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Natural deep eutectic solvents (NADESs) as green solvents have substantial potential for enhancing the solubility of pharmaceuticals in aqueous solutions. In this work, the solubility of two B vitamins (nicotinic acid and 4aminobenzoic acid) in aqueous solutions of two NADESs was measured at temperatures between 298.15 K and 313.15 K and different concentrations of NADESs. The studied NADESs were prepared by mixing dried choline chloride with urea or malonic acid with molar ratios of 1:2 and 1:1, respectively. Based on the results, chloride+malonic acid was more effective on the solubility of the pharmaceuticals in aqueous solutions. Furthermore, the DESs increased the aqueous solubility of 4aminobenzoic acid more than that of nicotinic acid. The experimental solubility data were modeled with the van Laar activity model as well as the modified Apelblat equation to correlate the solubility of the pharmaceuticals to the temperature and NADESs concentration. The molar enthalpy of dissolution (Δhd) and the molar enthalpy of mixing (Δhmix) of the pharmaceuticals in the aqueous solutions of the NADESs were calculated. These parameters showed that all the dissolution processes were endothermic, while the mixing process as a part of the dissolution process was exothermic.

Assessment of deep eutectic solvents (DESs) as a new generation of green solvent for different applications requires sufficient knowledge about their physical, chemical, and thermodynamic propertie...

Knowledge of solid–liquid equilibria (SLE) and vapor–liquid equilibria (VLE) for mixtures containing deep eutectic solvents (DESs) is of paramount importance. We have conducted experiments to obtain additional data for SLE systems and then used the e-NRTL model to predict the VLE behavior in mixtures of DESs comprised of choline chloride (ChCl), sorbitol (S), and water (W). The systems under investigation include three 2-component systems (S/W, ChCl/W, ChCl/S), and one 3-component system (DES/W) where the DES is obtained by mixing ChCl and S at a molar ratio of 1. Using the e-NRTL activity coefficient model, the adjusted binary interaction parameters of the proposed model are determined solely from the experimental SLE data obtained from this work and also those in the literature. There is an excellent agreement between the SLE solubility data and the e-NRTL results. The tuned model is then used to predict the bubble pressure in three aqueous systems (S/W, ChCl/W, and DES/W) with an overall AARD value of 2.21%. The excellent agreement between the experimental and predicted VLE data demonstrates the viability of the proposed thermodynamic modelling framework to represent the equilibrium systems containing DESs. The outcome from our study can be used to relate the SLE and VLE data without the need to perform experiments for both cases for the given system of interest. Compared to the VLE experiments, the SLE tests are much simpler and safer because there is little compressibility and the effect of pressure on solubility is minimal which makes it possible to conduct SLE tests at room conditions (at least for liquids with negligible non-ideality). Therefore, conducting the high-pressure VLE calculations without the need to perform VLE experiments and through SLE experimental data alone will be of practical significance. Publishers version

Understanding gas hydrates potential for gas storage purposes has fascinated scientists for decades. Structure H clathrate hydrate through its capacity and ability of sH hydrate formers in decreasing the gas hydrates formation pressures, introduces itself as an appropriate alternative for gas storage and career aims. Hence, this communication is aimed at estimating the capacity and phase equilibria of methane, ethane, propane, carbon dioxide, hydrogen sulfide, nitrogen, hydrogen, argon, krypton, xenon, and methyl fluoride sH hydrates in the presence of different sH hydrate formers at below and above the freezing point of water. By developing the artificial neural network (ANN) model and extending thermodynamic-based framework these targets have been undertaken. The ANN approach correlates the hydrate dissociation conditions as a function of critical properties of hydrate formers. The thermodynamic approach employs a two-step hydrate formation mechanism using the modified version of Cubic-Plus-Association (CPA) equation of state associated with modified Huron-Vidal mixing rule (MHVI) accompanied by UNIFAC model. Findings reveal that the most accurate ANN model is obtained through tan-sigmoid transfer function with 12 neurons number. The average absolute relative deviation percent (AARD%) and correlation coefficient (R2) for the ANN approach are determined 0.09% and 0.9981 while the thermodynamic framework provides 0.07% and 0.9987, respectively. The capacity storage evaluation results indicate that the minimum and maximum storage volume percent for unit cell of various sH clathrate hydrate systems are 42 and 58, respectively.

