Prediction Of Gas Permeability Research Articles

Using a New Model for Prediction of Gas Permeability through MMMs: Considering Effects of Particles Shape, Polymer Chain Rigidification, Partial Pore Blockage, and Void Formation

Many researchers used some models based on the assumption of spherical in shape particles. Particles shape is important and should be considered in models. In this work, a new model was used for prediction of gas permeability through the Mixed Matrix Membranes (MMMs). The effects of particles shape and undesirable defects (polymer chain rigidification, partial pore blockage and void formation) were introduced to the model simultaneously. The model was employed to predict some experimental data. The results showed that the less average absolute relative error (AARE) of the model for the MMMs were about 0.73–31.53%.

Modelling respiration in fresh-cut pineapple and prediction of gas permeability needs for optimal modified atmosphere packaging

This study investigated the effects of intrinsic factors (origin, physiological age and seasonality) and extrinsic factors (cut-size, blade-sharpness and dipping treatments) on respiration rate (RCO2) of fresh-cut pineapple chunks. A mathematical model for respiration rate based on exponential decay was developed that showed a gradual decrease in rate with time. The model parameters (Ri and Req, initial respiration rate and equilibrium respiration rate, respectively) were found useful to compare respiration rates for the factors studied. The average values were 8.52±4.68 and 2.64±0.68ml/kgh for Ri and Req, respectively. Ri was affected to a greater extent by physiological age and origin than by season. Cut size had a considerable effect on Ri and Req, with larger cut pieces having the lowest Ri and Req of 5.9 and 2.7ml/kgh, respectively. In contrast, smallest cut pieces had highest Ri (7ml/kgh) and Req (3.2ml/kgh). Cutting the fruit with a razor sharp blade versus a blunt blade decreased the Ri, while only caused a slight reduction in Req. The target O2 and CO2 transmission rate required for optimal modified atmosphere packaging were 7300–12,500 and 13,900–23,500ml/m2dayatm covering variability in respiration rate due to intrinsic and extrinsic factors studied.

Prediction of gas permeability in mixed matrix membranes using theoretical models

This paper extensively discussed the gas permeabilities in MMM using theoretical models and the results were compared with the published experimental data. The models considered are: Maxwell model modified Maxwell model, Lewis–Nielsen model, modified Lewis–Nielsen model and Felske model. The selected models were compared based on published experimental data (168 data point) of MMMs, inorganics including zeolites NaA and NaX and neat polymer permeabilities. The results generated based on standard deviation, σ and absolute average relative error, % AARE for the models studied show a decreasing order: Lewis–Nielsen model > Maxwell model > modified Lewis–Nielsen model> modified Maxwell model > Felske model. This indicates that the most reliable predictive model in predicting gas permeability in MMM is Felske model.

Prediction of gas permeability of polymer membrane materials using an improved empirical statistical method

The objective of this study was to develop a quantitative structure-property relationship (QSPR) using an artificial neural network (ANN) in order to improve the prediction of gas permeability coefficients of membranes for some major industrial gases, such as O2, N2, CO2, and CH4.Using a polymer data bank based on 149 polymers, a total of 21 descriptors were calculated for all polymers using the group contribution technique of Yampolskii, which decomposes polymers’ structures into their smallest groups and characterizes a polymer's repetitive units.These molecular descriptors represent the network inputs, whereas the gas permeability coefficients of the polymers constitute its output.Permeability prediction results of various calculations obtained with the developed ANN models provided highly encouraging results. Correlation factors R of 0.999, 0.999, 0.984 and 0.999 and low root mean square errors (RMS) of 1.054, 2.635, 150, and 2.46 were reached for N2, O2, CO2 and CH4, respectively. Significant improvements in the predictions of the proposed ANN models were observed compared with the previously published results, expressed in terms of performance factor C, which generally ranged from 0.0075 to 0.06 for all studied gases. A best value of 0.77 was achieved for the CO2 permeability prediction by a published study in this field.

Correlation and prediction of gas permeability in glassy polymer membrane materials via a modified free volume based group contribution method

Over the past decade or more an extensive amount of data on the permeation of gases such as helium, hydrogen, oxygen, nitrogen, methane, and carbon dioxide in a wide array of glassy polymers has been published. Much of this work has been motivated by the search for materials with high permeability and high selectivity for potential use as gas separation membranes. This paper attempts to develop a method for correlating this data in a way that permits prediction of permselectivity behavior of other polymer structures. The method used involves an empirical modification of a free volume scheme that has been used in the past with some success. The previous method requires an experimental density of the polymer and an estimate of occupied volume from a group contribution method developed by Bondi. The present method actually predicts the density and uses a refined estimate of occupied volume specific to each gas. The parameters in the model were deduced from a database including over one hundred polymers. The new method significantly improves the accuracy of correlation and of prediction.