Applied AI techniques in the process industry: from molecular design to process design and optimization
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Weinheim
Wiley-VCH
[2025]
|
| Ausgabe: | 1. Auflage |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | xi, 321 Seiten Illustrationen, Diagramme 24.4 cm x 17 cm |
| ISBN: | 3527353399 9783527353392 |
Internformat
MARC
| LEADER | 00000nam a22000008c 4500 | ||
|---|---|---|---|
| 001 | BV050141843 | ||
| 003 | DE-604 | ||
| 005 | 20250317 | ||
| 007 | t| | ||
| 008 | 250128s2025 gw a||| |||| 00||| eng d | ||
| 015 | |a 24,N24 |2 dnb | ||
| 016 | 7 | |a 1332166997 |2 DE-101 | |
| 020 | |a 3527353399 |9 3-527-35339-9 | ||
| 020 | |a 9783527353392 |c : EUR 119.00 (DE) (freier Preis), EUR 122.40 (AT) (freier Preis) |9 978-3-527-35339-2 | ||
| 024 | 3 | |a 9783527353392 | |
| 028 | 5 | 2 | |a Bestellnummer: 1135339 000 |
| 035 | |a (OCoLC)1510737895 | ||
| 035 | |a (DE-599)DNB1332166997 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-BW | ||
| 049 | |a DE-29T | ||
| 084 | |8 1\p |a 540 |2 23sdnb | ||
| 245 | 1 | 0 | |a Applied AI techniques in the process industry |b from molecular design to process design and optimization |c Edited by Chang He and Jingzheng Ren |
| 264 | 1 | |a Weinheim |b Wiley-VCH |c [2025] | |
| 300 | |a xi, 321 Seiten |b Illustrationen, Diagramme |c 24.4 cm x 17 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 653 | |a Artificial Intelligence | ||
| 653 | |a CG10: Prozesssteuerung | ||
| 653 | |a CHD0: Computational Chemistry u. Molecular Modeling | ||
| 653 | |a CSF0: Künstliche Intelligenz | ||
| 653 | |a Chemical Engineering | ||
| 653 | |a Chemie | ||
| 653 | |a Chemische Verfahrenstechnik | ||
| 653 | |a Chemistry | ||
| 653 | |a Computational Chemistry & Molecular Modeling | ||
| 653 | |a Computational Chemistry u. Molecular Modeling | ||
| 653 | |a Computer Science | ||
| 653 | |a Informatik | ||
| 653 | |a Künstliche Intelligenz | ||
| 653 | |a Process Engineering | ||
| 653 | |a Prozesssteuerung | ||
| 700 | 1 | |a He, Chang |0 (DE-588)1359339833 |4 edt | |
| 700 | 1 | |a Ren, Jingzheng |0 (DE-588)1129281965 |4 edt | |
| 710 | 2 | |a Wiley-VCH |0 (DE-588)16179388-5 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 978-3-527-84547-7 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-3-527-84548-4 |
