Acentric Factor Research Articles

Potential application of novel [formula omitted]-indices as molecular descriptors

A topological index or a descriptor is a graph invariant that describes the structure of a graph as a numerical value. This paper proposes eight novel indices called AL-indices, based on distance and degree, and analyzes their behavior to show that they can serve as potentially useful molecular descriptors. We find that among the proposed indices many have very good discriminative power when compared to the existing vertex-degree based indices and justifies the requirement of these new indices. Further, we propose a method to compute these indices and vertex-degree-based indices from a recently proposed graph matrix, referred to as neighborhood matrix.Computationally, we correlate the proposed indices’ efficiency against the octane isomers’ and polychlorobiphenyls’ physicochemical properties. We perform a comparative study of these indices with a few well-known vertex degree-based indices. Further, the proposed indices’ discriminative capacity is analyzed and shown to have higher discriminative power on the considered datasets. Among all the indices under study, four indices, namely AL4, AL5, AL7, and AL8 have shown the highest discriminative power on the set of octane isomers. While the indices AL4 and AL7 have the highest discriminative power on the set of PCB molecules compared to the other VDB indices.Among the proposed and considered indices, we show that the first index AL1 has a good correlation with the Acentric factor and entropy of octane isomer and with the total surface area, log-water-solubility, relative retention time, octanol–water-partition, and log-water-activity coefficient of PCBs.

Performance of the Corresponding States Model of Dual-Reference on the Determination of Viscosity According to the Pressure of Pure Hydrocarbons

The main idea of this method is to characterize reduced viscosity (r of a fluid from two given fluids taken as a reference through the decreased temperature and pressure. The viscosity formulation through the corresponding states of dual-reference method contains an influential coefficient also known as weighting factor K. Unlike the literature, it seemed that the choice of this factor according to the molar mass or the acentric factor is somehow too predictive, which led us to additionally test two other new factors, including one with adjustment for a more objective and characteristic representation. The overall results show that the introductions of adjusted coefficients in the weighting ratio are quite satisfactory. And can be qualified as better than the two existing factors. This method guided us to investigate two possibilities for dual-references and compare their results. On the one hand methane/decane and on the other hand decane/benzene, then their results are compared on the same database. The performance of this predictive model for non-cyclical elements is very convincing compared to other models, especially for heavy bodies. These results confirm those obtained and published on blends containing at least one aromatic element. The viscosity calculated for some heavy bodies close to experimental values. This relative improvement is a result of the introduction of the second reference body (C10) compared to the model of the corresponding states to a reference (C1).

Application of a Single Multilayer Perceptron Model to Predict the Solubility of CO2 in Different Ionic Liquids for Gas Removal Processes

In this work, 2099 experimental data of binary systems composed of CO2 and ionic liquids are studied to predict solubility using a multilayer perceptron. The dataset includes 33 different types of ionic liquids over a wide range of temperatures, pressures, and solubilities. The main objective of this work is to propose a procedure for the prediction of CO2 solubility in ionic liquids by establishing four stages to determine the model parameters: (1) selection of the learning algorithm, (2) optimization of the first hidden layer, (3) optimization of the second hidden layer, and (4) selection of the input combination. In this study, a bound is set on the number of model parameters: the number of model parameters must be less than the amount of predicted data. Eight different learning algorithms with (4,m,n,1)-type hidden two-layer architectures (m = 2, 4, …, 10 and n = 2, 3, …, 10) are studied, and the artificial neural network is trained with three input combinations with three combinations of thermodynamic variables such as temperature (T), pressure (P), critical temperature (Tc), critical pressure, the critical compressibility factor (Zc), and the acentric factor (ω). The results show that the 4-6-8-1 architecture with the input combination T-P-Tc-Pc and the Levenberg–Marquard learning algorithm is a very acceptable and simple model (95 parameters) with the best prediction and a maximum absolute deviation close to 10%.

