Databases reconstruction from operating modes recognition in dynamic processes

This work presents a methodology for data reconstruction based in operational modes recognition in dynamic processes, maintaining dynamic properties of registered variables in such database. To do this, an introduction of process and system is made, characterizing the source of databases. Also, a re...

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Autores:
Obando Montoya, Andrés Felipe
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86729
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86729
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Bases de datos
Servicios de información en línea
Sistemas de reconocimiento de configuraciones
Reconocimiento de modelos
Algoritmos
Data imputation
Operational modes
Pattern recognition
Dynamic process
Database
Imputación de datos
Modos de operación
Reconocimiento de patrones
Procesos dinámicos
Bases de datos
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_426277b14413124205b0a0c6d077ff6d
oai_identifier_str oai:repositorio.unal.edu.co:unal/86729
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv Databases reconstruction from operating modes recognition in dynamic processes
dc.title.translated.spa.fl_str_mv Recontrucción de bases de datos desde el reconocimiento de los modos de operación de procesos dinámicos
title Databases reconstruction from operating modes recognition in dynamic processes
spellingShingle Databases reconstruction from operating modes recognition in dynamic processes
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Bases de datos
Servicios de información en línea
Sistemas de reconocimiento de configuraciones
Reconocimiento de modelos
Algoritmos
Data imputation
Operational modes
Pattern recognition
Dynamic process
Database
Imputación de datos
Modos de operación
Reconocimiento de patrones
Procesos dinámicos
Bases de datos
title_short Databases reconstruction from operating modes recognition in dynamic processes
title_full Databases reconstruction from operating modes recognition in dynamic processes
title_fullStr Databases reconstruction from operating modes recognition in dynamic processes
title_full_unstemmed Databases reconstruction from operating modes recognition in dynamic processes
title_sort Databases reconstruction from operating modes recognition in dynamic processes
dc.creator.fl_str_mv Obando Montoya, Andrés Felipe
dc.contributor.advisor.none.fl_str_mv Alvarez Zapata, Hernán Dario
dc.contributor.author.none.fl_str_mv Obando Montoya, Andrés Felipe
dc.contributor.researchgroup.spa.fl_str_mv Grupo de investigación en Procesos Dinámicos KALMAN
dc.contributor.cvlac.spa.fl_str_mv OBANDO MONTOYA, ANDRÉS FELIPE
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Bases de datos
Servicios de información en línea
Sistemas de reconocimiento de configuraciones
Reconocimiento de modelos
Algoritmos
Data imputation
Operational modes
Pattern recognition
Dynamic process
Database
Imputación de datos
Modos de operación
Reconocimiento de patrones
Procesos dinámicos
Bases de datos
dc.subject.lemb.none.fl_str_mv Bases de datos
Servicios de información en línea
Sistemas de reconocimiento de configuraciones
Reconocimiento de modelos
Algoritmos
dc.subject.proposal.eng.fl_str_mv Data imputation
Operational modes
Pattern recognition
Dynamic process
Database
dc.subject.proposal.spa.fl_str_mv Imputación de datos
Modos de operación
Reconocimiento de patrones
Procesos dinámicos
Bases de datos
description This work presents a methodology for data reconstruction based in operational modes recognition in dynamic processes, maintaining dynamic properties of registered variables in such database. To do this, an introduction of process and system is made, characterizing the source of databases. Also, a review of data imputation methodology is presented, highlighting the main features of their procedures. Despite of count with several imputation methodologies, any of them are focused into conserving dynamic properties of variables contained in databases, only proposing different identification models sketchers without considering of a previous data selection step to assure the accuracy of predictive models. Taking into account this fact, the proposed data imputation methodology is based into Dynamical Operational Mode (DOM) recognition of processes, grouping data in clusters with similar dynamic properties, allowing the usage of correct information for auxiliary identification models. Under this considerations, Artificial Resonance Theory (ART2) is introduced as the algorithm for DOM recognition. Additionally, the proposed methodology verifies that imputations do not add uncertainty to original data, conserving initial dynamic information. (Tomado de la fuente)
publishDate 2015
dc.date.issued.none.fl_str_mv 2015-12
dc.date.accessioned.none.fl_str_mv 2024-08-15T15:44:12Z
dc.date.available.none.fl_str_mv 2024-08-15T15:44:12Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86729
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/86729
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.indexed.spa.fl_str_mv LaReferencia
dc.relation.references.spa.fl_str_mv Ibrahim Berkan Aydilek and Ahmet Arslan. A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Information Sciences, 233:25–35, June 2013.
