Early prediction of BMP tests: A step response method for estimating first-order model parameters. (April 2022)

• Record Type: Journal Article
• Authors: Catenacci, Arianna; Santus, Anna; Malpei, Francesca; Ferretti, Gianni
• Is Part Of: Renewable energy. Volume 188(2022)
• Page Start: 184
• Page End: 194
• Publication Date: 2022-04
• Subjects: Biochemical methane potential -- First-order kinetics -- Akaike and Bayesian criteria -- Parameter estimation -- Early prediction -- Step response based method; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.02.017
• Languages: English
• ISSNs: 0960-1481

Abstract

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, B 0, 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. Graphical abstract: Image 1