Analysis and application of ADM1 for anaerobic methane production

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dc.contributor.authorJeong, HSko
dc.contributor.authorSuh, CWko
dc.contributor.authorLim, JLko
dc.contributor.authorLee, SHko
dc.contributor.authorShin, Hang-Sikko
dc.date.accessioned2008-09-05T01:02:47Z-
dc.date.available2008-09-05T01:02:47Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2005-04-
dc.identifier.citationBIOPROCESS AND BIOSYSTEMS ENGINEERING, v.27, no.2, pp.81 - 89-
dc.identifier.issn1615-7591-
dc.identifier.urihttp://hdl.handle.net/10203/7315-
dc.description.abstractAn anaerobic model for the serum bottle test was developed and analyzed with sensitivities of stoichiometric and kinetic parameters to the components in order to establish a basis for appropriate application of the model. Anaerobic glucose degradation in a serum bottle was selected as an example. The anaerobic model was developed based on the anaerobic digestion model no. 1 (ADM1), which had five processes with 17 kinetic and stoichiometric parameters. Sensitivity analysis showed that the yield of product on the substrate (f) has high sensitivities to model components, and that the methane concentration was the most sensitive component. Important parameters including yield of product on the substrate (f), yield of biomass on the substrate (Y), and half-saturation values (K) were estimated using genetic algorithms, which optimized the parameters with experimental results. The Monod maximum specific uptake rate (k) was, however, so strongly associated with the concentration of biomass, that values could not be estimated individually. Simulation with estimated parameters showed good agreement with experimental results in the case of methane production. However, there were some differences in acetate and propionate concentrations.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherSPRINGER-
dc.subjectMODEL NO. 1-
dc.subjectGENETIC ALGORITHMS-
dc.subjectPARAMETERS-
dc.titleAnalysis and application of ADM1 for anaerobic methane production-
dc.typeArticle-
dc.identifier.wosid000227946200002-
dc.identifier.scopusid2-s2.0-17744365373-
dc.type.rimsART-
dc.citation.volume27-
dc.citation.issue2-
dc.citation.beginningpage81-
dc.citation.endingpage89-
dc.citation.publicationnameBIOPROCESS AND BIOSYSTEMS ENGINEERING-
dc.identifier.doi10.1007/s00449-004-0370-4-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorShin, Hang-Sik-
dc.contributor.nonIdAuthorJeong, HS-
dc.contributor.nonIdAuthorSuh, CW-
dc.contributor.nonIdAuthorLim, JL-
dc.contributor.nonIdAuthorLee, SH-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorADM1-
dc.subject.keywordAuthorsensitivity analysis-
dc.subject.keywordAuthorgenetic algorithms-
dc.subject.keywordAuthorparameter estimation-
dc.subject.keywordPlusMODEL NO. 1-
dc.subject.keywordPlusGENETIC ALGORITHMS-
dc.subject.keywordPlusPARAMETERS-
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