A. Bos

CO2-induced plasticization phenomena in glassy polymers

A typical effect of plasticization of glassy polymers in gas permeation is a minimum in the relationship between the permeability and the feed pressure. The pressure corresponding to the minimum is called the plasticization pressure. Plasticization phenomena significantly effect the membrane performance in, for example, CO2/CH4 separation processes. The polymer swells upon sorption of CO2 accelerating the permeation of CH4. As a consequence, the polymer membrane loses its selectivity. Fundamental understanding of the phenomenon is necessary to develop new concepts to prevent it. In this paper, CO2-induced plasticization phenomena in 11 different glassy polymers are investigated by single gas permeation and sorption experiments. The main objective was to search for relationships between the plasticization pressure and the chemical structure or the physical properties of the polymer. No relationships were found with respect to the glass-transition temperature or fractional free volume. Furthermore, it was thought that polar groups of the polymer increase the tendency of a polymer to be plasticized because they may have dipolar interactions with the polarizable carbon dioxide molecules. But, no dependence of the plasticization pressure on the carbonyl or sulfone density of the polymers considered was observed. Instead, it was found that the polymers studied plasticized at the same critical CO2 concentration of 36±7 cm3 (STP)/cm3 polymer. Depending on the polymer, different pressures (the plasticization pressures) are required to reach the critical concentration.

Plasticization-resistant glassy polyimide membranes for CO2/CH4 separations

It is known that CO2 acts as a plasticizer in CO2/CO4 membrane separations at elevated pressures. The polymer matrix swells upon sorption of CO2, accelerating the permeation of CH4. As a consequence, the polymer membrane loses its selectivity. To overcome this effect, plasticization should be minimized. We succeeded in stabilizing the polymer membrane by a thermal treatment. For this purpose the polyimide Matrimid 5218 is used as model polymer. In single gas experiments with CO2, the untreated membrane normally shows a minimum in its pressure dependence on permeability, whereas the treated membranes do not. Membrane performances for CO2/CO4 gas mixtures showed that the plasticizing effect indeed accelerates the permeation of methane. The heat treatment clearly suppresses this undesired methane acceleration. Additionally to the pure and mixed gas permeation results, process calculations reveal valuable information as to what extent the stabilized membranes show improved membrane performance. The favourable performance of the stabilized membrane can be attributed to less methane loss and therefore a higher recovery, resulting in higher profit from gas sales.

Suppression of CO2-plasticization by semiinterpenetrating polymer network formation

CO2-induced plasticization may significantly spoil the membrane performance in high-pressure CO2/CH4 separations. The polymer matrix swells upon sorption of CO2, which accelerates the permeation of CH4. The polymer membrane looses its selectivity. To make membranes attractive for, for example, natural gas upgrading, plasticization should be minimized. In this article we study a polymer membrane stabilization by a semiinterpenetrating polymer network (s-ipn) formation. For this purpose, the polyimide Matrimid 5218 is blended with the oligomer Thermid FA-700 and subsequently heat treated at 265°C. Homogeneous films are prepared with different Matrimid/Thermid ratios and different curing times. The stability of the modified membrane is tested with permeation experiments with pure CO2 as well as CO2/CH4 gas mixtures. The original membrane shows a minimum in its permeability vs. pressure curves, but the modified membranes do not indicating suppressed plasticization. Membrane performances for CO2/CH4 gas mixtures showed that the plasticizing effect indeed accelerates the permeation of methane. The modified membrane clearly shows suppression of the undesired methane acceleration. It was also found that just blending Matrimid and Thermid was not sufficient to suppress plasticization. The subsequent heat treatment that results in the s-ipn was necessary to obtain a stabilized permeability.

Processing of signals from an ion-elective electrode array by a neural network

Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous determination of potassium, calcium, nitrate and chloride in mixtures of potassium and calcium chlorides and ammonium nitrate. The measurements for the Ca2+/Cu2+ determinations were done with a pH-glass electrode and calcium and copper ion-selective electrodes; results were accurate to ±8%. For the K+/Ca2+NO−3/Cl− determinations, the measurements were made with the relevant ion-selective electrodes and a glass electrode; the mean relative error was ±6%, and for the worst cases the error did not exceed 20%.

Artificial neural networks as a tool for soft-modelling in quantitative analytical chemistry: the prediction of the water content of cheese

The application of artificial neural networks for the modelling of a complex process was examined. A real data set concerning the batch production of cheese from an actual plant was used to predict the resulting water content of the cheese from the milk composition and process parameters. Owing to the complex nature of the data and the limited number of available patterns, difficulties were encountered when the standard backward error propagation algorithm was applied and no solution was derived. Several adaptions to the algorithm as suggested in the literature were then examined, and several gave satisfactory solutions. The resulting mean of the absolute values of the absolute prediction errors was 0.25% and 0.29% for known and unknown patterns, respectively, with a worst case error of 0.8%.

Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K? and Ks lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges.

Modelling the permeability of polymers: a neural network approach

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a polymer to its permeability. The underlying assumption is that the chemical information hidden in the IR spectrum is sufficient for the prediction. The best neural network investigated so far does indeed show predictive capabilities.