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Characterizing nonzeolitic pores in MFI membranes / Yu Miao in Industrial & engineering chemistry research, Vol. 47 n°11 (Juin 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°11 (Juin 2008) . - p. 3943–3948 Titre : Characterizing nonzeolitic pores in MFI membranes Type de document : texte imprimé Auteurs : Yu Miao, Auteur ; Falconer, John L., Auteur ; Richard D. Noble, Auteur Année de publication : 2008 Article en page(s) : p. 3943–3948 Note générale : Bibliogr. p. 3947-3948 Langues : Anglais (eng) Mots-clés : n-hexane;  MFI  zeolite  membranes Résumé : Methods that use n-hexane (n-hexane permporosimetry and n-hexane/2,2-dimethylybutane (DMB) separation) are shown to not be effective for characterizing MFI zeolite membranes because n-hexane adsorption swells MFI crystals and shrinks the size of nonzeolitic pores. Measurements on a membrane in which 30% of its helium flux at 300 K was through nonzeolitic pores demonstrate that benzene permporosimetry and isooctane vapor permeation as a function of feed activity provide better characterizations. Isooctane condensed in nonzeolitic pores at high activities, and this was used to estimate the sizes of those pores. The average nonzeolitic pore size in this membrane decreased from approximately 3.0 to 1.5 nm as the temperature increased from 300 to 348 K, apparently due to thermal expansion of MFI crystals. Benzene permporosimetry yielded dramatically different results from n-hexane permporosimetry because benzene does not swell the MFI crystals significantly. Single-component pervaporation fluxes as a function of molecular kinetic diameter verified the results from benzene permporosimetry. Larger molecules had higher fluxes than n-hexane because they diffused through nonzeolitic pores that were shrunk by n-hexane adsorption. Nonzeolitic pores were estimated to account for only 0.5% of the membrane permeation area, but 30% of the helium flux, because these pores were significantly larger than MFI pores. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071577t [article] Characterizing nonzeolitic pores in MFI membranes [texte imprimé] / Yu Miao, Auteur ; Falconer, John L., Auteur ; Richard D. Noble, Auteur . - 2008 . - p. 3943–3948. Bibliogr. p. 3947-3948 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°11 (Juin 2008) . - p. 3943–3948 Mots-clés : n-hexane;  MFI  zeolite  membranes Résumé : Methods that use n-hexane (n-hexane permporosimetry and n-hexane/2,2-dimethylybutane (DMB) separation) are shown to not be effective for characterizing MFI zeolite membranes because n-hexane adsorption swells MFI crystals and shrinks the size of nonzeolitic pores. Measurements on a membrane in which 30% of its helium flux at 300 K was through nonzeolitic pores demonstrate that benzene permporosimetry and isooctane vapor permeation as a function of feed activity provide better characterizations. Isooctane condensed in nonzeolitic pores at high activities, and this was used to estimate the sizes of those pores. The average nonzeolitic pore size in this membrane decreased from approximately 3.0 to 1.5 nm as the temperature increased from 300 to 348 K, apparently due to thermal expansion of MFI crystals. Benzene permporosimetry yielded dramatically different results from n-hexane permporosimetry because benzene does not swell the MFI crystals significantly. Single-component pervaporation fluxes as a function of molecular kinetic diameter verified the results from benzene permporosimetry. Larger molecules had higher fluxes than n-hexane because they diffused through nonzeolitic pores that were shrunk by n-hexane adsorption. Nonzeolitic pores were estimated to account for only 0.5% of the membrane permeation area, but 30% of the helium flux, because these pores were significantly larger than MFI pores. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071577t

Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection / Yu Miao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Titre : Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection Type de document : texte imprimé Auteurs : Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur Année de publication : 2010 Article en page(s) : pp. 10903–10911 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Support--Vector--Regression--Approach--Simultaneous--Data--Reconciliation--Gross  Error--Outlier  Detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f [article] Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection [texte imprimé] / Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur . - 2010 . - pp. 10903–10911. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Mots-clés : Support--Vector--Regression--Approach--Simultaneous--Data--Reconciliation--Gross  Error--Outlier  Detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f

Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection / Yu Miao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Titre : Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection Type de document : texte imprimé Auteurs : Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur Année de publication : 2010 Article en page(s) : pp. 10903–10911 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Support  vector  regression  Outlier  detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f [article] Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection [texte imprimé] / Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur . - 2010 . - pp. 10903–10911. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Mots-clés : Support  vector  regression  Outlier  detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f