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In this paper, we apply the minimum information theory to the uncapacitated p-median facility location problem to determine the most probable allocation solution. Indeed, we investigate the bi-leve...

We propose a maximum probability model to estimate the origin-destination trip matrix in the networks, where the observed traffic counts of links and the target origin-destination trip demands are independent discrete random variables with known probabilities. The problem is formulated by using the least squares approach in which the objective is to maximize the probability that the sum of squared errors between the estimated values and the observed (target) ones does not exceed a pre-specified threshold. An enumeration so lution approach is proposed to solve the problem in small-sized networks, while a normal approximation based on the central limit theorem is applied in large-sized networks to transform the problem into a deterministic nonlin ear fractional model. Some numerical examples are provided to illustrate the efficiency of the proposed method.

In this paper, a bi-level model is introduced to evaluate the capacitated p-median facility location problem with the most likely allocation solution. The classical capacitated p-median problem is considered in the upper-level to minimize the cost of locating facilities and serving demands while a log-based model resulting from the minimum information approach is used in the lower-level to determine the most likely allocation solution based on the available information. Using Lagrangian dual theory, the proposed bi-level problem is reduced to a new one-level nonlinear mixed-integer problem whose solution is obtained by comparing two mixed-integer linear problems. Some numerical examples are provided to illustrate the added value of the proposed model.

In this paper, a Markov chain origin-destination matrix estimation problem is investigated in which the average time between two incoming streams to or outgoing streams from nodes in consecutive time periods is considered as a Markov chain. Along with, a normal distribution with pre-determined parameters in each period is considered for traffic counts on links. A bi-level programming problem is introduced where in its upper level the network flow pattern in the n th period is estimated so that the probability of the estimated traffic counts is maximized, while in the lower level a traffic assignment problem with the equilibrium conditions is solved. We reduce the proposed nonlinear bi-level model to a new one level linear programming problem, where by using a trust-region method the local optimal solutions are obtained. Some numerical examples are provided to illustrate the efficiency of the proposed method.

In this paper, a new method categorized as a modification to the Q–h equations is proposed for the analysis of pipe networks. The method is inspired by the Kani method which is used for analysis of structural frames. The new method can be considered as an iterative procedure which does not require a large system of nonlinear equations to be solved simultaneously. The main advantages of the proposed method are relative simplicity in formulation and programming, and rapid and smooth convergence of initially guessed values. To show the robustness and convergence of the present method, several pipe networks are analyzed, and the results are compared with those of the conventional methods.

In the current research, silver (Ag) nanoparticles were synthesized by chemical reduction method and polyethylene–silver nanocomposites containing different Ag contents (0–30 mass%) were fabricated by mechanical milling method. All samples were characterized by X-ray diffraction, UV–Vis spectroscopy, scanning electron microscopy, and transmission electron microscopy. Thermal behavior of these nanocomposites was investigated using differential scanning calorimetry (DSC) and thermogravimetric analysis. The results of the DSC tests showed that although the melting temperature (T m) of PE and its nanocomposites has the same value, the crystallization temperature (T c) increases by addition of silver nanoparticles (Ag NPs) in the PE matrix, indicating that the Ag NPs act as nucleation sites. Also, crystallinity index of PE and its nanocomposites was calculated. Results show that the crystallization index changes from 45.74 to 57.36% as Ag NPs content increases from 0 to 30%. The mass loss of nanocomposites was lower than that of the pure PE, which could be attributed to the undecomposed Ag NPs.

The general problem of estimating origin-destination matrices in congested traffic networks is formulated as a mathematical program with equilibrium constraints. In this paper a path flow entropy model based on an equilibrium assignment approach is presented. It is assumed the flows on links are disaggregated due to some external reasons. Using the L-shaped algorithm the problem is transformed into a problem with less constraints. Then applying the lagrangian dual function the original problem is reduced to a nonlinear convex problem with only one linear constraint.

In this work, the densities of 48 refrigerant systems from 5 different categories including hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), hydrofluoroethers (HFEs), perfluoroalkanes (PFAs), and perfluoroalkylalkanes (PFAAs) have been studied using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 3825 data points of liquid density at several temperatures and pressures have been used to train, validate and test the model. This study shows that the ANN-GCM model represents an excellent alternative to estimate the density of different refrigerant systems with a good accuracy. The average absolute deviations for train, validation, and test sets are 0.18, 0.26, and 0.28, respectively. A comparison between our results and those obtained from some previous methods shows that as well as generality, this model can predict the density of different refrigerants in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions.

Non-isothermal crystallization kinetics of pure medium density polyethylene (MDPE) and MDPE-clay nanocomposites have been investigated by differential scanning calorimeter. The modified Avrami, Ozawa, Liu and Ziabicki equations have been applied to describe non-isothermal crystallization process. The results of Avrami analysis showed a very complicated crystallization mechanism. Although, Ozawa equation failed to provide an adequate description for non-isothermal crystallization process, Liu equation could describe it well. The data showed the crystallization rate of MDPE and nanocomposites raises with increasing cooling rate and the crystallization rate of nanocomposite is faster than that of MDPE at a given cooling rate. Ziabicki’s kinetic crystallizability index showed that clay can increase the ability of MDPE to crystallize, when it is cooled at unit cooling rate. The activation energy of samples has been evaluated by Kissinger method. The results showed that the activation energy of nanocomposite was lower than that of MDPE.

