Computational evaluation for effects of feedstock variations on the sensitivities of biochemical mechanism parameters in anaerobic digestion kinetic models.
Saved in:
| Title: | Computational evaluation for effects of feedstock variations on the sensitivities of biochemical mechanism parameters in anaerobic digestion kinetic models. |
|---|---|
| Authors: | Fortela, Dhan Lord B.1,2 dhanlord.fortela@louisiana.edu, Sharp, Wayne W.1,3, Revellame, Emmanuel D.1,4, Hernandez, Rafael1,2, Gang, Daniel1,3, Zappi, Mark E.1,2 |
| Source: | Biochemical Engineering Journal. Mar2019, Vol. 143, p212-223. 12p. |
| Subjects: | Feedstock, Digestion, Clustering of particles, Anaerobic digestion, Sensitivity analysis |
| Abstract: | Highlights • fPCA projects 95–99% of model outputs to time-independent PC score and GSA indices. • Feedstock variations significantly alter the dominant modelled digestion mechanisms. • GSA-FPCA-clustering algorithm numerically elucidates the key digestion mechanisms. Abstract This work demonstrates a computational approach for the exploration of the importance of biochemical mechanism parameters in anaerobic digestion models subjected to concentration variations of digestion feedstock components. The methodology consists of an algorithm integrating global sensitivity analysis (GSA), functional principal component analysis (FPCA), and rank-clustering techniques. The GSA-FPCA integration removes the time-varying character of GSA (Morris') indices while the rank-clustering step provides a statistical approach to grouping the ranks of parameter sensitivities as affected by feedstock component variations. To substantiate generalizations of findings, two digestion models of differing complexity were used: Case 1 – single-component feedstock digestion model with substrate inhibition, and Case 2 – rigorous model Anaerobic Digestion Model No. 1 (ADM1). Results indicate that 95–99% of the variations in the time-dependent outputs can be captured by the principal components (PCs) after FPCA transformation, and that the first PC is sufficient to represent the model outputs. This capability of the FPCA to capture overall response curve patterns reduces the dimensionality of model response perturbation measures and makes the resulting data projections onto time-independent sensitivity indices intuitive of response curve perturbations. Ranked Morris sensitivity indices calculated from the first PC scores revealed stoichiometric parameters that dominantly affect kinetic responses as well as least sensitive parameters. The GSA-FPCA-Clustering approach elucidates digestion kinetic patterns only obvious under a systematic GSA methodology. [ABSTRACT FROM AUTHOR] |
| Copyright of Biochemical Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 134737475 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Computational evaluation for effects of feedstock variations on the sensitivities of biochemical mechanism parameters in anaerobic digestion kinetic models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fortela%2C+Dhan+Lord+B%2E%22">Fortela, Dhan Lord B.</searchLink><relatesTo>1,2</relatesTo><i> dhanlord.fortela@louisiana.edu</i><br /><searchLink fieldCode="AR" term="%22Sharp%2C+Wayne+W%2E%22">Sharp, Wayne W.</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Revellame%2C+Emmanuel+D%2E%22">Revellame, Emmanuel D.</searchLink><relatesTo>1,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Hernandez%2C+Rafael%22">Hernandez, Rafael</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Gang%2C+Daniel%22">Gang, Daniel</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zappi%2C+Mark+E%2E%22">Zappi, Mark E.</searchLink><relatesTo>1,2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biochemical+Engineering+Journal%22">Biochemical Engineering Journal</searchLink>. Mar2019, Vol. 143, p212-223. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feedstock%22">Feedstock</searchLink><br /><searchLink fieldCode="DE" term="%22Digestion%22">Digestion</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+of+particles%22">Clustering of particles</searchLink><br /><searchLink fieldCode="DE" term="%22Anaerobic+digestion%22">Anaerobic digestion</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights • fPCA projects 95–99% of model outputs to time-independent PC score and GSA indices. • Feedstock variations significantly alter the dominant modelled digestion mechanisms. • GSA-FPCA-clustering algorithm numerically elucidates the key digestion mechanisms. Abstract This work demonstrates a computational approach for the exploration of the importance of biochemical mechanism parameters in anaerobic digestion models subjected to concentration variations of digestion feedstock components. The methodology consists of an algorithm integrating global sensitivity analysis (GSA), functional principal component analysis (FPCA), and rank-clustering techniques. The GSA-FPCA integration removes the time-varying character of GSA (Morris') indices while the rank-clustering step provides a statistical approach to grouping the ranks of parameter sensitivities as affected by feedstock component variations. To substantiate generalizations of findings, two digestion models of differing complexity were used: Case 1 – single-component feedstock digestion model with substrate inhibition, and Case 2 – rigorous model Anaerobic Digestion Model No. 1 (ADM1). Results indicate that 95–99% of the variations in the time-dependent outputs can be captured by the principal components (PCs) after FPCA transformation, and that the first PC is sufficient to represent the model outputs. This capability of the FPCA to capture overall response curve patterns reduces the dimensionality of model response perturbation measures and makes the resulting data projections onto time-independent sensitivity indices intuitive of response curve perturbations. Ranked Morris sensitivity indices calculated from the first PC scores revealed stoichiometric parameters that dominantly affect kinetic responses as well as least sensitive parameters. The GSA-FPCA-Clustering approach elucidates digestion kinetic patterns only obvious under a systematic GSA methodology. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Biochemical Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=134737475 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.bej.2019.01.001 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 212 Subjects: – SubjectFull: Feedstock Type: general – SubjectFull: Digestion Type: general – SubjectFull: Clustering of particles Type: general – SubjectFull: Anaerobic digestion Type: general – SubjectFull: Sensitivity analysis Type: general Titles: – TitleFull: Computational evaluation for effects of feedstock variations on the sensitivities of biochemical mechanism parameters in anaerobic digestion kinetic models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fortela, Dhan Lord B. – PersonEntity: Name: NameFull: Sharp, Wayne W. – PersonEntity: Name: NameFull: Revellame, Emmanuel D. – PersonEntity: Name: NameFull: Hernandez, Rafael – PersonEntity: Name: NameFull: Gang, Daniel – PersonEntity: Name: NameFull: Zappi, Mark E. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 1369703X Numbering: – Type: volume Value: 143 Titles: – TitleFull: Biochemical Engineering Journal Type: main |
| ResultId | 1 |
