Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty

In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Processes Ročník 7; číslo 10; s. 744
Hlavní autoři: Zhao, Liang, Zhong, Weimin, Du, Wenli
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 2019
Témata:
Boilers
Case studies
Compressors
Dirichlet problem
Efficiency
Ethylene
Heat exchangers
Hypothesis testing
Integer programming
Linear programming
Mathematical models
Mathematical programming
Mixed integer
Operating costs
Optimization
Optimization techniques
Parameter uncertainty
Plant extracts
Power
Robustness (mathematics)
Steam electric power generation
Steam pressure
Steam turbines
Turbines
Valves
ISSN:2227-9717, 2227-9717
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction–exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method.
AbstractList In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction–exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method.
Author Zhong, Weimin
Du, Wenli
Zhao, Liang
Author_xml – sequence: 1
  givenname: Liang
  orcidid: 0000-0002-3361-0783
  surname: Zhao
  fullname: Zhao, Liang
– sequence: 2
  givenname: Weimin
  surname: Zhong
  fullname: Zhong, Weimin
– sequence: 3
  givenname: Wenli
  surname: Du
  fullname: Du, Wenli
BookMark eNptkM1OwzAQhC1UJErpgTewxIlDqL2x4-aI2vIjVQJRyjVyEke4SuxgO0jh6UkpQgixl93DN7uzc4pGxhqF0DklV3GcklnrBCVEMHaExgAgolRQMfo1n6Cp9zsyVErjOU_G6GUpg4yWTr8rg59s3vmAH9qgG_0hg7YGV9bhTVCywZveB9V4rA1ehde-Vkbhx1qa4HFnSuXw1hTKBalN6M_QcSVrr6bffYK2N6vnxV20fri9X1yvowJSCBEAlyByxhWRpYIyYXRvrZozyVNQhJJEsqRisoSSMgF5ngvKISElEYSneTxBF4e9rbNvnfIh29nOmeFkBpwTYHEMYqBmB6pw1nunqqzQ4eu94KSuM0qyfX7ZT36D4vKPonW6ka7_h_0EjptxEw
CitedBy_id crossref_primary_10_1016_j_conengprac_2022_105160
crossref_primary_10_1002_adsu_202400064
crossref_primary_10_1007_s41660_023_00354_7
crossref_primary_10_3390_pr8050622
crossref_primary_10_3390_pr8111495
crossref_primary_10_3390_ijerph182212267
crossref_primary_10_1007_s11081_021_09659_3
crossref_primary_10_1016_j_apenergy_2024_124595
crossref_primary_10_3390_pr11113171
crossref_primary_10_3390_en15207491
crossref_primary_10_3390_pr12030446
Cites_doi 10.1016/S0009-2509(97)00431-4
10.1016/j.compchemeng.2019.03.034
10.1002/aic.15792
10.1007/s10107-017-1125-8
10.1515/9781400831050
10.1287/mnsc.6.1.73
10.1016/0098-1354(83)85023-6
10.1109/ACC.2015.7171991
10.3390/pr7050312
10.1021/acs.iecr.8b05247
10.1021/ie970383i
10.1016/j.compchemeng.2003.09.017
10.1016/j.energy.2011.03.056
10.1021/acs.iecr.7b00111
10.1109/CDC.2016.7799219
10.1214/06-BA104
10.1016/j.apenergy.2016.05.036
10.1016/j.ces.2010.01.016
10.1205/cherd06062
10.1016/j.compchemeng.2017.12.002
10.1016/j.compchemeng.2017.07.004
10.1002/aic.15717
10.1016/S1004-9541(13)60530-3
10.1002/aic.14523
10.1021/ie402438t
10.3390/pr4040052
10.1002/aic.16500
ContentType Journal Article
Copyright 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
7SR
8FD
8FE
8FG
8FH
ABJCF
ABUWG
AFKRA
AZQEC
BBNVY
BENPR
BGLVJ
BHPHI
CCPQU
D1I
DWQXO
GNUQQ
HCIFZ
JG9
KB.
LK8
M7P
PDBOC
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
DOI 10.3390/pr7100744
DatabaseName CrossRef
Engineered Materials Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Natural Science Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials
Biological Science Collection
ProQuest Central
Technology Collection
Natural Science Collection
ProQuest One
ProQuest Materials Science Collection
ProQuest Central Korea
ProQuest Central Student
SciTech Premium Collection
Materials Research Database
Materials Science Database
ProQuest Biological Science Collection
Biological Science Database
Materials Science Collection
Proquest Central Premium
ProQuest One Academic (New)
ProQuest Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
DatabaseTitle CrossRef
Publicly Available Content Database
Materials Research Database
ProQuest Central Student
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
Materials Science Collection
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Natural Science Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
Engineered Materials Abstracts
Natural Science Collection
ProQuest Central Korea
Biological Science Collection
Materials Science Database
ProQuest Central (New)
ProQuest Materials Science Collection
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
ProQuest Technology Collection
Biological Science Database
ProQuest SciTech Collection
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: KB.
