Evaluating Commercial Solutions for Identifying and Sorting Recyclable Plastics and Textiles

Abstract

This thesis presents a comprehensive analysis of waste sorting methods, comparing established practices with emerging technologies to evaluate their effectiveness in enhancing recycling operations. The study addresses the growing demand for more efficient waste management systems by investigating the technologies employed by ten well-established companies in the field of waste sorting or sorting equipment manufacturing alongside seven innovative methods recently introduced to the market. Through detailed case studies and empirical data analysis, the research identifies key strengths and weaknesses across various sorting technologies, including optical sorting, robotic systems, and chemical recycling methods.

The findings indicate that while traditional methods, such as optical and robotic sorting, remain highly effective, the integration of artificial intelligence, machine learning, and multi-sensor systems significantly enhances sorting accuracy and operational efficiency. Specifically, traditional techniques like Near-Infrared (NIR) Spectroscopy continue to perform well, but the addition of AI-driven approaches and multi-sensor technologies greatly improves precision and process efficiency. Moreover, emerging innovations, such as enzymatic recycling and electrostatic separation, demonstrate promising potential, particularly in managing complex waste streams like textiles and multi-layered plastics.

The thesis concludes that a hybrid approach, combining established practices with cutting-edge innovations, offers the greatest potential to improve recycling rates and achieve broader sustainability goals. By adopting these advanced technologies, waste management facilities can more effectively address challenges related to contamination, cost, and material recovery, ultimately contributing to the advancement of a circular economy.