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Volatilization of fluorides is a common phenomenon and an environmental concern in metallurgy, welding, glass, and other industries. In order to understand the effect of Na2O on the volatilization behavior of fluorides in molten slags, thermogravimetric analysis together with molecular dynamic (MD) simulation was employed in the present study. The MD simulation results are consistent with the thermogravimetric analysis (TGA). That is, the increase of Na2O promotes the volatilization of fluoride. Investigation of melt microstructure showed that the diffusion of Na+ plays a key role in the volatilization of fluoride. The main contributing microstructural factors to the diffusion coefficient of Na+ are the extent of polymerization in Si O and Ca F networks and the Na F clustering. Collectively, this research offered a thorough understanding of the microstructure and process of fluorine volatilization in molten slags.

Measurement of CO 2 -brine interfacial tension (IFT) in laboratory is time consuming, expensive and may exhibit various sources of errors. Hence, developing precise models for estimation of the CO 2 -brine IFT sounds to be greatly important. In this work, it has been attempted to study the application of two computer based models, namely radial basis function neural network optimized by particle swarm optimization method (PSO-RBF) and adaptive neuro-fuzzy inference system trained by hybrid method (Hybrid-ANFIS) for estimation of CO 2 -brine IFT. It is worthwhile to state that the database of this work includes 1716 CO 2 -brine IFT data collected from literature. The brine consists of NaCl, KCl, Na 2 SO 4 , MgCl 2 or CaCl 2. The performance of these models was evaluated through various statistical and graphical methods. The results of the above-mentioned models were also measured against four correlations of IFT presented in former literature works. Consequently, it was found that the aforementioned models exhibit more accurate results than empirical correlations for different systems including pure CO 2 -pure water, pure CO 2 -brine and impure CO 2 -water systems. Furthermore, the Hybrid-ANFIS model provides superior and most precise outcomes compared to PSO-RBF model with an overall R 2 and AARD% values of 0.9892 and 1.96%, respectively.

Density of liquids is an important physical property which clear knowledge of its value is essential in various scientific and engineering applications such as design of equipment, liquid metering calculations and many other applications. Hence, developing accurate and reliable predictive tools for prediction of this property seems to be of great importance. In this communication, a model was developed based on radial basis function neural network (RBF-NN) for estimation of density values of 146 ionic liquids at various temperatures. The outputs of the study reveal that the new model is capable to predict density data with an acceptable accuracy. The reliability and precision of the model was validated by using various statistical and graphical approaches. Results of the developed model were put into comparison with literature correlations and it was observed that the model effectively outperforms other correlations and exhibit higher accuracy and reliability.

Heat capacity of ionic liquid (IL) is an important property which is needed in various scientific and engineering problems. Hence, it is required to develop accurate and general models for prediction of this property at various conditions from both academic and industrial perspectives. This work highlights the application of three models namely least square support vector machine optimized by coupled simulated annealing optimization algorithm (CSA-LSSVM), gene expression programming (GEP) and adaptive-neuro fuzzy inference system optimized by hybrid method (Hybrid-ANFIS) for prediction of heat capacity of ILs. An extensive data set including 2940 data points for 56 ILs was used to develop the models. Predictions of the developed models were evaluated by statistical and graphical validation approaches. Moreover, comparison was also made between outcomes of the developed models and predictions of recently developed literature correlations. Results show that the models are accurate and reliable. However, the predictions of CSA-LSSVM model are better than GEP and Hybrid-ANFIS models. In addition, the developed models outperform the literature correlations for prediction of heat capacities of ILs.

This study aims to develop four methods, namely GA-RBF, MLP-NN, CSA-LSSVM and CHPSO-ANFIS methods, to accurately estimate the Watson characterization factor for various hydrocarbons. The obtained results were analyzed graphically and statistically to check the validity and the reliability of the proposed models. Results reveal that the proposed models are robust and accurate for estimation of experimental data. Moreover, the GA-RBF model is superior to three other developed models in prediction of experimental data. Furthermore, the results of the GA-RBF model as the most accurate model in the present work are compared with the results of a previously published model in literature and it is concluded that the GA-RBF model outperforms the literature model thanks to higher values of correlation coefficient and lower values of Root Mean Squared Error (RMSE).

