High quality garbage: A neural network plastic sorter in hardware and software
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.
- Research Organization:
- Sandia National Labs., Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-76DP00789
- OSTI ID:
- 10191079
- Report Number(s):
- SAND-93-0521C; CONF-9311101-2; ON: DE93041143
- Resource Relation:
- Conference: GOMAC 93: government microcircuit application conference,New Orleans, LA (United States),1-4 Nov 1993; Other Information: PBD: [1993]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
SORTING
NEURAL NETWORKS
SOLID WASTES
PLASTICS
INFRARED SPECTROMETERS
COMPUTER ARCHITECTURE
320305
320604
990200
INDUSTRIAL WASTE MANAGEMENT
MUNICIPAL WASTE MANAGEMENT
MATHEMATICS AND COMPUTERS