The purpose of this work was to identify the sizes economically and environmentally sustainable of a sorting plant for the recovery of plastics, fabrics and metals dall'ASR. Given the uncertainty surrounding the waste sector both in terms of characteristics of the material to be treated and the cost, the approach adopted for the analysis integrates the sensitivity analysis with the risk analysis carried out with the Monte Carlo method. The calculation model developed was implemented using the software @ RISCAMP with which the distributions with relative probabilities of the main technical and economic indices were calculated. The distributions of probabilities derived from the analysis of data, made available by Danieli & C. Officine Meccaniche SpA resulting from data obtained from similar plants, were assigned to the input variables. In the study tackled, the method used has proved to be a valuable tool to consider the reliability in evaluating the performance even for systems in which it is difficult to pattern after. The study also demonstrated how the overcome of the concept of project risk management towards the one of uncertainty management, leads us to consider the risk not only as an element whose occurrence has a negative effect on performance expectations, but as an opportunity by identifying interesting considerations on the expected performances of these systems

Uncertainty Management as a tool of choice for plants: the case of mechanical separation of the automotive shredder residue (ASR)

SIMEONI, Patrizia;NARDIN, Gioacchino
2014-01-01

Abstract

The purpose of this work was to identify the sizes economically and environmentally sustainable of a sorting plant for the recovery of plastics, fabrics and metals dall'ASR. Given the uncertainty surrounding the waste sector both in terms of characteristics of the material to be treated and the cost, the approach adopted for the analysis integrates the sensitivity analysis with the risk analysis carried out with the Monte Carlo method. The calculation model developed was implemented using the software @ RISCAMP with which the distributions with relative probabilities of the main technical and economic indices were calculated. The distributions of probabilities derived from the analysis of data, made available by Danieli & C. Officine Meccaniche SpA resulting from data obtained from similar plants, were assigned to the input variables. In the study tackled, the method used has proved to be a valuable tool to consider the reliability in evaluating the performance even for systems in which it is difficult to pattern after. The study also demonstrated how the overcome of the concept of project risk management towards the one of uncertainty management, leads us to consider the risk not only as an element whose occurrence has a negative effect on performance expectations, but as an opportunity by identifying interesting considerations on the expected performances of these systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1037408
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