The sorption of methylene blue from aqueous solutions by the microalgae Scenedesmus was studied in batch and continuous systems. In the first part of the study, the sorption capacity of methylene blue on the Scenedesmus was obtained at various temperatures (22 °C, 30 °C and 37 °C) and pH values (5, 7 and 9). The maximum uptake was obtained to be 87.69 ± 3.22 (mg/g) at a pH of 9 and a temperature of 30 °C. The characterization technique of scanning electron microscopy (SEM) showed a high potential of the biosorbent. The Langmuir adsorption isotherm is employed to describe the equilibrium data with the root mean square error of 2.22 (mg/g) and R2 of 0.978. In the second part of the study, the breakthrough curves were obtained for different conditions in fixed bed columns with different lengths and diameters. To predict and describe the dynamic behavior of the system under different conditions, a predictive model is developed. Although no kinetic experimental data were used in the model development, the model predicts the experimental breakthrough curves in different conditions accurately. The present model can be employed to design a packed bed column for biosorption of methylene blue, as well as the optimization and control of this system.

The critical point and vapor pressure of pharmaceuticals are required in the calculation of their solubility in supercritical fluids by using an equation of state. The experimental critical point of many pharmaceuticals are not accessible due to their temperature sensitivity, also the experimental vapor pressure of many pharmaceuticals are not available at temperatures at which the supercritical extraction is usually accomplished. In this study, four group contribution methods are used to estimate the critical point and acentric factor of eight pharmaceuticals. The estimated properties are evaluated in predicting the vapor pressure and enthalpy of sublimation of the pharmaceuticals by using the Peng Robinson equation of state. The most accurate critical points are employed to calculate the solubility of the pharmaceuticals in the supercritical CO2 by using the Peng Robinson equation combined with two mixing rules. The AARD of 7.28% indicates the good performance of the Wong Sander mixing rule.

The solubility of methylene blue in methanol, ethanol, 2-propanol, acetone, ethyl acetate, and water was measured at 303.15 K and 0.1 MPa. The solubility of methylene blue in ethyl acetate was very low while the solubility in methanol and 2-propanol was very high and not precisely measurable. In addition, the effect of temperature on the solubility of methylene blue in ethanol, acetone, and water was measured in the temperature range of 278.15 K–318.15 K. The solubility data was modeled with the electrolyte activity model of symmetric e-NRTL. The overall AARD of the model was obtained to be 2.32%. According to the results, there is a good agreement between the experimental solubility data and the solubility estimated with the help of the electrolyte activity model. Furthermore, the solubility data were used to estimate the enthalpy of fusion and melting point of methylene blue, as well as the enthalpy of dissolution and heat capacity for the dissolution of methylene blue in ethanol, acetone, and water.

Aqueous two-phase systems are useful for separating biological materials. In this study, the phase diagram of the ternary system of choline chloride, K2HPO4 and water was determined at 298.15 K and 0.1 MPa. In this regard, the binodal curves between the single liquid phase, liquid-liquid two-phase region, solid-liquid-liquid three-phase region, and solid-liquid two-phase region were determined experimentally. Then, the liquid-liquid two-phase region as an aqueous two-phase system was studied in more details and tie lines of the aqueous two-phase system were obtained. Finally, the partition coefficient of penicillin-G was measured in this aqueous two-phase system. The effect of concentration of the constituents of the aqueous two-phase system on the partition coefficient of penicillin G was investigated. The results show that >96% of penicillin-G extracted to the top phase that is rich in choline chloride. Also, the maximum experimental partition coefficient of penicillin-G was found to be 323.5 that indicates the high capability of the current system in the separation of penicillin G.

Despite the importance of temperature-dependent density and viscosity data of ternary aqueous mixtures of DESs, experimental studies to determine these essential data have been rather limited. In the current study, the density and kinematic viscosity of the aqueous solutions of choline (chloride + glucose) DESs have been measured over the temperature range of (293.15–323.15) K and DESs concentration of about (1–10) mole%. Following the measurement of bubble temperature of the mixtures at 82.6 kPa, excess molar volumes, dynamic viscosities, viscosity deviations, and also activity coefficients of water in the mixture were calculated. In addition, suitable correlations have been proposed to represent the temperature and composition dependence of the density, kinematic viscosity and also dynamic viscosity data. All in all, the negative values of the excess molar volume, the positive values of dynamic viscosity deviation, alongside the activity coefficients with values less than unity denote negative deviation from ideality of the system studied. It was also observed that both the density and kinematic viscosity decrease with increasing temperature, while they tend to increase with higher mole fractions of DES.