One of the most important properties of ionic liquids (ILs) is their densities. Accurate knowledge of this property is required in engineering and scientific purposes including equipment design, liquid metering calculations, and other applications. As a result, it is important to develop accurate and effective models for prediction of this property. The present work aims to develop a model based on Gene Expression Programming (GEP) technique for prediction of experimental density values of 146 different ionic liquids at different temperatures. A databank containing 602 data gathered from the literature was utilized to develop the model. The input parameters of the model are temperature, molecular weight of IL, normal boiling point temperature of IL, critical pressure of IL, critical volume of IL, and acentric factor of IL. The outcomes of this work indicate that the developed model is capable of estimating target density data with an acceptable accuracy. The effectiveness and accuracy of the model were checked by utilizing different statistical and graphical techniques. Moreover, results of the proposed model are compared with predictions of literature correlations and it is shown that the GEP model is effectively superior to other correlations and exhibits higher accuracy and reliability.

Ionic liquids (ILs) have various desired properties which bring them as useful and applicable compounds in different industrial processes. Heat capacity of ILs is one of their main properties which is required in various engineering and design applications. Hence, developing accurate and general models for prediction of this property is important. In this communication, two accurate and general models based on Radial Basis Function Neural Network (RBF-NN) and Multilayer Perceptron Neural Network (MLP-NN) were developed for estimation of heat capacities of ILs. The input parameters of the models are temperature, molecular weight of IL and several structural related parameters for each IL. The models were developed based on 2940 experimental data for 56 ILs. The reliability and accuracy of predictions of the developed models were examined by using statistical and graphical methods as well as comparing the results of the models with outcomes of recently developed literature correlations. Results show that the developed models are accurate and reliable and are superior to literature correlations for predictions of heat capacity of ILs. The average absolute relative deviation of RBF-NN and MLP-NN models for predicted heat capacity data was 0.83% and 1.04% respectively.

In this study, we developed a novel strategy for in situ encapsulation of hydrophobic drugs such as curcumin into the magnetite-mesoporous silica nanocomposites with a core-shell structure. In the proposed method, a modified reverse microemulsion system was used for silica formation on the surface of curcumin-loaded Fe3O4 nanoparticles (Cur@Fe3O4 NPs) to render mesoporous silica-coated Cur@Fe3O4 NPs (SiO2/Cur@Fe3O4 NPs). The prepared SiO2/Cur@Fe3O4 NPs with a core-shell structure had a spherical shape with a mean particle size less than 100 nm. The heating efficacy of the prepared nanocomposites was examined for application in magnetic hyperthermia therapy by exposing them to different biological safe alternating magnetic fields. The maximum specific absorption rate (SAR) obtained by the prepared sample, was found to be 22.11 WgFe3O4−1at the magnetic field intensity of 28 kA m−1 and frequency of 120 kHz.. Also, the prepared nanocomposites exhibited a pH-responsive drug release behavior. The in vitro drug release studies showed that, only 8.9% of curcumin was releasedwas from SiO2/Cur@Fe3O4 NPs at pH 7.4, while about 40% of drug was released at pH 5.0, after 5 days. Moreover, the in vitro cytotoxicity analysis showed that, by encapsulation of curcumin in the prepared nanocomposites, the cytotoxicity of the drug was significantly increased against breast cancer MCF-7 cells, compared to the free drug, so that, at curcumin concentration of 40 μg mL−1, the viability of MCF-7 cells incubated with free curcumin was 62.0%, whereas by encapsulation of curcumin in SiO2/Cur@Fe3O4 NPs the viability was decreased to 26.7% (Pvalue ≤ 0.005).

The aim of this study is to implement an accurate model for prediction of density of ionic liquids at various temperatures based on an extensive data base gathered from literature published works. The data base includes 602 density data points of 146 ionic liquids at various temperatures. The developed model is based on adaptive neuro-fuzzy inference system (ANFIS) algorithms. The predictions of the developed model were analyzed by various methods including both statistical and graphical approaches. The predictions of the developed model were compared with outcomes of literature correlations for predictions of density of pure liquids. Results show that the developed model accurately predicts the experimental data with an overall R2 and AARD% values of 0.985 and 0.657%, respectively. Moreover, the developed model effectively outperforms literature correlations and presents more accurate and reliable predictions.

Developing accurate and reliable modeling techniques for prediction of different properties of supercritical fluids (SCFs) such as thermal conductivity is of crucial importance due to growing application of these fluids in various fields of industrial and supercritical processes. This study aims to develop a predictive model for CO2 thermal conductivity predictions based on Adaptive Neuro-Fuzzy Inference System (ANFIS). An extensive number of 1042 experimental data were gathered from the literature covering wide ranges of operational temperatures and pressures. The combination of Hybrid and Particle Swarm Optimization (PSO) methods were used to train the model. The predictions of the developed Conjugate ANFIS model were compared with two literature correlations. Results show that the developed model is accurate and effective in prediction of experimental data. In addition, the proposed model is superior to two literature correlations in prediction of actual thermal conductivity data.

