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

• Published in: Environmental research 2022-12, Vol.215, p.114286, Article 114286
• Main Authors: Karbassiyazdi, Elika, Fattahi, Fatemeh, Yousefi, Negin, Tahmassebi, Amirhessam, Taromi, Arsia Afshar, Manzari, Javad Zyaie, Gandomi, Amir H, Altaee, Ali, Razmjou, Amir
• Format: Article
• Language: English
• Subjects: Adsorbent; Artificial intelligence; Machine learning; PFAS Removal; Wastewater treatment
• ISSN: 0013-9351; 1096-0953
• DOI: 10.1016/j.envres.2022.114286

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. [Display omitted] •Poly- and perfluoroalkyl substances (PFAS) has environmental and health implications.•Conventional adsorbents used to remove and degrade PFAS do not have enough competence.•Developing advanced materials using traditional approaches are time consuming and costly.•XGBoost model, a machine learning approach, was used to predict PFAS removal parameters.•XGBoost can accurately predict adsorption capacity, equilibrium, and mechanism.