| 856 | 4 | 2 | |m X:MVB |q text/html |u http://www.wiley-vch.de/ISBN978-3-527-35339-2 |3 Inhaltstext |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035478314&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 883 | 1 | |8 1\p |a vlb |d 20240608 |q DE-101 |u https://d-nb.info/provenance/plan#vlb | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035478314 | |
Datensatz im Suchindex
| _version_ | 1828843414784835584 |
|---|---|
| adam_text |
CONTENTS
PREFACE
XI
1
AL
FOR
PROPERTY
MODELING,
SOLVENT
TAILORING,
AND
PROCESS
DESIGN
1
YUQIU
CHEN
1.1
AI-ASSISTED
PROPERTY
MODELING
1
1.2
AI-ASSISTED
SOLVENT
TAILORING
10
1.3
AI-ASSISTED
PROCESS
DESIGN
14
1.4
CONCLUSIONS
17
REFERENCES
19
2
HUNTING
FOR
BETTER
AROMATIC
CHEMICALS
WITH
AL
TECHNIQUES
23
QILEI
LIU,
HAITAO
MAO,
LU
WANG,
AND
LEI
ZHANG
2.1
INTRODUCTION
23
2.2
MACHINE
LEARNING-BASED
ODOR
PREDICTION
MODELS
25
2.2.1
ODOR
PREDICTIONS
FOR
PURE
AROMATIC
CHEMICALS
USING
GROUP-BASED
MACHINE
LEARNING
METHOD
25
2.2.1.1
DATABASE
PREPARATION
25
2.2.1.2
MOLECULAR
REPRESENTATION
26
2.2.1.3
MODEL
ARCHITECTURE
27
2.2.1.4
RESULTS
AND
DISCUSSIONS
27
2.2.2
ODOR
PREDICTION
FOR
MIXTURE
AROMATIC
CHEMICALS
USING
C-PROFILES-BASED
MACHINE
LEARNING
METHOD
29
2.2.2.1
DATABASE
PREPARATION
29
2.2.2.2
MOLECULAR
REPRESENTATION
31
2.2.23
MODEL
ARCHITECTURE
34
2.2.2.4
RESULTS
AND
DISCUSSIONS
35
2.3
COMPUTER-AIDED
AROMA
DESIGN
(CAAD)
FRAMEWORK
36
2.3.1
CAAD
FOR
PURE
AROMATIC
CHEMICALS
36
2.3.1.1
IDENTIFY
PRODUCT
ATTRIBUTES
36
2.3.1.2
CONVERT
PRODUCT
ATTRIBUTES
TO
PROPERTIES
AND
THEIR
CONSTRAINTS
38
2.3.1.3
CHOOSE
PROPERTY
PREDICTION
MODEL
FOR
ESTIMATING
PROPERTIES
38
2.3.1.4
FORMULATE
MILP/MINLP
MODEL
38
2.3.1.5
SOLVE
THE
MODEL
USING
DECOMPOSITION-BASED
ALGORITHM
38
VI
CONTENTS
2.3.1.6
VERIFICATION
39
2.3.2
CAAD
FOR
MIXTURE
AROMATIC
CHEMICALS
39
2.3.2.1
IDENTIFY
PRODUCT
ATTRIBUTES
41
2.3.2.2
CONVERT
PRODUCT
ATTRIBUTES
INTO
PROPERTIES
AND
CORRESPONDING
CONSTRAINTS
42
2.3.2.3
ESTABLISH
PROPERTY
MODELS
42
2.3.2.4
INGREDIENT
SCREENING
42
2.3.2.5
VERIFICATION
43
2.4
CASE
STUDIES
44
2.4.1
PURE
AROMA
DESIGN
FOR
SHAMPOO
ADDITIVES
44
2.4.2
PURE
AROMA
DESIGN
FOR
THE
INGREDIENT
IN
INSECT
REPELLENT
SPRAY
45
2.4.3
MIXTURE
AROMA
DESIGN
FOR
AROMA
SUBSTITUTES
48
2.4.4
MIXTURE
AROMA
DESIGN
FOR
ODOR
TUNING
51
2.5
CONCLUSIONS
53
2.
A
THE
CAS
NUMBER
OF
MOLECULES
AND
THE
SELECTED
GROUPS
54
2.B
THE
CALCULATION
FORMULA
OF
ODOR
SCORE
56
2.C
THE
PARAMETERS
AND
RESULTS
OF
THE
ANN
MODEL
57
2.D
THE
DESIGNED
RESULTS
OF
MOLECULES
FOR
CASE
STUDY
2
59
2.E
AROMA
COMPOUNDS
FOR
INGREDIENT
SCREENING
61
ACKNOWLEDGMENTS
75
REFERENCES
75
3
MACHINE
LEARNING-AIDED
RATIONAL
SCREENING
OF
TASK-SPECIFIC