GAS-CONDENSATE FLUID PVT MODEL QUALITY CHECK BASED ON THE CONCEPT OF A SINGLE-CELL SIMULATION MODEL

The problems of gas-condensate PVT-models (Pressure Volume Temperature, PVT) creation under limited input information were analyzed. Traditional fluid phase behavior characterization approach relies on creation of the equation of state (EOS) based on initial composition of reservoir fluid and its future regression for critical parameters (pressure and temperature), binary interaction coefficients, acentric factors of residual “plus” fraction or pseudo-components. The adjustment is done until the moment when EOS is reproducing the results of laboratory experiments. Classic PVT experiments performed on gas-condensates and volatile oils are constant composition expansion (CCE), constant volume depletion (CVD) and separator tests. However, in the case of most Ukrainian fields, discovered and explored in the last century, not only the reliable detailed initial fluid composition is not available, but phase behavior was studied with non-equilibrium method of so-called differential condensation, that does not allow their direct application for PVT models creation. Previously, the authors [1, 2] presented an alternative method for fluid characterization based on the fractional distillation test. At the same time, due to significant uncertainty in input data, particularly a) condensate production allocation; b) commingled production from multiple reservoirs with different C5+ yield; c) non-recorded change of separator conditions that affects liquid extraction and its density; d) technological production losses, issues of reproducing the condensate production during history matching of several models of Dniper-Donetsk Basin were faced. There was proposed and explained in detail an example of single-cell reservoir simulation model application concept for quality check of created PVT model for one of the fields with potential yield of 86 g/m3. The idea of the concept is based on the reproduction of material balance of gas-condensate reservoir through one conditional well controlled on a primary (gas) phase, that allows quick identification of changes into calculated gas-condensate yield curve, necessary for matching of condensate production. Implementation of these changes allows quick and precise full-field model calibration.

Modification of the Redlich-Kwong-Aungier Equation of State to Determine the Main Thermodynamic Parameters in the Pure Liquid CO2 Region

The most important parameters for determining the state of real gas and the thermodynamic properties of the working fluid in a pure liquid region are pressure, specific volume, enthalpy and entropy. The paper presents a modified Redlich-Kwong-Aungier equation of state for determining pressure, specific volume, enthalpy and entropy in the pure liquid phase of real gas. CO2 was selected as the studied working fluid. When solving this problem, the author identified the main parameters of liquid carbon dioxide thermo-dynamics with the least error in comparison with experimental data in a wide range from 220 K to 300 K. It is possible to calculate pressure, specific volume, density, enthalpy and entropy of liquid CO2 with the help of the proposed method, for which the initial data are temperature, density, critical properties, molar mass and acentric factor of the working fluid. In particular, a modified Redlich-Kwong-Aungier equation is used to calculate the pressure of the working fluid. The author proposes a correlation equation of the scale correction, which is used in the Redlich-Kwong-Aungier equation for CO2 in the region of pure liquid phase. The results obtained for the pressure, enthalpy and entropy of liquid CO2 showed good agreement with the basic values, which provides the application of the proposed method in the field of pure liquid CO2, limited by the temperature range from 220 K to 300 K. The simplicity of the equation of state and the small number of empirical coefficients allows to use this method to solve practical problems of computational gas dynamics without spending a lot of time on calculations.

An empirical correlation for predicting vapor pressure of ionic liquids

The accuracy of vapor pressure calculations is essential because it is used as a basis to estimate the thermal and equilibrium properties in energy industries. In this research work, a new empirical model for predicting the vapor pressure of ionic liquids is presented. The model was derived from 383 experimental data points of the vapor pressure of 32 different ionic liquids, as a function of temperature, critical pressure, critical temperature, acentric factor, critical compressibility factor, the number of carbons of the second alkyl group of the cation, mass connectivity index and molecular weight. The computational parameters have been determined using extended group contribution methods. The accuracy of the new model has been calculated by the absolute relative deviation percent (AARD%) of 6.82. The comparison between the current study and previous studies indicates that the proposed method provides more accurate results compared to other methods.

Computational and comparative aspects of two carbon nanosheets with respect to some novel topological indices

The recent discovery of various forms of carbon nanostructures inspired the researchers due to their uses in various fields. Numerous production approaches for carbon nanostructures have been presented; doping, functionalization, chemical modification, and filling have been achieved, and manipulation, characterization and separation of carbon nanostructures are now possible. On the other hand, the flexibility and strength of carbon nanosheets make them potential candidates in controlling further nanoscale structures, which indicates that they have an important role in nanotechnology engineering. Because of their uniform thickness of only around 1 nanometer( nm) , and their high mechanical, thermal and chemical stability, such materials are of high concern for a wide range of applications. A topological index or molecular descriptor is a mathematical formulation that can be applied to any graph which models some molecular structure. Based on the molecular descriptor, it is convenient to analyze mathematical values and advance investigation on some physicochemical properties of a molecule. Consequently, it is an effective method in replacing laborious, expensive and time-consuming laboratory experimentations. Molecular descriptors perform an important part in mathematical chemistry, especially during the investigation of Quantitative structure property relationships (QSPRs) and Quantitative structure-activity relationships (QSARs) that provide insight into animate effects based on chemical structures. In this paper, we have computed some novel neighborhood version molecular descriptors for the two carbon nanosheets V C 5 C 7 p , q and ( H C 5 C 7 p , q ) and derived formulas for them. Our computed results are surely helpful in understanding the topology of understudy nanosheets. These computed indices have best correlation with acentric factor and entropy, therefore, are effective in QSPRs and QSARs analysis with powerful accuracy.

