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Fabrication of Zr(CUR)/NiCo2S4/CuCo2S4 and Zr(CUR)/CuCo2S4/Ag2S composites as an efficient photocatalyst. Examination of the potential of a spiral-shaped photocatalytic reactor for degradation of the methyl parathion pesticide.

This paper reports an experimental investigation on the viscosity, thermal conductivity and density of water, ethylene glycol, and water–ethylene glycol mixture (60:40 vol%)-based carbon quantum dots (CQDs) nanofluids. Stable nanofluids were prepared by two-step technique at room temperature, and the thermophysical properties of them were measured at various temperatures and volume fractions (0.2–1 vol%). The presence of CQDs enhances the viscosity and thermal conductivity of nanofluids noticeably. The maximum thermal conductivity enhancement reaches up to 8.2%, 25.1%, and 13.3% for the nanofluid containing 1% CQDs at 50 °C in ethylene glycol, water, and water–ethylene glycol mixture (60:40) as base fluids, respectively. In addition, the viscosity of each solution was measured, and the results show that it increases with increasing volume fractions of CQDs nanoparticles and decreased with increasing temperature. Additionally, to correlate viscosity, thermal conductivity, and density of nanofluids, some new empirical equations are derived and compared with experimental data and other theoretical models. Besides, three artificial neural network models are applied to predict the viscosity, thermal conductivity, and density of nanofluids and they are in excellent agreement with experimental data with the AAD = 1.29% and R2 = 0.99994 for viscosity, AAD = 0.85% and R2 = 0.99867 for thermal conductivity, and AAD = 0.01% and R2 = 0.99999 for density of nanofluids.

Zinc oxide nanoparticle loaded on activated carbon (ZnO–NP–AC) in presence and absence of Na and K doping were prepared and fully characterized using different techniques such as SEM and XRD analysis. Then, this material was used for simultaneous ultrasound-assisted removal of Quinoline Yellow (QY) and Crystal Violet (CV) from aqueous solution. QY and CV removal percentage toward various parameters including pH, adsorbent mass, their initial concentration and sonication time were examined by central composite design (CCD) and best operational conditions achieved at 7 and 15 mg L−1 of QY and CV, 0.02 g ZnO–NP–AC, pH of 7 and 4 min sonication. The artificial neural networks (ANN) are applied to predict of dyes adsorption onto present adsorbent and the obtained results have good agreement with experimental data. The absolute average deviations (AADs) of QY and CV dyes adsorption onto ZnO–NP–AC with experimental data are 1.11 and 1.06, and the linear regression between the network outputs and the result and targets were established to be reasonable based on acceptable determination coefficient (R2) of 0.994 and 0.974, respectively. Also, the kinetic models and adsorption equilibrium isotherm investigation revealed the well fit of the experimental data to second-order kinetic model with constant rate value of 0.086 and 0.206 and Langmuir isotherm with maximum monolayer capacity (qMax) of 37.49 and 75.39 mg g−1 at optimum conditions for QY and CV removal onto ZnO–NP–AC.

IRMOF-3 and its modifications, known as IRMOF-3–MWCNT-OH and IRMOF-3–MWCNT-OH–Pd-NPs, are prepared and characterized using Fourier transform infrared spectroscopy, infrared spectroscopy, field emis...

This paper applies the model including back-propagation network (BPN) and principal component analysis (PCA) to estimate the excess molar volume and excess enthalpy of ionic liquid mixtures. The PCA was coupled with the BPN to optimize the BPN’s parameters and improve the accuracy of proposed model. The excess molar volume and excess enthalpy of ionic liquid mixtures are examined as a function of the temperature (T), mole fractions of compounds (x1 and x2), molar mass of pure ionic liquids (M1 and M2) and total molar mass (Mw) using artificial neural network. The obtained results by means of PCA–BPN model for excess molar volume and excess enthalpy have good agreement with the experimental data and absolute average deviations are 1.57 and 0.98%, respectively. Also, high coefficient of determination for excess molar volume and excess enthalpy are R2 = 0.9983 and 0.9999, respectively.

