Average Absolute Deviation Percent Research Articles

Surface Tension of Seawater

New measurements and a reference correlation for the surface tension of seawater at atmospheric pressure are presented in this paper. Surface tension of seawater was measured across a salinity range of 20 ⩽ S ⩽ 131 g/kg and a temperature range of 1 ⩽ t ⩽ 92 °C at atmospheric pressure using the Wilhelmy plate method. The uncertainty within measurements varied from 0.18 to 0.37 mN/m with the average uncertainty being 0.22 mN/m. The experimental procedures were validated with tests conducted on ACS reagent grade water and aqueous sodium chloride solutions. Literature data and present measurements were evaluated and a reference correlation was developed expressing surface tension of seawater as a function of temperature and salinity. The average absolute percentage deviation between measurements and the correlation was 0.19% while the maximum deviation was 0.60%.

Development of electrolyte SAFT-HR equation of state for single electrolyte solutions

The explicit version of the mean spherical approximation (MSA) is added to the SAFT-HR equation of state (EoS) to model aqueous alkali halide solutions. The proposed electrolyte equation of state (eEoS) has two parameters per each ion. Two methods are in common use for calculating ion parameters: ion-based and salt-based. In this work, the electrolyte parameters are obtained for 61 single electrolyte solutions using salt-based method. Using this approach, mean ionic activity coefficients of the 61 aqueous electrolyte systems were modeled with overall average absolute relative percent deviation (AAD%) of 3.91. Also, for testing the ability of the model in terms of ionic parameters, six salts (NaCl, NaBr, NaI, KCl, KBr and KI) were studied using ion-based method. The liquid densities, osmotic coefficients and salt mean ionic activity coefficients of 6 aqueous electrolyte solutions were modeled with overall AAD% of 0.68, 2.28 and 0.96, respectively.

Modeling and multi-response optimization of pressure die casting process using response surface methodology

Modeling of pressure die casting process is carried out in the present work. Design of experiment has been utilized to collect the experimental data. The input–output relations have been developed by using response surface methodology. Optimal process parameters have been determined by using desirability function. The process parameters, namely, fast shot velocity, injection pressure, phase changeover point, and holding time, have been considered as the input to the model. Porosity, surface roughness, and hardness are measured and represented as the responses. Based on the experiments carried out, two nonlinear models have been developed using central composite design and Box-Behnken design. These two models have been tested for their statistical adequacy and prediction accuracy through analysis of variance (ANOVA) and some practical test cases, respectively. The performance of central composite design is found to be better than Box-Behnken design (BBD) for the response surface roughness and hardness, whereas the latter is found better than the former for the response porosity. The performance is adjudged based on the average absolute percent deviation in predicting the responses. The absolute percent deviation values for the responses surface roughness, hardness, and porosity are found to be equal to 5.95, 1.29, and 63.94, respectively, in central composite design (CCD). On the other hand, corresponding values in BBD are found to be equal to 14.19, 3.04, and 4.94. Further, an attempt is made to minimize the porosity and surface roughness along with maximization of hardness of die cast component. The objective of multi-response optimization was met with a high desirability value of 0.9490.

Applying a new method to solve diffusivity equation for gas reservoirs

Diffusivity equation is the general partial differential equation used to describe the flow of any fluid flowing in a radial direction in porous media. The main objective of this study is to present a new numerical scheme based on orthogonal collocation (OC) method to solve the diffusivity equation for heterogeneous and homogeneous gas reservoirs. OC is an approximate analytical technique which categorizes in the weighted residuals methods. The advantage and priority of the OC method over exact analytical solution (i.e., Laplace transform) is in the cases that the heterogeneity and variation of reservoir properties such as porosity and permeability with position or pressure could not be neglected; in these cases, the exact analytical solution is very tedious and may be impossible. The diffusivity equation has been solved by both OC and exact analytical solutions. To demonstrate the reliability of the proposed method, the results of OC method have been compared with those achieved using exact analytical solution. Average absolute deviation percent (AAD%) has been used for determining the suitable number of collocation points to give acceptable error and best matching between approximate and analytical results. Sensitivity analysis indicates that increasing the numbers of collocation points result in significant improvement of OC method accuracy and its capability on dynamic pseudo pressure prediction. The minimum AAD% of 0.113 from the exact analytical predictions has been obtained with 25 collocation points. The results indicate that the proposed approximate method with these numbers of collocation points can predict the reservoir pseudo pressure trend with an acceptable accuracy in heterogeneous and homogeneous gas reservoirs.

