Hyperspectral image analysis to identify of components in municipal solid waste with feedstock potential

More than 65 million tons of plastics and organic materials from municipal solid waste (MSW) typically end up in landfills unless alternative routes can be found for their use. Given the high volume and low cost of these materials, they represent an attractive option to develop technologies for producing cost-competitive advanced biofuels from non-food biomass resources. These up-cycling technologies will require real-time composition analysis to operate properly and efficiently. A convolutional neural network (CNN) is being developed to classify components of MSW based on their visual and midinfrared (MIR) quantum cascade laser-based spectroscopy. We test this classifier by streaming labeled visual and MIR spectral images to simulate its operation in real-time on moving waste streams. These simulations allow us to explore how performance parameters like speed and accuracy are related to the required preprocessing of input the CNN and the structure of the network.