Density measurement was performed on S,N-GQDs nanoparticles dispersed in base fluids such as water, ethylene glycol and 60:40 water/ethylene glycol. Firstly, we synthesized S,N-GQDs nanoparticles by the chemical vapor deposition method. UV-Vis absorption spectrometry, XRD, FT-IR, EDX and SEM analysis were done to elucidate the structure and morphology of the as prepared samples. The measurements were performed with different weight percentages from 0.09 to 0.9%. The temperature range of the measurements ranged from 20° to 60° C. Also, artificial neural network method and improved Tao-Mason equation of state (TM EOS) were used to calculate the density of nanofluids. These models are in good agreement with experimental results.

The present study is aimed to measure the thermophysical properties of graphene quantum dots (GQDs) nanoparticles that dispersed in water, ethylene glycol and water-ethylene glycol mixture (60:40) as base fluids. The presence of GQDs enhance the viscosity and thermal conductivity of nanofluids considerably. The maximum thermal conductivity enhancement reaches up 53%, 21% and 18% for the nanofluid containing 0.5% GQDs at 50 °C in water, ethylene glycol and water- ethylene glycol mixture (60:40) as base fluids, respectively. In addition, the viscosity of each solution was measured, and the results show that it increases with increasing volume fractions of GQDs nanoparticles and decreased with increasing of temperature. Besides, an artificial neural network model presents to predict the thermal conductivity and viscosity of nanofluids. The overall predicted thermal conductivities and viscosities of nanofluids are in excellent agreement with experimental data with the AAD of 1.02% and R2 = 0.99929 for thermal conductivity and AAD = 1.09%, R2 = 0.99915 for viscosity.

Bi surface plasmon resonance (SPR)-promoted BiPO4/BiOI-BiOCl (Bi/BPCI) ternary heterostructure as plasmonic blue-light-driven photocatalyst was prepared by one-pot UV-photoreduction process assisted hydrothermal method with to require any additives. As-prepared photocatalyst was characterized by PL, DRS, FESEM, XRD, EDS and EIS methods and its hierarchical heterostructured exhibits excellent photocatalytic activity toward auramine O (AO) as typical cationic organic dyes under blue light irradiation. The band structures of the Bi/BiPO4, BiOI and BiOCl are determined combined with the flat potential (Vfb) and bandgap energy (Eg) evaluated by UV-Vis diffuse reflectance spectra. The founding illustrate that the as-synthesized composite of the three Bi-based semiconductors facilitates the photosensitized degradation of AO under blue light as well as we described the optical absorption originated from the SPR influence and direct band gap transition in an individual Bi and transportation of photogenerated carriers at the interface of Bi/BPCI. The photocatalytic mechanism is further proposed by identifying the photogenerated reactive species and intermediates by scavenger test. The OH radical generation confirmed that the OH and h+ were the key species in the present system, which lead to the elimination and part mineralization of the AO.

The objective of this article is to investigate nanofluids composed by S, N doped graphene quantum dots (S,N-GQDs) nanoparticles dispersed in water, ethylene glycol and mixture of water and ethylene glycol (60:40) in a concentration up to 0.9% in weight fraction. The S,N-GQDs have been prepared via simply chemical vapor deposition method. The characteristics of the powder are analyzed and discussed. Then the transient hot-wire technique is used to determine the thermal conductivity and the effects of both weight fraction and temperature are evaluated. The maximum thermal conductivity enhancement reaches up 66, 58 and 33% for the nanofluid containing 0.9% S,N GQDs at 50 °C in water, ethylene glycol and water- ethylene glycol mixture (60:40) as base fluids respectively. Also the viscosity of S,N-GQDs nanofluids are measured. The results showed that the viscosity of nanofluids increased with increasing weight percentage of S,N-GQDs nanoparticles and decreased with increasing of temperature. Finally, the experimental values are compared with artificial neural network (ANN) models proposed for thermal conductivity and viscosity estimation. The overall predicted thermal conductivities and viscosities of nanofluids are in excellent agreement with experimental data with AAD = 0.94% and R2 = 0.99912 for thermal conductivity and AAD = 0.95%, R2 = 0.99905 for viscosity.