Thermophysical investigations of exfoliated graphite nanoplatelets and active carbon in binary aqueous environments at different temperatures

Thermophysical investigations in binary liquid mixtures are important for many technological and environmental applications of exfoliated graphite nanostructures. The densities, speeds of sound, and refractive indices for the binary liquid mixture environments of water + exfoliated graphite nanoplatelets, and water + activated carbon have been measured versus composition, at different temperatures, at atmospheric pressure. The thermophysical properties such as the acoustic impedance, isentropic compressibility, specific refraction, space-filling factor, and relaxation strength have been evaluated over the composition range up to 100 kg m−3, based on the experimental data. The absolute average percentage deviations of the calculated thermophysical properties in both systems were computed. The obtained results are discussed and interpreted in terms of molecular interactions and structural changes of these carbon-based nanomaterials.

Corresponding states correlation for temperature dependent surface tension of normal saturated liquids

A new simple correlation based on the principle of corresponding state is proposed to estimate the temperature-dependent surface tension of normal saturated liquids. The correlation is a linear one and strongly stands for 41 saturated normal liquids. The new correlation requires only the triple point temperature, triple point surface tension and critical point temperature as input and is able to represent the experimental surface tension data for these 41 saturated normal liquids with a mean absolute average percent deviation of 1.26% in the temperature regions considered. For most substances, the temperature covers the range from the triple temperature to the one beyond the boiling temperature.

Predicting Nanofiltration Performance during Treatment of Welding Electrode Manufacturing Wastewater, Using Artificial Neural Networks

This paper presents artificial neural network (ANN) predictions for a nanofiltration membrane used to treat wastewater of welding electrode manufacturing in a cross flow set up. The main parameters were time, feed flow rate, and transmembrane pressure (TMP). The experimental data were correlated and analyzed using ANN. ANN’s prediction of the permeate flux, turbidity, total dissolved solids (TDS), hardness for various TMPs, and flow rates are discussed. The effects of the training algorithm, neural network architectures, and transfer function on the ANN performance, as reflected by the percentage average absolute deviation, are discussed. A network with one input layer, 50-100 hidden layers, and one output layer is found to be adequate for mapping input–output relationships and providing a good interpolative tool. A good agreement has been obtained between the ANN predictions and the experimental data with a deviation below 2% for all cases considered.

Pressure and temperature functionality of paraffin-carbon dioxide interfacial tension using genetic programming and dimension analysis (GPDA) method

A precise semi-empirical correlation for the calculation of interfacial tension (IFT) between the carbon dioxide and paraffin group to be used in an enhanced oil recovery process and the chemical industry is introduced. Genetic programming and dimension analysis (GPDA) are combined to create a correlation for the calculation of the equilibrium interfacial tension of the carbon dioxide and paraffin group, based on the explicit functionality of the pressure and temperature. The parameters of the correlation consist of critical temperature, critical pressure, density of paraffin at normal temperature, and diffusion coefficients. The pool of experimental data for developing the correlation consists of 400 randomly gathered data points. To check prediction capability of the correlation 200 data points which are not participated in developing part are used. The average absolute percent deviation (AAD %) for comparing the results with the experimental data is found to be 5%, which demonstrates the accuracy of the presented correlation.

English

This work presents two developments in Antoine vapor pressure correlation: (1) Generalization of the correlation using the principle of corresponding state; (2) Prediction of the Antoine coefficients form molecular weight. The coefficients of the generalized correlation are correlated to molecular weight (r2=0.99) for the group of normal alkanes (C1 to C20). The method is validated using experimental data obtained from reliable literature covering 169 vapor pressure data points covering 11 fluids. The method reproduced the experimental data with a grand absolute average percent deviation (AAPD) of 2.93 while the original Antoine correlation reproduced the same data with 1.90. The method provides a simple procedure for the prediction of Antoine coefficients. On the other hand, the developments, significantly reduces the number of coefficients of C1 to C20 from (3×20=) 60 to (3×3=) 9. This saves the labor work needed for storage and processing of the large number of coefficients values. Key words: Antoine coefficients, normal alkanes, vapor pressure.

