Moving horizon closed‐loop production scheduling using dynamic process models
The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be co...
Gespeichert in:
Veröffentlicht in: | AIChE journal 2017-02, Vol.63 (2), p.639-651 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 651 |
---|---|
container_issue | 2 |
container_start_page | 639 |
container_title | AIChE journal |
container_volume | 63 |
creator | Pattison, Richard C. Touretzky, Cara R. Harjunkoski, Iiro Baldea, Michael |
description | The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017 |
doi_str_mv | 10.1002/aic.15408 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1880020322</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1859500061</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5408-19aabaef44a2351d22729024827a747a8f099deba6b7b0dda8b4e5e94232f8543</originalsourceid><addsrcrecordid>eNqN0c9OwyAYAHBiNHFOD75BEy966AYUWnpcFv8smdlFz4QCdSy0TFg18-Qj-Iw-idR6MjHxwhfgxwd8HwDnCE4QhHgqjJwgSiA7AKMYi5SWkB6CEYQQpXEBHYOTEDZxhguGR2B1715M-5SsnTdvrk2kdUGrz_cP69w22XqnOrkzcSPItVad7W0X-lHtW9EY2RupQ0gap7QNp-CoFjbos584Bo831w_zu3S5ul3MZ8tU9o9LUSlEJXRNiMAZRQrjApcQE4YLUZBCsBqWpdKVyKuigkoJVhFNdUlwhmtGSTYGl0PeeP1zp8OONyZIba1otesCR4zFcsAM439QWtJYnxxFevGLblzn2_iRXlGUM4TzqK4GJb0Lweuab71phN9zBHnfBR67wL-7EO10sK_G6v3fkM8W8-HEF_xpiO8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1855168126</pqid></control><display><type>article</type><title>Moving horizon closed‐loop production scheduling using dynamic process models</title><source>Wiley Journals</source><creator>Pattison, Richard C. ; Touretzky, Cara R. ; Harjunkoski, Iiro ; Baldea, Michael</creator><creatorcontrib>Pattison, Richard C. ; Touretzky, Cara R. ; Harjunkoski, Iiro ; Baldea, Michael</creatorcontrib><description>The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017</description><identifier>ISSN: 0001-1541</identifier><identifier>EISSN: 1547-5905</identifier><identifier>DOI: 10.1002/aic.15408</identifier><identifier>CODEN: AICEAC</identifier><language>eng</language><publisher>New York: American Institute of Chemical Engineers</publisher><subject>closed‐loop scheduling ; dynamic constraints ; Dynamics ; Economics ; Feedback ; Horizon ; Mathematical models ; moving horizon ; Production scheduling ; rescheduling ; Schedules ; Scheduling</subject><ispartof>AIChE journal, 2017-02, Vol.63 (2), p.639-651</ispartof><rights>2016 American Institute of Chemical Engineers</rights><rights>2017 American Institute of Chemical Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5408-19aabaef44a2351d22729024827a747a8f099deba6b7b0dda8b4e5e94232f8543</citedby><cites>FETCH-LOGICAL-c5408-19aabaef44a2351d22729024827a747a8f099deba6b7b0dda8b4e5e94232f8543</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Faic.15408$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Faic.15408$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Pattison, Richard C.</creatorcontrib><creatorcontrib>Touretzky, Cara R.</creatorcontrib><creatorcontrib>Harjunkoski, Iiro</creatorcontrib><creatorcontrib>Baldea, Michael</creatorcontrib><title>Moving horizon closed‐loop production scheduling using dynamic process models</title><title>AIChE journal</title><description>The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017</description><subject>closed‐loop scheduling</subject><subject>dynamic constraints</subject><subject>Dynamics</subject><subject>Economics</subject><subject>Feedback</subject><subject>Horizon</subject><subject>Mathematical models</subject><subject>moving horizon</subject><subject>Production