Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Düren
Shaker Verlag
2022
|
| Series: | Schriftenreihe zur Aufbereitung und Veredlung
Band 80 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | VIII, 101 Seiten Illustrationen 30 cm, 170 g |
| ISBN: | 9783844084481 3844084487 |
Staff View
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| 245 | 1 | 0 | |a Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms |c Xiaozheng Chen |
| 264 | 1 | |a Düren |b Shaker Verlag |c 2022 | |
| 300 | |a VIII, 101 Seiten |b Illustrationen |c 30 cm, 170 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Schriftenreihe zur Aufbereitung und Veredlung |v Band 80 | |
| 502 | |b Dissertation |c RWTH Aachen University |d 2021 | ||
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| 653 | |a machine learning | ||
| 653 | |a shrink sleeve | ||
| 653 | |a near-infrared spectroscopy | ||
| 653 | |a plastic recycling | ||
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Record in the Search Index
| _version_ | 1804184667391262720 |
|---|---|
| adam_text | CONTENTS
LIST
OF
FIGURES
HI
LIST
OF
TABLES
VI
LIST
OF
ABBREVIATIONS
VII
1
INTRODUCTION
1
2
SHRINK
SLEEVES
AND
PLASTIC
BOTTLE
RECYCLING:
STATE
OF
THE
ART
3
2.1
SHRINK
SLEEVES
..............................................................................................................................
3
2.1.1
MATERIALS
........................................................................................................................
4
2.1.2
PRODUCTION
AND
SHRINK
PROCESS
....................................................................................
6
2.2
OVERVIEW
OF
PLASTIC
BOTTLES
.......................................................................................................
7
2.3
MECHANICAL
RECYCLING
OF
PLASTIC
BOTTLES
....................................................................................
9
2.3.1
COLLECTION
.......................................................................................................................
9
2.3.2
PREENRICHMENT
IN
SORTING
PLANTS
.................................................................................
10
2.3.3
REFINEMENT
IN
RECYCLING
PLANTS
....................................................................................
12
2.4
INFLUENCE
OF
SHRINK
SLEEVES
ON
BOTTLE
RECYCLING
PROCESS
........................................................
12
3
NEAR
INFRARED
(NIR)
SPECTROSCOPY
15
3.1
PHYSICAL
PRINCIPLE
.......................................................................................................................
15
3.1.1
VIBRATIONAL
SPECTROSCOPY
..............................................................................................
15
3.1.2
NIR-SENSITIVE
FUNCTIONALITIES
.......................................................................................
17
3.1.3
APPLICATION
IN
POLYMER
IDENTIFICATION
.......................................................................
18
3.2
DATA
ACQUISITION
...........................................................................................................................
18
3.2.1
NIR
LIGHT
SOURCES
...........................................................................................................
18
3.2.2
NIR
DETECTORS
.................................................................................................................
20
3.3
DATA
ANALYSIS
..............................................................................................................................
20
3.3.1
DATA
PREPROCESSING
.......................................................................................................
21
3.3.2
DATA
CLASSIFICATION
.......................................................................................................
26
3.4
LIMITATION
OF
NIR
IN
POLYMER
SORTING
.......................................................................................
26
4
MACHINE
LEARNING
FOR
CLASSIFICATION
29
4.1
DECISION
TREE
..............................................................................................................................
31
4.1.1
ALGORITHMS
TO
CONSTRUCT
DECISION
TREES
.............................................................
31
4.1.2
RANDOM
FOREST
...............................................................................................................
34
4.2
SUPPORT
VECTOR
MACHINE
(SVM)
.................................................................................................
34
4.2.1
BINARY
CLASSIFICATION
....................................................................................................
34
4.2.2
MULTICLASS
SVM
..........................................................................................................
36
4.3
PARTIAL
LEAST
SQUARES
(PLS)
.......................................................................................................
36
4.3.1
LINEAR
PLS
.....................................................................................................................
37
4.3.2
NONLINEAR
PLS
...............................................................................................................