Petr Kadlec, Bogdan Gabrys, and Sibylle Strandt. Data-driven Soft Sensors in the process industry. Computers & Chemical Engineering, 33(4):795–814, April 2009.
Jiu-sun Zeng and Chuan-hou Gao. Improvement of identification of blast furnace iron-making process by outlier detection and missing value imputation. Journal of Process Control, 19(9):1519–1528, October 2009.
B. Balasko and J. Abonyi. What happens to process data in chemical industry: From source to applications-An Overview. Hungarian Journal of Industrial Chemistry, 35: 75–84, 2007.
Will Bridewell, Pat Langley, Steve Racunas, and Stuart Borrett. Learning process models with missing data. Machine Learning: ECML, pages 557–565, 2006.
A.J. Isaksson. Identification of ARX-models subject to missing data. Automatic Control, IEEE Transactions on, 38(5):813–819, 1993.
Stavros Papadokonstantakis, Stephan Machefer, Klaus Schnitzlein, and Argyrios I. Lygeros. Variable selection and data pre-processing in NN modelling of complex chemical processes. Computers & Chemical Engineering, 29(7):1647–1659, June 2005.
B. Lamrini, El-K. Lakhal, M-V. Lann, and L. Wehenkel. Data validation and missing data reconstruction using self-organizing map for water treatment. Neural Computing and Applications, 20(4):575–588, February 2011.
R. Vijayabhanu and V. Radha. Recognition and elimination of missing values and outliers from an anaerobic wastewater treatment system using K-Means cluster. 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), pages V4–186–V4–190, August 2010.
Changkyu Lee, Sang Wook Choi, Jong-min Lee, and In-beum Lee. Reconstruction based sensor fault identification in chemical processes. Proceedings of the 2004 IEEE International Conference on Control Applications, 2004., 2:1096–1100, 2004.
Alan Olinsky, Shaw Chen, and Lisa Harlow. The comparative efficacy of imputation methods for missing data in structural equation modeling. European Journal of Operational Research, 151(1):53–79, November 2003.
Tiago J. Rato and Marco S. Reis. Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems, 125:101–108, June 2013.
Jean-Pierre Belaud, Stéphane Negny, Fabrice Dupros, David Michéa, and Benoît Vautrin. Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering. Computers in Industry, 65(3):521–535, April 2014.
Markus Schladt and Bei Hu. Soft sensors based on nonlinear steady-state data reconciliation in the process industry. Chemical Engineering and Processing: Process Intensification, 46(11):1107–1115, November 2007.
S. A. Imtiaz and S. L. Shah. Treatment of missing values in process data analysis. The Canadian Journal of Chemical Engineering, 86(5):838–858, October 2008.
Lang Wu and Hulin Wu. Missing time-dependent covariates in human immunodeficiency virus dynamic models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(3):297–318, July 2002.
Hilda Marcela Moscoso-Vásquez. A design procedure for a supervisory control structure in plantwide control. Master thesis, Universidad Nacional de Colombia - Sede Medellín, 2013.
Michael A. Henson and Dale E. Seborg. Input-output linearization of general nonlinear processes. AIChE Journal, 36(11):1753–1757, 1990.
A. Inselberg and Bernard Dimsdale. Parallel coordinates: a tool for visualizing multidimensional geometry. In Visualization, 1990. Visualization ’90., Proceedings of the First IEEE Conference on, pages 361–378, Oct 1990.
