Abstract

In this study we present a new methodology for correcting experimental Zero Length Column data, to account for contributions to the measured signal arising from extra-column volumes and the detector. The methodology considers the experimental setup as a series of mixing volumes with diffusive pockets whose contributions to the overall measured signal can be accurately described by simple model functions. The composite effect of the individual contributions is subsequently described through the method of convolution. It is shown that the model parameters are closely related to the physical characteristics of the setup components and as such they remain valid over a range of process conditions. The methodology is firstly validated through fitting to experimental experiments without adsorbent present. The inverse procedure of deconvolution can in turn be applied to experimental data with adsorbent, to yield corrected data which can readily be modelled using standard tools for equilibrium and kinetic analysis. A number of case studies is finally presented exemplifying the effect of applying accurate blank corrections, demonstrating also the application to a nonlinear adsorption system.

Highlights

  • Adsorption is a widely used technology for separation, purification and drying, due to its simplicity, reliability and scalability

  • The technology is run in a cyclical fashion, e.g. pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), etc

  • We have developed a correction procedure for adsorption column measurements, which accurately takes into account the extra-column effects due to the experimental setup configuration

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Summary

Introduction

Adsorption is a widely used technology for separation, purification and drying, due to its simplicity, reliability and scalability. Joss and Mazzotti use a similar approach based on dispersed plug-flow and by adding a stagnant volume to their ECV model manage to account for mass transfer and heat effects to accurately describe extra-column contributions for a range of pressures and flow rates (Joss and Mazzotti 2012). As their model is partly empirical and does not attempt to capture the physics of their experimental setup, model parameters require adjusting for different process conditions. The key assumption of the deconvolution procedure is linearity and this is typically the case for the dynamic response of all the components before the ZLC and the detector

Materials
Equipment
Simulation of blank experiments through the method of convolution
Effect of changing process conditions
Model transferability
Deconvolution of the experimental signal
Deconvolution of actual experimental data—case studies
Case study 4—effect of non‐linearity on deconvolution procedure
Conclusions
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