Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers-Reference-Cited by-同舟云学术

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Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers

Published:2024-09-13 Issue: Volume: Page:
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Koyama Yoko¹²,Fasaee Mohammad A. K.¹,Berglund Emily Z.¹^ORCID,Knappe Detlef R. U.¹^ORCID

Affiliation:

1. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695-7908, United States

2. Carollo Engineers, Inc., 10900 Stonelake Blvd. Bldg. 2, Suite 126, Austin, Texas 78759, United States

Funder

National Institute of Environmental Health Sciences

North Carolina State University

North Carolina Policy Collaboratory

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.4c01316

Reference50 articles.

1. Predicting the performance of fixed-bed granular activated carbon adsorbers

2. Modeling PFAS Removal Using Granular Activated Carbon for Full-Scale System Design

3. Full- and pilot-scale GAC adsorption of organic micropollutants

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