Prediction of gas chromatographic peak width in capillary columns at different temperatures, carrier gas flows, column lengths, inside diameters and carbon numbers

Krisnangkura, Kanit; Pongtonkulpanich, Varaporn

doi:10.1002/jssc.200500141

Cited by 4 publications

(1 citation statement)

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“…They subsequently demonstrated the relationship between p ′ and k and applied this to the prediction of w R of n ‐alkanes, FAMES and fatty alcohols in isothermal separations . Krisnangkura later improved on this approach to peak width prediction with the inclusion of terms to account for column geometry and carrier gas flow rates . A few years later, Aldaeus et al.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

Stevenson

Harynuk

2019

J of Separation Science

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Thermodynamics-based models have been demonstrated to be useful for predicting retention time and peak widths in gas chromatography and two-dimensional gas chromatography separations. However, the collection of data to train the models can be time consuming, which lessens the practical utility of the method. In this contribution, a method for obtaining thermodynamic-based data to predict peak widths in temperature-programmed gas chromatography is presented. Experimental work to collect data for peak width prediction is identical to that required to collect data for retention time prediction using approaches that we have presented previously. Using this combined approach, chromatograms including retention times and peak widths are predicted with very high accuracy. Typical errors in retention time are < 0.5%, while errors in peak width are typically < 5% as demonstrated using polycycic aromatic hydrocarbons and a mixture containing compounds with aldehyde, ketone, alkene, alkane, alcohol, and ester functionalities. K E Y W O R D Speak width prediction, retention prediction, thermodynamic modeling As such, over the last few decades, researchers have developed methods and tools for predicting and simulating GC separations. Among the approaches to predictive modeling of GC separations, thermodynamic-based models have received a great deal of attention over the years. Much of

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Section: Introductionmentioning

confidence: 99%