New Versions of Locating Indices and Their Significance in Predicting the Physicochemical Properties of Benzenoid Hydrocarbons

In this paper, we introduce some new versions based on the locating vectors named locating indices. In particular, Hyper locating indices, Randić locating index, and Sambor locating index. The exact formulae for these indices of some well-known families of graphs and for the Helm graph are derived. Moreover, we determine the importance of these locating indices for 11 benzenoid hydrocarbons. Furthermore, we show that these new versions of locating indices have a reasonable correlation using linear regression with physicochemical characteristics such as molar entropy, acentric factor, boiling point, complexity, octanol–water partition coefficient, and Kovats retention index. The cases in which good correlations were obtained suggested the validity of the calculated topological indices to be further used to predict the physicochemical properties of much more complicated chemical compounds.

Prediction of CO2 solubility in glymes and ionic liquids using modified generalized BWR EoS

Glymes or ionic liquids are considered excellent solvents for carbon capture because they are insoluble in the gas phase during CO2 recovery. Hence, it is beneficial to predict the solubility of CO2 in these solvents to plan appropriate experiments for achieving carbon neutrality. In this study, Joback's simple group contribution method (Joback and Reid, Chem. Eng. Commun. 57 (1987) 233–243) was used to predict the effectivity of ionic liquids or glymes toward CO2 dissolution. Using this method, the fundamental and critical properties, such as critical temperature, critical pressure, critical molar volume, and acentric factor, were predicted. A binary interaction parameter, mij, which is necessary for calculating the solubility of CO2 in glyme or an ionic liquid, was also predicted using the critical volume ratio, Vc,i/Vc,CO2. Using the modified generalized BWR EoS, the value was best fitted at approximately 300 and 370 K. In general, the method proposed was effective in predicting the amount of CO2 that will dissolve in an unknown ionic liquid, which is essential for achieving carbon neutrality.

A temperature-independent prediction model predicts the vapor-liquid equilibrium of CO2-based binary mixtures

CO2-based binary mixtures are considered good alternative working fluids in the refrigeration and power cycle due to their excellent performance and environmental friendliness. The vapor-liquid phase equilibrium of CO2-based binary mixtures is the basis for calculating mixing enthalpy and heat capacity, which is essential for thermodynamic analysis. In this work, based on the vdW mixing rules, we proposed a kij model (kij=θjki-θikj). In the proposed model, ki and kj are defined as pure interaction factors of the pure components, θj is the influence factor of component j to component i, and θi is the influence factor of component i to component j. After continuous fitting and trial calculation, we developed a semi-empirical formula, which is called a temperature-independent prediction model. The model does not have any adjustable parameters, which are only related to the physical properties (critical pressure (kPa), critical temperature (K), and acentric factor) and molecular structure (carbon-carbon double bonds, carbon-fluorine, chlorine, iodine, and oxygen atoms) of the components. The temperature-independent prediction model can predict the bubble-point pressures, vapor phase molar fractions, and relative volatilities very well, which is better than the prediction model based on the vdW mixing rules by others and the group contribution model based on the excess free energy (GE) mixing rules (PR+MHV1+UNIFAC). The total average absolute relative deviation of pressures, the total average absolute deviation of vapor phase molar fractions, and the total average absolute relative deviation of relative volatilities by the temperature-independent prediction model are 2.23%, 0.0095, and 5.21%, respectively.