38
4.3.3
PLS
FOR
CLASSIFICATION
.....................................................................................................
38
4.4
CONVOLUTIONAL
NEURAL
NETWORK
(CNN)
.....................................................................................
40
4.4.1
COMMON
ARTIFICIAL
NEURAL
NETWORK
(ANN)
...................................................................
40
4.4.2
ARCHITECTURE
OF
CNN
.....................................................................................................
42
4.5
APPLICATION
IN
NIR
SPECTROSCOPY
..............................................................................................
44
5
MATERIAL
AND
METHODS
47
5.1
MATERIAL
SELECTION
........................................................................................................................
47
5.1.1
LWP
SAMPLE
SET
..............................................................................................................
47
5.1.2
REFERENCE
SAMPLE
SET
.....................................................................................................
47
5.1.3
POST-CONSUMER
BOTTLES
..................................................................................................
49
5.2
SPECTRA
ACQUISITION
.....................................................................................................................
51
5.3
DATA
PREPROCESSING
.....................................................................................................................
52
5.3.1
SPECTRA
EXTRACTION
...........................................................................................................
52
5.3.2
SPECTRA
PREPROCESSING
..................................................................................................
53
5.4
MACHINE
LEARNING
ALGORITHMS
.....................................................................................................
54
5.4.1
IMPLEMENTATION
...............................................................................................................
55
5.4.2
HYPERPARAMETER
OPTIMIZATION
....................................................................................
57
6
RESULTS
AND
DISCUSSION
61
6.1
TRAINING
AND
SELECTION
OF
CLASSIFICATION
MODELS
........................................................................
61
6.1.1
TRAINING
OF
MODEL
SET
T
1
..............................................................................................
61
6.1.2
TRAINING
OF
MODEL
SET
T2
..............................................................................................
65
6.2
CLASSIFICATION
OF
REFERENCE
BOTTLES
...........................................................................................
67
6.2.1
DIFFERENCE
IN
CLASSIFICATION
ALGORITHMS
........................................................................
67
6.2.2
INFLUENCE
OF
BOTTLE
AND
SLEEVE
PARAMETERS
.................................................................
72
6.3
CLASSIFICATION
OF
POST-CONSUMER
BOTTLES
....................................................................................
80
6.3.1
CLASSIFIERS
IN
MODEL
SET
T1
...........................................................................................
81
6.3.2
CLASSIFIERS
IN
MODEL
SET
T2
...........................................................................................
83
6.3.3
NON-DETECTABLE
CASES
.....................................................................................................
85
7
CONCLUSION
AND
OUTLOOK
91
|
| adam_txt |
CONTENTS
LIST
OF
FIGURES
HI
LIST
OF
TABLES
VI
LIST
OF
ABBREVIATIONS
VII
1
INTRODUCTION
1
2
SHRINK
SLEEVES
AND
PLASTIC
BOTTLE
RECYCLING:
STATE
OF
THE
ART
3
2.1
SHRINK
SLEEVES
.
3
2.1.1
MATERIALS
.
4
2.1.2
PRODUCTION
AND
SHRINK
PROCESS
.
6
2.2
OVERVIEW
OF
PLASTIC
BOTTLES
.
7
2.3
MECHANICAL
RECYCLING
OF
PLASTIC
BOTTLES
.
9
2.3.1
COLLECTION
.
9
2.3.2
PREENRICHMENT
IN
SORTING
PLANTS
.
10
2.3.3
REFINEMENT
IN
RECYCLING
PLANTS
.
12
2.4
INFLUENCE
OF
SHRINK
SLEEVES
ON
BOTTLE
RECYCLING
PROCESS
.
12
3
NEAR
INFRARED
(NIR)
SPECTROSCOPY
15
3.1
PHYSICAL
PRINCIPLE
.
15
3.1.1
VIBRATIONAL
SPECTROSCOPY
.
15
3.1.2
NIR-SENSITIVE
FUNCTIONALITIES
.
17
3.1.3
APPLICATION
IN
POLYMER
IDENTIFICATION
.
