MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Chemical Engineering |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322018000301063 |
Resumo: | Abstract Research and development of new processes is a fundamental part of any innovative industry. For process engineers, finding optimal operating conditions for new processes from the early stages is a main issue, since it improves economic viability, helps others areas of R&D by avoiding product bottlenecks and shortens the time-to-market period. Model-based optimization strategies are helpful in doing so, but imperfect models with parametric or structural errors can lead to suboptimal operating conditions. In this work, a methodology that uses probabilistic tendency models that are constantly updated through experimental feedback is proposed in order to rapidly and efficiently find improved operating conditions. Characterization of the uncertainty is used to make safe predictions even with scarce data, which is typical in this early stage of process development. The methodology is tested with an example from the traditional innovative pharmaceutical industry. |
id |
ABEQ-1_139411d7da8d3de8dae857ac8919cea1 |
---|---|
oai_identifier_str |
oai:scielo:S0104-66322018000301063 |
network_acronym_str |
ABEQ-1 |
network_name_str |
Brazilian Journal of Chemical Engineering |
repository_id_str |
|
spelling |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENTProcess system engineeringProcess developmentExperimental designModeling for optimizationAbstract Research and development of new processes is a fundamental part of any innovative industry. For process engineers, finding optimal operating conditions for new processes from the early stages is a main issue, since it improves economic viability, helps others areas of R&D by avoiding product bottlenecks and shortens the time-to-market period. Model-based optimization strategies are helpful in doing so, but imperfect models with parametric or structural errors can lead to suboptimal operating conditions. In this work, a methodology that uses probabilistic tendency models that are constantly updated through experimental feedback is proposed in order to rapidly and efficiently find improved operating conditions. Characterization of the uncertainty is used to make safe predictions even with scarce data, which is typical in this early stage of process development. The methodology is tested with an example from the traditional innovative pharmaceutical industry.Brazilian Society of Chemical Engineering2018-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322018000301063Brazilian Journal of Chemical Engineering v.35 n.3 2018reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/0104-6632.20180353s20170212info:eu-repo/semantics/openAccessLuna,Martin F.Martínez,Ernesto C.eng2019-01-15T00:00:00Zoai:scielo:S0104-66322018000301063Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2019-01-15T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
title |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
spellingShingle |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT Luna,Martin F. Process system engineering Process development Experimental design Modeling for optimization |
title_short |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
title_full |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
title_fullStr |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
title_full_unstemmed |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
title_sort |
MODEL-BASED RUN-TO-RUN OPTIMIZATION FOR PROCESS DEVELOPMENT |
author |
Luna,Martin F. |
author_facet |
Luna,Martin F. Martínez,Ernesto C. |
author_role |
author |
author2 |
Martínez,Ernesto C. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Luna,Martin F. Martínez,Ernesto C. |
dc.subject.por.fl_str_mv |
Process system engineering Process development Experimental design Modeling for optimization |
topic |
Process system engineering Process development Experimental design Modeling for optimization |
description |
Abstract Research and development of new processes is a fundamental part of any innovative industry. For process engineers, finding optimal operating conditions for new processes from the early stages is a main issue, since it improves economic viability, helps others areas of R&D by avoiding product bottlenecks and shortens the time-to-market period. Model-based optimization strategies are helpful in doing so, but imperfect models with parametric or structural errors can lead to suboptimal operating conditions. In this work, a methodology that uses probabilistic tendency models that are constantly updated through experimental feedback is proposed in order to rapidly and efficiently find improved operating conditions. Characterization of the uncertainty is used to make safe predictions even with scarce data, which is typical in this early stage of process development. The methodology is tested with an example from the traditional innovative pharmaceutical industry. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322018000301063 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322018000301063 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0104-6632.20180353s20170212 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
dc.source.none.fl_str_mv |
Brazilian Journal of Chemical Engineering v.35 n.3 2018 reponame:Brazilian Journal of Chemical Engineering instname:Associação Brasileira de Engenharia Química (ABEQ) instacron:ABEQ |
instname_str |
Associação Brasileira de Engenharia Química (ABEQ) |
instacron_str |
ABEQ |
institution |
ABEQ |
reponame_str |
Brazilian Journal of Chemical Engineering |
collection |
Brazilian Journal of Chemical Engineering |
repository.name.fl_str_mv |
Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ) |
repository.mail.fl_str_mv |
rgiudici@usp.br||rgiudici@usp.br |
_version_ |
1754213176245747712 |