Matrix based method for synthesis of main intensified and integrated distillation sequences

The objective of many studies in this area has involved access to a column-sequencing algorithm enabling designers and researchers alike to generate a wide range of sequences in a broad search space, and be as mathematically and as automated as possible for programing purposes and with good generality. In the present work an algorithm previously developed by the authors, called the matrix method, has been developed much further. The new version of the algorithm includes thermally coupled, thermodynamically equivalent, intensified, simultaneous heat and mass integrated and divided-wall column sequences which are of gross application and provide vast saving potential both on capital investment, operating costs and energy usage in industrial applications. To demonstrate the much wider searchable space now accessible, a three component separation has been thoroughly examined as a case study, always resulting in an integrated sequence being proposed as the optimum.     

Ternary gas permeation through synthesized pdms membranes: Experimental and CFD simulation basedon sorption‐dependent system using neural network model

In this study, a predictive model for the separation of gases via a polydimethylsiloxane (PDMS) membrane has been developed. This model takes into account the effects of gas composition and pressure at the membrane surfaces on the gas sorption and diffusion coefficients in the membrane. Computational fluid dynamics (CFD) modeling has been employed in order to predict the behavior of a gas mixture containing C₃H₈, CH₄, and H₂ at various operating conditions and three zones (upstream, downstream, and membrane body). Artificial neural network (ANN) modeling has been applied to predict sorption and diffusion coefficients of each component of the gas mixture in the membrane. A procedure of calculation has been applied to combine the CFD modeling and the ANN modeling in order to predict sorption, diffusion, and composition of each component at various sites of the membrane. The results determined using the developed prediction model have been found to be in agreement with those determined using experimental investigations with an average error of 10.21%. POLYM. ENG. SCI., 54:215–226, 2014. © 2013 Society of Plastics Engineers     

Modified poly(vinyl alcohol)/chitosan blended membranes for isopropanol dehydration via pervaporation: Synthesis optimization and modeling by response surface methodology

Box–Behnken (BB) design of response surface methodology (RSM) was effectively applied to optimize fabrication conditions of modified poly(vinyl alcohol) (PVA) and chitosan (CS) blended pervaporation (PV) membranes. The PVA/CS membranes were crosslinked either by chemical reaction with glutaraldehyde (GA) or by heat‐treating at different temperatures. The main objectives were to determine the optimal levels of fabricating parameters and also to investigate interactions among the variables. CS content in the blended membranes, concentration of crosslinking agent and heat‐treating temperature were the fabrication parameters, the main effects and interaction effects of which on membrane structure and PV performance toward isopropanol (IPA)/water dehydration were investigated, and for which regression models were established. The modified PVA/CS blended membranes were characterized by means of scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) as well as X‐ray diffraction (XRD). It was found that the CS content is the most significant factor influencing flux and separation factor among the three studied variables and the experimental results are in excellent accordance with predicted values from the developed RSM regression models. The RSM results indicated that under preparation conditions of 80 wt % CS in the blended membrane, 0.58 wt % GA concentration, and 77 °C heat‐treating temperature, the maximum separation factor of 5222.8 and the normalized flux of 9.407 kg µm/m²h can be acquired with feed content of 85 wt % IPA at 25 °C, showing that the prepared membrane is highly efficient for PV dehydration of IPA. The models were satisfactorily validated against experimental data. Furthermore, the optimum membrane presents excellent separation performance at different feed compositions and temperatures. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44587.     

Microstructures and Properties of Nanocrystalline NiFe Alloy With and Without Particulate TiC Reinforcement by Mechanical Alloying

In the current study, Ni₅₀Fe₅₀ alloy powders were prepared using a high-energy planetary ball mill. The effects of TiC addition (0, 5, 10, 20, and 30 wt pct) and milling time on the sequence of alloy formation, the microstructure, and microhardness of the product were studied. The structure of solid solution phase, the lattice parameter, lattice strain, and grain size were identified by X-ray diffraction analysis. The correlation between the apparent densities and the milling time is explained by the morphologic evolution of the powder particles occurring during the high-energy milling process. The powder morphology was examined using scanning electron microscopy. It was found that FCC γ (Fe–Ni) solid solution was formed after 10 hours of milling, and this time was reduced to 7 hours when TiC was added. Therefore, brittle particles (TiC) accelerate the milling process by increasing crystal defects leading to a shorter diffusion path. Observations of polished cross section showed uniform distribution of the reinforcement particles. The apparent density increases with the increasing TiC content. It was also found that the higher TiC amount leads to larger lattice parameter, higher internal strain, and lower grain size of the alloy.     