scheduling</subject><subject>rescheduling</subject><subject>Schedules</subject><subject>Scheduling</subject><issn>0001-1541</issn><issn>1547-5905</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqN0c9OwyAYAHBiNHFOD75BEy966AYUWnpcFv8smdlFz4QCdSy0TFg18-Qj-Iw-idR6MjHxwhfgxwd8HwDnCE4QhHgqjJwgSiA7AKMYi5SWkB6CEYQQpXEBHYOTEDZxhguGR2B1715M-5SsnTdvrk2kdUGrz_cP69w22XqnOrkzcSPItVad7W0X-lHtW9EY2RupQ0gap7QNp-CoFjbos584Bo831w_zu3S5ul3MZ8tU9o9LUSlEJXRNiMAZRQrjApcQE4YLUZBCsBqWpdKVyKuigkoJVhFNdUlwhmtGSTYGl0PeeP1zp8OONyZIba1otesCR4zFcsAM439QWtJYnxxFevGLblzn2_iRXlGUM4TzqK4GJb0Lweuab71phN9zBHnfBR67wL-7EO10sK_G6v3fkM8W8-HEF_xpiO8</recordid><startdate>201702</startdate><enddate>201702</enddate><creator>Pattison, Richard C.</creator><creator>Touretzky, Cara R.</creator><creator>Harjunkoski, Iiro</creator><creator>Baldea, Michael</creator><general>American Institute of Chemical Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U5</scope><scope>8FD</scope><scope>C1K</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>201702</creationdate><title>Moving horizon closed‐loop production scheduling using dynamic process models</title><author>Pattison, Richard C. ; Touretzky, Cara R. ; Harjunkoski, Iiro ; Baldea, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5408-19aabaef44a2351d22729024827a747a8f099deba6b7b0dda8b4e5e94232f8543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>closed‐loop scheduling</topic><topic>dynamic constraints</topic><topic>Dynamics</topic><topic>Economics</topic><topic>Feedback</topic><topic>Horizon</topic><topic>Mathematical models</topic><topic>moving horizon</topic><topic>Production scheduling</topic><topic>rescheduling</topic><topic>Schedules</topic><topic>Scheduling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pattison, Richard C.</creatorcontrib><creatorcontrib>Touretzky, Cara R.</creatorcontrib><creatorcontrib>Harjunkoski, Iiro</creatorcontrib><creatorcontrib>Baldea, Michael</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>AIChE journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pattison, Richard C.</au><au>Touretzky, Cara R.</au><au>Harjunkoski, Iiro</au><au>Baldea, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Moving horizon closed‐loop production scheduling using dynamic process models</atitle><jtitle>AIChE journal</jtitle><date>2017-02</date><risdate>2017</risdate><volume>63</volume><issue>2</issue><spage>639</spage><epage>651</epage><pages>639-651</pages><issn>0001-1541</issn><eissn>1547-5905</eissn><coden>AICEAC</coden><abstract>The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017</abstract><cop>New York</cop><pub>American Institute of Chemical Engineers</pub><doi>10.1002/aic.15408</doi><tpages>13</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0001-1541 |
ispartof | AIChE journal, 2017-02, Vol.63 (2), p.639-651 |
issn | 0001-1541 1547-5905 |
language | eng |
recordid | cdi_proquest_miscellaneous_1880020322 |
source | Wiley Journals |
subjects | closed‐loop scheduling dynamic constraints Dynamics Economics Feedback Horizon Mathematical models moving horizon Production scheduling rescheduling Schedules Scheduling |
title | Moving horizon closed‐loop production scheduling using dynamic process models |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T20%3A03%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Moving%20horizon%20closed%E2%80%90loop%20production%20scheduling%20using%20dynamic%20process%20models&rft.jtitle=AIChE%20journal&rft.au=Pattison,%20Richard%20C.&rft.date=2017-02&rft.volume=63&rft.issue=2&rft.spage=639&rft.epage=651&rft.pages=639-651&rft.issn=0001-1541&rft.eissn=1547-5905&rft.coden=AICEAC&rft_id=info:doi/10.1002/aic.15408&rft_dat=%3Cproquest_cross%3E1859500061%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1855168126&rft_id=info:pmid/&rfr_iscdi=true |