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Efficient algorithmic coupling technique for precision recycling of seven types of mixed plastic waste
Summary
Researchers developed a two-step machine learning coupling technique combining Linear Support Vector Classification (Linear-SVC) with a Multi-layer Perceptron (MLP) to improve the precision of sorting seven types of mixed plastic waste. The coupling technique raised overall plastic identification accuracy from 94.7% to 97.7% and substantially improved classification accuracy for HDPE and LDPE from 79-94%, while also reducing computation time compared to the single-step MLP approach.
Abstract The annual global production of plastic waste, characterized by complex composition and challenges in separation, necessitates immediate and comprehensive measures for the recycling and disposal of mixed plastic waste in an environmentally friendly and meticulous manner. This study introduces an efficient two-step coupling technique, employing Linear Support Vector Classification (Linear-SVC) in tandem with Multi-layer Perceptron (MLP). The application of this coupling technique elevates the overall accuracy of identifying seven types of plastics from 94.7% to an impressive 97.7%. Furthermore, the method exhibits a reduced running time compared to the one-step method of MLP. Notably, the classification accuracy for high-density polyethylene (HDPE) and low-density polyethylene (LDPE) experiences a substantial improvement from 79–94%, outperforming the one-step MLP method. This coupling technique emerges as an effective strategy, contributing significantly to the harmless and precise recycling of waste plastics.