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dc.contributor.author
Carnero, Mercedes del Carmen
dc.contributor.author
Hernandez, Jose Luis
dc.contributor.author
Sanchez, Mabel Cristina
dc.date.available
2019-11-28T14:56:50Z
dc.date.issued
2018-08-20
dc.identifier.citation
Carnero, Mercedes del Carmen; Hernandez, Jose Luis; Sanchez, Mabel Cristina; Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms; American Chemical Society; Industrial & Engineering Chemical Research; 57; 36; 20-8-2018; 12149-12164
dc.identifier.issn
0888-5885
dc.identifier.uri
http://hdl.handle.net/11336/90764
dc.description.abstract
The optimal selection of sensor structures improves the knowledge of the current plant state, which is a central issue for the decision-making process. Instrumentation design is a challenging optimization problem that involves a huge amount of binary variables that represent the possible sensor locations. In this work, the limitations of the current design strategies are discussed, and they support the application of evolutionary solution methods. Among them, the estimation of distribution algorithms (EDAs) arises as a convenient alternative to solving the problem. These are stochastic optimization strategies devised to capture complex interactions among problem variables by learning the probabilistic model of candidate solutions and its sampling to generate the next population. From the broad spectrum of EDAs that use multivariate models, two representative procedures are selected that significantly differ in the methods used for learning and sampling those models. Furthermore, a comparative performance study is conducted to evaluate the benefits of increasing the complexity of the distribution model with respect to a memetic procedure based on univariate models.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
SENSOR NETWORK DESIGN
dc.subject
EVOLUTIONARY COMPUTATION
dc.subject
ESTIMATION OF DISTRIBUTION ALGORITHMS
dc.subject.classification
Ingeniería de Procesos Químicos
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-24T19:37:55Z
dc.journal.volume
57
dc.journal.number
36
dc.journal.pagination
12149-12164
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Washington
dc.description.fil
Fil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto; Argentina
dc.description.fil
Fil: Hernandez, Jose Luis. Universidad Nacional de Río Cuarto; Argentina
dc.description.fil
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina
dc.journal.title
Industrial & Engineering Chemical Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/abs/10.1021/acs.iecr.8b01680
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acs.iecr.8b01680
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