Olga Georgieva, Michael Wagenknecht, and Rainer Hampel. Takagi-Sugeno fuzzy model development of batch biotechnological processes. International Journal of Approximate Reasoning, 26(3):233–250, 2001.
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dc.format.extent.spa.fl_str_mv 90 páginas
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Medellín - Minas - Maestría en Ingeniería - Ingeniería Química
dc.publisher.faculty.spa.fl_str_mv Facultad de Minas
dc.publisher.place.spa.fl_str_mv Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Medellín
institution Universidad Nacional de Colombia
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Alvarez Zapata, Hernán Dariocce7b899711f765f68d214d094845caeObando Montoya, Andrés Felipe727668e0b9c5a9ec63ba4acbd78ded99Grupo de investigación en Procesos Dinámicos KALMANOBANDO MONTOYA, ANDRÉS FELIPE2024-08-15T15:44:12Z2024-08-15T15:44:12Z2015-12https://repositorio.unal.edu.co/handle/unal/86729Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/This work presents a methodology for data reconstruction based in operational modes recognition in dynamic processes, maintaining dynamic properties of registered variables in such database. To do this, an introduction of process and system is made, characterizing the source of databases. Also, a review of data imputation methodology is presented, highlighting the main features of their procedures. Despite of count with several imputation methodologies, any of them are focused into conserving dynamic properties of variables contained in databases, only proposing different identification models sketchers without considering of a previous data selection step to assure the accuracy of predictive models. Taking into account this fact, the proposed data imputation methodology is based into Dynamical Operational Mode (DOM) recognition of processes, grouping data in clusters with similar dynamic properties, allowing the usage of correct information for auxiliary identification models. Under this considerations, Artificial Resonance Theory (ART2) is introduced as the algorithm for DOM recognition. Additionally, the proposed methodology verifies that imputations do not add uncertainty to original data, conserving initial dynamic information. (Tomado de la fuente)En este trabajo se presenta una metodología para la reconstrucción de datos basados en el reconocimiento de los modos operacionales en procesos dinámicos, manteniendo las propiedades dinámicas de las variables contenidas en dichas bases de datos. Con este objetivo, se hace una introducción a los conceptos de proceso y sistema, caracterizando las fuentes de información de las bases de datos. También se realiza una revisión de las metodologías para la imputación de datos, resaltando las principales características de sus procedimientos. A pesar de contar con bastantes metodologías para la imputación, ninguna de ellas se especializa en la conservación de las propiedades dinámicas de las variables, y solo proponen diferentes esquemas para la identificación de modelos sin considerar pasos previos para la correcta selección de información para asegurar la precisión de sus predicciones. De este modo, la metodología propuesta se basa en el reconocimiento de los Modos de Operación Dinámicos (DOM) de los procesos, permitiendo el uso correcto de esta información para la identificación de modelos auxiliares. Con esto en mente, se propone el algoritmo ART2 para el reconocimiento de los DOM. Adicionalmente, la metodología propuesta verifica las imputaciones para no adicionar incertidumbre a los datos originales, conservando la información dinámica original.MaestríaMagíster en Ingeniería - Ingeniería QuímicaIngeniería Química E Ingeniería De Petróleos.Sede Medellín90 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería QuímicaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresBases de datosServicios de información en líneaSistemas de reconocimiento de configuracionesReconocimiento de modelosAlgoritmosData imputationOperational modesPattern recognitionDynamic processDatabaseImputación de datosModos de operaciónReconocimiento de patronesProcesos dinámicosBases de datosDatabases reconstruction from operating modes recognition in dynamic processesRecontrucción de bases de datos desde el reconocimiento de los modos de operación de procesos dinámicosTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferenciaIbrahim Berkan Aydilek and Ahmet Arslan. A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Information Sciences, 233:25–35, June 2013.Petr Kadlec, Bogdan Gabrys, and Sibylle Strandt. Data-driven Soft Sensors in the process industry. Computers & Chemical Engineering, 33(4):795–814, April 2009.Jiu-sun Zeng and Chuan-hou Gao. Improvement of identification of blast furnace iron-making process by outlier detection and missing value imputation. Journal of Process Control, 19(9):1519–1528, October 2009.B. Balasko and J. Abonyi. What happens to process data in chemical industry: From source to applications-An Overview. Hungarian Journal of Industrial Chemistry, 35: 75–84, 2007.Will Bridewell, Pat Langley, Steve Racunas, and Stuart Borrett. Learning process models with missing data. Machine Learning: ECML, pages 557–565, 2006.A.J. Isaksson. Identification of ARX-models subject to missing data. Automatic Control, IEEE Transactions on, 38(5):813–819, 1993.Stavros Papadokonstantakis, Stephan Machefer, Klaus Schnitzlein, and Argyrios I. Lygeros. Variable selection and data pre-processing in NN modelling of complex chemical processes. Computers & Chemical Engineering, 29(7):1647–1659, June 2005.B. Lamrini, El-K. Lakhal, M-V. Lann, and L. Wehenkel. Data validation and missing data reconstruction using self-organizing map for water treatment. Neural Computing and Applications, 20(4):575–588, February 2011.R. Vijayabhanu and V. Radha. Recognition and elimination of missing values and outliers from an anaerobic wastewater treatment system using K-Means cluster. 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), pages V4–186–V4–190, August 2010.Changkyu Lee, Sang Wook Choi, Jong-min Lee, and In-beum Lee. Reconstruction based sensor fault identification in chemical processes. Proceedings of the 2004 IEEE International Conference on Control Applications, 2004., 2:1096–1100, 2004.Alan Olinsky, Shaw Chen, and Lisa Harlow. The comparative efficacy of imputation methods for missing data in structural equation modeling. European Journal of Operational Research, 151(1):53–79, November 2003.Tiago J. Rato and Marco S. Reis. Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems, 125:101–108, June 2013.Jean-Pierre Belaud, Stéphane Negny, Fabrice Dupros, David Michéa, and Benoît Vautrin. Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering. Computers in Industry, 65(3):521–535, April 2014.Markus Schladt and Bei Hu. Soft sensors based on nonlinear steady-state data reconciliation in the process industry. Chemical Engineering and Processing: Process Intensification, 46(11):1107–1115, November 2007.S. A. Imtiaz and S. L. Shah. Treatment of missing values in process data analysis. The Canadian Journal of Chemical Engineering, 86(5):838–858, October 2008.Lang Wu and Hulin Wu. Missing time-dependent covariates in human immunodeficiency virus dynamic models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(3):297–318, July 2002.Hilda Marcela Moscoso-Vásquez. A design procedure for a supervisory control structure in plantwide control. Master thesis, Universidad Nacional de Colombia - Sede Medellín, 2013.Michael A. Henson and Dale E. Seborg. Input-output linearization of general nonlinear processes. AIChE Journal, 36(11):1753–1757, 1990.A. Inselberg and Bernard Dimsdale. Parallel coordinates: a tool for visualizing multidimensional geometry. In Visualization, 1990. Visualization ’90., Proceedings of the First IEEE Conference on, pages 361–378, Oct 1990.Olga Georgieva, Michael Wagenknecht, and Rainer Hampel. Takagi-Sugeno fuzzy model development of batch biotechnological processes. International Journal of Approximate Reasoning, 26(3):233–250, 2001.EstudiantesInvestigadoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86729/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1017181858.2015.pdf1017181858.2015.pdfTesis de Maestría en Ingeniería - Ingeniería Químicaapplication/pdf2601256https://repositorio.unal.edu.co/bitstream/unal/86729/2/1017181858.2015.pdfa1bdb39719f549df94a1b357def2f0d8MD52THUMBNAIL1017181858.2015.pdf.jpg1017181858.2015.pdf.jpgGenerated Thumbnailimage/jpeg4200https://repositorio.unal.edu.co/bitstream/unal/86729/3/1017181858.2015.pdf.jpg032a36ab53149b2facff982f7e2b4d6aMD53unal/86729oai:repositorio.unal.edu.co:unal/867292024-08-15 23:06:25.343Repositorio Institucional Universidad Nacional de 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