18
3.2
DATA
ACQUISITION
.
18
3.2.1
NIR
LIGHT
SOURCES
.
18
3.2.2
NIR
DETECTORS
.
20
3.3
DATA
ANALYSIS
.
20
3.3.1
DATA
PREPROCESSING
.
21
3.3.2
DATA
CLASSIFICATION
.
26
3.4
LIMITATION
OF
NIR
IN
POLYMER
SORTING
.
26
4
MACHINE
LEARNING
FOR
CLASSIFICATION
29
4.1
DECISION
TREE
.
31
4.1.1
ALGORITHMS
TO
CONSTRUCT
DECISION
TREES
.
31
4.1.2
RANDOM
FOREST
.
34
4.2
SUPPORT
VECTOR
MACHINE
(SVM)
.
34
4.2.1
BINARY
CLASSIFICATION
.
34
4.2.2
MULTICLASS
SVM
.
36
4.3
PARTIAL
LEAST
SQUARES
(PLS)
.
36
4.3.1
LINEAR
PLS
.
37
4.3.2
NONLINEAR
PLS
.
38
4.3.3
PLS
FOR
CLASSIFICATION
.
38
4.4
CONVOLUTIONAL
NEURAL
NETWORK
(CNN)
.
40
4.4.1
COMMON
ARTIFICIAL
NEURAL
NETWORK
(ANN)
.
40
4.4.2
ARCHITECTURE
OF
CNN
.
42
4.5
APPLICATION
IN
NIR
SPECTROSCOPY
.
44
5
MATERIAL
AND
METHODS
47
5.1
MATERIAL
SELECTION
.
47
5.1.1
LWP
SAMPLE
SET
.
47
5.1.2
REFERENCE
SAMPLE
SET
.
47
5.1.3
POST-CONSUMER
BOTTLES
.
49
5.2
SPECTRA
ACQUISITION
.
51
5.3
DATA
PREPROCESSING
.
52
5.3.1
SPECTRA
EXTRACTION
.
52
5.3.2
SPECTRA
PREPROCESSING
.
53
5.4
MACHINE
LEARNING
ALGORITHMS
.
54
5.4.1
IMPLEMENTATION
.
55
5.4.2
HYPERPARAMETER
OPTIMIZATION
.
57
6
RESULTS
AND
DISCUSSION
61
6.1
TRAINING
AND
SELECTION
OF
CLASSIFICATION
MODELS
.
61
6.1.1
TRAINING
OF
MODEL
SET
T
1
.
61
6.1.2
TRAINING
OF
MODEL
SET
T2
.
65
6.2
CLASSIFICATION
OF
REFERENCE
BOTTLES
.
67
6.2.1
DIFFERENCE
IN
CLASSIFICATION
ALGORITHMS
.
67
6.2.2
INFLUENCE
OF
BOTTLE
AND
SLEEVE
PARAMETERS
.
72
6.3
CLASSIFICATION
OF
POST-CONSUMER
BOTTLES
.
80
6.3.1
CLASSIFIERS
IN
MODEL
SET
T1
.
81
6.3.2
CLASSIFIERS
IN
MODEL
SET
T2
.
83
6.3.3
NON-DETECTABLE
CASES
.
