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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.
Roy Setiawan; Reza Daneshfar; Omid Rezvanjou; Siavash Ashoori; Maryam Naseri. Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence. Environment, Development and Sustainability 2021, 1 -22.
AMA StyleRoy Setiawan, Reza Daneshfar, Omid Rezvanjou, Siavash Ashoori, Maryam Naseri. Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence. Environment, Development and Sustainability. 2021; ():1-22.
Chicago/Turabian StyleRoy Setiawan; Reza Daneshfar; Omid Rezvanjou; Siavash Ashoori; Maryam Naseri. 2021. "Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence." Environment, Development and Sustainability , no. : 1-22.
The coupled flow in gas condensate reservoirs is investigated using a purely theoretical approach for the description of velocity effect on relative permeability in the absence of inertial effects. A combination of linear stability analysis and dynamic wetting theory is used to describe this coupled flow under the effect of viscous resistance and disjoining pressure. The role of capillary number and Scheludko number (i.e. the dimensionless ratio of disjoining pressure to capillary pressure) is clearly expressed through closed relative permeability formula for the limits of negligible and strong disjoining effect. The model predicts no velocity dependence at very low capillary numbers and a positive velocity effect at high capillary numbers but the negative velocity effect at very high velocities due to strong inertial effects is not predicted due to the model's basic assumptions. Although the model was not intended to serve as a substitute for the experiment, reasonable quantitative agreement is observed between its predictions and available experimental data as long as the Weber number (the dimensionless ratio of inertial resistance to capillary pressure) is smaller than 5 × 10−5, with an average and a maximum relative deviation of 16% and 67% respectively, for a total number of 49 data points.
Mohammad Mohammadi-Khanaposhtani; Yousef Kazemzadeh; Reza Daneshfar. Positive coupling effect in gas condensate flow: Role of capillary number, Scheludko number and Weber number. Journal of Petroleum Science and Engineering 2021, 203, 108490 .
AMA StyleMohammad Mohammadi-Khanaposhtani, Yousef Kazemzadeh, Reza Daneshfar. Positive coupling effect in gas condensate flow: Role of capillary number, Scheludko number and Weber number. Journal of Petroleum Science and Engineering. 2021; 203 ():108490.
Chicago/Turabian StyleMohammad Mohammadi-Khanaposhtani; Yousef Kazemzadeh; Reza Daneshfar. 2021. "Positive coupling effect in gas condensate flow: Role of capillary number, Scheludko number and Weber number." Journal of Petroleum Science and Engineering 203, no. : 108490.
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.
Yushan Gao; Zhidan Zhang; Shuangling Zhong; Reza Daneshfar. Preparation and Application of Aromatic Polymer Proton Exchange Membrane with Low-Sulfonation Degree. International Journal of Chemical Engineering 2020, 2020, 1 -9.
AMA StyleYushan Gao, Zhidan Zhang, Shuangling Zhong, Reza Daneshfar. Preparation and Application of Aromatic Polymer Proton Exchange Membrane with Low-Sulfonation Degree. International Journal of Chemical Engineering. 2020; 2020 ():1-9.
Chicago/Turabian StyleYushan Gao; Zhidan Zhang; Shuangling Zhong; Reza Daneshfar. 2020. "Preparation and Application of Aromatic Polymer Proton Exchange Membrane with Low-Sulfonation Degree." International Journal of Chemical Engineering 2020, no. : 1-9.
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 R2 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.
Reza Daneshfar; Amin Bemani; Masoud Hadipoor; Mohsen Sharifpur; Hafiz Ali; Ibrahim Mahariq; Thabet Abdeljawad. Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms. Applied Sciences 2020, 10, 6432 .
AMA StyleReza Daneshfar, Amin Bemani, Masoud Hadipoor, Mohsen Sharifpur, Hafiz Ali, Ibrahim Mahariq, Thabet Abdeljawad. Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms. Applied Sciences. 2020; 10 (18):6432.
Chicago/Turabian StyleReza Daneshfar; Amin Bemani; Masoud Hadipoor; Mohsen Sharifpur; Hafiz Ali; Ibrahim Mahariq; Thabet Abdeljawad. 2020. "Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms." Applied Sciences 10, no. 18: 6432.
A novel multilayer perceptron artificial neural network (MLP-ANN) model is proposed to estimate the dew-point pressure (DPP) of gas condensate reservoirs as a function of gas composition, reservoir temperature and, molecular weight and specific gravity of C7+. For this purpose, a comprehensive database was prepared by reviewing literature and the results of MLP-ANN are graphically and statistically compared with these actual values. The R-squared (R2) and mean relative error are determined to be 0.9868 and 1.5%, respectively, which reveals that DPP values are well predicted by this model. Furthermore, the MLP-ANN model is compared with previous developed models.
Reza Daneshfar; Farhad Keivanimehr; Mohammad Mohammadi-Khanaposhtani; Alireza Baghban. A neural computing strategy to estimate dew-point pressure of gas condensate reservoirs. Petroleum Science and Technology 2020, 38, 706 -712.
AMA StyleReza Daneshfar, Farhad Keivanimehr, Mohammad Mohammadi-Khanaposhtani, Alireza Baghban. A neural computing strategy to estimate dew-point pressure of gas condensate reservoirs. Petroleum Science and Technology. 2020; 38 (10):706-712.
Chicago/Turabian StyleReza Daneshfar; Farhad Keivanimehr; Mohammad Mohammadi-Khanaposhtani; Alireza Baghban. 2020. "A neural computing strategy to estimate dew-point pressure of gas condensate reservoirs." Petroleum Science and Technology 38, no. 10: 706-712.
Nanofluids and low-salinity water (LSW) flooding are two novel techniques for enhanced oil recovery. Despite some efforts on investigating benefits of each method, the pros and cons of their combined application need to be evaluated. This work sheds light on performance of LSW augmented with nanoparticles through examining wettability alteration and the amount of incremental oil recovery during the displacement process. To this end, nanofluids were prepared by dispersing silica nanoparticles (0.1 wt%, 0.25 wt%, 0.5 wt% and 0.75 wt%) in 2, 10, 20 and 100 times diluted samples of Persian Gulf seawater. Contact angle measurements revealed a crucial role of temperature, where no wettability alteration occurred up to 80 °C. Also, an optimum wettability state (with contact angle 22°) was detected with a 20 times diluted sample of seawater augmented with 0.25 wt% silica nanoparticles. Also, extreme dilution (herein 100 times) will be of no significance. Throughout micromodel flooding, it was found that in an oil-wet condition, a combination of silica nanoparticles dispersed in 20 times diluted brine had the highest displacement efficiency compared to silica nanofluids prepared with deionized water. Finally, by comparing oil recoveries in both water- and oil-wet micromodels, it was concluded that nanoparticles could enhance applicability of LSW via strengthening wettability alteration toward a favorable state and improving the sweep efficiency.
Amir Hossein Saeedi Dehaghani; Reza Daneshfar. How much would silica nanoparticles enhance the performance of low-salinity water flooding? Petroleum Science 2019, 16, 591 -605.
AMA StyleAmir Hossein Saeedi Dehaghani, Reza Daneshfar. How much would silica nanoparticles enhance the performance of low-salinity water flooding? Petroleum Science. 2019; 16 (3):591-605.
Chicago/Turabian StyleAmir Hossein Saeedi Dehaghani; Reza Daneshfar. 2019. "How much would silica nanoparticles enhance the performance of low-salinity water flooding?" Petroleum Science 16, no. 3: 591-605.