Within batch non-linear profile monitoring applied to shrimp farming: A case study
Descripción del Articulo
The operation of many batch processes involves critical characteristics that follow specific patterns over time. Sometimes the batch time is long enough that the operator can take corrective actions within it. This paper explores how to develop appropriate monitoring procedures for such situations w...
| Autores: | , , , , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2020 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/2630 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/2630 https://doi.org/10.1080/08982112.2020.1844894 |
| Nivel de acceso: | acceso abierto |
| Materia: | within batch monitoring Non-linear profile profile monitoring shrimp growth http://purl.org/pe-repo/ocde/ford#2.02.04 |
| id |
CONC_b826916579f5bdef85f7ead1b878165e |
|---|---|
| oai_identifier_str |
oai:repositorio.concytec.gob.pe:20.500.12390/2630 |
| network_acronym_str |
CONC |
| network_name_str |
CONCYTEC-Institucional |
| repository_id_str |
4689 |
| dc.title.none.fl_str_mv |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| title |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| spellingShingle |
Within batch non-linear profile monitoring applied to shrimp farming: A case study Quevedo A.V. within batch monitoring Non-linear profile profile monitoring shrimp growth http://purl.org/pe-repo/ocde/ford#2.02.04 |
| title_short |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| title_full |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| title_fullStr |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| title_full_unstemmed |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| title_sort |
Within batch non-linear profile monitoring applied to shrimp farming: A case study |
| author |
Quevedo A.V. |
| author_facet |
Quevedo A.V. Vegas S. Loda J. Cedillo G. Vining G.G. |
| author_role |
author |
| author2 |
Vegas S. Loda J. Cedillo G. Vining G.G. |
| author2_role |
author author author author |
| dc.contributor.author.fl_str_mv |
Quevedo A.V. Vegas S. Loda J. Cedillo G. Vining G.G. |
| dc.subject.none.fl_str_mv |
within batch monitoring |
| topic |
within batch monitoring Non-linear profile profile monitoring shrimp growth http://purl.org/pe-repo/ocde/ford#2.02.04 |
| dc.subject.es_PE.fl_str_mv |
Non-linear profile profile monitoring shrimp growth |
| dc.subject.ocde.none.fl_str_mv |
http://purl.org/pe-repo/ocde/ford#2.02.04 |
| description |
The operation of many batch processes involves critical characteristics that follow specific patterns over time. Sometimes the batch time is long enough that the operator can take corrective actions within it. This paper explores how to develop appropriate monitoring procedures for such situations within the context of a shrimp farming process. It uses a Gompetz non-linear model to describe the basic shrimp growth and defines the control limits through the prediction band around the estimated model. It proposes a method to generate a control chart soon enough within the batch so management can prioritize which ponds require the most attention. © 2021 Taylor & Francis Group, LLC. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2020 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/2630 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1080/08982112.2020.1844894 |
| dc.identifier.scopus.none.fl_str_mv |
2-s2.0-85100196047 |
| url |
https://hdl.handle.net/20.500.12390/2630 https://doi.org/10.1080/08982112.2020.1844894 |
| identifier_str_mv |
2-s2.0-85100196047 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
Quality Engineering |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Bellwether Publishing, Ltd. |
| publisher.none.fl_str_mv |
Bellwether Publishing, Ltd. |
| dc.source.none.fl_str_mv |
reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
| instname_str |
Consejo Nacional de Ciencia Tecnología e Innovación |
| instacron_str |
CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
| collection |
CONCYTEC-Institucional |
| repository.name.fl_str_mv |
Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
| _version_ |
1870084306669404160 |
| spelling |
Publicationrp06772600rp06771600rp06769600rp06770600rp06773600Quevedo A.V.Vegas S.Loda J.Cedillo G.Vining G.G.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2020https://hdl.handle.net/20.500.12390/2630https://doi.org/10.1080/08982112.2020.18448942-s2.0-85100196047The operation of many batch processes involves critical characteristics that follow specific patterns over time. Sometimes the batch time is long enough that the operator can take corrective actions within it. This paper explores how to develop appropriate monitoring procedures for such situations within the context of a shrimp farming process. It uses a Gompetz non-linear model to describe the basic shrimp growth and defines the control limits through the prediction band around the estimated model. It proposes a method to generate a control chart soon enough within the batch so management can prioritize which ponds require the most attention. © 2021 Taylor & Francis Group, LLC.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengBellwether Publishing, Ltd.Quality Engineeringinfo:eu-repo/semantics/openAccesswithin batch monitoringNon-linear profile-1profile monitoring-1shrimp growth-1http://purl.org/pe-repo/ocde/ford#2.02.04-1Within batch non-linear profile monitoring applied to shrimp farming: A case studyinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/2630oai:repositorio.concytec.gob.pe:20.500.12390/26302024-05-30 16:10:00.327http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a4508157-35ed-45b7-8a4f-d5883ab631cf"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Within batch non-linear profile monitoring applied to shrimp farming: A case study</Title> <PublishedIn> <Publication> <Title>Quality Engineering</Title> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <DOI>https://doi.org/10.1080/08982112.2020.1844894</DOI> <SCP-Number>2-s2.0-85100196047</SCP-Number> <Authors> <Author> <DisplayName>Quevedo A.V.</DisplayName> <Person id="rp06772" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Vegas S.</DisplayName> <Person id="rp06771" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Loda J.</DisplayName> <Person id="rp06769" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Cedillo G.</DisplayName> <Person id="rp06770" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Vining G.G.</DisplayName> <Person id="rp06773" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Bellwether Publishing, Ltd.</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>within batch monitoring</Keyword> <Keyword>Non-linear profile</Keyword> <Keyword>profile monitoring</Keyword> <Keyword>shrimp growth</Keyword> <Abstract>The operation of many batch processes involves critical characteristics that follow specific patterns over time. Sometimes the batch time is long enough that the operator can take corrective actions within it. This paper explores how to develop appropriate monitoring procedures for such situations within the context of a shrimp farming process. It uses a Gompetz non-linear model to describe the basic shrimp growth and defines the control limits through the prediction band around the estimated model. It proposes a method to generate a control chart soon enough within the batch so management can prioritize which ponds require the most attention. © 2021 Taylor & Francis Group, LLC.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
| score |
13.411838 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