Viscosity is one of the most important properties of nanofluids which play an important role in many applications of these fluids. There is no general model or equation for reliable prediction of viscosity of nanofluids in various thermodynamic conditions. In this work an intelligent model named radial basis function neural network (RBF-NN) is proposed to estimate the viscosity of nanofluids with different nanoparticle types and base fluid types. The model was constructed based on 1490 experimental data gathered from literature. The performance of model and its accuracy was investigated by utilizing various graphical and statistical approaches. The predictions of RBF-NN model were also compared with a literature model and several correlations. Results showed that the model provides good degree of accuracy. The overall R 2 , AARD% and RMSE of developed model are 0.99996, 0.2 and 0.0089 respectively. In addition, the developed model successfully outperforms literature model and correlations for viscosity prediction of nanofluids and present more accurate and reliable results. The sensitivity analysis of predictions of developed model also shows that the volume fraction of nanoparticle has the greatest impact on viscosity of nanofluid and temperature has the lowest impact.

It is important to develop accurate and efficient modeling approaches for estimation of interfacial tension between water and hydrocarbon gases because of notable importance of this parameter in various areas of chemical and petroleum engineering. This work presents three predictive models for hydrocarbon gas-water IFT prediction namely radial basis function networks optimized by genetic algorithm (GA-RBF), least square support vector machine optimized by coupled simulated annealing (CSA-LSSVM) and adaptive neuro-fuzzy inference system optimized by combination of PSO and hybrid methods (CHPSO-ANFIS). An extensive number of 1105 experimental data were collected from literature covering wide ranges of operational conditions. The outcomes of the implemented models were compared with four literature correlations. Results indicate that although all the developed computer-based models were effective and accurate in reproducing the target data, the GA-RBF model provides better results compared to CSA-LSSVM and CHPSO-ANFIS models. Moreover, the predictions of the developed models are better than four selected literature correlations.

This study presents the application of an intelligent algorithm in estimation of dew point pressure (DPP) of gas condensate systems. Radial Basis Function (RBF) network in conjunction with Genetic Algorithm (GA) as an optimization algorithm was utilized for this aim. The performance of the proposed GA-RBF model was investigated by statistical and graphical analysis of results. The analysis show that the implemented model is precise and robust in prediction of experimental DPP data. Furthermore, the GA-RBF model was compared with a literature intelligent approach (GEP model) as well as three well-known correlations. The comparison reveals that the GA-RBF model is superior to other model and correlations and successfully improves the predictions.

It is of great importance to know the phase behavior of systems containing water and hydrocarbon in petroleum industry because reservoir fluids are usually saturated with water at pressure and temperature condition of reservoir. There are various correlation/charts and thermodynamic models for prediction of water content of natural gases. The existing approaches usually do not present accurate predictions at low temperature conditions. In addition, since during the development of these correlations, there were not reported experimental data dealing with phase equilibrium in system containing water and hydrocarbons at low temperature conditions, these correlations and models need to be further verified. Present study aims to develop a method based on radial basis function networks optimized by genetic algorithm, namely GA-RBF for estimation of water content of natural gases. The model was proposed based on reported experimental data. Most of utilized experimental data were at low temperature conditions. The performance of the proposed model in prediction of experimental data was compared with literature correlations. Results show that the developed model exhibits accurate results, which are more accurate and superior to literature correlations.

The use of heavy oil is increasing due to the exhaustion of the light crude oil wells. Meanwhile, refineries try to intensify their capacity and process the heaviest fractions of petroleum to turn them into a more valuable form. To design and operate hydro-processes, it is of great importance to acquire information about the hydrogen solubility in heavy oils. However, there are some difficulties in the hydrogen solubility measurements and modeling. Consequently, prediction of hydrogen solubility in heavy oils in an accurate and easy way is essential. In this study, reliable model namely Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for prediction of hydrogen solubility in heavy oil fractions based on experimental data gathered from literature. The accuracy of the proposed model was checked using various statistical parameters and graphical representations. The model exhibit overall R2 and AARD% values of 0.99242 and 3.4 which indicate accuracy and robustness of developed model. Result show that the developed model presents accurate results by reproducing the experimental values with an acceptable error.