  name: Materials Science Database
  url: http://search.proquest.com/materialsscijournals
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Sciences (General)
EISSN 2227-9717
ExternalDocumentID 10_3390_pr7100744
GroupedDBID 5VS
8FE
8FG
8FH
AADQD
AAFWJ
AAYXX
ABJCF
ACIWK
ACPRK
ADBBV
ADMLS
AFFHD
AFKRA
AFZYC
ALMA_UNASSIGNED_HOLDINGS
BBNVY
BCNDV
BENPR
BGLVJ
BHPHI
CCPQU
CITATION
D1I
HCIFZ
IAO
IGS
KB.
KQ8
LK8
M7P
MODMG
M~E
OK1
PDBOC
PHGZM
PHGZT
PIMPY
PQGLB
PROAC
RNS
7SR
8FD
ABUWG
AZQEC
DWQXO
GNUQQ
JG9
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
ID FETCH-LOGICAL-c292t-225a27b45e0ade2d6410091f84a592e0106a46f4ad2d1472bbb715260d07059b3
IEDL.DBID BENPR
ISICitedReferencesCount 11
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000495436200106&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2227-9717
IngestDate Fri Jul 25 12:15:41 EDT 2025
Sat Nov 29 07:19:49 EST 2025
Tue Nov 18 21:41:52 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 10
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c292t-225a27b45e0ade2d6410091f84a592e0106a46f4ad2d1472bbb715260d07059b3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-3361-0783
OpenAccessLink https://www.proquest.com/docview/2550243327?pq-origsite=%requestingapplication%
PQID 2550243327
PQPubID 2032344
ParticipantIDs proquest_journals_2550243327
crossref_citationtrail_10_3390_pr7100744
crossref_primary_10_3390_pr7100744
PublicationCentury 2000
PublicationDate 2019-00-00
PublicationDateYYYYMMDD 2019-01-01
PublicationDate_xml – year: 2019
  text: 2019-00-00
PublicationDecade 2010
PublicationPlace Basel
PublicationPlace_xml – name: Basel
PublicationTitle Processes
PublicationYear 2019
Publisher MDPI AG
Publisher_xml – name: MDPI AG
References Papoulias (ref_7) 1983; 7
Aguilar (ref_24) 2007; 85
Shang (ref_26) 2018; 110
ref_14
Ning (ref_21) 2017; 63
ref_13
ref_12
ref_10
Li (ref_2) 2013; 21
Zhao (ref_19) 2018; 65
Shang (ref_20) 2017; 106
Qin (ref_16) 2014; 60
Bertsimas (ref_17) 2017; 167
Charnes (ref_11) 1959; 6
Blei (ref_28) 2006; 1
Sun (ref_15) 2017; 186
(ref_25) 2010; 65
Luo (ref_4) 2011; 36
Ning (ref_18) 2019; 125
ref_23
Mavromatis (ref_6) 1998; 53
Li (ref_5) 2014; 53
Ning (ref_22) 2017; 63
ref_27
Sahinidis (ref_9) 2004; 28
Shen (ref_3) 2019; 58
Xu (ref_1) 2017; 56
Iyer (ref_8) 1998; 37
References_xml – volume: 53
  start-page: 1585
  year: 1998
  ident: ref_6
  article-title: Conceptual optimisation of utility networks for operational variations—I. targets and level optimisation
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/S0009-2509(97)00431-4
– volume: 125
  start-page: 434
  year: 2019
  ident: ref_18
  article-title: Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2019.03.034
– volume: 63
  start-page: 4343
  year: 2017
  ident: ref_21
  article-title: A data-driven multistage adaptive robust optimization framework for planning and scheduling under uncertainty
  publication-title: AICHE J.
  doi: 10.1002/aic.15792
– volume: 167
  start-page: 235
  year: 2017
  ident: ref_17
  article-title: Data-driven robust optimization
  publication-title: Math. Program.
  doi: 10.1007/s10107-017-1125-8
– ident: ref_10
  doi: 10.1515/9781400831050
– volume: 6
  start-page: 73
  year: 1959
  ident: ref_11
  article-title: Chance-constrained programming
  publication-title: Manag. Sci.
