Sorting of Waste Wood by NIR Imaging Techniques

Sorting of Waste Wood by NIR Imaging
Techniques
Enrico Pigorsch, Gerhard Gärtner
Frank Hollstein
Peter Meinlschmidt
PTS Papiertechnische Stiftung
Pirnaer Straße 37
D-01809 Heidenau
RTT Steinert GmbH
Hirschfelder Ring 9
D-02763 Zittau
Fraunhofer Institut für Holzforschung WKI
Bienroder Weg 54e
D-38108 Braunschweig
Keywords: Recycling, waste wood, sorting, NIR imaging
1
Introduction
Wood is an important renewable resource as raw material and for energy production. Furthermore,
in waste wood coming from building demolition and furniture there is a significant amount of wood
based material that can be re-used. But at present, about three quarters of the identified resource of
waste wood is either not recycled at all or has a low-value use.
One main obstacle to the re-use of waste wood is that a significant proportion of the material is
chemically treated which makes it unsuitable for many end users. This aspect makes the separation
of the treated wood from clean wood a major element of the wood recycling process.
Today, the separation of treated wood and other contaminations from clean wood is mainly based
on visual, mechanical, magnetic or gravity sifting techniques and is done at different steps along the
waste wood processing chain [1]. Sensor based sorting techniques could help to achieve higher levels of recovery and quality of waste wood and to considerably increase the proportion of a high value material use of waste wood.
NIR spectroscopy, especially the NIR imaging technique, is best suited for the automated online
sorting of high volumes of waste wood. It has a high discrimination power for organic contaminations and the necessary measuring speed and spatial resolution.
In our paper, we present results of online NIR sorting trials with different waste wood mixtures including a presentation of the used NIR measuring system and information on the development of
the NIR classification methods.
This research work was done within the European research project DEMOWOOD “Optimisation of
material recycling and energy recovery from waste and demolition wood in different value chains”
(2011-2014) with the support of Wood Wisdom Net (2nd Call) (www.wwnet-demowood.eu).
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2
Experimental
2.1
NIR Imaging System
The NIR measurements were performed by means of a NIR imaging camera KUSTA2.2MSI (LLA
Instruments GmbH). The NIR spectral imaging technique enables to obtain spatial and chemical
information characterising material samples with high speed and high spatial resolution and offers
new possibilities for classification and sorting.
For the measurements the camera was connected to a computer. The illumination of the samples is
done with halogen lamps. The main parameters of the NIR camera are:
•
•
•
•
Spectral range
Spectral resolution
2D detector array
Frame frequency
2.2
1229 to 2157 nm
4 nm
InGaAs detector with 320 spatial pixel x 256 spectral pixel
160 Hz
Classification Methods
The different components in waste wood, like wood, paints or plastic coatings, differ in their chemical compositions, which result in specific spectral differences in the NIR spectra.
Figure 1 shows NIR spectra of different waste wood types measured with the NIR camera
KUSTA2.2MSI. Characteristic NIR bands of some contaminations are indicated.
Figure 1:
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NIR spectra of waste wood samples
Sensor-Based Sorting 2014
Sorting of Waste Wood by NIR Imaging Techniques
The spectral differences can be used for the classification of the different contaminations. To distinguish or separate all the possible variations in the NIR absorptions, spectra pre-treatment procedures
and chemometric methods are used.
For the development of the classification methods, representative samples of the different waste
wood types were collected and separated in different fractions.
From all waste wood samples the NIR reference spectra were measured (see Fig. 1). The calculation
of the classification methods for each waste wood type was done on the basis of the recorded NIR
spectra using the discriminant PLS method. Several classification PLS methods have been calculated. Each of them differentiates between two waste wood types. The individual classification methods were then combined to a classification algorithm similar to that shown in Figure 2.
Figure 2:
Algorithm for classification of different waste wood types
This classification algorithm allows the detection and separation of the following waste wood types
from a waste wood mixture:
W
clean waste wood
WC
wood with coatings (paint, plastics, decor paper)
P
particle board (uncoated)
PC
particle board coatings (plastics, decor paper)
The structure of the classification algorithm is variable which makes it possible to change or to add
sorting criteria (e. g. identification of specific glues) in a simple and easy manner.
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2.3
Sorting Installation
The sorting trials were done using a sorting machine of the project partner RTT Steinert GmbH in
Zittau (Germany). Figure 3 shows the sorting installation with the machine and the NIR camera
used in the sorting trials.
