In this report, we describe the current state of progress of WP3 (shredding process modelling and simulation) at the end of the first project year and its associated deliverable: \An optimized stochastic model which, for every configuration of the process parameters, predicts the distribution of particle size and liberation degree classes". The following main results are achieved within WP3 and are reported in this deliverable: the development of a model for predicting the evolution of particle size distribution in a batch shredding process; the development of a method for designing the shredding experiments aiming at estimating the key parameters of the model; the development of a model estimation approach; a preliminary experimentation to investigate the dependency of the output size distribution on the process parameters and to choose a suitable chamber saturation for the next experimental validations. The experiments will be completed in the next two months and the model will be validated, with appropriate experiments, and optimized, as stated in the deliverable description. Further experiments will be designed to optimize a model for the discharge process. The analysis of the particle liberation degree will be carried out as part of the second project year activity, based on hyperspectral imaging or XRF particle analysis technologies. The modelling of the particle liberation degree as a function of the process parameters will be investigated, according to the description of work reported in the project proposal. This deliverable is organized as follows. In Section 2.2 we explain brie y the importance of modeling size reduction processes in mechanical recycling systems. In Section 2.3 we describe the Markov model for the evolution of the particle size distribution in a batch process. Simplifying assumptions are introduced with the purpose of minimizing the set-up time for model estimation (or \optimization") in different applied contexts, in view of the wide range of PCBs that could be processed in real life. Section 2.4 establishes requirements for model estimation, which contribute to determine the criteria for the design of experiments. Section 2.5 illustrates a preliminary experiment to choose a suitable comminution chamber saturation, to be used throughout the main experimental plan. In Section 2.6 we use information obtained from Section 2.5 to determine the number of replicated runs in the main experimental plan.

ZeroWaste PCBs project D3.1: Report on shredding process modeling and simulation

A Pievatolo;E Lanzarone;L Martin Fernandez;F Ruggeri;C Brambilla;G Copani
2013

Abstract

In this report, we describe the current state of progress of WP3 (shredding process modelling and simulation) at the end of the first project year and its associated deliverable: \An optimized stochastic model which, for every configuration of the process parameters, predicts the distribution of particle size and liberation degree classes". The following main results are achieved within WP3 and are reported in this deliverable: the development of a model for predicting the evolution of particle size distribution in a batch shredding process; the development of a method for designing the shredding experiments aiming at estimating the key parameters of the model; the development of a model estimation approach; a preliminary experimentation to investigate the dependency of the output size distribution on the process parameters and to choose a suitable chamber saturation for the next experimental validations. The experiments will be completed in the next two months and the model will be validated, with appropriate experiments, and optimized, as stated in the deliverable description. Further experiments will be designed to optimize a model for the discharge process. The analysis of the particle liberation degree will be carried out as part of the second project year activity, based on hyperspectral imaging or XRF particle analysis technologies. The modelling of the particle liberation degree as a function of the process parameters will be investigated, according to the description of work reported in the project proposal. This deliverable is organized as follows. In Section 2.2 we explain brie y the importance of modeling size reduction processes in mechanical recycling systems. In Section 2.3 we describe the Markov model for the evolution of the particle size distribution in a batch process. Simplifying assumptions are introduced with the purpose of minimizing the set-up time for model estimation (or \optimization") in different applied contexts, in view of the wide range of PCBs that could be processed in real life. Section 2.4 establishes requirements for model estimation, which contribute to determine the criteria for the design of experiments. Section 2.5 illustrates a preliminary experiment to choose a suitable comminution chamber saturation, to be used throughout the main experimental plan. In Section 2.6 we use information obtained from Section 2.5 to determine the number of replicated runs in the main experimental plan.
2013
Istituto di Matematica Applicata e Tecnologie Informatiche - IMATI -
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato - STIIMA (ex ITIA)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/245976
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