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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1016/j.wasman.2021.08.020
|2 doi
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|a pubmed25n1100.xml
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|a (DE-627)NLM330168037
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|a (NLM)34474306
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|a (PII)S0956-053X(21)00452-9
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Bareha, Y
|e verfasserin
|4 aut
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|a A simple mass balance tool to predict carbon and nitrogen fluxes in anaerobic digestion systems
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 19.10.2021
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|a Date Revised 19.10.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2021 Elsevier Ltd. All rights reserved.
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|a The increase in anaerobic digestion systems has profoundly affected the waste management of territories, particularly for agricultural systems. Changes in cultural practices and imports of organic waste modify the carbon (C) and nitrogen (N) fluxes on territories where anaerobic digestion is implemented. Successful anaerobic digestion can increase the economic and ecological efficiency of the waste management system. Conversely, poor anaerobic digestion leads to low economic and environmental efficiency due to greenhouse gas emissions and nutrient loss. Modeling the impact of anaerobic digestion on the systems integrating anaerobic digestion can improve the efficiency of these practices. The aim of this study was to develop, analyze, and evaluate a simple mass balance tool able to predict carbon and nitrogen fluxes in anaerobic digestion systems. The tool is composed of an exhaustive substrate database used by three models: (i) an anaerobic digestion model that predicts C and N contents in biogas and digestate; (ii) a phase separation model that predicts C and N content in liquid and solid phase digestates; and (iii) a storage model that predicts C and N content in raw, liquid phase, and solid phase digestates, as well as C and N emissions during storage. Sensitivity analyses were performed on the tool to determine critical inputs. Sensitivity analysis showed that outputs were highly sensitive to their respective inputs and to total inputs of solids. Performance evaluation showed that the tool can provide good quality predictions with R2 correlations between observation and prediction varying from 0.72 to 0.99 with the best predictions obtained for raw digestate
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|a Journal Article
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|a Anaerobic digestion
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|a Digestate
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|a Digestate storage
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|a Modeling
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|a Phase separation
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|a Biofuels
|2 NLM
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|a Carbon
|2 NLM
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| 650 |
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|a 7440-44-0
|2 NLM
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| 650 |
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|a Nitrogen
|2 NLM
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| 650 |
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|a N762921K75
|2 NLM
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| 700 |
1 |
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|a Affes, R
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Moinard, V
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Buffet, J
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Girault, 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 135(2021) vom: 15. Nov., Seite 47-59
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnas
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| 773 |
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|g volume:135
|g year:2021
|g day:15
|g month:11
|g pages:47-59
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|u http://dx.doi.org/10.1016/j.wasman.2021.08.020
|3 Volltext
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