Correction procedures for extra-column effects in dynamic column breakthrough experiments

• Main Authors: Rajendran, Arvind, Kariwala, Vinay, Farooq, Shamsuzzaman
• Other Authors: School of Chemical and Biomedical Engineering
• Format: Article
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Chemical engineering::Chemical plants; DRNTU::Engineering::Chemical engineering
• Online Access: https://hdl.handle.net/10356/90917; http://hdl.handle.net/10220/4514; http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:ELSEVIER_SCIRUS&id=doi:&genre=&isbn=&issn=&date=2008&volume=63&issue=10&spage=2696&epage=2706&aulast=Rajendran&aufirst=%20A&auinit=&title=Chemical%20Engineering%20Science&atitle=Correction%20procedures%20for%20extra%2Dcolumn%20effects%20in%20dynamic%20column%20breakthrough%20experiments
• Institution: Nanyang Technological University

Dynamic column breakthrough experiments, routinely used to complement adsorption and diffusion studies at the particle scale, constitute an important step in the development and verification of dynamic models for simulation of adsorption processes. Various parts of the experimental set-up contribute to the retention time and band broadening of the experimental breakthrough curve. However, the effect of the extra-column contributions have to be properly accounted for in order to compare the experimental results with theoretical calculations. A common practice is to measure a blank response under the same flow rate, pressure and temperature conditions as the actual experiment by simply bypassing the adsorption column with a tube (or a connector) of negligible volume. This blank response is then subtracted point-by-point from the composite response (i.e., including the adsorption column) to account for extra-column contributions. The underlying assumption here is that blank and column responses are linearly additive, both in terms of mean residence time and band broadening. It is shown that this method of correction can, under certain operating conditions, lead to erroneous results. An alternative procedure based on linear regression is introduced and the improvements achieved by this method are illustrated using simulation examples.