: pMuTTProgram files doi: http://dx.doi.org/10.17632/b7f7d28ynd.1Licensing provisions: MIT license (MIT)Programming language: PythonExternal routines: ASE, NumPy, Pandas, SciPy, Matplotlib, Pygal, PyMongo, dnspythonNature of problem: Conversion of ab-initio properties to thermochemical properties and rate constants is time consuming and error-prone.Solution method: Python package with a modular approach to statistical thermodynamics and rate constant estimation.

Thermodynamic database of zeolites and new method of their thermodynamic properties evaluation for a wide temperature range

A database on the thermodynamic properties of zeolites has been created. It contains the published raw experimental measurements of heat capacities, heat contents and enthalpies of formation for 46 zeolites at T=0−1000K. This is about 1/3 higher than in previous reviews and databases. The zeolites from the database consist from H2O and oxides of Li, Na, K, Tl, Ca, Mg, Sr, Ba, Fe, Al and Si. All experimental data were approximated by sums of Einstein-Planck functions in the entire temperature range. Heat capacity anomalies were described by addition of extra terms to the sum. A new additive composition dependent model for estimation of thermodynamic properties of zeolites was suggested. It's based on the collected data and is valid for a wide temperature range.