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     

Gas sorption in H2-selective mixed matrix membranes: Experimental and neural network modeling

Robust artificial neural network (ANN) was developed to forecast sorption of gases in membranes comprised of porous nanoparticles dispersed homogenously within polymer matrix. The main purpose of this study was to predict sorption of light gases (H₂, CH₄, CO₂) within mixed matrix membranes (MMMs) as function of critical temperature, nanoparticles loading and upstream pressure. Collected data were distributed into three portions of training (70%), validation (19%), and testing (11%). The optimum network structure was determined by trial-error method (4:6:2:1) and was applied for modeling the gas sorption. The prediction results were remarkably agreed with the experimental data with MSE of 0.00005 and correlation coefficient of 0.9994.     

Polyurethane-SAPO-34 mixed matrix membrane for CO2/CH4 and CO2/N2 separation

SAPO-34 nanocrystals (inorganic filler) were incorporated in polyurethane membranes and the permeation properties of CO₂, CH_4, and N₂ gases were explored. In this regard, the synthesized PU-SAPO-34 mixed matrix membranes (MMMs) were characterized via SEM, AFM, TGA, XRD and FTIR analyses. Gas permeation properties of PU-SAPO-34 MMMs with SAPO-34 contents of 5 wt%, 10 wt% and 20 wt% were investigated. The permeation results revealed that the presence of 20 wt% SAPO-34 resulted in 4.45%, 18.24% and 40.2% reductions in permeability of CO_2, CH_4, and N₂, respectively, as compared to the permeability of neat polyurethane membrane. Also, the findings showed that at the pressure of 1.2 MPa, the incorporation of 20 wt% SAPO-34 into the polyurethane membranes enhanced the selectivity of CO₂/CH₄ and CO₂/N₂, 14.43 and 37.46%, respectively. In this research, PU containing 20 wt% SAPO-34 showed the best separation performance. For the first time, polynomial regression (PR) as a simple yet accurate tool yielded a mathematical equation for the prediction of permeabilities with high accuracy (R² > 99%).     

H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS

The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H₂-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH₄, CO₂, C₃H₈ and H₂ was used for ANFIS modeling. In this manner, the H₂/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R²) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R² values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively.     

Sorption properties of hydrogen-selective PDMS/zeolite 4A mixed matrix membrane

The present study explores the fundamental science of estimating sorption of gases in membranes comprised of inorganic porous fillers within a polymer matrix with a novel semi-empirical correlation. The sorption properties of H₂, C₃H₈, CO₂ and CH₄ were determined in polydimethylsiloxane (PDMS)/zeolite 4A mixed matrix membranes (MMMs) to assess the viability of these membranes for hydrogen purification and natural gas sweetening. Zeolite filling in MMMs results an increase in solubility over neat PDMS membrane. In addition, incorporation of zeolite 4A to PDMS membrane improved H₂ permeation and H₂/CH₄ selectivity. The results confirmed that zeolite 4A can significantly improve the separation properties of poorly H₂-selective PDMS membrane from 0.7 up to 11 and this overcomes the Robeson upper-bound limitation. This improvement was explained referring the Flory–Huggins interaction parameter within MMMs.     

ILSBMR: improved LSBMR (ILSBMR) method

The Least Significant Bit Matching Revisited (LSBMR) is among the most commonly used methods on image steganography, aiming to make only smaller changes in an image. While security is considered as one of the basic evaluation criteria for steganography techniques and notably, LSBMR can be easily recognized due to its well-known structure, it is important to find a way to help improve this approach so that it might cause less variation in the image and also increase security. To this end, the current paper deploys divided blocks of the original cover image and a selection of the best layout to embed secret message bits from each block into color images by using LSBMR. The simulation results indicate that this improved method makes small changes in the image and increases its security, compared to the LSBMR technique.     

State-of-the-art membrane based CO2 separation using mixed matrix membranes (MMMs): An overview on current status and future directions

The main purpose of research in membrane gas separation is to develop membranes with high permeability and selectivity. Historically, the gas separation performance of polymeric membranes has been constrained to an upper performance limit. Hence, different methods have been investigated to prepare membranes that can exceed this limitation including the incorporation of inorganic materials into polymer matrices. Membranes formed by this method are called mixed matrix membranes (MMMs). The major challenge is to prepare a defect-free polymer/inorganic nanoparticles interfaces with enhanced separation performance and mechanical and thermal stability. For this purpose, various types of nanoparticles have been proposed and examined experimentally. This review is especially devoted to summarize the fundamental concepts that have to be considered to prepare various types of MMMs, including considerations for the design novel MMMs that will eventually surpass the Robeson's trade-off upper bound. In addition, it provides the pros and cons of various factors that affect the MMM preparation especially for CO₂ separation processes.     

Pervaporation study of ethylene glycol dehydration through synthesized (PVA–4A)/polypropylene mixed matrix composite membranes

Poly(vinyl alcohol) (PVA)/zeolite 4A mixed matrix composite membranes supported on polypropylene microfiltration membranes were prepared by solution casting method and crosslinked with glutaraldehyde to investigate their pervaporation (PV) separation properties of water–ethylene glycol mixtures. Scanning electron microscopy images showed homogeneous distribution of zeolite nanoparticles within the polymer matrix without any visible macroscopic voids at the zeolite–polymer interface. The PV experiments were accomplished to investigate the effects of water concentration (10–40 wt%) and temperature (60–80°C) on separation performance of the membranes. It was found out that 5 wt% loading of the 4A nanoparticles into the PVA matrix is optimal to obtain the best separation performance. The experimental results revealed that loading of zeolite 4A enhanced the membrane performance [both permeation flux (5%) and separation factor (32%) at 5 wt% zeolite loading]. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers     

Transient computational fluid dynamics modeling of pervaporation separation of aromatic/aliphatic hydrocarbon mixtures using polymer composite membrane

Pervaporation (PV) separation of toluene/n‐heptane mixtures was studied experimentally and theoretically by means of a molecular surface engineering (MSE) polymer composite membrane. A comprehensive mathematical model was developed to predict unsteady state transport of toluene and n‐heptane (nC₇) through the membrane. Conservation equations including continuity, and heat transfer equations were solved using finite element method (FEM). Computational fluid dynamics (CFD) technique was applied to solve the model equations. The model was then verified with PV experimental data. The simulation results were in good agreement with the experimental data. The simulation results revealed that the proposed model could provide a general simulation of transport in the PV process. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers     

Water Desalination Using Solar Thermal Collectors Enhanced by Nanofluids

The absorption efficiency of a solar collector using different types of nanofluids was improved. Experimental work was carried out to investigate the flat-plate and evacuated-tube collectors under outdoor conditions to produce distilled water. A pilot plant was designed and installed. The yield of distilled water at different seawater flow rates and the physical properties of nanofluids were determined. Solar intensity, water mass flow rate, and temperature were measured. The performance of the desalination unit was evaluated in the presence of carbon nanotubes in paraffin wax and ethylene glycol nanofluids. The evaporation efficiency of the flat-plate collector was improved up to 36 % in the presence of ethylene glycol nanofluid at 80–100 °C.