Kinetics and modeling of carbon dioxide absorption into aqueous solutions of piperazine

In this work, the kinetics of the reaction between CO₂ and aqueous piperazine (PZ) have been estimated over the temperature range of 298-313 K from the absorption data obtained in a wetted wall contactor. The absorption data are obtained for the PZ concentrations of 0.2- and for CO₂ partial pressures up to 5 kPa. A coupled mass transfer-kinetics-equilibrium mathematical model based on Higbie's penetration theory has been developed with the assumption that all reactions are reversible. The model is used to estimate the rate constants from the experimental data for absorption of CO₂ in aqueous PZ. The estimated rate constants of this study are in good agreement with those reported in the literature.     

Artificial neural network modeling for prediction of binary surface tension containing ionic liquid

In this study, a feed-forward multilayer perceptron neural network is applied to predict the surface tension of 32 binary ionic liquids (ILs)/non-ILs systems using melting point (Tm), molecular weight (Mw) and mole fraction of ILs as well as Tm and Mw of non-IL components. The data are divided into two different subsets, namely training and testing subsets, to obtain the optimum parameters of the used network and to evaluate the correlative capability of the trained network. The results of the test stage show excellent capability of the proposed network to predict/correlate the binary surface tension of ILs/non-ILs systems (AARD%: 0.93, MSE: 6.67 × 10^?7 and R²: 0.9950).     

Effect of surface tension gradient on gas hold-up enhancement in aqueous solutions of electrolytes

The effect of addition of electrolytes on gas hold-up of air/water system was investigated experimentally in a laboratory scale bubble column. The experiments were carried out with four electrolytes, namely, NaCl, MgSO₄·7H₂O, Na₂SO₄ and CaCl₂·2H₂O and the concentrations of the solutions were varied from 0 to 0.3 mol/l. Enhancement of gas hold-up was observed for all four electrolytes at concentrations less than 0.1 mol/l. With the increase in concentration, the gas hold-up showed two different trends; in Na₂SO₄ and CaCl₂·2H₂O solutions, gas hold-up formed a sharp peak after the enhancement and leveled off at a value somewhat higher than that in water, whereas in NaCl and MgSO₄·7H₂O solutions, gas hold-up leveled off immediately after the enhancement without forming any peak. Experiments were also conducted to measure the surface tensions of the solutions with special focus in the low concentration region. A strong relation between the gas hold-up enhancement and the change of surface tension with the addition of electrolyte was found. It was also observed that the concentration at which maximum value of C(dσ/dC)² i.e. (concentration × surface tension gradient with respect to concentration²) is obtained corresponds to the concentration at which maximum gas hold-up enhancement occurs.     

Modeling bubble column reactor with the volume of fluid approach: Comparison of surface tension models

This work aims at comparing surface tension models in VOF (Volume of Fluid) modeling and investigating the effects of gas distributor and gas velocity. Hydrodynamics of a continuous chain of bubbles inside a bubble column reactor was simulated. The grid independence study was first conducted and a grid size of 1.0 mm was adopted in order to minimize the computing time without compromising the accuracy of the results. The predictions were validated by comparing the experimental studies reported in the literature. It was found that all surface tension models can describe the bubble rise and bubble plume in a column with slight deviations.     

Prediction of air pollutants by using an artificial neural network

The purpose of this study is to predict the amount of primary air pollution substances in Seoul, Korea. An artificial neural network (ANN) was used as a prediction method. The ANN with three layers is learned with past data, and the concentrations of air pollutants are predicted based on the pre-learned weights. The error back propagation method that has a powerful application to various fields was adopted as the learning rule. The concentrations of air pollutants from one to six hours in the future were predicted with the ANN. To verify the performance of the prediction method used in the present study, the predicted concentrations of air pollutants were compared with the measured data. From the comparison, it was found that the prediction method based on the ANN gives an acceptable accuracy for the limited prediction horizon.     

