Karbassiyazdi Elika, Fattahi Fatemeh, Yousefi Negin, Tahmassebi Amirhessam, Taromi Arsia Afshar, Manzari Javad Zyaie, Gandomi Amir H, Altaee Ali, Razmjou Amir
Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Australia.
Department of Chemical Engineering, Isfahan University of Technology, Isfahan, Iran.
Environ Res. 2022 Dec;215(Pt 1):114286. doi: 10.1016/j.envres.2022.114286. Epub 2022 Sep 9.
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.
由于多氟烷基和全氟烷基物质（PFAS）对环境和公众健康的影响，近来人们高度关注寻找去除PFAS的创新材料和方法。在这项工作中，PFAS被视为普遍存在的污染物，可在许多废水流中发现。用于去除和降解PFAS的传统材料和工艺没有足够的能力解决该问题，特别是在消除短链PFAS方面。这主要是由于材料和工艺设计中涉及大量复杂参数。在此，我们利用人工智能引入了一个模型（XGBoost），其中同时考虑了材料和工艺因素。本研究采用机器学习方法，利用从已发表文章中收集的数据来预测PFAS去除因素。XGBoost建模提供了准确的吸附容量、平衡和去除估计，并能够预测吸附机制。尽管已经开展了许多研究，通过离子交换、纳滤和活性炭（AC）等各种吸附方法来开发PFAS去除技术，但首次对吸附剂的性能比较以及人工智能在其中一个主要方面的作用进行了研究和综述。该模型表明，pH是预测PFAS去除的最有效参数。本工作中提出的模型可扩展用于其他微污染物，并可作为未来吸附剂设计和工艺优化的基本框架。