In the previous studies, the GMA (Goharshadi–Morsali–Abbaspour) equation has been extended to the long chain n-alkanes, alcohols, amines, ketones, 1-carboxylic acids and esters via the group contribution method (GCM). The new proposed equation was named GCM-GMA equation. In this work, we have extended the GCM-GMA equation to predict the molar density of long chain ethers and glycol ethers and the binary and ternary mixtures of different organic compounds with ethers and glycol ethers. Each of these organic compounds has been assumed as a hypothetical mixture of methyl, methylene, and a functional group. Then, the GMA equation has been modified for such a hypothetical mixture. Propane, n-butane, n-hexane, 1-pentanol, 2-pentanol, and diethyl ether have been used to investigate the contribution of the different segments in the GCM-GMA equation parameters. The calculated parameters along with the GCM-GMA equation have been used to calculate the molar density of different long chain ethers and glycol ethers and their binary and ternary mixtures. The values of statistical parameters between experimental and calculated density show the ability of this equation to predict the density of these organic compounds. The GCM-GMA equation has been compared with four cubic equations of state. The results show that the GCM-GMA gives better results than other methods. Also, the excess molar volumes of some binary mixtures have been calculated from the density data at different temperatures.

In this work, the extended Lennard–Jones potential-based equation of state (ELJ-based EoS) on which the effective near-neighbor pair interactions are LJ (12,6,3) type has been extended to predict the density and other thermodynamic properties of hydrocarbons up to extremely high temperature, high pressure (HTHP) conditions. It seems that, at least in the dense regions, there are no upper and lower density limitations in the applicability of the model for different hydrocarbon systems. Having the temperature dependence of the parameters of new EoS, the parameters can be determined at any temperature for each of 38 studied compounds, including short- and long-chain alkanes ranging from CH 4 to n-C 40 H 82 , and also several cycloalkanes, highly branched alkanes and aromatic hydrocarbons. The calculated parameters have been used to calculate the density and other derived thermodynamic properties of different hydrocarbons at any temperature and pressure. The results show that ELJ-based EoS is clearly superior to those obtained using previous works in accord with experimental data up to HTHP conditions.

Magnetic nanofluids, ferrofluids, are a special category of smart nanomaterials, consisting of stable dispersion of magnetic nanoparticles in different fluids. In this study, magnetic nanoparticles of hematite, α-Fe2O3, were prepared by solvothermal method using Fe(NO3)3 as a starting material. The nanoparticles were characterized by X-ray diffraction (XRD) and transmission electronic microscope (TEM). To the best of our knowledge, this is the first research on the rheological properties of nanofluids of α-Fe2O3 nanoparticles and glycerol. The experimental results showed that the viscosity of α-Fe2O3-glycerol nanofluids increases with increasing the particle volume fraction and decreases with increasing temperature. Our results clearly showed that the α-Fe2O3-glycerol nanofluids are non-Newtonian shear-thinning and their shear viscosity depends strongly on temperature. The experimental data were compared with some theoretical models. The measured values of the effective viscosity of nanofluids are underestimated by the theoretical models.

A convenient microwave method in preparation of zinc oxide nanoparticles (ZnONPs) using an ionic liquid, trihexyltetradecylphosphonium bis{(trifluoromethyl)sulfonyl}-imide, [P 66614 ][NTf 2 ], as a green solvent is described in this paper. To the best of our knowledge, there is no report for synthesizing any nanoparticle using this ionic liquid. Trihexyltetradecylphosphonium bis{(trifluoromethyl)sulfonyl}-imide has low interface tension and thus it can enhance the nucleation rate, which is favorable to the formation of smaller ZnONPs. The fabricated ZnONPs were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and UV-vis spectroscopy. The XRD pattern reveals that the ZnONPs have hexagonal wurtzite structure. The strong intensity and narrow width of ZnO diffraction peaks indicate that the resulting nanoparticles are of high crystallinity. The synthesized ZnONPs show direct band gap of 3.43 eV. The UV–vis absorption spectrum of ZnONPs dispersed in ethylene glycol at room temperature revealed a blue-shifted onset of absorption.

Magnetite Fe 3 O 4 nanoparticles were synthesized by a co-precipitation method at different pH values. The products were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and transmission electronic microscopy. Their magnetic properties were evaluated on a vibrating sample magnetometer. The results show that the shape of the particles is cubic and they are superparamagnetic at room temperature. Magnetic nanofluids were prepared by dispersing the Fe 3 O 4 nanoparticles in water as a base fluid in the presence of tetramethyl ammonium hydroxide as a dispersant. The thermal conductivity of the nanofluids was measured as a function of volume fraction and temperature. The results show that the thermal conductivity ratio of the nanofluids increases with increase in temperature and volume fraction. The highest enhancement of thermal conductivity was 11.5% in the nanofluid of 3 vol% of nanoparticles at 40 °C. The experimental results were also compared with the theoretical models.