In this study, the artificial neural network (ANN) and response surface methodology (RSM) based on central composite design (CCD) were applied for modeling and optimization of the simultaneous ultrasound-assisted removal of quinoline yellow (QY) and eosin B (EB). The MWCNT-NH2 and its composites were prepared by sonochemistry method and characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analysis’s. Initial dyes concentrations, adsorbent mass, sonication time and pH contribution on QY and EB removal percentage were investigated by CCD and replication of experiments at conditions suggested by model has results which statistically are close to experimented data. The ultrasound irradiation is associated with raising mass transfer of process so that small amount of the adsorbent (0.025 g) is able to remove high percentage (88.00% and 91.00%) of QY and EB, respectively in short time (6.0 min) at pH = 6. Analysis of experimental data by conventional models is good indication of Langmuir efficiency for fitting and explanation of experimented data. The ANN based on the Levenberg–Marquardt algorithm (LMA) combined of linear transfer function at output layer and tangent sigmoid transfer function at hidden layer with 20 hidden neurons supply best operation conditions for good prediction of adsorption data. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer, while data was divided into training, test and validation sets which contained 70, 15 and 15% of data points respectively. The Average absolute deviation (AAD)% of a collection of 128 data points for MWCNT-NH2 and composites is 0.58%.for EB and 0.55 for YQ.

In this study, an experimental study on the effect of temperature and particles concentration on the dynamic viscosity of MWCNT-OH and their composites with Ag, Au and Pd in water, ethylene glycol and ethylene glycol/water (60:40 vol%) is presented. The experiments were carried out in the solid weight fraction range of 0.0125–0.1 under the temperature range from 10 to 40 °C. The results show that the nanofluids behave as a Newtonian fluid for all solid mass fractions and temperatures considered. In addition, the dynamic viscosity increases with increasing the solid mass fraction and decreases with the temperature rising. Additionally, the performance of the artificial neural network (ANN) based on back propagation training with 20 neurons in hidden layer for predicting of behavior of above mention nanofluids was investigated. The AAD% of a collection of 192 data points for all nanofluids using the ANN at various temperatures, solid mass fractions, viscosity of based fluids, molar mass of based fluids and diameter of nanoparticles is 0.98%.

Highly crystalline water stable monolithic HKUST-1 MOF by a straightforward conversion of Cu(OH)2-based monoliths was prepared and characterized via FE-SEM, XRD and EDS analysis. The prepared water stable monolithic HKUST-1 MOF as a new adsorbent was applied for the removal of eosin yellow (EY) and malachite green (MG) dyes from a binary aqueous solution. A central composite design as one type of experimental design method was used to investigate the main effect and interaction effect of experimental variables such as the initial dye concentration, monolithic HKUST-1 MOF mass, pH and sonication time while the dye removal percentage (R%) was considered as the response. A maximum removal efficiency of 83.4 and 94.9% for MG and EY pollutants, respectively, was obtained at the optimized settings at: 8.0 mg L−1 of EY, 8.0 mg L−1 of MG, 0.015 g of monolithic HKUST-1 MOF mass, 3.0 min sonication time and pH 6.0. A flexible mathematic relationship between the operational factor and responses was modelled by an artificial neural network (ANN). The model predicted results that show a good agreement with the experimental data. Absolute average deviations (AADs) of 1.07% and 0.49%, R2 values of 0.9974 and 0.9963 and mean square error (MSE) of 1.75 × 10−5 and 7.43 × 10−5 were obtained for the MG and EY models, respectively. Isotherm and kinetic investigations revealed that a pseudo second order and Langmuir isotherm model have the best behavior for both dye adsorptions onto monolithic HKUST-1 MOF.

This work presents mixed-anion blends (e.g., oxynitrides and oxysulfides) as prospective representatives for visible-light sono-photocatalyst. While generally tolerate oxidative degradation of photogenerated holes causes to medium durability and stability. Hence, an exceptionally stable, mixed-anion oxyfluoride photocatalyst was designed and prepared based on CuFe2F8(H2O)2, decorated by gold nanoparticles (Au-NPs) as a sink for photogenerated electron and sacrificial electron mediator. Electrochemical analysis revealed Au-NPs can appearance Schottky barriers with CuFe2F8(H2O)2, injection of electrons into CuFe2F8(H2O)2, cause to electron capturing at profound surface pitfall states of CuFe2F8(H2O)2. Au-NPs sensitized CuFe2F8(H2O)2 were applied as a robust photocatalyst in pairs with ultrasound (US) waves for Rhodamine B (RhB) dye degradation. Quenching investigations indicated that •OH and •O2− were productive species for RhB degradation on Au/CuFeF8(H2O)2 which the kinetics of it was the first order and fitted to the Langmuir-Hinshelwood model. This study displays the first type of sonophotocatalytic decomposition (SPD) of an organic dye driven by visible-light excitation of a metal-doped oxyfluoride with Schottky barrier and surface plasmonic resonance (SPR).