The development of van der Waals equation of state for ionic liquids

The main purpose of this paper is to develop van der Waals equation of state for calculating the PρT properties of some ionic liquids. This equation of state is based on the generic van der Waals equation of state in which the parameters of this equation of state are dependent of density and temperature. In order to improve the predictive power of the mentioned equation of state for calculation of density of ionic liquids, modifications on the parameters of this equation of state have been considered. The ionic liquids under consideration in this work include the cation: imidazolium and anions: tetrafluoroborate, thiocyanate, trifluoromethanesulfonate, ethylsulfate, bis (trifluoromethylsulfonyl) imide and hexafluorophosphate. The densities of these compounds were calculated from the modified equation of state at different temperatures and pressures. We will see that the comparison of predicted densities from the modified equation of state and experimental data shows high ability of this equation in predicting PρT properties of ionic liquids. The overall average absolute deviation percent of the calculated density of 15 ionic liquids obtained by this equation of state is 0.09%.

Modeling the thermodynamic behavior of copolymers using equation of state

A simplified procedure with minimum input information for calculating an analytical equation of state for copolymer melts from density and surface tension at the room temperature, as scaling constants, is presented. The second virial coefficients are calculated from a two-parameter corresponding states correlation, which is constructed with two constants as scaling parameters, i.e., the molar density (ρ r) and surface tension at room temperature (γ r). This new correlation has been applied to the Tao–Mason equation of state to calculate the volumetric behavior of copolymer melts including poly(ethylene-co-propylene), poly(ethylene-co-vinyl acetate), poly(ethylene-co-metacrylic acid), poly(ethylene-co-acrylic acid), poly(ethylene-co-vinyl alcohol), poly(styrene-co-acrylonitrile), and poly(acrylonitrile-co-butadiene). The experimental specific volumes were correlated satisfactorily with our procedure and average absolute deviation percent for 7,431 data point is within 0.93 %.

Ground reaction force estimates from ActiGraph GT3X+ hip accelerations.

Simple methods to quantify ground reaction forces (GRFs) outside a laboratory setting are needed to understand daily loading sustained by the body. Here, we present methods to estimate peak vertical GRF (pGRFvert) and peak braking GRF (pGRFbrake) in adults using raw hip activity monitor (AM) acceleration data. The purpose of this study was to develop a statistically based model to estimate pGRFvert and pGRFbrake during walking and running from ActiGraph GT3X+ AM acceleration data. 19 males and 20 females (age 21.2±1.3 years, height 1.73±0.12 m, mass 67.6±11.5 kg) wore an ActiGraph GT3X+ AM over their right hip. Six walking and six running trials (0.95–2.19 and 2.20–4.10 m/s, respectively) were completed. Average of the peak vertical and anterior/posterior AM acceleration (ACCvert and ACCbrake, respectively) and pGRFvert and pGRFbrake during the stance phase of gait were determined. Thirty randomly selected subjects served as the training dataset to develop generalized equations to predict pGRFvert and pGRFbrake. Using a holdout approach, the remaining 9 subjects were used to test the accuracy of the models. Generalized equations to predict pGRFvert and pGRFbrake included ACCvert and ACCbrake, respectively, mass, type of locomotion (walk or run), and type of locomotion acceleration interaction. The average absolute percent differences between actual and predicted pGRFvert and pGRFbrake were 8.3% and 17.8%, respectively, when the models were applied to the test dataset. Repeated measures generalized regression equations were developed to predict pGRFvert and pGRFbrake from ActiGraph GT3X+ AM acceleration for young adults walking and running. These equations provide a means to estimate GRFs without a force plate.