IONIC
LIQUIDS
79
RUOFAN
GU
AND
ZHEN
SONG
3.1
INTRODUCTION
79
3.2
MOLECULE
REPRESENTATION
OF
ILS
80
3.2.1
GROUPS
OR
FRAGMENT-BASED
REPRESENTATION
81
3.2.2
COSMO-DERIVED
DESCRIPTORS
OR
FINGERPRINTS
83
3.2.3
MACHINE-LEARNED
REPRESENTATIONS
83
3.3
MACHINE
LEARNING-BASED
STRUCTURE-PROPERTY
MODELS
84
3.3.1
MACHINE
LEARNING
FOR
COSMO-BASED
MODELS
84
3.3.2
MACHINE
LEARNING
FOR
UNIFAC
EXTENSIONS
86
3.3.3
PURE
MACHINE
LEARNING
MODELS
89
3.4
APPLICATIONS
90
3.4.1
COMPUTER-AIDED
IL
SCREENING
90
3.4.2
COMPUTER-AIDED
IL
DESIGN
93
3.4.3
EXTENSIONS
TO
IL
MIXTURES
95
3.5
CONCLUSION
AND
PERSPECTIVES
97
REFERENCES
99
4
INTEGRATION
OF
OBSERVED
DATA
AND
REACTION
MECHANISMS
IN
DEEP
LEARNING
FOR
DESIGNING
SUSTAINABLE
GLYCOLIC
ACID
105
XIN
ZHOU
4.1
INTRODUCTION
105
CONTENTS
VII
4.2
METHODOLOGY
107
4.2.1
DATABASE
GENERATION
109
4.2.2
DEEP
LEARNING
111
4.2.2.1
DEEP
NEURAL
NETWORKS
111
4.2.2.2
DEEP
BELIEF
NETWORKS
112
4.2.2.3
FULLY
CONNECTED
RESIDUAL
NETWORKS
114
4.2.2.4
RANDOM
FOREST
115
4.2.3
'
OPTIMIZATION
AND
PREDICTION
116
4.2.4
LIFE
CYCLE
MULTIDIMENSIONAL
EVALUATION
116
4.3
RESULTS
AND
DISCUSSION
117
4.3.1
DATA
ANALYSIS
AND
STATISTICS
BEFORE
MODELING
117
4.3.1.1
ANALYSIS
OF
EXPERIMENTAL
DATA
117
4.3.1.2
DATA
DEPENDENCE
ANALYSIS
117
4.3.2
MODEL
COMPARISON
AND
FEATURE
IMPORTANT
ANALYSIS
118
4.3.2.1
MODEL
COMPARISON
118
4.3.2.2
FEATURE
IMPORTANT
ANALYSIS
123
4.3.3
PERFORMANCE
AND
FEATURE
ANALYSIS
OF
THE
OPTIMIZED
FC-RESNET-GA
MODEL
125
4.3.4
PROCESS
MULTI-OBJECTIVE
OPTIMIZATION
AND
EXPERIMENTAL
VERIFICATION
126
4.3.5
LCSA
BASED
ON
THE
OPTIMIZED
PARAMETERS
128
4.3.5.1
ORIGINAL
LIFE
CYCLE
FRAMEWORK
128
4.3.5.2
LIFE
CYCLE
INVENTORY
ANALYSIS
128
4.3.5.3
LIFE
CYCLE
SUSTAINABLE
INTERPRETATION
AND
ASSESSMENT
129
4.4
CONCLUSION
131
4
.
A
PARETO
OPTIMIZATION
SET
132
4
.B
EXPERIMENTAL DATA
133
4
.C
CONSTRUCTION
METHOD
OF
PROCESS
SIMULATION
DATABASE
USING
REACTION
MECHANISM
134
4
.C.1
ELIMINATION
OF
THE
DIFFUSION
LIMITATIONS
136
4
.C.2
REACTION
KINETICS
138
REFERENCES
139
5
INNOVATION
OF
GAS
SEPARATION
PROCESSES:
INTEGRATING
COMPUTATIONAL
MOF
DESIGN
AND
ADSORPTION
PROCESS
OPTIMIZATION
145
XIANG
ZHANG
AND
TENG
ZHOU
5.1
INTRODUCTION
145
5.2
STEP
ONE:
DESCRIPTOR
OPTIMIZATION
147
5.2.1
MATERIAL-PROPERTY
RELATIONSHIP
OF
MOFS
148
5.2.1.1
MOF
REPRESENTATION
148
5.2.1.2
DATA-DRIVEN
MODEL
FOR
SINGLE-COMPONENT
ADSORPTION
ISOTHERM
149
5.2.1.3
MULTICOMPONENT
DUAL-SITE
LANGMUIR
ISOTHERM
MODEL
150
5.2.2
INTEGRATED
OPTIMIZATION
OF
MOF
DESCRIPTORS
AND
PSA
OPERATING
CONDITIONS
151