The use of extractive fermentation in penicillin production can overcome the low yield of penicillin. In this study, the liquid-liquid equilibrium (LLE) data of the ternary aqueous two-phase system (ATPS) of (polyethylene glycol 6000 + dipotassium hydrogen phosphate + water) at 298.2 K were measured. Then, penicillin G was added to the ternary ATPS and the LLE of the quaternary system at 298.2 K was measured. The maximum distribution coefficient of 15.63 was experimentally observed for penicillin G that indicates the good performance of the system in separating penicillin G from the fermentation broth. The experimental LLE data were modeled with the NRTL model with average mass fraction deviations of 0.004 for both ternary and quaternary systems. The model is employed to investigate the effect of mixture composition on the distribution coefficient of penicillin G. The results show that the decrease of water in the mixture increases significantly the distribution coefficient of penicillin G, while increasing K2HPO4 has a small positive effect on the distribution coefficient.

Water content is one of the most important quality indexes of biodiesel. In this study, the group interaction parameters of UNIFAC model are optimized to improve this model in predicting the solubility of water in fatty acid methyl esters and biodiesel. To determine the binary interaction parameters between water and the main functional groups attached to the molecules of the biodiesel compounds, 55 experimental data points related to the solubility of water in nine pure fatty acid methyl esters in the temperature range of 282.2 K–323.2 K are employed. Then, the model is used to predict the water solubility in 10 different biodiesels (60 data points), to evaluate the calculated interaction parameters in predicting the solubility of water in the mixture of fatty acid methyl esters. The average absolute relative deviations of the model in predicting the water content of pure fatty acid methyl esters and biodiesel are 4.31% and 4.69%, respectively. To show the superior performance of the model, the results of the model are compared with the results of previous models.

In this study, the biosorption of cadmium ions from aqueous solutions by different microalgae i.e., Parachlorella sp., Spirulina sp., Scenedesmus sp., and Nannochloropsis sp. was examined. The sorption capacity of Parachlorella sp. at 30 °C and pH of 7 was determined to be 90.72 mg/g that was shown to be 1.5 to 3 times higher than the sorption capacity of the other sorbents studied. The two characterization techniques employed, namely, scanning electron microscopy and Fourier-transform infrared spectroscopy confirmed the high potential of Parachlorella sp. in the biosorption of cadmium ions. Therefore, this microalga was studied in more details. In doing so, the effect of temperature, pH, and the initial concentration of cadmium ions on the uptake amount was investigated. The maximum experimental uptake occurred at 96.20 mg/g at pH of 7 and 35 °C, which is higher than the uptake of the other biosorbents reported in the literature. Some relevant thermodynamic parameters were also calculated. The negative value of the Gibbs free energy (−30.81 to −25.13 kJ/mol) and the positive values of the enthalpy change (+77.01 to +91.86 kJ/mol) and also the entropy change (+349.01 to +393.71 J/mol K) indicate the spontaneity, endothermicity, and desirability of the process of interest. In addition, the effect of mixing speed on the adsorption rate was investigated in a bioreactor. The rate of adsorption and the maximum uptake of cadmium ions at different mixing speeds were measured. The increase of the mixing speed to 250 rpm or higher decreased the uptake by less than half.

In this study, the capacity of artificial neural networks (ANNs) and genetic programming (GP) in making possible, fast and reliable predictions of equilibrium compositions of alkane binary mixtures is investigated. A data set comprising 847 data points was gathered and used in both training the proposed ANN and generating the closed-form expressions of the GP procedure. The results obtained demonstrate the relative precision of the proposed ANN, while, on the other hand, exhibit that the GP model, although less precise, affords high CPU time efficiency and simplicity. Concisely, the proposed models can serve the purpose of being close first estimates for more thermodynamically rigorous vapor–liquid equilibrium calculation procedures and do obviate the necessity for the availability of a large set of experimental binary interaction coefficients. Mean absolute errors of 0.0100 and 0.0404 for liquid compositions and of 0.0054 and 0.0254 for vapor-phase mole fractions, for the proposed ANN and GP models, respectively, are a testament to the reliability of the proposed models.