Vaporization enthalpy is an essential property in various areas of science and engineering applications such as optimizing the transportation processes, designing the oil and gas production and processing facilities, and heat flux calculations. This work aims to develop an intelligent technique, namely Radial Basis Function (RBF) approach to predict the vaporization enthalpy of petroleum fractions and pure hydrocarbons. The model was coupled with an optimization algorithm namely Genetic Algorithm (GA) to determine the tuning parameters of RBF model. The model performance was evaluated through various graphical and statistical approaches. Results of the developed GA-RBF model were also compared with other literature correlations and intelligent models. It was found that the proposed GA-RBF model exhibits reliable results with acceptable accuracy for the prediction of experimental vaporization enthalpy data. In addition, results show that the model outperforms other literature models and correlations and exhibits better performance for prediction of experimental data.

Surfactants have ability to reduce the interfacial tension between residual oil and water and give high recovery factor. Adsorption of surfactant onto the solid surface makes it less effective in reduction of interfacial tension in enhanced oil recovery (EOR) applications. Adsorption of Trigoonella foenum-graceum (TFG) as a nonionic surfactant on crushed sandstone rock was investigated in this work. Conductivity technique was used to measure amount of surfactant adsorbed on crushed rock. Batch experiments were used to investigate the adsorption of surfactant on solid rock surface. In addition, different adsorption equilibrium and kinetic models were fitted to experimental data and parameters for each model were determined. Among the two and three equilibrium models, the Jovanovic and Brouers-Sotolongo models exhibited better predictions and were most suitable for prediction of surfactant adsorption. The kinetic adsorption of surfactant was best suited to pseudo-second order model. Furthermore, the experimental adsorption data were modeled using a method named GA-RBF. It was concluded that the proposed GA-RBF model is accurate in prediction of kinetic adsorption behavior of surfactant. The results of this study can be useful in surfactant selection in EOR processes especially for chemical flooding schemes.

This study highlights the application of artificial neural network for prediction of density values in a binary mixture of water and ionic liquids at different temperature for different imidazolium-based ionic liquids. Two intelligent models named multilayer perceptron model and radial basis function model were developed and the accuracy of two models was examined by different graphical and statistical methods. The input parameters were temperature, water/ionic liquid ratio, molecular weight, critical temperature, critical pressure and boiling point temperature of ionic liquids and the model output was density. The molecular weight, critical temperature, critical pressure and boiling point temperature were used to discriminate among different ionic liquids. Results showed that the both models are accurate and effective in prediction of density of ionic liquid + water mixture. However, based on graphical and statistical representations, the multilayer perceptron model exhibits better performance and is superior compared to radial basis function model.

Interfacial tension (IFT) is a key parameter which affects the remaining oil in place and fluid distributions in an oil reservoir. Mobilization of trapped oil in a reservoir after primary and secondary recovery schemes, as part of a tertiary oil recovery, requires a true and accurate understanding of interfacial interactions between oil, brine and reservoir rock. This study presents an investigation of temperature, pressure and synthetic formation water salinity effects on interfacial tension of two carbonate oil reservoirs. A pendant drop instrument was used to perform the measurements. In addition, Least Square Support Vector Machine (LS-SVM) in combination with Coupled Simulated Annealing (CSA) was used to model the values of IFT. Results show that the IFT of reservoir A increases with increasing temperature, pressure and salinity of synthetic formation water and the IFT data for reservoir B increases with increasing pressure and salinity of synthetic formation water but decreases with increasing pressure. The results of modeling studies show that the developed model is accurate for prediction of experimental IFT data.

Despite recent efforts to understand the wettability alteration process in limestone rocks during low salinity waterflooding, the findings related to wettability alteration due to changes in salinity and in the presence of a thin water film are still inconclusive. In this work, the effect of ions, temperature and solution pH on the rock and oil surface charges were explored by measuring zeta potential and predicting it using double layer surface complexation modelling (SCM). SCM fitted the trends of the measured zeta potential for both rock-brine and oil-brine interfaces by varying the surface equilibrium constants, particularly the equilibrium constant for the Ca2+ binding site. The SCM was used to predict 32 zeta potential values either measured experimentally or extracted from the literature. Zeta potential and isoelectric potential of rock-brine interface is dependent on brine salinity, sulfate concentrations and solution pH. The isoelectric point (IEP) for rock-brine interface decreased from pH of 8.5 for formation water to 7.8 for low salinity brine. Increasing the sulfate concentration in the seawater brine resulted in shifting the IEP to a lower pH value, indicating that sulfate ion shifts the zeta potential of rock-brine interface to negative over a wide pH range, due to the increased site density of >CaSO4− and the simultaneous reduction in the site densities of both CaOH2+ and CO3− as sulfate concentration increases. Bond product sum (BPS), together with total disjoining pressure calculations were used to predict wettability trends, supported by contact angle measurement, where the total BPS was observed to be the lowest and repulsive disjoining pressure generated at the COBR for LSW brines. This was observed to be significantly influenced by [COOCa+][CO3−] and [COOMg+][CO3−] bond linkages at low pH (below 8) whiles the bond linkages of [COOCa+][CaCO3−] and [COOMg+][CaCO3−] dominated the COBR interface at higher pH (above 8).

