A Novel Application of Evolutionary Computing in Process Systems Engineering
In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algori...
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| creator | Carballido, Jessica Andrea Ponzoni, Ignacio Brignole, Nélida Beatriz |
| description | In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algorithm with a three-objective fitness function based on cost, reliability and observability metrics. MOGAI’s special features are: dynamic adaptive bit-flip mutation and guided generation of the initial population, both giving a special treatment to non-feasible individuals, and an adaptive genotypic convergence criterion to stop the algorithm. The algorithmic behavior was evaluated through the analysis of the mathematical model that represents an ammonia synthesis plant. Its efficacy was assessed by comparing the performance of the OA algorithm with and without MOGAI initialization. The genetic algorithm proved to be advantageous because it led to a significant reduction in the number of iterations required by the OA algorithm. |
| doi_str_mv | 10.1007/978-3-540-31996-2_2 |
| format | Conference Proceeding |
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Systems</subject><subject>Exact sciences and technology</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Logical, boolean and switching functions</subject><subject>Multi-Objective Genetic Algorithm</subject><subject>Observability Analysis</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Process control. Computer integrated manufacturing</subject><subject>Process-Plant Instrumentation Design</subject><subject>PSE</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540253372</isbn><isbn>3540253378</isbn><isbn>9783540319962</isbn><isbn>3540319964</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNo9kFtLAzEQheMNLLW_wJe8-BjNZHJpHkupFygqqM8hm2bL6nazbGrBf2_aivMyzDmH4fARcg38Fjg3d9ZMGTIlOUOwVjPhxAmZFBWLdpDEKRmBBmCI0p79e0IhGnFORhy5YNZIvCSTnD95GQSjjBmR5Yw-p11s6azv2yb4bZM6mmq62KX2e3_44YfO06YvR7emTUdfhxRizvTtJ2_jJtNFt266GIdiX5GL2rc5Tv72mHzcL97nj2z58vA0ny1ZL4TZMu25raOGEITSklfWxCDRBrFaBV1JwKCUNNVKT43yGqRWUimItQqqUhoMjsnN8W_vc_BtPfguNNn1Q7MpdR0YLrTRtuTgmMv9vl4cXJXSV3bA3Z6sK5wcukLKHTC6QhZ_Af-NZeY</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Carballido, Jessica Andrea</creator><creator>Ponzoni, Ignacio</creator><creator>Brignole, Nélida Beatriz</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>A Novel Application of Evolutionary Computing in Process Systems Engineering</title><author>Carballido, Jessica Andrea ; Ponzoni, Ignacio ; Brignole, Nélida Beatriz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p227t-6a09fe61cc25640b97ec439c2ddc6b413c5547bd6875a614654551ef5c5b56173</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Combinatorial Optimization Problem</topic><topic>Computer science; control theory; systems</topic><topic>Control theory. Systems</topic><topic>Exact sciences and technology</topic><topic>Flows in networks. Combinatorial problems</topic><topic>Logical, boolean and switching functions</topic><topic>Multi-Objective Genetic Algorithm</topic><topic>Observability Analysis</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Process control. Computer integrated manufacturing</topic><topic>Process-Plant Instrumentation Design</topic><topic>PSE</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Carballido, Jessica Andrea</creatorcontrib><creatorcontrib>Ponzoni, Ignacio</creatorcontrib><creatorcontrib>Brignole, Nélida Beatriz</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Carballido, Jessica Andrea</au><au>Ponzoni, Ignacio</au><au>Brignole, Nélida Beatriz</au><au>Raidl, Günther R.</au><au>Gottlieb, Jens</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Novel Application of Evolutionary Computing in Process Systems Engineering</atitle><btitle>Evolutionary Computation in Combinatorial Optimization</btitle><date>2005</date><risdate>2005</risdate><spage>12</spage><epage>22</epage><pages>12-22</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540253372</isbn><isbn>3540253378</isbn><eisbn>9783540319962</eisbn><eisbn>3540319964</eisbn><abstract>In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Evolutionary Computation in Combinatorial Optimization, 2005, p.12-22 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17026769 |
| source | Springer Books |
| subjects | Applied sciences Combinatorial Optimization Problem Computer science control theory systems Control theory. Systems Exact sciences and technology Flows in networks. Combinatorial problems Logical, boolean and switching functions Multi-Objective Genetic Algorithm Observability Analysis Operational research and scientific management Operational research. Management science Process control. Computer integrated manufacturing Process-Plant Instrumentation Design PSE Theoretical computing |
| title | A Novel Application of Evolutionary Computing in Process Systems Engineering |
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