 Early prediction of BMP tests: A step response method for estimating first-order model parametersArianna Catenacci, Anna Santus, Francesca Malpei and Gianni Ferretti Renewable Energy, 2022, vol. 188, issue C, 184-194 Abstract: The Biochemical Methane Potential (BMP) test is an essential tool for supporting real-scale facilities, for instance to derive practical knowledge about a digester performance. However, its broader application is limited by long test duration and high cost. This work proposes a new method for early prediction of BMP first-order kinetic parameters (the maximum methane yield, B0, and the kinetic constant rate k), based on the analysis of a part of data collected from the experiment. Akaike and Bayesian information criteria were used to verify that the prevailing degradation kinetics is that of first-order, for many substrates. An algorithm was developed, providing good early estimates within a short time (4–10 days): in 92.5% of cases, the relative error of the final BMP estimate was found to be in the 1–13% range, with a relative Root Mean Squared Errors (rRMSE) of below 10%. Results suggest that it’s possible to shorten BMP test duration by leveraging data collected in the first part of the experiment. Keywords: Biochemical methane potential; First-order kinetics; Akaike and Bayesian criteria; Parameter estimation; Early prediction; Step response based method (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:188:y:2022:i:c:p:184-194 DOI: 10.1016/j.renene.2022.02.017 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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