VILI
CONTENTS
5.2.2.1
DESCRIPTOR
DESIGN
SPACE
151
5.2.2.2
P/VSA
PROCESS
MODEL
152
5.2.2.3
INTEGRATED
DESIGN
FORMULATION
153
5.2.3
RESULTS
154
5.2.3.1
BENCHMARK
PROCESS
USING
CU-BTC
154
5.2.3.2
OPTIMAL
MOF
AND
PROCESS
FROM
INTEGRATED
DESIGN
154
5.3
STEP
TWO:
MOF
MATCHING
157
5.3.1
MATERIAL-PROPERTY
RELATIONSHIP
OF
MOFS
157
5.3.1.1
PROPERTY-PERFORMANCE
RELATIONSHIP
FOR
PE/PA
SEPARATION
157
5.3.1.2
VALIDATION
WITH
471
CORE
MOFS
159
5.3.2
FROM
COMPUTATIONAL
MOF
DESIGN
TO
MODEL-BASED
MOF
SCREENING
161
5.3.2.1
IDENTIFICATION
OF
MOF
BUILDING
BLOCKS
161
5.3.2.2
IN
SILICO
SYNTHESIS
OF
HYPOTHETICAL
MOFS
162
5.3.2.3
MOF
SCREENING
VIA
VALIDITY
AND
FEASIBILITY
CONSTRAINTS
164
5.3.2.4
MOF
SCREENING
VIA
GCMC
SIMULATIONS
165
5.3.2.5
MOF
SCREENING
VIA
PSA
PROCESS
OPTIMIZATION
165
5.3.2.6
OPTIMAL
RESULTS
OF
SMOF-1
165
5.4
CONCLUSION
167
REFERENCES
168
6
REVERSE
DESIGN
OF
HEAT
EXCHANGE
SYSTEMS
USING
PHYSICS-INFORMED
MACHINE
LEARNING
173
CHANG
HE
AND
YUNQUAN
CHEN
6.1
INTRODUCTION
173
6.2
PINN-BASED
INVERSE
DESIGN
METHOD
176
6.2.1
OVERVIEW
OF
INVERSE
DESIGN
176
6.2.1.1
STANDARD
PHYSICS-INFORMED
NEURAL
NETWORKS
177
6.2.1.2
DESIGN
OPTIMIZATION
AND
DECISION-MAKING
METHODS
180
6.3
EXAMPLE
1:
FINNED
HEAT
SINK
MODEL
181
6.3.1
SYSTEM
DESCRIPTION
AND
OBJECTIVES
181
6.3.2
IMPROVED
PINN
STRUCTURE
185
6.3.3
RESULTS
185
6.4
ILLUSTRATIVE
EXAMPLE
2:
TUBULAR
AIR
COOLER
MODEL
191
6.4.1
SYSTEM
DESCRIPTION
AND
OBJECTIVES
191
6.4.2
IMPROVED
PINN
STRUCTURE
195
6.4.3
TRANSFER
LEARNING
196
6.4.4
RESULTS
199
6.5
CONCLUSION
204
REFERENCES
205
7
INTEGRATING
INCOMPLETE
PRIOR
KNOWLEDGE
INTO
DATA-DRIVEN
INFERENTIAL
SENSOR
MODELS
UNDER
VARIATIONAL
BAYESIAN
FRAMEWORK
211
ZHICHAO
CHEN,
HAO
WANG,
YIRAN
MA,
CHENG
QIU,
LE
YAO,
XINMIN
ZHANG,
AND
ZHIHUON
SONG
7.1
INTRODUCTION
211
CONTENTS
IX
7.2
LITERATURE
REVIEW
213
7.2.1
TRANSPORT
PROCESS
SCALE
213
7.2.2
UNIT
OPERATION
SCALE
214
7.2.3
OVERALL
SUMMARY
AND
TECHNICAL
GAP
214
13
PROPOSED
APPROACH
214
13.1
LOSS
FUNCTION
DERIVATION
215
13.2
KNOWLEDGE
REPRESENTATION
216
7.3.2.
1
KNOWLEDGE
DESCRIPTION
216
13.2.2
KNOWLEDGE
SECTION
VIA
SELF-ATTENTION
MECHANISM
216
13.23
SIMILARITY
OF
GCN
AND
SAM
217
13.2.4
SAMPLING
FROM
POSTERIOR
219
133
MODEL
EXPRESSIONS
221
7
.4
EXPERIMENTAL
RESULTS
223
7.4.1
EVALUATION
METRICS
224
7.4.2
PROCESS
DESCRIPTION
224
7.4.3
PRIOR
KNOWLEDGE
ANALYSIS
225
1AA
BASELINE
MODELS
227
7.4.5
MODEL
PERFORMANCE
COMPARISONS
228
7.4.6
COMPARISON
WITH
LI
AND
L2
REGULARIZATION
TERMS
229
1A.1
SENSITIVITY
ANALYSIS
230
7
.5
CONCLUSIONS
230
7
.
