Micropollutant rejection by nanofiltration membranes: a mini review dedicated to the critical factors and modelling prediction

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dc.contributor.authorXu, Rui
dc.contributor.authorZhang, Zeqian
dc.contributor.authorDeng, Chenning
dc.contributor.authorNie, Chong
dc.contributor.authorWang, Lijing
dc.contributor.authorShi, Wenqing
dc.contributor.authorLyu, Tao
dc.contributor.authorYang, Queping
dc.date.accessioned2024-01-18T11:08:01Z
dc.date.available2024-01-18T11:08:01Z
dc.date.issued2023-12-16
dc.description.abstractNanofiltration (NF) membranes, extensively used in advanced wastewater treatment, have broad application prospects for the removal of emerging trace organic micropollutants (MPs). The treatment performance is affected by several factors, such as the properties of NF membranes, characteristics of target MPs, and operating conditions of the NF system concerning MP rejection. However, quantitative studies on different contributors in this context are limited. To fill the knowledge gap, this study aims to assess critical impact factors controlling MP rejection and develop a feasible model for MP removal prediction. The mini-review firstly summarized membrane pore size, membrane zeta potential, and the normalized molecular size (λ = rs/rp), showeing better individual relationships with MP rejection by NF membranes. The Lindeman-Merenda-Gold model was used to quantitatively assess the relative importance of all summarized impact factors. The results showed that membrane pore size and operating pressure were the high impact factors with the highest relative contribution rates to MP rejection of 32.11% and 25.57%, respectively. Moderate impact factors included membrane zeta potential, solution pH, and molecular radius with relative contribution rates of 10.15%, 8.17%, and 7.83%, respectively. The remaining low impact factors, including MP charge, molecular weight, logKow, pKa and crossflow rate, comprised all the remaining contribution rates of 16.19% through the model calculation. Furthermore, based on the results and data availabilities from references, the machine learning-based random forest regression model was trained with a relatively low root mean squared error and mean absolute error of 12.22% and 6.92%, respectively. The developed model was then successfully applied to predict MPs’ rejections by NF membranes. These findings provide valuable insights that can be applied in the future to optimize NF membrane designs, operation, and prediction in terms of removing micropollutants.en_UK
dc.identifier.citationXu R, Zhang Z, Deng C, et al., (2024) Micropollutant rejection by nanofiltration membranes: a mini review dedicated to the critical factors and modelling prediction. Environmental Research, Volume 244, March 2024, Article number 117935en_UK
dc.identifier.issn0013-9351
dc.identifier.urihttps://doi.org/10.1016/j.envres.2023.117935
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20677
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAdvanced wastewater treatmenten_UK
dc.subjectEmerging contaminantsen_UK
dc.subjectEnvironmental sustainabilityen_UK
dc.subjectMembrane technologyen_UK
dc.subjectModeling assessmenten_UK
dc.titleMicropollutant rejection by nanofiltration membranes: a mini review dedicated to the critical factors and modelling predictionen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-12-11

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