Abstract
This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles’ (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.
Highlights
Worldwide the automotive industry has become one of the largest and most developed industries over the last two decades
PP/EPDM is a kind of thermoplastic elastomer (TPE) material, but due to its mechanical properties it performs like a plastic, in this research the PP/EPDM was considered as a kind of vehicle plastic material
In this study all the impact acoustic emissions (AEs) signals were captured in form of 440–500 samples with 8 bit within 10–100 ms
Summary
Worldwide the automotive industry has become one of the largest and most developed industries over the last two decades. Nowadays both vehicle production and ownership have reached extremely high levels around the world. Has estimated that the vehicle growth from the 1990s to 2020s could reach more than 30% [1,2,3,4]. In China in particular, the automotive industry has maintained an annual average growth rate of about 20–25% for the last ten years. In 2014 the total vehicle production reached about 24 million, which made China the largest automotive producer and consumer in the world [4,5,6]. The boom of Sensors 2017, 17, 1325; doi:10.3390/s17061325 www.mdpi.com/journal/sensors
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