Airan Hu, Yanling Liu, Xiaomao Wang, Shengji Xia, Bart Van der Bruggen
{"title":"基于机器学习的框架,定制纳滤膜和反渗透膜的特性,有针对性地去除有机微污染物","authors":"Airan Hu, Yanling Liu, Xiaomao Wang, Shengji Xia, Bart Van der Bruggen","doi":"10.1016/j.watres.2024.122677","DOIUrl":null,"url":null,"abstract":"Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which puts higher demands on the customization of membranes suitable for OMPs removal based on the rejection mechanisms. Here, the pathways of OMPs-targeted optimization for membranes were constructed by using machine learning (ML) to capture the correlations between OMPs removal efficiency with properties of membranes and OMPs. Through expertise assistance and rigorous modeling methodology, an accurate and robust Extreme Gradient Boosting (XGBoost) model was established, which could well recognize the dominant rejection mechanisms of OMPs (i.e., the size exclusion effect and electrostatic interactions). An exemplary application to another dataset of several high-risk OMPs showed how the optimized model could be used to estimate the overall efficiency of OMPs risk control and, more importantly, to provide quantitative guidance on membrane properties for specific removal targets. The satisfying prediction results demonstrated the good generalization of the ML model and consequently its ability to sensitively define the ideal membrane properties for the targeted removal of these (and any other concerned) OMPs. This study provides a feasible and universal ML-based framework to achieve the tailored selection and design of NF/RO membranes for OMPs risk control.","PeriodicalId":443,"journal":{"name":"Water Research","volume":null,"pages":null},"PeriodicalIF":11.4000,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A machine learning based framework to tailor properties of nanofiltration and reverse osmosis membranes for targeted removal of organic micropollutants\",\"authors\":\"Airan Hu, Yanling Liu, Xiaomao Wang, Shengji Xia, Bart Van der Bruggen\",\"doi\":\"10.1016/j.watres.2024.122677\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which puts higher demands on the customization of membranes suitable for OMPs removal based on the rejection mechanisms. Here, the pathways of OMPs-targeted optimization for membranes were constructed by using machine learning (ML) to capture the correlations between OMPs removal efficiency with properties of membranes and OMPs. Through expertise assistance and rigorous modeling methodology, an accurate and robust Extreme Gradient Boosting (XGBoost) model was established, which could well recognize the dominant rejection mechanisms of OMPs (i.e., the size exclusion effect and electrostatic interactions). An exemplary application to another dataset of several high-risk OMPs showed how the optimized model could be used to estimate the overall efficiency of OMPs risk control and, more importantly, to provide quantitative guidance on membrane properties for specific removal targets. The satisfying prediction results demonstrated the good generalization of the ML model and consequently its ability to sensitively define the ideal membrane properties for the targeted removal of these (and any other concerned) OMPs. This study provides a feasible and universal ML-based framework to achieve the tailored selection and design of NF/RO membranes for OMPs risk control.\",\"PeriodicalId\":443,\"journal\":{\"name\":\"Water Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.4000,\"publicationDate\":\"2024-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.watres.2024.122677\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.watres.2024.122677","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
A machine learning based framework to tailor properties of nanofiltration and reverse osmosis membranes for targeted removal of organic micropollutants
Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which puts higher demands on the customization of membranes suitable for OMPs removal based on the rejection mechanisms. Here, the pathways of OMPs-targeted optimization for membranes were constructed by using machine learning (ML) to capture the correlations between OMPs removal efficiency with properties of membranes and OMPs. Through expertise assistance and rigorous modeling methodology, an accurate and robust Extreme Gradient Boosting (XGBoost) model was established, which could well recognize the dominant rejection mechanisms of OMPs (i.e., the size exclusion effect and electrostatic interactions). An exemplary application to another dataset of several high-risk OMPs showed how the optimized model could be used to estimate the overall efficiency of OMPs risk control and, more importantly, to provide quantitative guidance on membrane properties for specific removal targets. The satisfying prediction results demonstrated the good generalization of the ML model and consequently its ability to sensitively define the ideal membrane properties for the targeted removal of these (and any other concerned) OMPs. This study provides a feasible and universal ML-based framework to achieve the tailored selection and design of NF/RO membranes for OMPs risk control.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.