Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics

In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based...

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Veröffentlicht in:Algorithms Jg. 2; H. 1; S. 259 - 281
Hauptverfasser: Carnero, Mercedes, Hernández, José L., Sánchez, Mabel C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2009
Schlagworte:
Population based incremental learning algorithms
Scatter search
Sensor location
Stochastic optimization
Tabu search
ISSN:1999-4893, 1999-4893
Online-Zugang:Volltext
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Zusammenfassung:In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the required quantity and quality of process information. Ad-hoc strategies based on Tabu Search, Scatter Search and Population Based Incremental Learning Algorithms are proposed. Regarding Tabu Search, the intensification and diversification capabilities of the technique are enhanced using Path Relinking. The strategies are applied for solving minimum cost design problems subject to quality constraints on variable estimates, and their performances are compared.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1999-4893
1999-4893
DOI:10.3390/a2010259