烷基聚葡糖苷水溶液动态表面张力研究

采用最大泡压法研究了表面活性剂烷基聚葡糖苷（APG或CiGj）C8／10 G1.31和C12／14G1．43水溶液的动态表面张力（DST）;重点考察了胶束溶液中的DST曲线。将得到的13ST对时间的曲线,用Rosen模型进行处理,计算了DST的各项参数（n,ti、t^＊、tm、γm、γ0-γm、R1／2）。结果表明,随浓度增加,n、ti、t^＊、tm以及）γm减小,R1／2增大。当浓度大于cmc时,仍可用Rosen经验方程对数据进行处理,提出改进模型对胶束溶液中的动态吸附现象进行了解释。     

Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions

A novel high-pressure apparatus with various abilities in hydrate investigation fields has been designed, constructed and fully described in the present paper. In order to achieve an appropriate understanding of the gas hydrate behavior in formation and destabilization, series of laboratory experiments with six different gas mixtures were done and more than 130 hydrate equilibrium points in the pressure range of about 450–3000 psia were recorded. Different methods of hydrate formation prediction were discussed and finally the new promising neural networks method was used. Because of the previous works defects in accurate hydrate formation prediction via neural networks, a new use of neural networks was introduced. Testing and validation of the new neural networks method indicates that it is a reliable technique for the accurate prediction of hydrate formation conditions for generalized gas systems and can be used in future automatic inhibitor dosing devices.     

Surface tension prediction and thermodynamic analysis of the surface for binary solutions

The surface tensions of 100 aqueous and 200 non-aqueous binary solutions are correlated by Shereshefsky model and excellent results are obtained. The average percent deviations are about ∼1.8% for aqueous and ∼0.56% for non-aqueous binary solutions. The free energy change in the surface region is calculated and is used to obtain the excess number of molecular layers in the surface region. Furthermore, the model is used to derive an equation for the standard Gibbs energy of adsorption. Where experimental data is available for the standard Gibbs energy of adsorption, the agreement between the calculated and the experimental data is found to be very good.     

Implementation of electrolyte CPA EoS to model solubility of CO_2 and CO_2+ H_2S mixtures in aqueous MDEA solutions

The electrolyte version of SRK plus association equation of state(e SRK-CPA Eo S) was employed to correlate CO_2 solubility in MDEA aqueous solutions. The applied model comprises the classic form of CPA Eo S including SRK Eo S plus Wertheim association term in addition to MSA theory and Born terms so that the two last terms are responsible for the long-range interactions. A reaction-containing bubble pressure computation technique comprising two nested loops was utilized to model the systems. The internal loop, calculates the liquid phase concentrations via reaction, mass and charge balance equation solving, whereas, the vapor phase concentrations will be obtained in the external one. 470 experimental data were used to correlate binary subsystems and the H_2O + MDEA + CO_2 ternary system. Since, there not exist any binary VLE data for MDEA + CO_2 subsystem, two fitting scenarios were applied. At the first scenario, the binary interaction parameter was assumed equal to zero, while, in second approach the parameter was obtained through ternary system correlation. Both scenarios show very good accuracy in that the Absolute Average Deviation percentages(AAD) obtained were 19.12% and 18.85%, respectively. Also, to show the efficiency of the used model, a comparison between our results and those of the best-known models was made.Finally, having model parameters for H_2S solubility from our previous work [A. Afsharpour, Petroleum Science and Technology 35(3)(2017) 292-298], simultaneous solubility of CO_2+ H_2S mixtures in MDEA solutions was predicted using the e SRK-CPA Eo S with no new optimizable parameters. As the results show,the applied model has a good performance for correlation and prediction of acid gas solubility in a wide range of pressures, temperatures, acid gas loadings, and MDEA concentrations.     

Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling

Carbonation on concrete structures in underground sites or metropolitan cities is one of the major causes of steel corrosion in RC (Reinforced Concrete) structures. For quantitative evaluation of carbonation, physico-chemo modeling for reaction with dissolved CO₂ and hydrates is necessary. Amount of hydrates and CO₂ diffusion coefficient play an important role in evaluation of carbonation behavior, however, it is difficult to obtain a various CO₂ diffusion coefficient from experiments due to limited time and cost.In this paper, a numerical technique for carbonation behavior using neural network algorithm and carbonation modeling is developed. To obtain the comparable data set of CO₂ diffusion coefficient, experimental results which were performed previously are analyzed. Mix design components such as cement content, water to cement ratio, and volume of aggregate including exposure condition of relative humidity are selected as neurons. Training of learning for neural network is carried out using back propagation algorithm. The diffusion coefficient of CO₂ from neural network are in good agreement with experimental data considering various conditions such as water to cement ratios (w/c: 0.42, 0.50, and 0.58) and relative humidities (R.H.: 10%, 45%, 75%, and 90%). Furthermore, mercury intrusion porosimetry (MIP) test is also performed to evaluate the change in porosity under carbonation. Finally, the numerical technique which is based on behavior in early-aged concrete such as hydration and pore structure is developed considering CO₂ diffusion coefficient from neural network and changing effect on porosity under carbonation.     

Measurement and correlation of physical properties of aqueous solutions of tetrabutylammonium hydroxide,piperazine and their aqueous blends

The density, viscosity and refractive index of aqueous solutions of tetrabutylammonium hydroxide (TBAOH), pi-perazine (PZ) and their aqueous blends are determined at several temperatures (303.15 to 333.15 K). All these measured physicochemical properties decreases with an increase in temperature. The density data is used to cal-culate the coefficient of thermal expansion and excess molar volume of al aqueous binary and ternary solutions. The coefficient of thermal expansion increases with increase in temperatures and concentrations. The negativity of excess molar volume for al the aqueous solution decreased with increase in temperature. Each physical prop-erty is correlated with temperature by least square method and the corresponding coefficients for each property are presented. The prediction values from correlations for the physical properties are in good agreement with the experimental values.     

Experimental investigation,modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm

In this work, treatment of oily wastewaters with commercial polyacrylonitrile (PAN) ultrafiltration (UF) membranes was investigated. In order to do these experiments, the outlet wastewater of the API (American Petroleum Institute) unit of Tehran refinery, is used as the feed. The purpose of this paper was to predict the permeation flux and fouling resistance, by applying artificial neural networks (ANNs), and then to optimize the operating conditions in separation of oil from industrial oily wastewaters, including trans-membrane pressure (TMP), cross-flow velocity (CFV), feed temperature and pH, so that a maximum permeation flux accompanied by a minimum fouling resistance, was acquired by applying genetic algorithm as a powerful soft computing technique. The experimental input data, including TMP, CFV, feed temperature and pH, permeation flux and fouling resistance as outputs, were used to create ANN models. This fact that there is an excellent agreement between the experimental data and the predicted values was shown by the modeling results. Eventually, by multi-objective optimization, using genetic algorithm (GA), an optimization tool was created to predict the optimum operating parameters for desired permeation flux (i.e. maximum flux) and fouling resistance (i.e. minimum fouling) behavior. The accuracy of the model is confirmed by the comparison between the predicted and experimental data.     

溴化锂及离子液体水溶液密度、黏度和表面张力测定与计算

常压条件下,实验测定了不同温度（283.15~343.15 K）下吸收制冷/热泵工质对——溴化锂（LiBr）、1-乙基-3-甲基咪唑醋酸盐（[EMIM][OAC]）和1-丁基-3-甲基咪唑硫氰酸盐（[BMIM][SCN]）水溶液的密度、黏度和表面张力,借助线性方程和Vogel-Tammann-Fulcher（VTF）模型,分别成功关联了密度和黏度实验值。研究结果表明：在相同条件下,溴化锂水溶液的密度大于离子液体水溶液的密度,而前者的黏度小于后者;对表面张力,随着溴化锂含量增加,其水溶液的表面张力值增加,而少量离子液体可使水的表面张力快速下降。根据实验黏度和表面张力分别获得了能量势垒和表面熵/焓,表明各水溶液中分子或离子迁移难易程度遵循[EMIM][OAC] > [BMIM][SCN] > LiBr,表面有序性遵循[BMIM][SCN] > [EMIM][OAC] > LiBr。研究结果可为吸收制冷/热泵工质对及低温余热回收系统的设计和计算提供可靠的数据支撑。     

Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.     