Revisiting the Corresponding-States-Based Correlation for Pool Boiling Critical Heat Flux

A corresponding-states correlation for predicting the critical heat flux (CHF) in pool boiling conditions is proposed, and only requires knowledge of physical property constants of the fluid at any fluid temperature: molar mass, critical temperature, critical pressure, and the Pitzer acentric factor. If a fourth corresponding equation of state (EoS) parameter is added, a more accurate CHF correlation is obtained and matches Kutateladze–Zuber prediction within ±10% in the reduced temperature range of 0.55–0.95. This way, CHF can be easily predicted for any reduced temperature within the range of correlation’s validity by only knowing basic properties of the fluid. Additionally, two corresponding-states correlations for determining the capillary length are proposed and also do not rely on any temperature- and pressure-dependent fluid properties. A simpler correlation only using the Pitzer acentric factor is shown to be imprecise, and a more complex correlation also accounting for the fourth corresponding EoS parameter is recommended. These correlations are fundamental for further developments, which would allow for accurate prediction of CHF values on functionalized surfaces through further studies on the influence of interactions of fluid properties with other parameters, such as wetting and active nucleation site density.

How to Quickly Evaluate the Thermodynamic Performance and Identify the Optimal Heat Source Temperature for Organic Rankine Cycles?

Abstract Organic Rankine cycle (ORC) is a promising technology to convert low- and medium-temperature energy into power. Identifying the optimal working fluids and heat source temperature are always the focuses in the ORC field. This paper presents a new methodology to evaluate the thermodynamic performance of ORC with different working fluids and identify the optimal heat source temperature. Initially, the parameterization model is developed to characterize the working fluids by thermodynamic property parameters including critical temperature (Tc), critical pressure (pc), acentric factor (ω), and ideal gas isobaric heat capacity (cp0). Subsequently, the simultaneous optimization of thermodynamic property parameters and cycle parameters is conducted to obtain the thermodynamic performance limits of simple and regenerative ORCs at six typical geothermal heat source temperatures. By comparing the thermodynamic performance limits of ORC under different heat source temperatures, the optimal heat source temperature is identified. Then, ten commonly used working fluids are selected as reference working fluids, and the thermodynamic property parameters comparisons between reference and ideal working fluids, which can be characterized by the optimized thermodynamic property parameters, are investigated. Finally, multiple linear regression models are developed to evaluate the thermodynamic performance. The numerical differences of thermodynamic property parameters between the ideal reference and reference working fluids are chosen as initial variables, while the thermal efficiency and volumetric power output are used as thermodynamic performance indicators. The results show that the optimal heat source temperature is 250 °C, which is independent of cycle configuration. The thermodynamic performance of ORCs can be evaluated accurately by the multiple linear regression models. The maximum relative error of the multiple linear regression models is 3.02%. Moreover, Tc is the most dominant thermodynamic property parameter.

Vapour–liquid coexistence of natural phenolic monoterpenoid, thymol: comparison with structural isomer, carvacrol

Thymol, a naturally occurring structural isomer of carvacrol, is known to exhibit medicinal properties. Since vapour–liquid equilibria data are critical for the design of efficient separation processes, theoretical methods and molecular simulations in conjunction with a simple united atom force field are employed to predict the coexistence properties; also at high temperatures where experimental data are scarce. Process design simulators used to model separation processes employ the equation of state approach which requires as input the critical state properties and acentric factor. Here, these inputs are computed using structure–property relations for use with the theoretical approach and are also determined from the more fundamental molecular simulations to be Tc = 70216 K, Pc = 336 bar, Vc = 48637 cc/mol and ω = 0.459. The structure of the coexisting liquid phase has been investigated using the radial distribution functions and number integrals to determine the extent of association present. The simple united atom force field captures the differences in the phase behaviour of thymol with that of carvacrol that arise due to steric effects of the relative placement of the hydroxyl group with respect to the methyl and isopropyl groups on the aromatic ring.

Algorithm for estimating SARA fraction properties and its application in predicting the densities of heavy oils, bitumen and diluted bitumen with solvents

Using aromatic ring index (ARI) and the densities of saturates, aromatics, resins and asphaltenes (SARA) at any temperature, an algorithm was developed to iteratively estimate the molecular weights and specific gravities (60 °F) of the SARA fractions. The algorithm circumvents molecular weight measurements in reporting SARA fractions properties. Then, using the corresponding state principles the critical properties and acentric factor of the fractions were estimated using the correlations available in literature. Further, applying regular solution theory the densities of heavy oils, bitumen, and five diluted Athabasca bitumen with 5 solvents were predicted from Hankinson-Thomson and a compressed liquid density correlation. The average absolute deviation (AAD) in predicting the densities was found to be 1.33% for 1524 points with temperature range from 288 K to 463 K and pressure from 0.1 to 10 MPa. The method exhibits predictive ability and can be used satisfactorily for any heavy oil/bitumen/solvent system. In order to improve the accuracy of the calculated densities, a constant density translation method can also be applied to the bitumen/oil mixtures. When the translation method was used, the AAD of mixtures was improved to 0.69%. The proposed method was found to be better or comparable to other models in predicting densities.

