High-Speed Hyperspectral Imaging facilitates Advanced Technologies like those for Plastic Recycling

Introduction

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Modern society requests high standards from its industry: It should be efficient, economical, ecologically sustainable and at the same time offer the best possible working conditions for all concerned. Hence relentless working towards the fulfillment of these legit demands is implied.

One powerful way to tackle this goal is the intelligent utilization of novel sophisticated technologies. If used judiciously they can bring about substantial improvements in all four above mentioned criteria. With this article I would like to exemplify such an commendable implementation.

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^{Source: pixabay}

Recycling of Plastics

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Plastics are state-of-the-art materials whose mechanical, electronic, chemical and optical properties can be precisely adjusted during their production. Because they are typically lightweight, resources are used efficiently. However, as bulk plastics are not biodegradable, efficient recycling must be installed to take the ecological aspect into account.

In principle, both material and energetical recycling are possible. In the first case, the plastic is reused as a material and in the second case it is used as an energy source. For both cases, it is essential to know the exact nature of the plastic, so whether it is PE, PP, PET, PVC, PU or any other.

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Challenges of Recycling

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Traditional plastic separation methods are based on physical properties, e.g. separation by density. However, such simple procedures are often not sufficient to achieve complete separation. Basically, this is only possible by a chemical-physical analysis of each piece of waste. An enormous workload!

A prime example for this are the following plastics, PVC and PET:

• PVC - Polyvinylchloride: d = 1.10-1.61 g/mL
  recycling methodlogy must consider high chlorine content

• PET - Polyethylenterephthalate: d = 1.33-1.37 g/mL
  material recycling is easily possible and advised

Due to the low costs of virgin polymers the utilization of very efficient, low cost techniques and processes is indicated for the recycling of plastics.

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^{Source: pixabay}

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Hyperspectral Imaging

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Hyperspectral imaging (short: HSI) is a complex technique that generates multivariable images. This means that the two-dimensional pixel of normal images is transferred into a three-dimensional voxel. Today many different physical principles can be used to generate the third dimension of these images, e.g. near infrared or visible light spectra. One typical characteristic of these images is that they cover many wavelenghts, thus each voxel contains an almost continuous spectrum.

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Using sophisticated methodology and relying on good calibration, these spectra can be evaluated very quickly and precisely. From the enormous data density that is generated, a lot of information can be extracted. Due to its considerable strengths, this technique is used in many fields:

• Agriculture: detection of grape variety and disease outbreaks
• Eye Care: detection of early retinopathy and other diseases
• Food Processing: identification of defects and foreign materials
• Mineralogy: rapid mapping of minerals in geological samples
• and for many more like Plastic Identification and Sorting!

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The Technological Application

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You now may wonder how exactly the previously presented technology is used. Letting aside all the complex evaluation techniques, this is relatively easy to understand. Advanced plastic sorting is performed by specialized equipment that consists of two major components:

1. A sensing system that utilizes HSI technology to identify plastics.
2. A device that usually uses a rapid stream of air to sort plastic objects.

The recognition of the exact type of plastic is based upon the NIR (near infrared) spectral components, which are contained in the HSI cube. The next figures show characteristic NIR spectra of various PET & PVC samples.

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PET NIR Spectra: V...virigin - F...flakes - P...Pieces - R...Regenerated Flakes
^{click to enlarge - data taken from Ref. [1]}

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PVC NIR Spectra: V...virigin - F...flakes - P...Pieces - R...Regenerated Flakes
^{click to enlarge - data taken from Ref. [1]}

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However, it must also be separated according to other criteria like additives. Otherwise, dyes would cause unwanted discoloration in the recycled PET product. Therefor the spectral VIS data (visible light spectrum) is utilized.

Finally, the whole process can be represented as follows:

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^{Source: Van Dyk Recycling Solutions}

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Modern versions of these machines allow astonishing conveying and sorting speeds, which are up to 40 times higher than with previous sorting techniques. Furthermore, advanced learning software, sophisticated identification algorithms as well as high-precision air blast mechanics are used to easily achieve classification results of greater 90 %.

If you want to see the application of this process during the operation of a complete plastic sorting system, you should not miss this impressive video:

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Conclusion

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PET as a readily recyclable plastic shows continual growth in its' recycling rates in Europe. In 2016, this rate was just under 60 %. Also the recycling of other plastics is substantially facilitated by this technology.

With the technology presented here, the adequate waste management of plastics in general does not really have an economic barrier anymore, e.g. through old-fashioned time-consuming sorting processes. This shifts the core task to collecting and disposing, ie to a societal-logistical challenge.

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Further videos can be found here:

• REDWAVE plastic sorting plant in Austria
• MSS CIRRUS Optical Sorter for HDPE and PET

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Best,
mountain,phil28

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References:

1. M. Moroni, et. al. Sensors 2015, 15, pp. 2205-2227
2. H. F. Grahn, P. Geladi (2007) Techniques and Applications of Hyperspectral Image Analysis. John Wiley & Sons, Ltd.
3. PVC-Recycling-Technologien, vinylPLUS
4. R. Humphreys (2017) More About Waste Plastic Sorting Technologies. Polymer Innovation Blog
5. PET collection and recycling rates in Europe, petcore Europe, 2017
6. MSS Sorting Technology, Homepage
7. A Wikipedia Article