In this research, the volumetric properties of sixteen lubricant/refrigerant mixtures are predicted using the developed statistical mechanical equation of state at a broad range of temperatures, pressures and mole fractions. The equation of state have been examined using corresponding states correlation based on just one input parameter (density at room temperature) as scaling constants. Besides, the artificial neural network (ANN) based on back propagation training with 19 neurons in hidden layer was tested to predict the behavior of binary mixtures of lubricant/refrigerant. The AADs% of a collection of 3961 data points for all binary mixtures using the EOS and the ANN at various temperatures and mole fractions are 0.92% and 0.34%, respectively. Furthermore, the excess molar volume of all binary mixtures calculated from obtained densities of ANN, and the results shown these properties have good harmony with literature.

Multiwalled carbon nanotubes (MWCNTs) functionalized with (N-3-phenylallaylidene)-N′-trimethoxysilylpropyl-ethane-1,2-diamine (PATMSPEDA) were prepared and subsequently the MWCNTs-PATMSPEDA-Pd-NPs nanohybrids with high shape selectivity and high specific surface area have been prepared by single step synthesis of Pd nanoparticles (Pd-NPs) loaded on MWCNTs-PATMSPEDA. These materials were characterized by different techniques such as XRD, SEM, FT-IR and TGA-DTA and subsequently were used for the simultaneous ultrasound-assisted removal of erythrosine (ER) and sunset yellow (SY) dyes from aqueous solution. The influences of important variables such as initial dyes concentration, adsorbent dosage, pH and sonication time on the efficiency of ultrasound-assisted removal process were investigated by central composite design (CCD) and the optimization conditions were obtained 4.39 and 4.33 mg L−1 of ER concentration, 11.76 and 11.90 mg L−1 of SY concentration, 0.02 and 0.019 g adsorbent mass, 7.0 and 7.0 for pH value and 3.88 and 3.75 min sonication time for MWCNTs-PATMSPEDA and MWCNTs-PATMSPEDA-Pd-NPs, respectively. The artificial neural network (ANN) model was used for constructing an empirical model to predict the understudy dyes removal behavior onto these adsorbents and the obtained results have good agreement with experimental data. The absolute average deviations (AADs) of ER and SY dyes adsorption by MWCNTs-PATMSPEDA are 0.62% and 0.58%, and the determination coefficient (R2) values are 0.971 and 0.978, respectively. Also, the AADs of ER and SY dyes adsorption by MWCNTs-PATMSPEDA-Pd-NPs are 0.48% and 0.34%, and the R2 values are 0.971 and 0.972, respectively. Finally, it was found that the equilibrium isotherm of adsorption process follows the Langmuir isotherm. From the Langmuir isotherm, maximum monolayer capacity (qmax) were obtained to 30.58 and 38.76 mg g−1 and 55.25 and 59.17 mg g−1 at optimum conditions for ER and SY removal onto MWCNTs-PATMSPEDA and MWCNTs-PATMSPEDA-Pd-NPs, respectively.

In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network (ANN) with principle component analysis (PCA) based on back propagation training with 28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.

Removal of noxious Sunset Yellow CFC (SY) from the liquid phase using Cu doped ZnS nanoparticles loaded on activated carbon (Cu: ZnS-NPs-AC) was well examined and investigated. The adsorbent was characterized using various analytical techniques such as Field emission Scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction. The very good fitting of developed equation for prediction of experimental data is judged with their acceptable R2 adjusted correlation coefficients (R2-Adj: 0.9941). The predicted model show maximum SY removal conditions adjusted at 6.0, 0.025 g, 20 mg L− 1 and 3 min) correspond to pH, adsorbent dosage, SY concentration and sonication time, respectively. From the results obtained it was found that the removal of Sunset Yellow CFC dye show positive relation with adsorbent i.e. with increase in adsorbent dose, removal efficiency increases and negative trend with the initial SY concentration i.e. with decrease in initial SY concentration, removal efficiency decreases. Additionally increase in SY removal on increasing the sonication time lead to simultaneous increase in SY diffusion coefficient. The adsorption equilibrium and kinetic data was found to be in well fitted and in good agreement with Langmuir isotherm and pseudo-second-order reaction kinetic model. The results from the studies of sequential adsorption–desorption cycles showed that Cu: ZnS-NPs-AC adsorbent held good desorption and reusability.

