Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Treatment of Water.
| dc.contributor.advisor | Detlef Knappe, Co-Chair | |
| dc.contributor.advisor | Emily Berglund, Co-Chair | |
| dc.contributor.advisor | Joel Ducoste, Member | |
| dc.contributor.author | Koyama, Yoko | |
| dc.date.accepted | 2021-10-27 | |
| dc.date.accessioned | 2021-11-03T12:30:24Z | |
| dc.date.available | 2021-11-03T12:30:24Z | |
| dc.date.defense | 2021-07-19 | |
| dc.date.issued | 2021-07-19 | |
| dc.date.released | 2021-11-03 | |
| dc.date.reviewed | 2021-07-20 | |
| dc.date.submitted | 2021-07-19 | |
| dc.degree.discipline | Environmental Engineering | |
| dc.degree.level | thesis | |
| dc.degree.name | Master of Science | |
| dc.description | North Carolina State University Theses Civil Engineering. | |
| dc.format | M.S. North Carolina State University, 2021. | |
| dc.identifier.other | deg26422 | |
| dc.identifier.uri | https://www.lib.ncsu.edu/resolver/1840.20/39151 | |
| dc.title | Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Treatment of Water. | |
| dcterms.extent | 1 online resource (ix, 342 pages) : color illustrations |
