XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions

Document Type

Journal Article

Publication Title

Environmental Research

Volume

215

PubMed ID

36096170

Publisher

Elsevier

School

School of Engineering

RAS ID

52352

Funders

University of Technology Sydney fund (PRO20-11072)

Comments

Karbassiyazdi, E., Fattahi, F., Yousefi, N., Tahmassebi, A., Taromi, A. A., Manzari, J. Z., ... & Razmjou, A. (2022). XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions. Environmental Research, 215, Article 114286. https://doi.org/10.1016/j.envres.2022.114286

Abstract

Due to the implications of poly- and perfluoroalkyl substances (PFAS) on the environment and public health, great attention has been recently made to finding innovative materials and methods for PFAS removal. In this work, PFAS is considered universal contamination which can be found in many wastewater streams. Conventional materials and processes used to remove and degrade PFAS do not have enough competence to address the issue particularly when it comes to eliminating short-chain PFAS. This is mainly due to the large number of complex parameters that are involved in both material and process designs. Here, we took the advantage of artificial intelligence to introduce a model (XGBoost) in which material and process factors are considered simultaneously. This research applies a machine learning approach using data collected from reported articles to predict the PFAS removal factors. The XGBoost modeling provided accurate adsorption capacity, equilibrium, and removal estimates with the ability to predict the adsorption mechanisms. The performance comparison of adsorbents and the role of AI in one dominant are studied and reviewed for the first time, even though many studies have been carried out to develop PFAS removal through various adsorption methods such as ion exchange, nanofiltration, and activated carbon (AC). The model showed that pH is the most effective parameter to predict PFAS removal. The proposed model in this work can be extended for other micropollutants and can be used as a basic framework for future adsorbent design and process optimization.

DOI

10.1016/j.envres.2022.114286

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