  doi: 10.1287/mnsc.6.1.73
– volume: 7
  start-page: 707
  year: 1983
  ident: ref_7
  article-title: A structural optimization approach in process synthesis—II: Heat recovery networks
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/0098-1354(83)85023-6
– ident: ref_27
  doi: 10.1109/ACC.2015.7171991
– ident: ref_14
  doi: 10.3390/pr7050312
– volume: 58
  start-page: 1686
  year: 2019
  ident: ref_3
  article-title: Modeling and Optimization of a Large-Scale Ethylene Plant Energy System with Energy Structure Analysis and Management
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.8b05247
– volume: 37
  start-page: 474
  year: 1998
  ident: ref_8
  article-title: A bilevel decomposition algorithm for long-range planning of process networks
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie970383i
– volume: 28
  start-page: 971
  year: 2004
  ident: ref_9
  article-title: Optimization under uncertainty: State-of-the-art and opportunities
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2003.09.017
– volume: 36
  start-page: 3501
  year: 2011
  ident: ref_4
  article-title: Modeling and optimization of a utility system containing multiple extractions steam turbines
  publication-title: Energy
  doi: 10.1016/j.energy.2011.03.056
– volume: 56
  start-page: 7984
  year: 2017
  ident: ref_1
  article-title: Process Synthesis of Mixed Refrigerant System for Ethylene Plants
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.7b00111
– ident: ref_23
  doi: 10.1109/CDC.2016.7799219
– volume: 1
  start-page: 121
  year: 2006
  ident: ref_28
  article-title: Variational inference for Dirichlet process mixtures
  publication-title: Bayesian Anal.
  doi: 10.1214/06-BA104
– ident: ref_12
– volume: 186
  start-page: 450
  year: 2017
  ident: ref_15
  article-title: Site utility system optimization with operation adjustment under uncertainty
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2016.05.036
– volume: 65
  start-page: 2811
  year: 2010
  ident: ref_25
  article-title: Modelling the power production of single and multiple extraction steam turbines
  publication-title: Chem. Eng. Sci.
  doi: 10.1016/j.ces.2010.01.016
– volume: 85
  start-page: 1136
  year: 2007
  ident: ref_24
  article-title: Design and optimization of flexible utility systems subject to variable conditions: Part 1: Modelling framework
  publication-title: Chem. Eng. Res. Des.
  doi: 10.1205/cherd06062
– volume: 110
  start-page: 53
  year: 2018
  ident: ref_26
  article-title: Distributionally robust optimization for planning and scheduling under uncertainty
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2017.12.002
– volume: 106
  start-page: 464
  year: 2017
  ident: ref_20
  article-title: Data-driven robust optimization based on kernel learning
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2017.07.004
– volume: 63
  start-page: 3790
  year: 2017
  ident: ref_22
  article-title: Data-driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty
  publication-title: AICHE J.
  doi: 10.1002/aic.15717
– volume: 21
  start-page: 520
  year: 2013
  ident: ref_2
  article-title: Modeling and Optimization of the Steam Turbine Network of an Ethylene Plant
  publication-title: Chin. J. Chem. Eng.
  doi: 10.1016/S1004-9541(13)60530-3
– volume: 60
  start-page: 3092
  year: 2014
  ident: ref_16
  article-title: Process data analytics in the era of big data
  publication-title: AICHE J.
  doi: 10.1002/aic.14523
– volume: 53
  start-page: 11021
  year: 2014
  ident: ref_5
  article-title: Modeling and Optimization of a Steam System in a Chemical Plant Containing Multiple Direct Drive Steam Turbines
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie402438t
– ident: ref_13
  doi: 10.3390/pr4040052
– volume: 65
  start-page: e16500
  year: 2018
  ident: ref_19
  article-title: Operational optimization of industrial steam systems under uncertainty using data-driven adaptive robust optimization
  publication-title: AICHE J.
  doi: 10.1002/aic.16500
SSID ssj0000913856
Score 2.1288776
Snippet In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is...
SourceID proquest
crossref
SourceType Aggregation Database
Enrichment Source
Index Database
StartPage 744
SubjectTerms Boilers
Case studies
Compressors
Dirichlet problem
Efficiency
Ethylene
Heat exchangers
Hypothesis testing
Integer programming
Linear programming
Mathematical models
Mathematical programming
Mixed integer
Operating costs
Optimization
Optimization techniques
Parameter uncertainty
Plant extracts
Power
Robustness (mathematics)
Steam electric power generation
Steam pressure
Steam turbines
Turbines
Valves
Title Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty
URI https://www.proquest.com/docview/2550243327
Volume 7
WOSCitedRecordID wos000495436200106&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: M~E
  dateStart: 20130101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Biological Science Database
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: M7P
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/biologicalscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Materials Science Database
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: KB.