A comparison of critical distance methods for fracture prediction

This paper is concerned with four methods which are used to predict the failure of bodies containing notches and other stress concentration features. Two of these methods, which we call the line method (LM) and point method (PM), use parameters taken from the elastic stress field ahead of the notch. The other two methods make use of linear elastic fracture mechanics (LEFM): we call these the imaginary crack method (ICM) and finite fracture mechanics (FFM). A common feature of all the methods is the use of a material constant with the units of length, which we call the critical distance.In this work we test the hypothesis that these four methods all give similar predictions. Firstly, we show analytically that, for the simple case of a straight, through-thickness crack in an infinite body, predictions using the LM are identical to those of the ICM and FFM and, in addition, there is a very simple relationship between the critical distances for the three methods. For notches no precise relationship exists; we used both closed-form solutions and finite element analyses (FEA) to compare predictions of failure from common types of notches, such as circular holes, edge notches and slots, in large (essentially infinite) bodies. We modelled both isotropic and anisotropic materials. In all cases, we found that predictions from the four different methods were of similar magnitude, always falling within an error band of ±10%. However, large differences emerge in the case of finite bodies when the remaining width is similar in size to the critical distance; then predictions become asymptotic in different ways. These findings have practical consequences for the use of these methods in engineering design. The results also promote some discussion about the theoretical basis of critical distance approaches.     

Wettability and Its Effects on Oil Recovery in Fractured and Conventional Reservoirs

Abstract

The lattice Boltzmann method is a relatively new simulation technique of computational fluid dynamic class. Its several advantages such as dealing with complex boundary and incorporating of microscopic interaction make it an alternative and promising numerical scheme for simulating fluid flow in porous media. Three lattice Boltzmann equation models are introduced and used for calculating permeability of a 2D porous media. Analytical solutions of Poiseuille flow between infinite parallel plates is used for validating lattice Boltzmann equation models. In the numerical simulations the effects of grid resolution and viscosity on the predicted permeability are checked.     

Growth,corrosion and wear resistance of SiC nanoparticles embedded MAO coatings on AZ31B magnesium alloy

In this research, nanocomposite coating was deposited on magnesium matrix AZ31B alloy using the micro arc oxidation (MAO) method. MAO was carried out in SiC-nanoparticles containing suspension using the sodium silicate and sodium aluminate bases at constant current density. The effect of nanopowder addition and MAO periods were also investigated in the present work. Using the Scanning electron microscopy (SEM), the thickness and surface morphology of the coatings were studied. The coefficient of friction and abrasion rate curves were used to analyze nanopowder addition on resistance to abrasion, while the potentiodynamic curves were used for assessing the resistance to corrosion in the ceramic nanocomposite coating deposited on surface of alloy AZ31B. The morphological studies on surface of coatings revealed that the cavitation level and size increases with the increasing coating duration. Besides, Energy Dispersive X-Ray Diffraction (EDS) analyses from cross section and surface of the prepared coatings revealed that nanopowder distribution on interface of coating with matrix and boundaries of the cavities is almost uniform. The cross section studies of the coatings revealed that their thickness increases, as coating duration prolongs. Furthermore, the corrosion behavior of the samples indicated that presence of nanopowder does not significantly affect the resistance to corrosion of the coatings; however, coefficient of friction and abrasion rate of coatings indicates a respective rise and drop in presence of these nanopowders.     

Improved Permeability Prediction of a Heterogeneous Carbonate Reservoir Using Artificial Neural Networks Based on the Flow Zone Index Approach

Abstract

Permeability is one of the most important parameters required for reservoir characterization. Although core analysis provides more exact information, core data do not exist for all wells in the reservoir because coring is expensive and time consuming. Therefore, another approach should be sought for permeability determination. The objective of this study was to create an artificial neural network (ANN) model in order to use well log data to predict permeability in uncored wells/intervals. The well log, core, and other data were gathered from an Iranian heterogeneous carbonate reservoir. A flow zone indicator was then predicted using an ANN approach with well logs as input variables. The reservoir was thus classified into different zones based on hydraulic flow units to overcome the extreme heterogeneity. Then, a separate ANN training procedure was followed for each flow zone with log data as input variables and permeability as output. This improved method is capable of permeability prediction in heterogeneous carbonate reservoirs in uncored wells/intervals with an average error of less than 10.9%.