This paper applies the model including back-propagation network (BPN) and principal component analysis (PCA) to estimate the effective viscosity of carbon nanotubes suspension. The effective viscosities of multiwall carbon nanotubes suspension are examined as a function of the temperature, nanoparticle volume fraction, effective length of nanoparticle and the viscosity of base fluids using artificial neural network. The obtained results by BPN–PCA model have good agreement with the experimental data.

This paper applies a model including back-propagation network (BPN) and principal component analysis (PCA) to compute the effective thermal conductivities of nanofluids such as Al2O3/(60:40)EG:H2O, Al2O3/W, Al2O3/(20:80)EG:W, Al2O3/(50:50)EG:W, ZnO/(60:40) EG:W, CuO/(60:40)EG:W, CuO/W, CuO/(50:50)EG:W, TiO2/W, TiO2/(20:80)EG:W, Fe3O4/(20:80) EG:W, Fe3O4/(60:40) EG:W, Fe3O4/(40:60) EG:W and Fe3O4/W, as a function of the temperature, thermal conductivity of nano particle, volume fraction of nanoparticle, diameter of nanoparticle and the thermal conductivity of base fluids. The obtained results by BPN–PCA model have good agreement with the experimental data with absolute average deviation and high correlation coefficients 1.47 % and 0.9942, respectively.

the thermal conductivity of OH functionalized multiwalled carbon nanotube and its composites with Ag, Au and Pd in water, ethylene glycol and ethylene glycol-water (60:40 vol%) mixtures is studied using a combined experimental and theoretical approach. The experimental study was carried out in the 0.01 to 0.20 solid mass fraction range from 10 °C to 60 °C. The results show a large effect of the considered solvent on the thermal conductivity as well as increasing values with solid mass fraction and temperature. Artificial neural network (ANN) methods were successfully applied for the prediction of thermal conductivity of the considered nanofluids. The nanoscopic structuring of the studied nanofluids was analyzed by using molecular dynamics simulations, with particular attention to the nanotubes' solvation as well as the changes in the base fluids by the presence of the nanomaterials.

The behavior of 2-hydroxyethylammonium acetate ionic liquid under high-pressure was studied using a combined experimental and molecular simulation approach. PVT behavior was experimentally inferred by density measurements using high-pressure vibrating tube densimetry in the 298.15–328.15 K and 0.1–40 MPa temperature and pressure ranges. Experimental data were correlated by the use of a Tait-type equation, from which the derived mechanical coefficients were calculated. Likewise, a molecular dynamics study in the same pressure – temperature range was carried out to infer the changes in fluid’s properties upon compression. The evolution of hydrogen bonding, ions packing and additional nanoscopic features were studied by simulation results, thus allowing to analyze the relationships between micro and macroscopic features.

The aim of the present study is experimental design optimization applied to the removal of malachite green (MG) from aqueous solution by ultrasound-assisted removal onto zinc sulfide nanoparticles loaded on activated carbon (ZnS-NP-AC). The nanomaterial was characterized using different techniques such as FESEM, BET, XRD and UV-Vis measurements. The effects of variables such as pH, initial dye concentration, adsorbent dosage (g) and sonication time on MG removal were studied using central composite design (CCD) and the optimum experimental conditions were found with a desirability function (DF) combined with response surface methodology (RSM). Fitting the experimental equilibrium data to various isotherm models showed the suitability and applicability of the Langmuir model and the second-order equation model controls the kinetics of the adsorption process. A small amount of proposed adsorbent (0.025 g) is applicable for successful removal of 22 mg L−1 MG (>99%) in a short time (5.0 min).

A simplified procedure with minimum input information for predicting of the densities of copolymer melts is presented. This new correlation has been applied to the Tao-Mason (TM) equation of state to predict the volumetric behavior of copolymer melts including poly(ethylene-co-propylene) (PEP), poly(ethylene-co-vinyl acetate) (PEVA), poly(ethylene-co-metacrylic acid) (PEMA), poly(ethylene-co-acrylic acid) (PEAA), poly(ethylene-co-vinyl alcohol) (PEVOH), poly(styrene-co-acrylonitrile) (PSAN), and poly(acrylonitrile-co-butadiene) (PANB). Also another model such as an artificial neural network (ANN) based on backpropagation training with 13 neurons was used. The obtained results by TM and ANN models had good agreement with the experimental data with absolute average deviations of 1.34 and 0.49 %, respectively.

