Loading…

Selective adsorption processes for fructooligosaccharides separation by activated carbon and zeolites through machine learning

Fructooligosaccharides (FOS) separation and purification are crucial for industrial applications where adsorption methods are widely used. However, some specific process characteristics can affect the selectivity of the adsorption. In this work, differences between the variables affecting the FOS se...

Full description

Saved in:
Bibliographic Details
Published in:Chemical engineering research & design 2023-02, Vol.190, p.379-394
Main Authors: Piazzi Fuhr, Ana Carolina Ferreira, Vieira, Yasmin, Kuhn, Raquel Cristine, Salau, Nina Paula Gonçalves
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fructooligosaccharides (FOS) separation and purification are crucial for industrial applications where adsorption methods are widely used. However, some specific process characteristics can affect the selectivity of the adsorption. In this work, differences between the variables affecting the FOS separation by fixed-bed column with activated carbon and zeolite as adsorbents were identified and analyzed. Extreme Gradient Boosting (XGBoost) and Shapley Additive explanation (SHAP) methodology were used to determine the best process conditions for each adsorbent considering the most relevant variables such as temperature, time and ethanol concentration. XGBoost showed high predictive power for both adsorbents, reaching R of 0.84–0.91 for activated carbon and 0.87–0.98 for zeolite. According to SHAP, the order of importance of the variables for FOS separation for the two adsorbents was time, ethanol concentration, and temperature. Activated carbon shows selectivity for FOS at low ethanol concentrations (7.95% v/v). Zeolite required ethanol concentrations about 8 times higher than activated carbon. From SHAP it can be concluded that activated carbon is impressively efficient in improving FOS selectivity. Results showed that machine learning is a valuable tool to better understand the optimal conditions for FOS separation, allowing for higher recovery rates, efficiencies at lower ethanol concentrations, and consequently lower costs. [Display omitted] •Saccharide adsorption process using fixed-bed column.•Comparison between active carbon and zeolite as adsorbents for FOS selectivity.•SHAP and ExplainerDashboard methodology to propose conditions for FOS selectivity.•Activated carbon and low ethanol concentration are selective for FOS.•Experimental conditions analyzed showed the zeolite with low FOS selectivity.
ISSN:0263-8762
DOI:10.1016/j.cherd.2022.12.041