Machine learning model and input batch management tool for the composition of new recycled polypropylene plastic material with reduced variability in target properties

Abstract

Reducing the variability of recycled plastic materials is a prerequisite to encourage the safe substitution of virgin materials in new components manufacturing, hence promoting the circular economy (CE) to achieve the recyclability objectives set by the European Commission. To increase the use of recycled materials in some industrial sectors, such as in automotive, it is necessary to control and reduce their batch-to-batch variability to ensure that the required properties lay in reasonable ranges. In this work, through the analysis of the accumulated historical data from a recycling company, and with the help of machine learning (ML) techniques, a model is developed that predicts key material properties (melt flow index, ash content, Izod impact strength and percentage of shrinkage) in a mixture of recycled polypropylene (PP) batches from different sources. The model is validated correlating predictions with experimental measurements over new batch mixtures compounded on purpose. A batch management algorithm considers the entry flow of material batches received in the recycling company and proposes mixtures for the production of new recycled batches with reduced variability and controllable target properties. Furthermore, it proposes solutions for batch management to maximize the production output while maintaining the properties of the recycled material within target variability limits.