A comminution model with homogeneity and multiplication assumptions for the Waste Electrical and Electronic Equipment recycling industry. (20th February 2019) - Diani, Marco; Pievatolo, Antonio; Colledani, Marcello; Lanzarone, Ettore
Abstract: Sustainable circular economy is introducing new industrial challenges, including recycling, to move from the cradle-to-grave paradigm to the innovative cradle-to-cradle concept. The core of several recycling systems consists of mechanical comminution, which aims at producing homogeneous mixtures of highly liberated particles. However, the complexity of comminution processes requires adequate models to control and optimize their behavior within the entire recycling system. In the literature, the so-called Population Balance Model has been proposed, which is however limited to the mining industry. In this paper, we examine the applicability of the Population Balance Models in the Waste Electrical and Electronic Equipment recycling industry. In particular, we include the homogeneity and multiplication assumptions, and analyze the impact of the comminution chamber saturation. These simplifications reduce the number of model parameters to be estimated, as the set of parameters remains the same over a wide range of working conditions. We tested our assumptions on a real system for recycling Printed Circuit Boards. Results showed the validity of the multiplication assumption and a marginal effect of saturation ( p values for rotor velocity and saturation were always above 25% in an ANOVA with the Bonferroni correction). The homogeneity assumption is also tenable, with the exception of the initial transient, as the null hypothesis of constant output mass distribution under equivalent shredding times was never rejected at the 5% significance level. Outcomes of an out-of-sample validation confirmed the effectiveness of our simplified Population Balance Model (values of the Kolmogorov-Smirnov metric lower than 0.1). Highlights: A Population Balance Model is applied to WEEE recycling. Simplifying assumptions are included in the model. The assumptions reduce the number of parameters, to allow practical applicability. Results from a real system confirm model effectiveness and applicability.