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Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms

; ; ; ; ;

In
Waste management 136, Seiten/Artikel-Nr.:253-265

ImpressumAmsterdam [u.a.] : Elsevier Science

ISSN1879-2456

Online
DOI: 10.1016/j.wasman.2021.10.017


Einrichtungen

  1. Lehrstuhl für Anthropogene Stoffkreisläufe und Institut für Aufbereitung, Kokerei und Brikettierung (512110)
  2. Fachgruppe für Rohstoffe und Entsorgungstechnik (510000)


Thematische Einordnung (Klassifikation)
DDC: 330


Dokumenttyp
Journal Article

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85117696392
WOS Core Collection: WOS:000713295000012
PubMed: pmid:34710801

Interne Identnummern
RWTH-2021-10057
Datensatz-ID: 834671

Beteiligte Länder
Germany

Lizenzstatus der Zeitschrift

 GO

Related:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Dissertation / PhD Thesis  ;  ;
Sensor-based characterization of anthropogenic material systems: developing characterization methods and novel applications for optimizing the mechanical recycling of lightweight packaging waste
Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme () [10.18154/RWTH-2023-11638] = Dissertation, RWTH Aachen University, 2023, Kumulative Dissertation  GO OpenAccess  Download fulltext Files BibTeX | EndNote: XML, Text | RIS

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The record appears in these collections:
Document types > Articles > Journal Articles
Faculty of Georesources and Materials Engineering (Fac.5) > Division of Mineral Resources and Raw Materials Engineering
Public records
Publications database
512110
510000
 Record created 2021-10-29, last modified 2025-08-21
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