In this paper, a novel nanoemulsion-based strategy used to improve the corrosion inhibition effect of plant extracts. A corrosion inhibitor was Zataria multiflora (Shirazi thyme) extract. GC–MS analyzes, FTIR spectroscopy, and dynamic light scattering were used to study the composition, chemical structure, and particle size of Thyme extract and nanoemulsion. Nanoemulsion prepared with anionic surfactant (sodium dodecyl sulfate) had hydrodynamic diameters smaller than 20 nm with narrow particle-size distribution. The inhibition efficiency was calculated using weight loss tests, electrochemical impedance spectroscopy, and dynamic polarization plots. The results revealed that the inhibition efficiency in the presence of 800 ppm of Thyme extract was 76%. Thyme extract nano-emulsification showed that greater efficiency is achieved in the presence of very small quantity of inhibitor. Corrosion efficiency exposed to 40 ppm of nanoemulsion reached 94%. The inhibitory mechanism of the extract was physical adsorption on the metal surface and followed up the Langmuir adsorption isotherm. The amount of free energy adsorbed by the Thyme extract is about −23 kJ·mol−1. This value reached to −30 kJ·mol−1 by nano-emulsification, which indicates better adsorption and physically chemically adsorption orientation. The Yield of Thyme extracts decreased significantly over long periods when nanoemulsion led to less reduction of yield and also provided a long-term inhibitory effect. The thermodynamic parameters were calculated for further investigation, and the performance mechanism based on the nanoemulsion inhibition was proposed.

This paper presents a novel method based on an inverse microemulsion system to synthesize monodisperse magnetic mesoporous silica nanoparticles (MMSN) with core-shell structure. In this method, the water-in-oil microemulsion system was prepared using of cyclohexane containing silica precursor as a continuous oil phase, discrete water droplets containing magnetic seeds (Fe3O4 nanoparticles) and urea as an aqueous phase, and cetyltrimethylammonium bromide (CTAB) and 1-butanol as a surfactant and co-surfactant, respectively. Unlike the traditional reverse microemulsion method, the magnetic seeds used in this system were first covered by a self-organized two-layer surfactant including oleic acid and CTAB as a good host for silica formation. Hence, by removing the CTAB template from the silica structure, a mesoporous silica shell remains on the surface of Fe3O4 nanoparticles. The effects of catalyst types (urea and NaOH), TEOS content, and reaction temperature on the morphology and size of the prepared samples were investigated. It was found that by rising the reaction temperature from 70 to 120 °C, the thickness of the silica layer was increased from 3 to 17 nm. Moreover, a thicker silica coating (26 nm) was obtained by increasing the TEOS content. Also, the performance of the prepared nanocomposite for drug delivery applications was investigated using 5-fluorouracil (5-Fu) as a drug model in a physiological medium. The obtained results showed that the prepared magnetic mesoporous silica nanocomposite has great potential for biomedical applications.

Molecular dynamics (MD) simulations were carried out to investigate the melting transition of iron with an aim to determine the accuracy and reliability of simulated melting points (MP), computing efficiency, and the role played by initial configurations. Two simulation techniques with equilibrium and non-equilibrium heating, and two well-known potentials: Mendelev (EAM) and Etesami (MEAM) were used to investigate a variety of initial configurations containing perfect crystal solid lattice and solid-liquid (S-L) coexistence regions with a number of S-L interfaces and relative S-L proportions (PC, SLSS-2, SLSS-4, SLSS-2 (20%L) and SLSS-2(80%L)). The simulated magnitude of MPs using the non-equilibrium heating method was found to be ~2020 K, which is about 200 K higher than experimental MP of iron (1811 K). Similar values were obtained for both potentials, PC and SLSS-2 configurations; there was a strong evidence for hysteresis effects as well. Simulations with equilibrium heating technique showed that MPs were found to be identical for the wide range of S-L configurations investigated: Mendelev potential (1769 ± 1 K) and Etesami potential (1811 ± 1 K). Corresponding results from the purely solid PC configurations were found to be much higher (1981 ± 1 K and 1951 ± 1 K respectively). Results on other system properties including pair distribution functions, density, local melting and solidification are also reported. These studies have helped identify optimal simulation parameters, potentials and computational approaches to probe the melting region of iron towards applications to problems of industrial importance.