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 %.

A statistical mechanical equation of state is developed to predict the volumetric properties of pure and mixture liquid alkali metals at different temperatures, pressures and compositions. The temperature dependent parameters of the equation of state have been calculated using corresponding states correlation based on the normal boiling point parameters as scaling constants. It is shown that the knowledge of just normal boiling point and its liquid density are sufficient to estimate the thermodynamic properties of pure and mixture liquid alkali metals in different conditions. Besides, the performance of artificial neural network (ANN) based on back propagation training with 10 neurons in hidden layer for prediction of behavior of presented systems was investigated. A collection of 512 data points for above systems in different temperatures and pressures was used. The Tao–Mason equation of state (TM EOS) and ANN model results have good agreement with the experimental data with absolute average deviations of 0.74% and 0.299%, respectively.

Vapor-liquid equilibria (VLE) of polymer/solvent solutions is a topic of importance because of several areas of applications, including the designing of process equipment. Theoretical and thermodynamic models are reported in the literature for the estimation of VLE. However, up until now, the simultaneous representation of VLE and pressure-volume-temperature data is not satisfactory enough with respect to experimental accuracies. New models are therefore highly required. In the present study, a hybrid model including artificial neural networks (ANN) and genetic algorithm (GA) were applied to estimating the VLE data of seven binary polystyrene (PS)/solvents. The ranges of variables used were 283.15–343.15 K and 0.105–7.46 MPa. The VLE data of these systems were taken from the literature. The net was trained, validated and tested with randomly 65% (108 data points), 10% (17 data points) and the 25% (42 data points), respectively. The mean deviations from the experimental data were determined for the model. The ability of the proposed model was compared with cubic equations of state (CEOS). It was observed that the data found by the ANN model was in excellent agreement with the experimental data, while the CEOS models showed more deviations, particularly at low pressures. In fact, the ANN model can be treated as a powerful technique for VLE data prediction in a fast and reliable way compared with the conventional thermodynamic models.

In our previous paper we extended the Tao and Mason equation of state (TM EOS) to refrigerant fluids, using the speed of sound data. This is a continuation for evaluating TM EOS in predicting PVT properties of heavy n-alkanes. Liquid density of long-chain n-alkane systems from C9 to C20 have been calculated using an analytical equation of state based on the statistical-mechanical perturbation theory. The second virial coefficients of these n-alkanes are scarce and there is no accurate potential energy function for their theoretical calculation. In this work the second virial coefficients are calculated using a corresponding state correlation based on surface tension and liquid density at the freezing point. The deviation of calculated densities of these alkanes is within 0.5% from experimental data. The densities of n-alkanes obtained from the TM EOS are compared with those calculated from Ihm-Song-Mason equation of state and the corresponding-states liquid densities (COSTALD). Our results are in favor of the preference of the TM EOS over other two equations of state.

In our previous work (Int. J. Thermal Sci. 50 (2011) 44–52), we developed diffusional neural network scheme to model thermal conductivity of several nanofluids. In this paper, we have extended the neural networks method to predict the relative viscosity of nanofluids including nanoparticles CuO suspended in propylene glycol + water, CuO suspended in ethylene glycol + water,SiO2 suspended in water, SiO2 suspended in ethanol, Al2O3suspended in water, and TiO2 suspended in water. The results obtained have been compared with other theoretical models as well as experimental values. The predicted relative viscosities of suspensions using diffusional neural networks (DNN) are in accordance with the literature values.

In our previous paper, we extended the Tao and Mason equation of state (TM EOS) to pure ionic liquids. Here we apply TM EOS based on statistical–mechanical perturbation theory to binary mixtures of ionic liquids. Three temperature-dependent quantities are needed to use the equation of state: the second virial coefficient, B2, effective van der Waals co-volume, b, and a scaling factor, α. The second virial coefficients are calculated from a correlation that uses the normal boiling temperature and normal boiling density. α and b can also be calculated from the second virial coefficient by scaling. In this procedure, the number of input parameters, for calculation of B2, α, and b reduced from 5 (i.e., critical temperature, critical pressure, acetric factor, Boyle temperature TB, and the Boyle volume υB) to 2 (i.e., Tbp and ρbp). At close inspection of the deviations given in this work, the TM EOS predicts the densities with a mean AAD of 1.69%. The density of selected system obtained from the TM EOS has been compared with those calculated from perturbed-hard-sphere equation of state. Our results are in favor of the preference of the TM EOS over another equation of state. The overall average absolute deviation for 428 data points that calculated by perturbed-hard-sphere equation of state is 2.60%.