Liquid density of HFE-7000 and HFE-7100 from T = (283 to 363) K at pressures up to 100 MPa

In this paper, liquid densities for HFE-7000 (1-methoxyheptafluoropropane) and HFE-7100 (methoxy-nonafluorobutane) are reported in the temperature range from (283 to 363)K and pressures up to 100MPa. The measurements were conducted with a high pressure vibrating-tube densimeter with combined expanded uncertainties (k=2) of 16mK in temperature, 0.062MPa (p<60MPa) and 0.192MPa (60MPa<p<100MPa) in pressure, and 0.6kg·m−3 in density. The experimental densities were correlated to the modified Tait equation with an absolute average percentage deviation of 0.019% and 0.034% for HFE-7000 and HFE-7100, separately. In addition, the isothermal compressibility and isobaric thermal expansivity have been derived from these density data.

Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network

Natural gas usually contains a large amount of water and is fully saturated during production operations. In natural gas dehydration units' water vapor is removed from natural gas streams to meet sales specifications or other downstream gas processing requirements. Many methods and principles have been developed in the natural gas dehydration process for gaining high level of triethylene glycol (TEG) purity. Among them, reducing the pressure in the reboiler at a constant temperature results in higher glycol purity. The main objective of this communication is the development of an intelligent model based on the well-proven standard feed-forward back-propagation neural network for accurate prediction of TEG purity based on operating conditions of reboiler. Capability of the presented neural-based model in estimating the TEG purity is evaluated by employing several statistical parameters. It was found that the proposed smart technique reproduces the reported data in the literature with average absolute deviation percent being around 0.30%.

Development of a new workflow for pseudo-component generation of reservoir fluid detailed analysis: a gas condensate case study

In this work, a new automatic workflow for accurate optimal pseudo-component generation from gas condensate mixtures with a large number of components is presented. This workflow has a good insight into thermo-physical and critical properties and introduces only a small amount of loss of information and EOS flexibility. In this regard, the fuzzy clustering is used to classify the components in the mixture based on the similarities in the critical properties. The mixing rules are then applied to find group properties. Two different approaches for components association in clustering process are investigated with several numbers of groups. The mathematical validity of the groups is controlled with a proper validity index. The fluid phase behaviour is analysed to investigate the proposed workflow under physical feedback for different numbers of groups. The comparison of equilibrium calculations, for the extended and grouped mixtures shows a close agreement. The average absolute deviation percent (AAD%) from the extended analysis for the liquid dropout percent in constant volume depletion reaches to 0.32 for 20 groups and 0.93 for 14 groups. The AAD% for the gas compressibility factor over the pressure steps is 0.00 for 20 groups and 0.04 for 14 groups.

A Simple Predictive Tool for Estimating the Water Removal Rate in Solid Desiccant Dehydrators

In the natural gas industry, there are several solid desiccants that possess the physical characteristic to adsorb water from natural gas. In processes where cryogenic temperatures are encountered, solid desiccant dehydration is usually preferred over conventional methanol injection to prevent hydrate and ice formation. Capacity and performance data for solid desiccants are usually presented based on a static test. However, under operating conditions (dynamic) the performance data may be significantly different. Therefore, in this work an attempt has been made to develop a simple-to-use mathematical predictive tool for estimating the water equilibrium adsorption behavior for various solid desiccants and in wide range of conditions in contact with natural gas. This mathematical predictive tool shows consistently accurate results across the proposed pressure and temperature ranges for a typical solid desiccant dehydrator wherein the average absolute deviation percent is 1.3%.

The Estimation of Heat-capacity Ratios of Hydrocarbon Gases Using a Simple Predictive Tool

