Abstract
Zhang et al.1 published a paper on machine learning basedpredictions of organic contaminant sorption ontocarbonaceous materials and resins. The authors provide anovel approach to predict concentration-dependent sorptiondistribution coefficients (KD) to these materials, without theneed to link it to any specific isotherm model. This study is avaluable contribution to the field that can stimulate thescientific discussion in the adsorption-modeling communityregarding (i) mechanistic assumptions prior to model building,(ii) the parametrization of the model based on theseassumptions, (iii) the grouping of data to train the algorithm,and (iv) data filtering strategies. We recently published a paperon a similar topic2 and are confident that this discussion isvaluable to improve the future applicability of machine learningtechniques to sorption phenomena.
Original language | English |
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Pages (from-to) | 11636-11637 |
Number of pages | 2 |
Journal | Environmental Science & Technology |
Volume | 54 |
Issue number | 18 |
DOIs | |
Publication status | Published - 15 Sept 2020 |
Austrian Fields of Science 2012
- 105906 Environmental geosciences
Keywords
- sorption
- Organic compounds
- Sorbents
- Machine Learning
- Materials