Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology

Abstract

The management and disposal of waste is a severe social issue and an essential part of ecological sustainability. As an important component of the green, low-carbon, and recycling economic system, the identification and classification of recyclable waste is the premise of its reuse and energy conservation. The main issues at hand are to improve the classification accuracy and reliability of recyclable waste and to achieve automatic classification. The methods based on physical characteristics and image-based methods are inaccurate and unreliable. The current spectroscopy methods need to process the detected samples in advance, unsuitable for automatic detection. Based on material composition properties, the Laser-Induced Breakdown Spectroscopy (LIBS) technology is here proposed to accurately and reliably identify and classify recyclable waste into six categories at the level of consumer, such as paper, plastic, glass, metal, textile, and wood. The method is also used to subclassify the same category of waste for reuse at the level of a recycling factory. We subclassified metals into iron, stainless steel, copper, and aluminum and plastics into polyvinylchloride, polyoxymethylene, acrylonitrile-butadiene-styrene, polyamide, polyethylene, and polytetrafluoroethylene. The drop-dimension methods of LIBS spectra of waste were researched to eliminate noise and redundant information by principal component analysis (PCA) and linear discriminant analysis (LDA), respectively. Their clustering effects were analyzed to choose a suitable dimension. Combining the random forest (RF), back propagation neural network (BPNN), and convolutional neural network (CNN), we established and compared five classification models, PCA + RF, PCA + BPNN, LDA + RF, LDA + BPNN, and 1D-CNN. For the classification of six categories, the accuracies of proposed classification models are all more than 96%, and LDA(5D) + RF has 100% accuracy and optimal classification performance indices. For the subclassification of metals and plastics, PCA(8D) + RF has the highest classification accuracy of 98.77% and 99.52%, respectively.