Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering-Reference-Cited by-同舟云学术

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Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering

Published:2024 Issue:27 Volume:14 Page:19331-19348
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Yogarathinam Lukka Thuyavan¹^ORCID,Abba Sani I.¹^ORCID,Usman Jamilu¹,Lawal Dahiru U.¹²,Aljundi Isam H.¹³^ORCID

Affiliation:

1. Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

2. Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

3. Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Abstract

Predicting the efficacy of micropollutant separation through self-fabricated membranes is an arduous endeavor.

Funder

King Fahd University of Petroleum and Minerals

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D4RA02475C

Reference47 articles.

1. Toward a Global Understanding of Chemical Pollution: A First Comprehensive Analysis of National and Regional Chemical Inventories

2. An integrated approach for chemical water quality assessment of an urban river stretch through Effect-Based Methods and emerging pollutants analysis with a focus on genotoxicity

3. A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment

4. Organic pollutants in protected plain areas: The occurrence of PAHs, musks, UV-filters, flame retardants and hydrocarbons in woodland soils

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