All compression processes of practical importance are normally adiabatic. The term adiabatic does not adequately describe this process, since it only implies no heat transfer. The ideal process also follows a path of constant entropy and should be called isentropic. The heat capacity varies considerably with temperature, since the temperature of the gas increases as it passes from suction to discharge in the compressor. For a multi-component gas, the mole weighted average value of molar heat capacity must be determined at average temperature; however, if only the molecular weight of the gas is known and not its composition, an approximate value for heat-capacity ratios of hydrocarbon gases or isentropic exponent (k) is required as a function of temperature and molecular weight for engineering calculations. The aim of this study is to develop a simple-to-use predictive that is easier than existing approaches and less complicated with fewer computations for predicting the approximate heat-capacity ratios of hydrocarbon gases. Results show that the proposed predictive tool has a very good agreement with the reported data where the average absolute deviation percent is 0.3%. The proposed method is superior owing to its accuracy and clear numerical background, wherein the relevant coefficients can be retuned quickly if new data become available. This proposed simple-to-use approach can be of immense practical value for engineers and scientists to have a quick check on approximate value for heat-capacity ratios of hydrocarbon gases or isentropic exponent (k) for wide range of operating conditions without the necessity of any experimental trials. In particular, practice engineers would find the proposed approach to be user friendly involving transparent calculations with no complex expressions for their applications compression process.

A Mathematical Predictive Tool for Determining the Time to Water Cone Breakthrough in Horizontal Wells

Production of associated water from the oil wells is a common but undesirable occurrence when the reservoir is underlain by a water layer. If the drawdown exerted on the reservoir exceeds a limiting value, the water cone can become unstable and water can start replacing the oil in the production stream. During past decades, several methods have been proposed in the form of mathematical expressions in determining the time to water cone breakthrough in horizontal wells. In the present work, simple-to-use predictive tool, which is easier than existing approaches, less complicated with fewer calculations, is formulated to arrive at an appropriate estimation of the sweep efficiency to investigate the water cone breakthrough in horizontal oil wells. The results can be used in follow-up calculations for calculating time to breakthrough in a bottom-water-drive reservoir. Results show that the proposed predictive tool has a good agreement with the reported data wherein the average absolute deviation percent found to be less than 2.6%. The proposed simple-to-use approach can be of immense practical value for engineers to have a quick check on the time to water breakthrough in a bottom-water-drive reservoir for wide range of operating conditions without the necessity of any field test trials. In particular, petroleum engineers would find the proposed approach to be user friendly involving transparent calculations with no complex expressions for their applications.

High pressure phase equilibrium of wax: A new thermodynamic model

A problem in the thermodynamic modeling of wax precipitation at high pressure is the extension of liquid to solid fugacity ratio of pure component to high pressure, related to the molar volume integral of pure component in the liquid and solid phases. In this work, overcoming to this is achieved by using the Clapeyron equation to make a relationship between the thermophysical properties and changing of the pure component molar volume during the phase change. These thermophysical properties are enthalpies and pressure–temperature (PT) saturation slope in the solid–liquid (SL) and order–disorder (OD) solid regions. In this work, by the available experimental data, dependency of saturation pressure on temperature is obtained for some pure normal alkanes (n-alkanes). Based on these functions, the average values of 4.5 and 3.5 are calculated for the PT slope in two regions of the SL and OD solid, respectively. By these findings, a new approach is constructed for the liquid to solid fugacity ratio of pure component at high pressure. To demonstrate the priority of the new method, 458 data points of the SL phase change temperature of different n-alkanes mixtures at a wide range of carbon number, composition and pressure are employed. Then, three UNIQUAC models are investigated to combine with the new approach to describe the non-ideal behavior of the solid wax phase. The results justify the reliability of the new model using a recent modified version of UNIQUAC with an average absolute deviation percent (AAD%) of 0.8%.

A hybrid GMDH neural network to investigate partition coefficients of Penicillin G Acylase in polymer–salt aqueous two-phase systems

To model partition coefficients of Penicillin G Acylase in polymer–salt aqueous two-phase systems (ATPS), a hybrid GMDH neural network is presented on the basis of original GMDH approach. We performed two major amendments to the structure of original approach in order to enhance the model complexity and power to trace high order of non-linearity. An extensive data set observed by Pazuki et al. is examined by original GMDH approach and hybrid GMDH neural network model. Compared to results generated by original GMDH approach and UNIFAC-FV model, hybrid model stands out with a noticeable superiority in tracking data trend. Average Absolute Deviation percent (AAD%) of proposed hybrid model is 4.04% which indicates a superior accuracy in comparison with those of original GMDH approach and UNIFAC-FV model with AADs% of 6.91% and 5.58%.