Dynamic and equilibrium surface tensions of surfactin aqueous solutions

A homologous series of surfactins containing β-hydroxy fatty acids having 13, 14, or 15 carbon atoms were isolated from the supernatant of Bacillus subtilis strain S499 cultures. Their surface-active properties at the air-water interface were then evaluated. Dynamic surface tension data were analyzed by the relaxation function γ_t=γ_m+(γ_o−γ_m)/[1+(t/t^*)ⁿ]. Based on various parameters t^*, n, v_max, γ_m calculated from this equation, the dynamic surface properties of surfactin were found to depend on both bulk concentration and hydrophobic character of the alkyl chain. At low concentrations of surfactin, the dynamic surface tension (γ_d) decreased with increasing carbon atom number of the surfactin alkyl chain (n=13 to 15). However, at high concentrations, the maximum decrease of 41-4 was achieved by surfactin-C₁₄. In contrast, more strongly hydrophobic alkyl chains in surfactins always enhanced their ability in reducing the equilibrium surface tensions and their aptitude in forming micelles.     

Viscosity prediction by computational method and artificial neural network approach: The case of six refrigerants

There are some computational models for fluids viscosity calculation. However, each of these models is reliable in confined density. In this comparative study two methods are evaluated for viscosity prediction in all range of density. We determine the effectiveness of each of the models and we demonstrate the strengths and weaknesses of them. Viscosity of the six refrigerants is calculated by some computational models based on Chapman⿿Enskog and Rainwater⿿Friend theories. Then a feed forward artificial neural network (ANN) with multilayer perceptrons is used to viscosity prediction and finally two methods (computational models and artificial neural network) are comparing. It is concluded that there is no opinion by computational methods to calculate viscosity from low to high density. The results show that prediction accuracy of computational models in low and moderate densities is good as ANN method. However artificial neural network has very good accuracy in high densities while computational method is defeated when the density is more than 8.     

Experimental and modeling of CO2 capture by dry sodium hydroxide carbonation

In this work, CO₂ capture from the air using dry NaOH sorbents has been studied. The influences of the main operating parameters such as temperature, air humidity, and NaOH loading on the CO₂ removal rate have been experimentally investigated using Taguchi method. The results revealed that the appropriate value of the temperature to maximize the rate was in the range of 35–45 °C. A multilayer artificial neural network (ANN) was also used to model the process in order to find the optimal conditions. A procedure reported in the literature was modified and applied to design the ANN model. The model predictions were validated by conducting some more experiments. The experimental results proved the accuracy of the model to predict the optimal conditions. The effects of NaOH particle size and multiple carbonation cycles have also been investigated.     

Bulk and interfacial physical properties of aqueous solutions of sodium lauryl sulphate: Part IV: Dilute aqueous solution behavior by electron spin resonance studies and by pH and surface tension measurements

The electron spin resonance spectrum of the nitroxide label 2,2,6,6 – TetraMethyl – 4 – Piperidone – Oxide (TEMPO) has been investigated in aqueous solutions of a purified and commercially available impure samples of sodium lauryl sulphate (NaLS) for a wide range of concentrations. The spectra were recorded at 24°C as a function of surfactant concentration. The reorientational correlation times T_θ of the TEMPO label in aqueous solutions were calculated using the Kivelson's theory. Using the T_θ data qualitative analysis has been carried out in an attempt to understand the microscopic effects produced by the hydrophobic part of the surfactant on the water structure and the hydrophobic-hydrophobic interactions in water. Four major discontinuities in the τ_θ results have been identified with the purified NaLS system. They are found to occur at NaLS concentrations of 0.008 wt.%, 0.015 wt.%, 0.23 and 1.73 wt.%. The latter two concentrations represent the first and the second critical micelle concentration (CMC) of the surfactant while the first two concentrations reveal the existence of premicellar association and dissociation processes, respectively. These discontinuities have been also confirmed by the pH data and by surface tension results from the du Noüy ring method. The effect of addition of lauryl alcohol and sodium chloride on τ_θ values was also studied. The additive action has been found to produce an additional discontinuity, corresponding to the mixture CMC, in the τ_θ versus concentration plot. Analysis of other effects such as solution aging and hydrolysis of NaLS are also included. The applicability of the spin labelling technique for the study of aqueous solution behavior of NaLS is discussed by comparing data reported in the literature.