2023
DOI: 10.1016/j.resconrec.2023.106873
|View full text |Cite
|
Sign up to set email alerts
|

Near-infrared-based determination of mass-based material flow compositions in mechanical recycling of post-consumer plastics: Technical feasibility enables novel applications

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
14
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 14 publications
(14 citation statements)
references
References 26 publications
0
14
0
Order By: Relevance
“… The dataset is intended for researchers investigating novel applications of inline-sensor technology for the optimization of (mechanical) recycling processes [2] . Machine learning and computer vision researchers can use this dataset to train and assess different (machine learning) algorithms for predicting (mass-based) material flow compositions [1] . Machine learning and image processing algorithms can be trained and assessed on predicting material flow compositions from near-infrared-based false-color images [1] .…”
Section: Value Of the Datamentioning
confidence: 99%
See 4 more Smart Citations
“… The dataset is intended for researchers investigating novel applications of inline-sensor technology for the optimization of (mechanical) recycling processes [2] . Machine learning and computer vision researchers can use this dataset to train and assess different (machine learning) algorithms for predicting (mass-based) material flow compositions [1] . Machine learning and image processing algorithms can be trained and assessed on predicting material flow compositions from near-infrared-based false-color images [1] .…”
Section: Value Of the Datamentioning
confidence: 99%
“…Machine learning and computer vision researchers can use this dataset to train and assess different (machine learning) algorithms for predicting (mass-based) material flow compositions [1] . Machine learning and image processing algorithms can be trained and assessed on predicting material flow compositions from near-infrared-based false-color images [1] . Furthermore, this dataset enables researchers to assess the accuracy of near-infrared-based inline material flow characterization under different measurement situations and can help gaining a better understanding of segregation effects of anthropogenic material flows [2] .…”
Section: Value Of the Datamentioning
confidence: 99%
See 3 more Smart Citations