Ionic liquids (ILs) have been of particular interest to researchers in recent years. Viscosity is considered as one of the most important thermophysical properties of ILs. This paper uses a genetic programming approach to propose a simple equation, with only one parameter to adjust, for predicting the viscosity of pure ILs and their binary mixtures as a function of temperature. The adjustable parameter of the equation was calculated for 103 pure ILs. The average absolute relative deviation of 4.60% obtained for 1374 data points confirms the good accuracy of the model. Also, an acceptable average absolute relative deviation of 12.83% at temperatures outside of the temperature range used to develop the model confirms the reliability of the model in the extrapolation of the temperature effect. To predict the viscosity of the binary mixture of ionic liquids by the proposed equation, the volumetric average of the parameter was applied to the proposed equation without introducing any new adjustable parameter. T...

The complexity, coupled with the high cost of the experimental determination of the vapor pressure of pure ionic liquids (ILs), which at moderate temperatures typically assume values below 0.1 (Pa), make the development of predictive models for this important thermophysical property crucial. Employing 309 experimental data points belonging to 20 ILs of different classes and based on the truncated Pitzer expansion, in the current work a simple analytical expression has been developed for the prediction of the vapor pressure of pure ILs. In doing so, all the required critical properties have been estimated using a well-known group contribution method. Subsequently, utilizing the Clausius-Clapeyron equation, a very simple expression has been derived for the prediction of the enthalpy of vaporization of ILs. Total AARDs of 6.97% in the prediction of the available experimental vapor pressure data, and of 5.89% in the prediction of the molar heat of vaporization of 62 ILs demonstrate the satisfactory accuracy and predictive performance of the model developed in the current study. A thorough comparison has also been made with two previously published generalized expressions based on the zero-pressure liquid fugacity approach, through which the superiority of the proposed model has been established.

In this study, a mathematical model is developed for predicting the effect of cosolvents on the biodiesel production, by taking into account the fact that the process consists of two liquid phases. The proposed model consists of a detailed chemical kinetic reaction mechanism and the phase equilibrium of the two phases. The experimental data for estimating the reaction rate constants consists of 1580 dynamic concentration data related to cosolvent-free systems. Then, the calculated reaction rate constants are used to model the biodiesel production process in systems containing cosolvents to investigate the cosolvent effects on the process. The average absolute relative deviation of 1.0% for all the experimental data points is a testament to the accuracy of the predictive model. Hence, the model was used to investigate the effect of methanol to oil ratio, the ratio of cosolvent to methanol, and the amount of catalyst on the biodiesel production rate and yield, in the presence of cosolvents tetrahydrofuran and diethyl ether. According to the results, the use of cosolvent leads to increase the reaction rates. In the other words, the process time is reduced; however, the final yield of biodiesel does not change. Comparison between two cosolvents shows that diethyl ether is more appropriate than tetrahydrofuran, because, in the use of diethyl ether, less cosolvent to methanol ratio is needed. Also, the effect of catalyst concentration is similar to the effect cosolvent.

A thermodynamic model is presented for predicting the phase stability conditions of clathrate hydrates for methane + water-soluble organic promoter aqueous solution. A new equation is then proposed to estimate the enthalpy of hydrate dissociation for methane + aqueous solution of water-soluble organic promoter using Genetic Programming (GP) and Teaching–Learning-Based Optimization (TLBO) evolutionary algorithm. The model reliably predicts the hydrate dissociation conditions for methane + aqueous solutions of tetrahydrofuran, 1,3-dioxolane, 1,4-dioxane and acetone. The van Laar model is used to calculate the activity coefficient of water in aqueous solution of water-soluble organic promoter. About 30% of the reported experimental data were used for finding the empirical relationships to estimate the enthalpy of hydrate dissociation and the remaining 70% was used to test the accuracy and the predictive capability of the correlation. The average absolute error for methane hydrate dissociation temperatures was found to be 0.33 K, which indicates the accuracy of the model.

