Computational evaluation for effects of feedstock variations on the sensitivities of biochemical mechanism parameters in anaerobic digestion kinetic models.
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| Title: | Computational evaluation for effects of feedstock variations on the sensitivities of biochemical mechanism parameters in anaerobic digestion kinetic models. |
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| 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] |
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| Database: | Engineering Source |
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