A Statistical Approach to Model Selection for Dynamic Adsorption Columns


A Statistical Approach to Model Selection for Dynamic Adsorption Columns

Paul Musonge, Manqoba Shezi

A variety of models have been used to describe and predict breakthrough curves for dynamic adsorption systems, in order to scale up laboratory and pilot plant systems. There are however limitations in the applicability of existing models. The study is aimed at providing unambiguous approaches in selecting the best performing model between Thomas, Yoon-Nelson and Bohart-Adams (B-A) models for three dynamic adsorption systems. Three approaches were implemented in this study using published experimental data of three adsorption systems. The first approach was the application of statistical analysis between actual and predicted breakthrough curves without modifying the models. The second and third approaches were application of local mean values (LMV) and global mean values (GMV) of empirical constants to predict breakthrough curves. Predictive and generalization performances of the three models were evaluated using the statistical criteria of Mean Absolute Error (MAE), Root mean Squared Error (RMSE) and Correlation Coefficient (R2).

Acid Mine Drainage, Adsorption, Yoon-Nelson Model, Thomas Model, B-A Model

Published online 5/1/2021, 40 pages

Citation: Paul Musonge, Manqoba Shezi, A Statistical Approach to Model Selection for Dynamic Adsorption Columns, Materials Research Foundations, Vol. 102, pp 128-167, 2021

DOI: https://doi.org/10.21741/9781644901397-5

Part of the book on Advances in Wastewater Treatment II

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