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/materialsscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: BENPR
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Publicly Available Content Database
  customDbUrl:
  eissn: 2227-9717
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913856
  issn: 2227-9717
  databaseCode: PIMPY
  dateStart: 20130301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8MwDLZ4HeDAY4B4ThHiAIdCm6btekLAhkCIUU0MwalKmkyaBNtoCxIXfjv2lg2QEBcO7SVRVcX2Z8dxPgPsx9oPtXSFE8lAO8JzMyd2jY-6HCgMjwKeCT1sNhE1m7WHhzixCbfCllWOMXEI1LqfUY78GENfIs_zeXQyeHGoaxSdrtoWGtMwS0xlqOezZ41m0ppkWYj1shaEI0ohH_f3x4Oc-GwiIX46op84PHQuF0v__a1lWLRhJTsd6cEKTJleBRa-kQ1WYMWaccEOLNf04Src12UpnXpOmMdaffValOwWUeTZXs9kGNMyqvl9ZpbbnHV7rIHSRW9lGLU8KgtGF9Fy1saPD-sLyvc1aF807s4vHdtqwcl4zEsHrVrySInAuFIbrkOUGq5dpyZkEHNDG0cpwo6QmmtPRFwpFaHnD12NkBHEyl-HmV6_ZzaAac8zSnRCgZAuIuMp4hgUCp9AYfSQbcLBeN3TzPKQUzuMpxT3IySidCKiTdibTB2MyDd-m7Qzlkxq7a9Iv8Sy9ffwNsxjCBSPkio7MFPmr2YX5rK3slvkVatOVZi-PjuqUl1oQu-PBo4kVzfJ4yfBOtkz
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NS8NAEB1qFdSD3-JH1UUU9BBMNpukOYiorSitVUSlt7ib3YKgbU2i4p_yNzrbbKqCePPgIacsS5J9eTM7O_MGYCuUri-5zayAe9Jijh1boa1cxLIn0D3yaMzkoNlE0GpV2-3wsgTvRS2MTqssOHFA1LIX6xj5Hrq-WjzPpcFB_8nSXaP06WrRQiOHRUO9veKWLd0_q-H6blN6Ur8-PrVMVwErpiHNLAQwp4FgnrK5VFT6-IBoNDtVxr2QKr1H4szvMC6pdFhAhRABGjnflvh3eKFwcd4RGGUuUkwZRo_qrcurYVRHq2xWPT-XMHLd0N7rJ1o_J2Dsu-H7zvsDY3Yy_d8-wwxMGbeZHOY4n4WS6s7B5BcxxTmYNTSVkh2jpb07D7c1nnGrlmhOJ1c98Zxm5AJZ8tGUnxL02YnOaX4kRrud3HdJHdGL1lgR3dIpS4kutEvIDU4-yJ_I3hbg5k_edhHK3V5XLQGRjqME6_gMTRYLlCO0hiITeHkCvaN4GXaKdY5io7Ou2308RLjf0pCIhpBYhs3h0H4uLvLToEqBhMjwSxp9wmDl99sbMH56fd6MmmetxipMoLsX5gGkCpSz5FmtwVj8kt2nybqBMoG7v4bNB5ZRLnQ
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8QwEB58IXrwLb4NoqCHsm2atpuDiLq7KMq6iIq3mjRZEHR3baviX_PXOdmmq4J48-Chp4bQNl_mm0lnvgHY5soPlXCZE4lAOcxzE4e72kcsBxLdo4AmTPWbTUTNZvX2lreG4L2shTFplaVN7Btq1U3MGXkFXV8jnudjqN62aRGtWuOg9-SYDlLmT2vZTqOAyJl-e8XwLds_reFa71DaqF8dnzi2w4CTUE5zB8EsaCRZoF2hNFUhPiwSaLvKRMCpNvGSYGGbCUWVxyIqpYyQ8EJX4U4JuPRx3mEYjXzu4Y4aPao3W5eDEx6juFkNwkLOyPe5W-mlRksnYuw7CX7ngD6xNab_8yeZgSnrTpPDAv-zMKQ7czD5RWRxDmat-crIrtXY3puHm5rIhVNLja0nl135nOXkAq3noy1LJejLE5Pr_Eispju575A6ohpZWhPT6inPiCnAS8k1Tt7Pq8jfFuD6T952EUY63Y5eAqI8T0vWDhlSGYu0J422IpN4BRK9pmQZdss1jxOrv27agDzEGIcZeMQDeCzD1mBorxAd-WnQWomK2NqdLP6ExMrvtzdhHLESn582z1ZhAr1AXpwrrcFInj7rdRhLXvL7LN2wqCZw99eo-QBEtjc3
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data-Driven+Robust+Optimization+for+Steam+Systems+in+Ethylene+Plants+under+Uncertainty&rft.jtitle=Processes&rft.au=Zhao%2C+Liang&rft.au=Zhong%2C+Weimin&rft.au=Du%2C+Wenli&rft.date=2019&rft.pub=MDPI+AG&rft.eissn=2227-9717&rft.volume=7&rft.issue=10&rft.spage=744&rft_id=info:doi/10.3390%2Fpr7100744&rft.externalDBID=HAS_PDF_LINK
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-9717&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-9717&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-9717&client=summon