A
EXPERIMENTAL
SETTINGS
232
REFERENCES
233
8
DATA-DRIVEN
AND
PHYSICS-BASED
REDUCED-ORDER
MODELING
AND
OPTIMIZATION
OF
COOLING
TOWER
SYSTEMS
239
CHANG
HE
AND
ZHIQIANG
WU
8.1
INTRODUCTION
239
8.2
FULL-SCALE
PHYSICAL
MODEL
OF
COOLING
TOWERS
241
8.3
BI-LEVEL
REDUCED-ORDER
MODELS
244
8.3.1
DESIGN
OF
OPTIMAL
EXPERIMENTS
245
8.3.2
MULTI-SAMPLE
CFD
SIMULATIONS
247
8.3.3
MODEL
REDUCTION
248
8.4
THERMODYNAMIC
PERFORMANCE
INDICATORS
251
8.5
OPTIMIZATION
MODEL
253
8.6
ILLUSTRATIVE
EXAMPLE
254
8.7
CONCLUSION
261
REFERENCES
261
9
AI-AIDED
HIGH-THROUGHPUT
SCREENING
AND
OPTIMISTIC
DESIGN
OF
MOF
MATERIALS
FOR
ADSORPTIVE
GAS
SEPARATION
265
LI
ZHOU,
MIN
CHENG,
SHIHUI
WANG,
AND
XU
JI
9.1
INTRODUCTION
265
9.2
METHODOLOGY
266
CONTENTS
9.2.1
MOLECULAR
LEVEL
SIMULATION
AND
SCREENING
DRIVEN
BY
RIGOROUS
MOLECULAR
SIMULATION
AND
MACHINE
LEARNING
266
9.2.1.1
MOLECULAR
CHARACTERIZATION
266
9.2.1.2
STRUCTURAL/CHEMICAL
ANALYSIS-BASED
PRESCREENING
267
9.2.1.3
DIVERSITY
ANALYSIS
AND
DATASET
SPLITTING
268
9.2.1.4
MOLECULAR
SIMULATION
AND
PERFORMANCE
EVALUATION
METRICS
268
9.2.1.5
AI-AIDED
QUANTITATIVE
STRUCTURE-PROPERTY
RELATIONSHIP
DEVELOPMENT
AND
RAPID
SCREENING
269
9.2.1.6
PROCESS
LEVEL
SIMULATION
270
9.2.1.7
REVERSE
MOLECULAR
DESIGN
270
9.3
CASE
STUDIES
273
9.3.1
HIGH-THROUGHPUT
SCREENING
OF
METAL-ORGANIC
FRAMEWORKS
FOR
HYDROGEN
PURIFICATION
273
9.3.1.1
PRESCREENING
273
9.3.1.2
RAPID
SCREENING
274
9.3.1.3
RIGOROUS
VALIDATION
274
9.3.1.4
STRUCTURE-PROPERTY
RELATIONSHIP
ANALYSIS
276
9.3.1.5
INVESTIGATION
ON
PRACTICAL
FACTORS
277
9.4
CONCLUSIONS
283
REFERENCES
283
10
SURROGATE
MODELING
FOR
ACCELERATING
OPTIMIZATION
OF
COMPLEX
SYSTEMS
IN
CHEMICAL
ENGINEERING
287
JIANZHAO
ZHOU
AND
JINGZHENG
REN
10.1
INTRODUCTION
287
10.2
SURROGATE
MODELING
TECHNIQUES
289
10.2.1
POLYNOMIAL
REGRESSION
(PR)
290
10.2.2
POLYNOMIAL
CHAOS
EXPANSION
291
10.2.3
KRIGING
291
10.2.4
RADIAL
BASIS
FUNCTIONS
(RBF)
292
10.2.5
HIGH-DIMENSIONAL
MODEL
REPRESENTATION
(HDMR)
293
10.2.6
DECISION
TREE
(DT)
294
10.2.7
SUPPORT
VECTOR
MACHINE
(SVM)
295
10.2.8
ARTIFICIAL
NEURAL
NETWORK
(ANN)
296
10.3
APPLICATION
OF
SURROGATE
MODEL
IN
OPTIMIZATION
OF
CHEMICAL
PROCESSES
297
10.3.1
REACTION
ENGINEERING
297
10.3.2
SEPARATION
ENGINEERING
299
10.3.3
HEAT
EXCHANGE
AND
INTEGRATION
300
10.3.4
PROCESS
DESIGN
AND
SYNTHESIS
301
10.4
CONCLUSION
303
ACKNOWLEDGMENT
303
REFERENCES
303
INDEX
313 |
| any_adam_object | 1 |
| author2 | He, Chang Ren, Jingzheng |
| author2_role | edt edt |
| author2_variant | c h ch j r jr |
| author_GND | (DE-588)1359339833 (DE-588)1129281965 |
| author_facet | He, Chang Ren, Jingzheng |
| building | Verbundindex |
| bvnumber | BV050141843 |
| ctrlnum | (OCoLC)1510737895 (DE-599)DNB1332166997 |
| edition | 1. Auflage |
| format | Book |
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| id | DE-604.BV050141843 |
| illustrated | Illustrated |
| indexdate | 2025-04-08T14:03:13Z |
| institution | BVB |
| institution_GND | (DE-588)16179388-5 |
| isbn | 3527353399 9783527353392 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035478314 |