An accurate and efficient artifcial neural network based on mega-trend diffusion algorithm (MD) is developed for predicting CO2 diffusivity in biodegradable polymers. This mega-trend diffusion neural network (MD-NN) model could predict diffusivities close to experimental data. Furthermore, the comparison of MD-NN model with free-volume model and conventional back-propagation model demonstrates that proposed model is a powerful method and has better precision.

In our previous paper, we extended the Tao and Mason equation of state (TM EOS) to refrigerant fluids, using the speed of sound data. Here, we predict the equation of state for ionic liquids (ILs). The considered ILs are [Bmim][PF6], [C2mim][NtF2], [C3mim][NtF2], [C6mim][NtF2], [C7mim][NtF2], [C2mim][EtOSO3], [Bmim][MeSO4], [Bmim][OcSO4], and [C4mim][dca]. The equation of state consists of three temperature-dependent parameters: the second virial coefficient, a constant for scaling the softness of repulsive force, and an effective hard-sphere diameter equivalent to the van der Waals co-volume. The second virial coefficients of ILs are scare and there is no accurate potential energy function to allow their theoretical calculation. In this work, the second virial coefficient have been calculated using corresponding states correlation based on temperature and density at normal boiling point. The other two parameters of the equation of state can be calculated using a scaling rule. Analysis of our predicted results shows that the Tao–Mason equation of state is capable of accurately predicting the density of ILs at any temperature and pressure. The overall average absolute deviation densities for 1,633 data points are 2.05%. Also, the density of ILs obtained from the TM EOS has been compared with those calculated from vdW–CS–β and Peng–Robinson (PR) equation of state. Our results are in favor of the preference of the TM EOS over the two other equations of state. The overall average absolute deviation for 1,633 data points calculated by vdW–CS–β and PR equation of state are 6.63% and 12.19%, respectively.

A statistical-mechanical-based equation of state (EOS) for pure substances, the Tao-Mason equation of state, is successfully extended to prediction of the ( p-v-T ) properties of fourteen natural gas mixtures at temperatures from 225 K to 483 K and pressures up to 60.5 MPa. This work shows that the Tao-Mason equation of state for multicomponent natural gas is predictable with minimal input information, namely critical temperature, critical pressure, and the Pitzer acentric factor. The calculated results agree well with the experimental data. From a total of 963 data of density and 330 data of compressibility factor for natural gases examined in this work, the average absolute deviations (AAD) are 1.704% and 1.344%, respectively. The present EOS is further assessed through the comparisons with Peng-Robinson (PR) equation of state. For the all of mixtures Tao-Mason (TM) EOS outperforms the PR EOS.

In a previous paper, we extended the Tao and Mason equation of state (TM EOS) to refrigerant fluids using speed of sound data. Herein, we employ the TM EOS to predict volumetric properties of multi-component mixtures of liquefied natural gas (LNG). The second virial coefficient, B2(T), necessary for the mixture version of the TM EOS, in the absence of sufficient experimental data on B2(T), were calculated from the corresponding state correlation. Analysis of our predicted results shows that the TM EOS is capable of accurately predicting the densities of multi-component liquefied natural gas mixtures over wide range of temperatures and pressures. The overall average absolute deviation (AAD) of the calculated densities from the literature ones for 222 data points was found to be 2.37%. Furthermore, the densities of LNG mixtures obtained from the TM EOS have been compared with those calculated from Peng–Robinson (PR) and Ihm–Song–Mason (ISM) equations of state. Generally, our results show that the TM EOS is favorable over the two other equations of state. The overall average absolute deviation for the 222 data points calculated by ISM and PR equations of state were of the order of 2.90% and 4.85%, respectively.

