G. A. Hebner’s research while affiliated with Sandia National Laboratories and other places

While plastic recycling efforts have expanded during the past several years, the cost of recovering plastics is still a major impediment for recyclers. Several factors contribute to the prohibitive cost of recycled resins, including the present low marketability of products made with mixed recycled materials, and costs of collecting, sorting and reprocessing plastic materials. A method for automatic sorting of post-consumer plastics into pure polymer streams is needed to overcome the inaccuracies and low product throughput of the currently used method of hand sorting of waste plastics for recycling. The Society of Plastics has designated seven categories as recyclable: Polyethylene terephthalate (PET); High Density Polyethylene (HDPE); Polyvinyl Chloride (PVC); Low Density Polyethylene (LDPE); Polypropylene (PP); Polystyrene (PS); and Other (mixtures, layered items, etc.). With these categories in mind, a system for sorting of waste plastics using near-infrared reflectance spectra and a backpropagation neural network classifier has been developed. A solution has been demonstrated in the laboratory using a high resolution, and relatively slow instrument. A faster instrument is being developed at this time. Neural network hardware options have been evaluated for use in a real-time industrial system. In the lab, a Fourier transform Near Infrared (FT-NIR) scanning spectrometer was used to gather reflectance data from various locations on samples of actual waste plastics. Neural networks were trained off-line with this data using the NeuralWorks Professional II Plus software package on a SparcStation 2. One of the successfully trained networks was used to compare the neural accelerator hardware options available. The results of running this ``worst case`` network on the neural network hardware will be presented. The AT&T ANNA chip and the Intel 80170NX chip development system were used to determine the ease of implementation and accuracies for this network.

In order to produce pure polymer streams from post-consumer waste plastics, a quick, accurate and relatively inexpensive method of sorting needs to be implemented. This technology has been demonstrated by using near-infrared spectroscopy reflectance data and neural network classification techniques. Backpropagation neural network routines have been developed to run real-time sortings in the lab, using a laboratory-grade spectrometer. In addition, a new reflectance spectrometer has been developed which is fast enough for commercial use. Initial training and test sets taken with the laboratory instrument show that a network is capable of learning 100% when classifying 5 groups of plastic (HDPE and LDPE combined), and up to 100% when classifying 6 groups. Initial data sets from the new instrument have classified plastics into all seven groups with varying degrees of success. One of the initial networks has been implemented in hardware, for high speed computations, and thus rapid classification. Two neural accelerator systems have been evaluated, one based on the Intel 8017ONX chip, and another on the AT&T ANNA chip.