Figure 3:
Sorting machine (RTT Steinert) that was used in the sorting trials
During the sorting experiments the waste wood particles were manually evenly distributed across
the working width of the belt and moved under the NIR camera. The distance between the NIR
camera and the conveyor belt was 700 mm resulting in a measuring width of 500 mm and a spatial
resolution over the belt of about 2 mm. The conveyor belt had a speed of 2 m/s which gave in connection with the frame frequency of the camera of 160 Hz a spatial resolution of about 13 mm in
belt direction.
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3
Results of Sorting Trials for Model Mixtures
3.1
Classification Experiments
In a first step, the NIR classification methods were tested by measuring and classifying single waste
wood samples. The following six sample types have been measured:
W
fresh spruce wood
WC
window frames with white paint
P-UF
particle board (uncoated) with urethane-formaldehyde (UF) glue
P-MUF
particle board (uncoated) with melamine-urethane-formaldehyde (MUF) glue
PC-HPL
particle board coated with decor paper (HPL)
PC-PVC
particle board coated with poly-vinyl chloride (PVC)
The samples were manually put on the conveyor belt as an array of 25 objects (see Figure 4) and
moved under the NIR camera. Three measurements were performed for each sample type. For each
measurement, the classification results were visualized using the classification algorithm and compared with a visual image of the sample array. Figure 4 gives an example for the measurement of
the PC-PVC samples.
Figure 4:
Visual and classification images of the sample array PC-PVC
A comparison of the visual and classification images in Figure 4 shows a very good agreement between the classification and the composition of the measured wood pieces. The same or similar results were found for all other measurements (see also Table 1) demonstrating a high or sufficient
accuracy of the classification algorithm. Only few spectra are wrongly classified. Consequently,
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using the majority-criteria for the classification of the whole object, only very few objects have
been given a wrong classification. In Table 1, an analysis of all measured spectra of the samples W,
P-UF and P-MUF is given.
Table 1:
Classification results for the spectra of the samples W, P-UF and P-MUF
Spectra Classification
Wood Type Measurement
Number
of Spectra
WC or PC
Spectra
filtered out
Classification
Accuracy [%]
W
P
W 01
1931
1062
444
3
422
70
W 02
1579
1080
146
5
348
88
W 03
1655
1103
170
2
380
86
P-UF 01
2760
18
2210
45
487
97
P-UF 02
2594
36
2068
42
448
96
P-UF 03
2707
20
2109
33
545
97
P-MUF 01
2885
68
2203
249
365
87
P-MUF 02
1396
70
1703
199
424
86
P-MUF 03
2387
44
1710
212
421
87
The P-UF samples show the highest classification accuracy of over 90 per cent. But, also for the
other samples, the classification accuracy of over 85 per cent should be sufficiently high.
3.2
Results of the Sorting Trials
The sorting trials were done in three experiments (Table 2). For each experiment, mixtures of wood
pieces (size about 3 x 3 cm) of two different waste wood types were prepared. The separation was
done according to the chosen sorting criteria.
Table 2:
Composition of model mixtures and sorting criteria
Experiment
Component 1 [kg]
Component 2 [kg]
Sorting Criteria
1
W
2.08
P-UF
3.2
W – clean wood
2
W
1.5
WC
0.6
WC - white paint coating
3
P-UF
5.0
PC-HPL
3.4
PC-HPL – HPL coating
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The waste wood mixtures were manually poured from buckets on the conveyor belt (speed 2 m/s).
After the sorting, the two sorted fractions (accept and reject) were weighted and manually analysed.
The first experiment has been repeated with the same mixture. In the two other experiments, the
accept fraction (wanted fraction) was again put on the conveyor belt, sorted and analysed.
The sorting results are given in Table 3. Figure 5 gives a classification picture from the third experiment showing the classification of the two components of the mixture, uncoated and coated particle
board.
