Étude expérimentale des méthodes de chargement des particules et développement d'un algorithme d'IA pour déterminer l'état de leur charge électrostatique

Abstract

This work focuses on the experimental study of electrostatic charging methods for granular particles used in plastic material separation processes. Experiments were carried out using charging devices based on particle sliding through aluminum and PVC tubes, as well as a flu- idized-bed system. The influence of particle mass, charging time, and fluidization velocity on the acquired electrostatic charge was investigated. The experimental results were then used to develop an artificial intelligence algorithm in Python capable of predicting the electrostatic charge state of the particles. The obtained results demonstrate that this approach is effective for analyzing and optimizing triboelectric charging processes.