IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v188y2022icp184-194.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122001562
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.02.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sohoo, Ihsanullah & Ritzkowski, Marco & Heerenklage, Jörn & Kuchta, Kerstin, 2021. "Biochemical methane potential assessment of municipal solid waste generated in Asian cities: A case study of Karachi, Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Dandikas, Vasilis & Heuwinkel, Hauke & Lichti, Fabian & Eckl, Thomas & Drewes, Jörg E. & Koch, Konrad, 2018. "Correlation between hydrolysis rate constant and chemical composition of energy crops," Renewable Energy, Elsevier, vol. 118(C), pages 34-42.
    3. Jinming Liu & Changhao Zeng & Na Wang & Jianfei Shi & Bo Zhang & Changyu Liu & Yong Sun, 2021. "Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics," Energies, MDPI, vol. 14(5), pages 1-17, March.
    4. Peng, Wei & Beggio, Giovanni & Pivato, Alberto & Zhang, Hua & Lü, Fan & He, Pinjing, 2022. "Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    5. Wu, Di & Li, Lei & Zhao, Xiaofei & Peng, Yun & Yang, Pingjin & Peng, Xuya, 2019. "Anaerobic digestion: A review on process monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 1-12.
    6. Posom, Jetsada & Saechua, Wanphut & Sirisomboon, Panmanas, 2017. "Evaluation of pyrolysis characteristics of milled bamboo using near-infrared spectroscopy," Renewable Energy, Elsevier, vol. 103(C), pages 653-665.
    7. Audrey Lallement & Christine Peyrelasse & Camille Lagnet & Abdellatif Barakat & Blandine Schraauwers & Samuel Maunas & Florian Monlau, 2023. "A Detailed Database of the Chemical Properties and Methane Potential of Biomasses Covering a Large Range of Common Agricultural Biogas Plant Feedstocks," Waste, MDPI, vol. 1(1), pages 1-33, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:188:y:2022:i:c:p:184-194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.