Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artifcial intelligence

The surface tension (ST) of ionic liquids (ILs) and their accompanying mixtures allows engineers to accurately arrange new processes on the industrial scale. Without any doubt, experimental methods for the specification of the ST of every supposable IL and its mixtures with other compounds would be an arduous job. Also, experimental measurements are effortful and prohibitive; thus, a precise estimation of the property via a dependable method would be greatly desirable. For doing this task, a new modeling method according to artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching�learning-based optimization (TLBO), particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA), has been suggested to estimate ST of the binary ILs mixtures. For training and testing the applied network, a set of 748 data points of binary ST of IL systems within the temperature range of 283.1�348.15 K was utilized. Furthermore, an outlier analysis was used to discover doubtful data points. Gained values of MSE & R2 were 0.0000007 and 0.993, 0.0000002 and 0.998, 0.0000004 and 0.996 and 0.0000006 and 0.994 for the ICA-ANN, TLBO-ANN, PSO-ANN and GA-ANN, respectively. Results demonstrated that the experimental data and predicted values of the TLBO-ANN model for such target are wholly matched

Estimating the heat capacity of non-Newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms

This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R 2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.https://www.mdpi.com/journal/applscipm2021Mechanical and Aeronautical Engineerin

Determination of Phenol and Carvacrol in Honey Samples Using Dispersive Liquid-Liquid Microextraction and Experimental Design for Optimization

A very simple, rapid and sensitive dispersive liquid-liquid microextraction (DLLME) followed by gas chromatography and flame ionization detection (GC-FID) was developed for the determination of phenol and carvacrol in honey samples. A mixture of 100 µl dichloromethane (extraction solvent) and 0.5 ml acetonitrile (disperser solvent) was rapidly injected into sample solution. Thereby a cloudy solution was formed. After centrifuging, the fine droplets of extraction solvent were sedimented in the bottom of the conical test tube. Sedimented phase (0.6 µl) was injected into the GC-FID system. Experimental parameters which control the performance of DLLME, such as type and volumes of extraction and disperser solvents, pH, salt effect and extraction time were investigated. Under optimum conditions obtained by the response surface methodology, the method was found to be linear in the range of 10-200 mg l-1. The limits of detection for phenol and carvacrol were 4.15 and 3.9, respectively, and the extraction recovery ranged from 67-97.3%

Preparation and Application of Aromatic Polymer Proton Exchange Membrane with Low-Sulfonation Degree

4,4′-Dichlorodiphenylsulfone-3,3′-disulfonic acid (disodium) salt and 4,4′-difluorodiphenylsulfone were used as sulfonated monomer. 4,4′-Fluorophenyl sulfones were used as the nonsulfonated monomer. 4,4′-Dihydroxy diphenyl ether or 4,4′-thiodibenzenethiol was used as the comonomer. The sulfonated poly (aryl ether sulfone) (SPES) and sulfonated poly (arylene thioether sulfone) (SPTES) with sulfonation degree of 30% and 50% were successfully prepared by nucleophilic polycondensation. Two kinds of aromatic polymer proton exchange membranes were prepared by using sulfonated poly phthalazinone ether ketone (SPPEK) material and fluidization method. The performance of the prepared aromatic polymer proton exchange membrane was researched by the micromorphology, ion exchange capacity, water absorption and swelling rate, oxidation stability, tensile properties, and proton conductivity. Experimental results show that there is no agglomeration in the prepared aromatic polymer proton exchange membrane. The ion exchange capacity is 0.76–1.15 mmol/g. The water absorption and swelling rate increase with the increase of sulfonation degree. The sulfonated poly (aryl ether sulfone) membrane shows better oxidation stability than sulfonated poly (aryl sulfide sulfone). They have good mechanical stability. The prepared aromatic polymer proton exchange membrane with low sulfonation degree has good performance, which can be widely used in portable power equipment, electric vehicles, fixed power stations, and other new energy fields

Assessment of the Specimen Size Effect on the Fracture Energy of Macro-Synthetic-Fiber-Reinforced Concrete

The most frequently used construction material in buildings is concrete exhibiting a brittle behaviour. Adding fibers to concrete can improve its ductility and mechanical properties. To this end, a laboratory study was conducted to present an experimental model for the specimens’ size effect of on macro-synthetic fiber-reinforced concrete using variations in fracture energy. Composite concrete beams with different thicknesses and widths were made and tested under mode I to obtain (1) fracture toughness, (2) fracture energy, and (3) critical stress intensity factor values. Results indicated that by increasing the thickness and the width, fracture toughness and fracture energy were enhanced. Moreover, increasing the thickness and width of the beam led to critical stress intensity factors enhancement respectively by 35.01–41.43% and 7.77–8.09%

Predicting the condensate viscosity near the wellbore by ELM and ANFIS-PSO strategies

By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate calculation of pressure decline and optimization operations in these reservoirs need to know and predict the specific properties such as liquid viscosity. Empirical models have already been developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a large data bank of reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.762 and 0.15, respectively, while these values are 0.941 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG, and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software. © 2021 Elsevier B.V

Copper ferrite nanoparticles catalyzed formation of β-Ketophosphonates via oxyphosphorylation of styrenes with H-phosphonates: A DFT study on UV–vis absorption spectra

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