The Practical Aspects in the Determination of Membrane Properties for Gas Permeation
Notice bibliographique
Résumé
Membrane-based pressure driven processes have been widely studied and have been used in a myriad of applications.The objective of the characterization of membranes in which gas sorption obeys Henry's law is to determine the solubility S and diffusivity D of the membrane.The time-lag method, developed by Daynes [1] and Barrer [2], is commonly used as the integral approach [3] in the characterization of membranes by measuring the cumulated amount of permeated gas after a sudden change of the upstream boundary condition [3].One of the important assumptions of the time-lag method is that the amount of permeate gas accumulating in the downstream receiver is small enough to have a negligible effect on the driving force.To better satisfy this assumption, it was claimed that a capacity parameter (related to the receiver volume) should be small enough [4].Nevertheless, due to the existence of measurement noise in experimental data, the accurate determination of the time lag is difficult and a small capacity parameter will intensify the effect of noise.The accuracy of the estimated time lag is also influenced by the noise level and the data analysis procedure.An alternative to obtain the membrane properties is to fit the variation of the pressure change in the downstream reservoir as a function of time using the nonlinear regression method [5].By minimizing the sum of squares of the differences between the experimental data and the predictions from the numerical model, the best combinations of the membrane properties can be obtained.The latter method also allows using the real rather than ideal boundary conditions at the membrane interfaces.In this paper, the experimental results are obtained from the constant volume membrane system which consists of two fixed volumes separated by a membrane cell module [7].The system is initially evacuated using a vacuum pump prior to each experiment.The permeation process is initiated by performing a step change of the gas pressure at the upstream side of the membrane.The progressive permeation of the gas within the membrane leads to a pressure increase at the downstream side of the membrane which is recorded by a high precision pressure transducer.The time lag can be determined from the time-axis intercept of the quasi steady-state portion of the pressure rise curve plotted versus time.To gain a better understanding of the gas permeation process, a numerical model was also used to simulate the real experimental process and to predict the behaviours of membranes and various boundary conditions [9,10].In this paper, practical suggestions for the determination of membrane properties are given based on the commonly used time-lag method and on the nonlinear regression method.For the time-lag method, the major sources of noise were analysed and the level of noise as well as the length of the evaluation window were evaluated to estimate the accuracy of time lag results.Suggestions are given based on how to reduce the noise by properly selecting the design parameters of the experiments, such as the downstream volume, and the data analysis.For the nonlinear regression method, results show that it is nearly impossible to recover the real values of the membrane permeation properties, i.e. S, D due to the very strong correlations that prevail between S and D. Instead of exploring the effect of individual values of the membrane properties, combinations of the property coefficients that lead to same accuracy were studied.Contour maps and suggestions on the relative weights of each section of the pressure rise versus time curve are provided in the paper to assist researchers to better use the characterization method of the membrane.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».