In the present investigation, neural network method is employed to estimate thermal conductivity of nanofluids consisting of multi-walled carbon nanotubes (MWCNTs) suspended in oil (α-olfin), decene (DE), distilled water (DW), ethylene glycol (EG) and also single-walled carbon nanotubes (SWCNTs) in epoxy and poly methylmethacrylate (PMMA). The results obtained have been compared with other theoretical models as well as experimental values. The predicted thermal conductivities are in good agreement with the literature values.

It is the purpose of this paper to extract unlike intermolecular potential energies of five carbon dioxide-based binary gas mixtures including CO2–He, CO2–Ne, CO2–Ar, CO2–Kr, and CO2–Xe from viscosity data and compare the calculated potentials with other models potential energy reported in literature. Then, dilute transport properties consisting of viscosity, diffusion coefficient, thermal diffusion factor, and thermal conductivity of aforementioned mixtures are calculated from the calculated potential energies and compared with literature data. Rather accurate correlations for the viscosity coefficient of afore-cited mixtures embracing the temperature range 200 K

Tao and Mason (J. Chem. Phys. 1994, 100, 9075−9084) developed a statistical-mechanical-based equation of state (EOS) for pure substances. In the present study, we have successfully extended this EOS to fluid mixtures, selecting refrigerant fluid mixtures as the test systems. The considered refrigerant mixtures are R32 + R125, R32 + R134a, R134a + R152a, R125 + R143a, R125 + R134a, R32 + R227ea, R134a + R290, and R22 + R152a. The second virial coefficient, B(T), necessary for the mixture version of the Tao−Mason (TM) EOS, was determined using a two-parameter corresponding-states correlation obtained from the analysis of the speed of sound data and two constants: the enthalpy of vaporization ΔHvap and the molar density ρnb, both at the normal boiling point. Other temperature-dependent quantities, including the correction factor α(T) and van der Waals covolume b(T), were obtained from the Lennard-Jones (12−6) model potential. The cross parameters B12(T), α12(T), and b12(T), required by the EOS for mixtures, ...

In this paper, a back propagation artificial neural network (BP-ANN) model is presented for the simultaneous estimation of vapour liquid equilibria (VLE) of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature (222.04–343.23K) and pressure (0.105 to 7.46MPa). BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054–0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave (RKS) equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation (the root mean square error in the range of 0.115–0.1546).

In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R2 and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.

Impact operational parameters such as initial dyes concentration, adsorbent mass, pH and sonication time on the efficiency of ultrasound-assisted adsorption of sunset yellow (SY) and azur (II) (AZ) onto MWCNT and MWCNT-Pd-NPs was studied by central composite design (CCD) and back propagation artificial neural network (ANN) models and resulted for tow adsorbent were compared to each other. Optimize conditions from CCD in presence of MWCNT as adsorbent was found to be 6.01 and 9.98 mg L −1 AZ and SY concentration, 0.02 of adsorbent mass, pH 7.0 and 3.8 min sonication time, respectively, while in the presence of MWCNT-Pd-NPs optimize value were obtained at 10.0 and 8.0 mg L − 1 AZ and SY concentration, 0.018 of adsorbent mass and pH 5.0 and 3.5 min sonication time, respectively. In these conditions and at desirability value of 0.99, the maximum adsorption efficiency for AZ onto MWCNT and MWCNT-Pd-NPs are 82.97 and 94.95, respectively. Also, the maximum adsorption efficiency for SY onto MWCNT and MWCNT-Pd-NPs are 73.13 and 85.55, respectively. This values show that MWCNT-Pd-NPs in comparison with MWCNT at the lower contact time and adsorbent mass was able to remove greater amounts of under study dyes. Based on the ANN model the absolute average deviations (AADs) of AZ and SY dyes adsorption by MWCNT from experimental data are 0.27% and 0.43%, and the R 2 values are 0.911 and 0.954, respectively. Also, the AADs of AZ and SY dyes adsorption by MWCNTs-Pd-NPs are 0.44% and 0.65%, and the R 2 values are 0.925% and 0.927%, respectively. This values show that ANN for MWCNT-Pd-NPs in comparison with MWCNT was a more powerful tool for building up to construct an empirical model to predict under study dyes adsorption behavior. In addition, the obtained results have good agreement with experimental data for MWCNT-Pd-NPs in comparison with MWCNT. Finally, the study of equilibrium isotherm and kinetic models of adsorption process are shown that the Langmuir isotherm model and pseudo second order kinetic model were the best models for both adsorbent.