An Effective Recycling Tool
Hyperspectral imaging can greatly assist in sorting and recycling a variety of materials.
Production of plastics is growing at an alarming rate, projected to double in demand by 2050 and already responsible for 4.5% of global greenhouse gas emissions. This is triggering governmental actions and research on faster and more efficient ways to recycle plastics.
Because effective recycling is only possible if the mixed plastic waste can be separated into different polymers, factors such as transparency and color might not be the most accurate way to differentiate materials. Hyperspectral imaging is being used as a more accurate part of the sorting process.
Of the many efforts of conservation and reduction of waste, textiles remain one of the greatest challenges. The United States EPA estimates that of the 25 billion pounds of post-consumer textile waste recycled, only 15% is recycled and repurposed, while the
remaining 85% of it ends up in landfills. The challenge facing
textile recycling projects is discerning between similar looking fabrics at a high throughput.
The ideal solution for this challenge would be a non-contact classifier that can sort the different fabrics and blends at high speeds. With Headwall’s hyperspectral imaging (HSI) sensors, and
perClass Mira’s machine learning software, Headwall provides a potential solution to this problem.
Case studies
Real-Time, Non-Contact Sorting of Plastics for Recycling Using Hyperspectral Imaging
This feasibility study utilized an inno-spec GmbH (now a Headwall Group company) hyperspectral imaging system consisting of a RedEye 1.7 (950 – 1700 nm) push-broom camera, compact scanning stage, tungsten halogen lighting, and a host computer running perClass Mira software. Plastic samples of known composition were scanned and a model created to show which samples were made of which polymer. Once properly trained, mixed batches of plastic were passed under the RedEye sensor, detected and false-colored using perClass Mira in real time! In a factory scenario, the positions of each plastic type is sent downstream.
Sorting Textiles for Recycling
Of the many efforts of conservation and reduction of waste, textiles remain one of the greatest challenges. The United States EPA estimates that of the 25 billion pounds of post-consumer textile waste recycled, only 15% is recycled and repurposed, while the remaining 85% of it ends up in landfills.1 The challenge facing textile recycling projects is discerning between similar looking fabrics at a high throughput. The ideal solution for this challenge would be a non-contact classifier that can sort the different fabrics and blends at high speeds. With Headwall’s hyperspectral imaging (HSI) sensors, and
perClass Mira’s machine learning software, Headwall provides a potential solution to this problem.