The rate of percutaneous absorption of micro-particles is very important due to its effects on human health. In this study, a new approach is proposed to investigate both the lateral and the longitudinal diffusions during the absorption of a micro-particle. The results of the current 2D model are compared with the results of a previous 1D model. According to the results, the influence of lateral diffusion on the rate of percutaneous absorption is important, especially for the lipohilic materials. On the other words, the difference between the results of the two models is more significant, at lower values of the ratio of viable-epidermis-diffusivity to the stratum-corneum-diffusivity (DVE/DSC), or at lower values of the viable epidermis to the stratum corneum partition-coefficient (kVE/kSC). For instance, when DVE/DSC equals to 1 and kVE/kSC is 0.33, for a particle with a radius of 10 µm, the steady-state-molar-flux estimated by the 2D model is 2.37 times greater than that estimated by the 1D model...

The surface tension of binary mixtures at different temperatures and compositions is required in much scientific and technological research. Therefore, having an exact correlation between surface tension and easily accessible physical properties is essential. In this work, the sensitivity of the surface tension to some physical properties was studied by using artificial neural networks (ANNs) to find the most effective ones. Furthermore, ANNs were used to estimate the surface tension of binary systems as a function of the most effective physical properties including critical pressure, reduced temperature, acentric factor, and molar density. The experimental data, collected for training and verifying the networks, include various materials such as alkanes, alkenes, aromatics, alcohols, organic acids, as well as chlorine, iodine, sulfur, nitrogen, and fluorine-containing compounds in the composition ranges from 0 to 100 mole percent, and temperatures between 116 K and 393 K. The average absolute relative de...

This article presents a new approach to investigate the kinetics of sunflower and rapeseed oils methanolysis. Due to its heterogeneous nature, the methanolysis reaction is affected by different physical properties such as mass transfer coefficients and specific surface area of the dispersed phase. Considering these parameters, a model was developed, and was evaluated by comparing the results of the model with the experimental data found in the literature. The mean absolute deviation obtained for sunflower and rapeseed oils is 0.039 mol L−1, which demonstrates the accuracy of the model. The results show that the mixing speed is more effective in the first few minutes of the process. Furthermore, at mixing speed above 700 rpm, the process is controlled only by the reactions. The rate of biodiesel production increases with increasing catalyst concentration; however, catalyst concentration above 1.5 wt% has little or no significant effect on the rate of biodiesel production. In addition, because of its higher...

Extractive fermentation processes involving aqueous two-phase systems (ATPSs) are considered as viable means of overcoming the problems associated with product inhibition. Practical development of these processes requires accurate knowledge of the liquid–liquid equilibrium (LLE) of the ATPS forming components alongside the substrate and product of the fermentation process. In this work, the quinary aqueous two-phase LLE of poly(ethylene glycol) 6000 + sodium sulfate + water in the presence of glucose and ethanol have been experimentally determined at 298.15 K using spectrophotometric methods. The resulting LLE data were then satisfactorily correlated by the non-random two-liquid (NRTL) activity coefficient model based on mass fractions. In doing so, the binary energy interaction parameters of the NRTL activity coefficient model were obtained and reported. Calculated RMS deviations below 0.6% demonstrate that the original NRTL activity coefficient model can accurately correlate the LLE data of the quinary aqueous biphasic system of interest.

Acetaminophen is a poorly water-soluble drug whose solubility in water can be increased by adding cosolvents. Using non-toxic or low toxic cosolvents is of interest in the pharmaceutical industry. In this work, the effect of three natural deep eutectic solvents (NADESs), choline chloride + l -malic acid, choline chloride + malonic acid, and choline chloride + urea, was experimentally studied on the aqueous solubility of acetaminophen in the temperature range of 293.15 K to 323.15 K. Besides the NADESs, the individual components of the NADESs were also examined to increase the aqueous solubility of acetaminophen. The results showed that the solubility of acetaminophen increased with an increase in the temperature or an increase in the concentration of the studied cosolvents. The most effective and least effective cosolvents were choline chloride and urea, respectively. For instance, an aqueous solution of 6.45% choline chloride by mole increased the solubility of acetaminophen to more than 4 times the solubility in deionized water. The solubility of acetaminophen in the aqueous solutions of the individual components of the NADESs was used for estimating the binary interaction parameters of the NRTL for these systems. The AARD of the model was obtained to be 3.2% for calculating the solubility of acetaminophen in the aqueous solutions of the individual components of the NADESs. Then, the binary interaction parameters were employed to predict the solubility of acetaminophen in the aqueous NADESs. The AARD of the model was obtained to be 6.2% for predicting the solubility of acetaminophen in the aqueous NADESs which indicate the reliable predictability of the model.

