Environmental sustainability: A machine learning approach for cost analysis in plastic recycling classification

Abstract

Recycling plastics can reduce waste generation and improve waste management, but the recycling industry needs both cost reduction and increased revenue to be economically viable. Recently, recycling plastic classification techniques with Artificial Intelligence have gained popularity, as they can avoid manual sorting, which is time-consuming and economically less profitable than automatidc processing. In this paper, we provide an economic framework for quality sorting control by classifying plastics based on the infrared spectrum of polymers and machine learning algorithms. In addition, the suggested framework offers a method for selecting the algorithm according to the polymer's income class and the highest economic advantages. Furthermore, our experiments probe that Fourier-transform infrared (FTIR) and near-infrared (NIR) spectroscopies combined with machine learning algorithms are suitable for plastic classification as four datasets and seven machine learning algorithms have been tested to classify Polyethylene (PE), Polypropylene (PP), Polyethylene terephthalate (PET), polystyrene (PS), and Polyvinyl chloride (PVC).