Study of Transformed ηζ Networks via Zagreb Connection Indices

A graph is a tool for designing a system’s required interconnection network. The topology of such networks determines their compatibility. For the first time, in this work we construct subdivided ηζ network S(ηζΓ) and discussed their topology. In graph theory, there are a variety of invariants to study the topology of a network, but topological indices are designed in such a way that these may transform the graph into a numeric value. In this work, we study S(ηζΓ) via Zagreb connection indices. Due to their predictive potential for enthalpy, entropy, and acentric factor, these indices gain value in the field of chemical graph theory in a very short time. ηζΓ formed by ζ time repeated process which consists ηζ copies of graph Γ along with η2|V(Γ)|ζηζ−1 edges which used to join these ηζ copies of Γ. The free hand to choose the initial graph Γ for desired network S(ηζΓ) and its relation with chemical networks along with the repute of Zagreb connection indices enhance the worth of this study. These computations are theoretically innovative and aid topological characterization of S(ηζΓ).

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction.

Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluid-based solar energy technologies more cost-effective.

A generalized linear secant bulk modulus based correlation for the prediction of compressed liquid density

AbstractA generalized correlation for the prediction of compressed liquid density has been developed based on the linear secant bulk modulus (LSM) concept. The correlation was developed using a dataset containing over 25 000 density data points from non‐polar and polar components collected from the literature. The LSM correlation fitted the data in this dataset with an overall average absolute deviation of 6 kg/m3. The developed correlation applies to pure components at reduced temperatures up to 0.99 and pressures up to 1 × 106 kPa and requires the following inputs: critical properties, acentric factor, saturated liquid density, and saturation pressure. The developed LSM correlation was tested on predicting the density of 18 components (over 23 000 data points) from assorted chemical families, including alcohols. The overall average absolute deviation was 5 kg/m3. For comparison, the Chang–Zhao correlation predicted the densities in the same Test Dataset with an overall average absolute deviation of 6 kg/m3. However, at pressures above 1 × 105 kPa, the LSM correlation predicted more accurate densities with an overall average absolute deviation of 11 kg/m3, whereas the Chang–Zhao correlation predicted the densities with an overall average absolute deviation of 26 kg/m3.

Accurate, cost-effective strategy for lean gas condensate sampling, characterization, and phase equilibria study

Accurate recognition of the fluid phase behaviour is the most important and first step for the preservation and hydrocarbon reservoir management. Among the various hydrocarbon fluids, the lean gas condensate fluids show complex and unique behaviour. Due to the nature of these fluids, errors in the laboratory constant volume depletion (CVD) experimental data are inevitable. In this study, the significance of using CVD data in the EoS tuning process is debated using six samples of lean gas condensate. According to the negative composition in the CVD data material balance of all samples, just data of the constant composition expansion (CCE) experiment, saturation pressure, and liquid density in the stock tank are used for tuning the equation of state (EoS) as fundamental experimental data. The results of this study demonstrate that without using any CVD experimental data in the EoS tuning process, this data can be generated accurately using tuned EoS with fundamental data. Therefore, there is no need to do CVD experiments for lean gas condensate fluids characterization, which leads to reduces not only the error in the simulation process but also the laboratory costs. Furthermore, the tuning strategy in this study demonstrates that molecular weight of plus fraction and the volume shift parameter of the heaviest pseudo-component are the effective parameters to adjust the EoS. The objective function has reduced from 0.7360 to 0.1538 (79.10% improvement) just by using these two parameters. A constrained tuning strategy was proposed to keep the critical properties and acentric factor expected trends versus carbon number. The results show uniform and swing trends in the case of constrained and unconstrained tuning strategies, respectively. Therefore, using a constrained tuning strategy is essential to obtain a fluid with an authentic thermodynamic background after the tuning process. The proposed analysis method of CVD experimental data suggests that gas compressibility factor and gas composition as the main sources of error, while the experimental parameters of retrograde liquid and cumulative gas production have ignorable errors.