TY - JOUR

T1 - Linear regression model of structured column packing form factor

AU - Schlager, Marcus

AU - Tous, Francesca Capo

AU - Wolf-Zöllner, Verena Maria

N1 - Publisher Copyright: © 2024 The Authors

PY - 2024/11/30

Y1 - 2024/11/30

N2 - Semi-empirical correlations are commonly used to predict packed column pressure drop. As a downside, these models rely on packing-specific parameters that are obtained through laborious experiments. This work aims to eliminate packing-specific parameters for modeling pressure drop of structured packings. A multiple linear regression model is used to predict the packing form factor directly from packing properties. The form factor is then used with the model of Maćkowiak (2010) to calculate pressure drop. This tandem model approach therefore allows for pressure drop predictions directly from packing properties. The regression model includes model features to describe packing properties, such as packing geometry, perforations, packing material or packing surface. Model coefficients are determined using experimental pressure drop results of 28 different structured column packings manufactured from metal, plastic or ceramic. The fitted regression model yields very good (mean absolute error 1.86%) approximation of packing form factor. Model feature importance analysis suggests packing porosity, packing angle and packing perforations exert the greatest influence on the form factor. Leave-one-out cross-validation is performed to evaluate predictive model performance beyond training data. Results indicate the model's reliable predictions of the form factor (mean absolute error 3.1%) and strong agreement between pressure drop predictions and experimental data (mean absolute error 23.4%).

AB - Semi-empirical correlations are commonly used to predict packed column pressure drop. As a downside, these models rely on packing-specific parameters that are obtained through laborious experiments. This work aims to eliminate packing-specific parameters for modeling pressure drop of structured packings. A multiple linear regression model is used to predict the packing form factor directly from packing properties. The form factor is then used with the model of Maćkowiak (2010) to calculate pressure drop. This tandem model approach therefore allows for pressure drop predictions directly from packing properties. The regression model includes model features to describe packing properties, such as packing geometry, perforations, packing material or packing surface. Model coefficients are determined using experimental pressure drop results of 28 different structured column packings manufactured from metal, plastic or ceramic. The fitted regression model yields very good (mean absolute error 1.86%) approximation of packing form factor. Model feature importance analysis suggests packing porosity, packing angle and packing perforations exert the greatest influence on the form factor. Leave-one-out cross-validation is performed to evaluate predictive model performance beyond training data. Results indicate the model's reliable predictions of the form factor (mean absolute error 3.1%) and strong agreement between pressure drop predictions and experimental data (mean absolute error 23.4%).

KW - Distillation and absorption

KW - Multiple linear regression

KW - Packed column hydraulics

KW - Pressure drop modeling

UR - http://www.scopus.com/inward/record.url?scp=85211175879&partnerID=8YFLogxK

U2 - 10.1016/j.cherd.2024.11.008

DO - 10.1016/j.cherd.2024.11.008

M3 - Article

AN - SCOPUS:85211175879

VL - 213.2025

SP - 172

EP - 183

JO - Chemical Engineering Research and Design

JF - Chemical Engineering Research and Design

SN - 0263-8762

IS - January

ER -