The New Techpap NIR spectroscopy for Recycled Paper Bales Inspection

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1 The New Techpap NIR spectroscopy for Recycled Paper Bales Inspection Speaker: Didier Rech (Techpap) Authors: -Alain Cochaux (CTP France) -Pascal Borel (CTP France) -Guy Eymin Petot Tourtollet (CTP France) - Didier Rech (Techpap)

2 Content of the presentation General presentation of topic : problems & solutions proposed NIR spectroscopy First trials in laboratory with NIR spectroscopy Our industrial recovered paper bales quality control sensors First industrial trials in a mill : procedures & results obtained Last industrial trials... completed! 2

3 1.1 Problem: recovered paper bales quality control Increasing necessity for papermakers to control, at delivery, recovered paper bales to ensure raw material quality & to pay the right price Crucial interest for papermakers: Measurement of moisture Detection of possible presence of unusable materials (metals, plastics, etc) Distinction of different raw materials (paper / board) for coherence with EN643 standard (1.04, 1.05 ) 3

4 1.2 Current control procedures used Generally speaking, they are : Manually made Very long and tiresome for operators example: core sampling & traditional measurement of moisture Limited objectivity example: visual and/or manual controls Consequence : Low efficiency so papermakers certainly lose money 4

5 1.3 Solution proposed The objective of MONITOR project is to develop an easyto-use sensor: Direct, rapid, objective, reliable, low cost To control in recovered paper bales at delivery: Moisture Unusable materials Grades To do that, we have developed a sensor based on a new approach : Core-drilling device & Near Infra-Red (NIR) Spectroscopy 5

6 2. NIR spectroscopy (in a few words ) Already used since several years in different sectors (food processing, chemicals, drugs ) to study the composition of samples (in particular: moisture measurement) Papermaking sector: «emerging» and «promising» technology for the future Main advantages: powerful technique to precisely study samples composition no need of sample preparation low price very rapid (~ 50 ms / spectrum) ~ 200 measurements in only 10 seconds 6

7 2. NIR spectroscopy (in a few pictures ) Light source with 800nm < λ < 2500nm Reflected photons NIR Spectrometer Sample to study Black Box Composition of sample Black Box Black Box = Multi-Linear statistics models (for predictions) obtained by calibration / learning 7

8 3. First trials in laboratory with NIR spectroscopy Development of a lab prototype to simulate the behaviour of a coring device used with our NIR equipment: Fibre with measurement head Displacement of sample Measurement window 8

9 3.2 Moisture measurement Samples (cores) were made with different moisture contents For each core, we have done: Acquisitions of NIR spectrum (= inlet data X) An accurate traditional measurement of moisture: weighing, complete oven drying, re-weighing (= outlet data Y) 9

10 Two «λ bands» seem interesting (around 1440 and 1940 nm) Dynamic more important around 1940 nm 20 % 18 % 13 % 12 % 10 % 8 % 7 % Using statistical methods, several moisture prediction models were built and tested : Δ = «accuracy» of the prediction model = ± 0.35 % 10

11 3.3 Unusable & raw materials measurements Same procedure as for moisture : making of cores with different compounds, building of prediction models ( = calibration) & tests Choice of the different compounds to take into account Aluminium (metals), 3 kinds of plastics (types:pp, PE, PET), paper and corrugated board Acquisitions of core spectra with 100% of each compound (for calibration) Acquisitions of core spectra with blends of several compounds (for tests) 8 blends cores were made with random % 11

12 Using this «λ band», the system is able to distinguish the different compounds 1700 nm < λ < 2150 nm Plastics Water / Papers Papers 12

13 Conclusion of these first laboratory studies The first results obtained in lab with the NIR spectroscopy have allowed us to check the use and the discriminant capability of this approach for moisture and unusable materials that we would like to characterize (metals, plastics) vs raw materials (paper, board) Manufacture of an industrial prototype 13

14 4. Our industrial sensor for recovered paper bales quality control Two versions of the sensor have been developed: An embedded version : to instrument an industrial coring device, in order to perform direct control at delivery on trucks. This sensor was made to be fixed directly on the output pipe of any coring device. A transportable version : less precise and efficient than the first one, but also much less expensive (no need of a coring device). This sensor is hand-held and it has to be introduced in a hole done with a simple drill. 14

15 4.1 Our monitor sensor: embedded version screen Synopsis of our complete sensor: control buttons PC optical fiber / power wire measurement head Spectrometer Light source power coring device Industrial rack 15

16 Some details about the measurement head pipe with optical fibre & power wire measurement head reflected signal collection NIR light source The head is very light and very rigid Total length of the head = 20 cm Inside the head : optical device to optimize the light illumination & the signal collection in the optical fibre 16

17 4.2 Our MONITOR sensor: transportable version All the acquisition and data processing device is the same as for the embedded version, except for the measurement head which is longer (1m), to be introduced directly in a hole done with any perforating device 17

18 5. First industrial trials in a mill: procedures & results obtained We have used an industrial coring device with our MONITOR sensors for industrial trials programs Trials have been carried out on ~ 70 bales (= ~ 150 cores), and for each bale core: NIR spectra acquisition Recording of the corresponding outlet data wanted Black Box Moisture spectra Black Box Black Box % of unusable / raw materials EN643 classification (1.04 / 1.05) 18

19 5.1 Results obtained: Moisture prediction «accuracy»of the prediction model = ± 0.75 % 19

20 5.2 Results obtained: Unusable materials prediction For metals prediction : we can t conclude with the data obtainable (because only 5 values are not 0 % and the max. value is only 0.5 %) For plastics prediction : «accuracy»of the prediction model = ± 0.45 % 20

21 6. Last industrial trials performed After the first encouraging trials in a mill, different adaptations (mechanic, hardware, software interface) were done on our prototype to fit it to an autonomous and continuous use in mill by operators 21

22 6. Last industrial trials performed A new trials program (still in progress) has started for a long duration period with one of our industrial partners These trials have allowed us to identify and to correct some adverse industrial conditions relevant to the industry (clogging, mechanical constraints, ) Now, the sensor seems to work well, and we are beginning to obtain interesting results 22

23 6. Last industrial trials performed (still in progress) : a few results Moisture prediction «accuracy» of the model = ± 1 % Plastics prediction «accuracy» of the model = ± 0.5 % 23

24 the recovered paper bales quality control sensor 2 versions available : Easy-to-use Direct Rapid Objective Reliable Short pay-back 24

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