Using information of the separation process of recycling scrap tires for process modelling
A. Pehlken and
D.H. Müller
Resources, Conservation & Recycling, 2009, vol. 54, issue 2, 140-148
Abstract:
Modelling recycling processes is a challenge because there is few specific data available. Various materials are firmly connected to each other that makes an ideal separation success nearly impossible. Scrap tire recycling is discussed as the case study with respect to some of the main key processes such as crushing, grinding and sieving. Useful information can be accumulated with data received from particle size distribution. Assessing the information of the separation for modelling purposes with the availability of data ranges and identifying uncertainties is the focus of the article. Modelling recycling processes and the assessment of uncertainty are firmly connected, otherwise the model provides a lack of reliability due to parameter uncertainties. Modelling a recycling process the known and unknown uncertainties become important parameters to consider. Due to the fact that residues as scrap tires always vary in their composition and material flows only data ranges can be used as input parameter.
Keywords: Separation; Particle size distribution; Uncertainty; Scrap tires; Modelling (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0921344909001554
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:recore:v:54:y:2009:i:2:p:140-148
DOI: 10.1016/j.resconrec.2009.07.008
Access Statistics for this article
Resources, Conservation & Recycling is currently edited by Ming Xu
More articles in Resources, Conservation & Recycling from Elsevier
Bibliographic data for series maintained by Kai Meng ().