Deep eutectic solvents (DESs) as green cosolvents can be added to water for increasing the solubility of poorly water-soluble drugs. In this study, the solubility of cefixime is studied in three aqueous solutions of DES, including choline chloride+malonic acid, choline chloride+urea, and choline chloride+glucose. The experiments are conducted in the temperature range of 293.15 K to 333.15 K, pressure of 0.1 MPa and different compositions of the DESs. The results show that adding DES to water increases the solubility of cefixime significantly. For instance, adding 6% (mol/mol) of choline chloride+malonic acid to water increases the cefixime solubility by more than 17 times. The DESs of choline chloride+malonic acid and choline chloride+urea are more effective cosolvents compared with the DES of choline chloride+glucose in increasing the mole fraction of cefixime. In addition, the solubility data are successfully correlated with the activity models of NRTL, Wilson and van Laar. The overall AARD% of the NRTL, Wilson and van Laar models in estimating the mole fraction of cefixime in aqueous solutions of the DESs are 3.88, 3.62 and 10.14, respectively.

In this study, a new approach is proposed to investigate the kinetics of sunflower oil and rapeseed oil transesterification in the presence of potassium hydroxide. Transesterification is a heterogeneous process which affected by a number of parameters, that are not readily available in the literature, such as mass transfer coefficients, partition coefficients, and specific surface area of the dispersed phase. However, under intense agitation condition, mass transfer restrictions may be neglected, and the two phases are supposed to remain in thermodynamic equilibrium, during the process. Therefore, a model was developed independent of the mass transfer coefficient and specific surface area, which is reliable for the intense agitation condition. According to the results, the model is valid at least for mixing rates over 500 rpm. The results of the model were used to study the effects of temperature, methanol-to-oil ratio, and catalyst concentration on the biodiesel conversion. Biodiesel production rate increases with increasing temperature, although rapeseed oil transesterification is more temperature dependent. The results show that the maximum amount of catalyst concentration is less than 1% (by weight); however, the optimum value depends on the operating temperature. The optimum value of the methanol-to-oil-ratio decreases with increasing temperature. Thus, at higher temperatures, less amount of methanol and catalyst are required, which leads to easier purification of biodiesel.

Ionic liquids can dramatically improve the efficiency of absorption refrigeration processes due to their negligibly small vapor pressure; where the IL absorbs the refrigerant gas in one stage and subsequently releases the high-pressure gas upon addition of heat. In the current study, phase equilibrium data (P-T-x) of a diverse set of refrigerants in different types of ionic liquids are modeled with the aid of two equations of state based on lattice fluid theory, i.e. Sanchez–Lacombe and ϵ*-Modified Sanchez–Lacombe EOSs. The characteristic parameters of the pure components were determined from a database of 3086 experimental liquid density datapoints for 11 refrigerants and 12 ILs. According to the results obtained by considering the experimental data of 25 binary IL/refrigerant mixtures comprised of 88 isotherms with a total of 658 datapoints, both EOSs can successfully correlate the solubility data with AARDs of %5.773 and %5.771 for SL and ϵ*-Modified SL EOSs, respectively.

The bubble pressure of aqueous solutions of two natural deep eutectic solvents (DESs) was measured at temperatures 303.15 K–343.15 K and different concentration of the DESs. DESs were formed with combining choline chloride and a sugar (glucose or fructose) in a choline chloride: sugar molar ratio of 2:1. In addition, the bubble pressure of aqueous solutions of fructose, glucose, and choline chloride, were measured to define the impact of the individual components of the DESs on the bubble pressure. According to the results, choline chloride had the greatest effect and sugars had the least effect on the bubble pressure. Experimental bubble point data was used to estimate the binary interaction parameters of the NRTL activity model. An overall AARD of 1.91% indicates the good accuracy of the model in estimating the bubble pressure of aqueous solutions studied in this work. To verify the calculated binary interaction parameters, the NRTL model was used to estimate the solubility of glucose and fructose in water and the results were compared with the experimental solubility available in the literature.