| oclc_num | 1510737895 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xi, 321 Seiten Illustrationen, Diagramme 24.4 cm x 17 cm |
| publishDate | 2025 |
| publishDateSearch | 2025 |
| publishDateSort | 2025 |
| publisher | Wiley-VCH |
| record_format | marc |
| spelling | Applied AI techniques in the process industry from molecular design to process design and optimization Edited by Chang He and Jingzheng Ren Weinheim Wiley-VCH [2025] xi, 321 Seiten Illustrationen, Diagramme 24.4 cm x 17 cm txt rdacontent n rdamedia nc rdacarrier Artificial Intelligence CG10: Prozesssteuerung CHD0: Computational Chemistry u. Molecular Modeling CSF0: Künstliche Intelligenz Chemical Engineering Chemie Chemische Verfahrenstechnik Chemistry Computational Chemistry & Molecular Modeling Computational Chemistry u. Molecular Modeling Computer Science Informatik Künstliche Intelligenz Process Engineering Prozesssteuerung He, Chang (DE-588)1359339833 edt Ren, Jingzheng (DE-588)1129281965 edt Wiley-VCH (DE-588)16179388-5 pbl Erscheint auch als Online-Ausgabe, PDF 978-3-527-84547-7 Erscheint auch als Online-Ausgabe, EPUB 978-3-527-84548-4 X:MVB text/html http://www.wiley-vch.de/ISBN978-3-527-35339-2 Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035478314&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20240608 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Applied AI techniques in the process industry from molecular design to process design and optimization |
| title | Applied AI techniques in the process industry from molecular design to process design and optimization |
| title_auth | Applied AI techniques in the process industry from molecular design to process design and optimization |
| title_exact_search | Applied AI techniques in the process industry from molecular design to process design and optimization |
| title_full | Applied AI techniques in the process industry from molecular design to process design and optimization Edited by Chang He and Jingzheng Ren |
| title_fullStr | Applied AI techniques in the process industry from molecular design to process design and optimization Edited by Chang He and Jingzheng Ren |
| title_full_unstemmed | Applied AI techniques in the process industry from molecular design to process design and optimization Edited by Chang He and Jingzheng Ren |
| title_short | Applied AI techniques in the process industry |
| title_sort | applied ai techniques in the process industry from molecular design to process design and optimization |
| title_sub | from molecular design to process design and optimization |
| url | http://www.wiley-vch.de/ISBN978-3-527-35339-2 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035478314&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hechang appliedaitechniquesintheprocessindustryfrommoleculardesigntoprocessdesignandoptimization AT renjingzheng appliedaitechniquesintheprocessindustryfrommoleculardesigntoprocessdesignandoptimization AT wileyvch appliedaitechniquesintheprocessindustryfrommoleculardesigntoprocessdesignandoptimization |