85
7
CONCLUSION
AND
OUTLOOK
91 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Chen, Xiaozheng |
| author_GND | (DE-588)125143990X |
| author_facet | Chen, Xiaozheng |
| author_role | aut |
| author_sort | Chen, Xiaozheng |
| author_variant | x c xc |
| building | Verbundindex |
| bvnumber | BV048612022 |
| classification_rvk | ZQ 9990 |
| ctrlnum | (OCoLC)1333211327 (DE-599)DNB1250594677 |
| dewey-full | 668.4970286 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 668 - Technology of other organic products |
| dewey-raw | 668.4970286 |
| dewey-search | 668.4970286 |
| dewey-sort | 3668.4970286 |
| dewey-tens | 660 - Chemical engineering |
| discipline | Chemie / Pharmazie Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Chemie / Pharmazie Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV048612022 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:12:12Z |
| indexdate | 2024-07-10T09:42:59Z |
| institution | BVB |
| institution_GND | (DE-588)1064118135 |
| isbn | 9783844084481 3844084487 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033987376 |
| oclc_num | 1333211327 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | VIII, 101 Seiten Illustrationen 30 cm, 170 g |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Shaker Verlag |
| record_format | marc |
| series | Schriftenreihe zur Aufbereitung und Veredlung |
| series2 | Schriftenreihe zur Aufbereitung und Veredlung |
| spelling | Chen, Xiaozheng Verfasser (DE-588)125143990X aut Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms Xiaozheng Chen Düren Shaker Verlag 2022 VIII, 101 Seiten Illustrationen 30 cm, 170 g txt rdacontent n rdamedia nc rdacarrier Schriftenreihe zur Aufbereitung und Veredlung Band 80 Dissertation RWTH Aachen University 2021 Kunststoffflasche (DE-588)4755491-5 gnd rswk-swf NIR-Spektroskopie (DE-588)4298282-0 gnd rswk-swf Sortieren (DE-588)4181872-6 gnd rswk-swf Infrarotdetektor (DE-588)4161692-3 gnd rswk-swf Flaschenetikett (DE-588)4154569-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf plastic sorting machine learning shrink sleeve near-infrared spectroscopy plastic recycling (DE-588)4113937-9 Hochschulschrift gnd-content Kunststoffflasche (DE-588)4755491-5 s Flaschenetikett (DE-588)4154569-2 s Sortieren (DE-588)4181872-6 s NIR-Spektroskopie (DE-588)4298282-0 s Infrarotdetektor (DE-588)4161692-3 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Shaker Verlag (DE-588)1064118135 pbl Schriftenreihe zur Aufbereitung und Veredlung Band 80 (DE-604)BV014023681 80 B:DE-101 application/pdf https://d-nb.info/1250594677/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033987376&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p dnb 20220617 DE-101 https://d-nb.info/provenance/plan#dnb |
| spellingShingle | Chen, Xiaozheng Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms Schriftenreihe zur Aufbereitung und Veredlung Kunststoffflasche (DE-588)4755491-5 gnd NIR-Spektroskopie (DE-588)4298282-0 gnd Sortieren (DE-588)4181872-6 gnd Infrarotdetektor (DE-588)4161692-3 gnd Flaschenetikett (DE-588)4154569-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4755491-5 (DE-588)4298282-0 (DE-588)4181872-6 (DE-588)4161692-3 (DE-588)4154569-2 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms |
| title_auth | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms |
| title_exact_search | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms |
| title_exact_search_txtP | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms |
| title_full | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms Xiaozheng Chen |
| title_fullStr | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms Xiaozheng Chen |
| title_full_unstemmed | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms Xiaozheng Chen |
| title_short | Enhancing plastic bottle sorting through shrink sleeve detection with near-infrared spectroscopy by validation of machine learning algorithms |
| title_sort | enhancing plastic bottle sorting through shrink sleeve detection with near infrared spectroscopy by validation of machine learning algorithms |
| topic | Kunststoffflasche (DE-588)4755491-5 gnd NIR-Spektroskopie (DE-588)4298282-0 gnd Sortieren (DE-588)4181872-6 gnd Infrarotdetektor (DE-588)4161692-3 gnd Flaschenetikett (DE-588)4154569-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Kunststoffflasche NIR-Spektroskopie Sortieren Infrarotdetektor Flaschenetikett Maschinelles Lernen Hochschulschrift |
| url | https://d-nb.info/1250594677/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033987376&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV014023681 |
| work_keys_str_mv | AT chenxiaozheng enhancingplasticbottlesortingthroughshrinksleevedetectionwithnearinfraredspectroscopybyvalidationofmachinelearningalgorithms AT shakerverlag enhancingplasticbottlesortingthroughshrinksleevedetectionwithnearinfraredspectroscopybyvalidationofmachinelearningalgorithms |
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