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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1016/j.wasman.2020.10.037
|2 doi
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|a pubmed24n1058.xml
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|a (DE-627)NLM31766610X
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|a (NLM)33199242
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|a (PII)S0956-053X(20)30608-5
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Curtis, A
|e verfasserin
|4 aut
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|a Real time material flow monitoring in mechanical waste processing and the relevance of fluctuations
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 29.12.2020
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|a Date Revised 29.12.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2020 Elsevier Ltd. All rights reserved.
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|a To achieve the goals of the circular economy, significant improvements in non-hazardous solid waste processing/treatment must be made. Large deficits in the digitalization of mechanical waste treatment plants (smart waste factory) offer great potential for improvement. Real-time material flow monitoring is carried out in very few plants, thus wasting considerable potential for improving plant performance. This article describes results from the authors' own practical analyses using sensor-based technologies for monitoring material flows, an on-site investigation in a large waste treatment plant and also in a pilot-scale plant (Technical Line 4.0) using mixed commercial waste (MCW) from Austria. The obtained data shows that the quantitative monitoring of volume and mass flow (via laser triangulation as well as near-infrared (NIR) based monitoring) and material composition (NIR sensor) is possible. The observed fluctuations were categorised in short-, mid- and long-term fluctuations and were led back to their causes, i.e. discontinuous feeding process, material and machine-specific characteristics. Using the quotient of the 90% (Q90) and 10% (Q10) quantiles of time-resolved volume-flow data for the assessment of fluctuations, for the considered time-intervals, resulted in Q90 / Q10 ratios between 3.39 and 4.58. If short-term fluctuations (within the observed time-intervals) are related to the 29.6 s moving average, deviations between 1.8% and 6.8% result. To verify the relevance of such fluctuations, sensor-based sorting (SBS) experiments were conducted, revealing a reduced product purity of 6% due to short-term fluctuations in the feed of the SBS-Machine using light packaging waste (LPW)
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|a Journal Article
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|a Digitalization
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|a Fluctuations
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|a Mixed commercial waste (MCW)
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|a Non-hazardous solid waste processing/treatment
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|a Smart waste factory
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|a Volume and mass flow monitoring
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| 650 |
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|a Solid Waste
|2 NLM
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| 700 |
1 |
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|a Küppers, B
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Möllnitz, S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Khodier, K
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sarc, R
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Waste management (New York, N.Y.)
|d 1999
|g 120(2021) vom: 01. Feb., Seite 687-697
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnns
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| 773 |
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|g volume:120
|g year:2021
|g day:01
|g month:02
|g pages:687-697
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|u http://dx.doi.org/10.1016/j.wasman.2020.10.037
|3 Volltext
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|d 120
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