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Early prediction of BMP tests: A step response method for estimating first-order model parameters

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  • Catenacci, Arianna
  • Santus, Anna
  • Malpei, Francesca
  • Ferretti, Gianni

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.

Suggested Citation

  • Catenacci, Arianna & Santus, Anna & Malpei, Francesca & Ferretti, Gianni, 2022. "Early prediction of BMP tests: A step response method for estimating first-order model parameters," Renewable Energy, Elsevier, vol. 188(C), pages 184-194.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:184-194
    DOI: 10.1016/j.renene.2022.02.017
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    References listed on IDEAS

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    1. Triolo, Jin M. & Ward, Alastair J. & Pedersen, Lene & Løkke, Mette M. & Qu, Haiyan & Sommer, Sven G., 2014. "Near Infrared Reflectance Spectroscopy (NIRS) for rapid determination of biochemical methane potential of plant biomass," Applied Energy, Elsevier, vol. 116(C), pages 52-57.
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