Zinc oxide nanoparticles have been synthesized by microwave decomposition of zinc acetate precursor using an ionic liquid, 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide, [bmim][NTf 2 ] as a green solvent. The structure and morphology of ZnO nanoparticles have been characterized using X-ray diffraction and transmission electron microscopy. The ZnO nanofluids have been prepared by dispersing ZnO nanoparticles in glycerol as a base fluid in the presence of ammonium citrate as a dispersant. The antibacterial activity of suspensions of ZnO nanofluids against ( E. coli ) has been evaluated by estimating the reduction ratio of the bacteria treated with ZnO. Survival ratio of bacteria decreases with increasing the concentrations of ZnO nanofluids and time. The results show that an increase in the concentrations of ZnO nanofluids produces strong antibacterial activity toward E. coli .

In this work, the excess thermodynamic properties, namely excess molar Gibbs energy, excess molar enthalpy, excess molar entropy, excess molar internal energy, and excess molar Helmholtz energy for four polymer mixtures and blends at different temperatures, pressures, and compositions have been calculated using the GMA equation of state. We have also calculated the activity coefficient for these polymeric mixtures using the GMA equation of state. The values of statistical parameters between experimental and calculated properties show the ability of this equation of state in reproducing and predicting the excess thermodynamic functions and activity coefficients for studied polymeric mixtures.

Medium-density polyethylene (MDPE) matrix nanocomposites reinforced with different clay contents were produced by using a planetary ball mill. The morphology of all the milled samples, including pure polyethylene and the polyethylene-clay nanocomposites, was examined by scanning electron microscopy. To investigate the effects of milling time and clay content on the particle size of polyethylene powder, sieve analysis was used. The results showed that during milling, the regular shape of pure polyethylene powder converts into flake shapes and the average particle size of the powder increases upon increasing the milling time because the welding mechanism is predominant. Also, the presence of the clay can increase the maximum-particle size, and increasing the clay content can decrease the milling time required to reach the same maximum particle size.

In the present work, we have used a simple equation of state called the GMA EoS to calculate the density of three ionic liquid mixtures including 1-butyl-3-methylimidazolum hexafluorophosphate, [BMIM] [PF6] + methanol, 1-butyl-3-methylimidazolum tetrafluoroborate, [BMIM] [BF4] + methanol, and [BMIM] [BF4] + ethanol at different temperatures, pressures, and compositions. The isothermal compressibility, excess molar volumes, and excess Gibbs molar energy of these mixtures have been computed using this equation of state. The values of statistical parameters show that the GMA EoS can predict these thermodynamic properties very well within the experimental errors. The results show that isothermal compressibility of ionic liquids is lower than alcohols and the effect of temperature and pressure on the isothermal compressibility of ionic liquids is lower than alcohols. The excess molar volumes and excess molar Gibbs energy for these ionic liquid mixtures with alcohols are all negative at various temperatures and pressures over the whole composition range. The results have been interpreted in terms of intermolecular interactions and structural factors of the ionic liquids and alcohols.

Polymer-clay nanocomposites were fabricated from medium-density polyethylene and organically modified Na-montmorillonite (MMT) using the planetary ball milling as a new method. The materials were characterized using scanning electron microscopy, transmission electron microscopy, and X-ray diffraction (XRD). The crystalline size of polyethylene (PE) filled with MMT was studied using XRD. These studies indicated that the addition of MMT decreases the crystalline size of the polymer. Moreover, the degree of crystallinity of both ball-milled and un-milled PE and PE-clay nanocomposites was measured by applying the XRD technique. The results show that both milling time and clay content decrease the degree of crystallinity of PE.

In this work, we have calculated the densities of two mixtures composed of 1-propanol + toluene and ethanol + toluene at different temperatures, pressures, and compositions using GMA equation of state (EoS). We have also calculated some thermodynamic properties of these mixtures such as isobaric thermal expansion coefficient, isothermal compressibility coefficient, internal pressure, and excess functions such as the excess volumes, excess Gibbs free energy, excess enthalpy, excess entropy, excess internal energy, and excess Helmholtz free energy. A wide comparison with the experimental data shows the ability of this equation of state to reproduce and predict different thermodynamic properties. The composition dependence of the parameters of this equation of state has been assumed as quadratic functions of mole fraction. Using these quadratic mixing rules, the excellent agreement has been obtained between the calculated values and experimental data. To assess the validity of GMA EoS, the density of studied mixtures has been calculated based on this equation of state and Tait equation and compared using statistical parameters. The lower values of statistical parameters obtained by GMA EoS for these mixtures confirm the ability of this EoS to reproduce and predict the density of mixtures.