Figure 5:
NIR classification image of Experiment 3
Table 3:
Analysis of the sorting experiment results
Input
Comp. 1
Exp. [kg]
[%]
Comp. 2
[kg]
[%]
Accept (Passing)
Reject (Ejection)
Comp. 1
Comp. 2
Comp. 1
Comp. 2
[kg]
[%]
[kg]
[%]
[kg]
[%]
[kg]
96.6
W
1.95
P-UF
86.7 0.3
13.3
96.6
1.85
84.1 0.35
15.9
WC
20.0 0.2
80.0
18.2 0.09
81.8
1A
W
2.08
P-UF
39.4 3.2
W
60.6 0.1
3.4
P-UF
2.85
1B
1.95
38.2 3.15
61.8 0.1
3.4
2.8
2A
W
1.5
WC
73.2 0.55
W
26.8 1.45
WC
80.6 0.35
19.4
W
0.05
2B
1.45
80.6 0.35
19.4 1.43
84.6 0.26
15.4
0.02
3A
P-UF
5
59.5
30.4
P-UF
0.2
13.3
3B
4.8
22.3
0.1
PC-HPL
3.4
69.8 2.1
40.5
P-UF
4.8
69.8
30.4 4.7
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PC-HPL
2.1
77.7 1.35
[%]
PC-HPL
1.3
11.8 0.75
86.7
88.2
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3.3
Discussions of the Results
Evaluating the achieved accuracy and efficiency in the sorting experiments, one has to consider
that:
•
The control of the ejection valves was not optimized. There was no object recognition. Therefore, the activation signal for the ejection valves was generated using the classification of single
spectra.
•
In the experiments 2 and 3, all wood pieces had two different surfaces, with and without contaminations. Consequently, the probability of the presentation of the contaminated surface to the
NIR sensor is only about 50 %, considerably lowering the sorting efficiency.
Experiment 1
In this case, the reject was the wanted sorting fraction. Both trials (1 A and 1B) show for the clean
wood fraction an increase of share from 39 % to about 85 %. The yield of the clean wood was about
94 %.
Experiment 2 and 3
In these experiments, the coating contaminations were the sorting criteria. The accept was the wanted fraction.
In both cases, the first sorting step (2A and 3A) showed an increase of share of the clean fraction in
the accept of 7 and 10 %, respectively. This poor sorting efficiency was due to the above mentioned
50/50 probability of the presentation of the contaminated surface to the sensor. In contrast, the reject showed a high share of contaminated pieces of 80 and 87 % which demonstrates the high accuracy of the NIR classification.
The second sorting step showed a further but, still small increase of share of the wanted fraction in
the accept of 4 and 8 %. Again, the share of contaminated pieces in the reject was high with 81 and
88 %.
The poorer efficiency of sorting of only surface contaminated material is a general problem encountered in recycling. To liberate the contaminated parts from the clean bulk material it is necessary to
shred the wood in smaller pieces. The yield of the uncontaminated material will increase with the
decrease in size of the pieces. Conversely, smaller pieces are more difficult to identify by sensors
and to eject from the material stream. This conflict is illustrated in Figure 6.
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Figure 6:
Connection between particle size of only surface contaminated material and contamination detectability and sorting efficiency
The sensor based sorting of small sized material with high speed has already improved considerably
in recent years. Especially, the NIR imaging technology will show further progress in this direction.
In this context, the results of our sorting experiments are a demonstration of the possibilities of this
new technology.
Another possibility to improve the efficiency of sorting of material with different surfaces that can
be presented to the sensor are the circulation of the material stream, realizing an automated sorting
in multiple steps.
4
Conclusions
The objective of our work was to demonstrate the feasibility of a NIR sensor based separation of
contaminations in waste wood with a high precision and efficiency. From the results of the sorting
trials presented in this report can be concluded:
•
The NIR imaging technology allows the detection of chemical contaminations in waste wood
with a high precision and at a high speed. Consequently, the use of this technology makes it
possible to sort waste wood with a high efficiency and with a high throughput.
•
The NIR imaging system used in the sorting trials was able to detect small pieces of waste wood
material of sizes from 10 to 50 mm at a speed of 2 m/s. Further optimization of the detection
and ejection will allow further decreasing the particle size and increasing the speed of the conveyor belt.
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•
The NIR classification methods are able to detect most of the organic chemical contamination,
like paint, plastics coating, decor papers and different glues. The developed classification algorithm can be further extended and improved by changing or additing of individual classification
methods.
•
Additional sensor systems, like XRF or LIBS, are necessary for the detection of inorganic contaminations, especially heavy metals.
Hence, it can be said that NIR imaging is a very promising technology to reach the target to considerably increase the proportion of a high value material use of waste wood. It makes it possible to
develop an automated online sorting system for waste wood that would be efficient and economically feasible.
References
[1]
BAV - Bundesverband der Altholzaufbereiter und -verwerter e.V. (Hrsg.), Leitfaden der Altholzverwertung, 7. Auflage, Berlin, 2012
[2]
E. Pigorsch et al., Review on Current Practices on Detection and Sorting Technologies for
Waste Wood, Deliverable DL-WP2.1, EU research project DEMOWOOD within Wood Wisdom Net (2nd Call), 2011
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