The potentials of gas hydrates to store large quantities of gases such as H2 and CH4 make them suitable candidates for energy storage applications. The presence of insoluble liquid hydrocarbons can lead to the formation of four-phase hydrates, and also affects the hydrate formation conditions. In this study, we propose a robust multilayer perceptron feed-forward artificial neural network (MLP-FF-ANN) and least squares support vector machines (LSSVM kernel RBF type) to forecast the four-phase equilibrium conditions of structure II (sII) gas hydrates. A total of 474 equilibrium data points for four-phase hydrate conditions are collected from the literature that are used to develop the models. The optimal ANN model structure contains a single hidden layer with 12 tangent-Sigmoid neurons, which is trained with the back-propagation algorithm. The magnitudes of average absolute relative deviation percent (AARD%) while employing the ANN and LSSVM models are 0.08 and 0.21, respectively. Although for the systems consisting of C2H6, N2, H2, and CO2, both methods feature a similar performance in estimating the equilibrium hydrate dissociation conditions, the ANN outperforms the LSSVM in systems including fluoromethane (R-41), difluoromethane (R-32), Kr, and H2S. Moreover, the performances of the ANN and LSSVM are compared to two thermodynamic (analytical) models and the ANN model is found the most accurate. This performance superiority brings potentials in demanding applications such as adaptive control, online state estimation, and process optimization in energy storage systems.

Surface tension as an important characteristic in much scientific and technological research is a function of liquid materials’ physical properties. Thus, it is desirable to have an accurate correlation between effective parameters and surface tension. This study investigates the applicability of artificial neural networks as an efficient tool for the prediction of pure organic compounds’ surface tensions for a wide range of temperatures. The experimental data gathered for training and verification of the network are related to a wide variety of materials such as alkanes, alkenes, aromatics, and sulfur, chlorine, fluorine, and nitrogen containing compounds. The most accurate network among several constructed configurations has one hidden layer with 20 neurons. The average absolute deviation percentage obtained for 1048 data points related to 82 compounds is 1.57%. The results demonstrate that the multilayer perceptron network could be an appropriate lookup table for the determination of surface tension as...

In this study, biological sulfide removal is investigated in a fed batch bioreactor. In this process, sulfide is converted into elemental sulfur particles as an intermediate in the oxidation of hydrogen sulfide to sulfate. The main product is sulfur at low dissolved oxygen or at high sulfide concentrations and also more sulfates are produced at high dissolved oxygen. According to the carried out reactions, a mathematical model is developed. The model parameters are estimated and the model is validated by comparing with some experimental data. The results show that, the proposed model is in a good agreement with experimental data. According to the experimental result and mathematical model, sulfate and sulfur selectivity are sensitive to the concentration of dissolved oxygen. For sulfide concentration 0.2 (mM) in the bioreactor and dissolved oxygen of 0.5 ppm, only 10% of sulfide load is converted to sulfate, while it is 60% at the same sulfide concentration and dissolved oxygen of 4.5 ppm. At high sulfide load to the bioreactor, the concentration of uneliminated sulfide increases; it leads to more sulfur particle selectivity and consequently, less sulfate selectivity.

In this study, biological sulfide removal from natural gas in a continuous bioreactor is investigated for estimation of the optimal operational parameters. According to the carried out reactions, sulfide can be converted to elemental sulfur, sulfate, thiosulfate, and polysulfide, of which elemental sulfur is the desired product. A mathematical model is developed and was used for investigation of the effect of various parameters on elemental sulfur selectivity. The results of the simulation show that elemental sulfur selectivity is a function of dissolved oxygen, sulfide load, pH, and concentration of bacteria. Optimal parameter values are calculated for maximum elemental sulfur selectivity by using genetic algorithm as an adaptive heuristic search. In the optimal conditions, 87.76% of sulfide loaded to the bioreactor is converted to elemental sulfur.