IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v298y2021ics030626192100670x.html
   My bibliography  Save this article

Retraining prior state performances of anaerobic digestion improves prediction accuracy of methane yield in various machine learning models

Author

Listed:
  • Park, Jun-Gyu
  • Jun, Hang-Bae
  • Heo, Tae-Young

Abstract

The prediction of anaerobic digestion (AD) performance using numerical models, which are based on mathematics and kinetics, is being challenged by poor mechanistic understanding and the non-linear relationships between performance and operational parameters. This study demonstrated that various machine learning (ML) models using the 1-step ahead with the retraining method, which utilized AD performance data from prior states, can improve the prediction accuracy of ML models. For the four types of ML models studied, the 1-step ahead with the retraining method could improve the root mean square errors by 32–49% compared to the conventional multi-step ahead method, which was particularly noteworthy during the transition period when AD reactors were faced with loading shocks and showed inhibited methane yields. Moreover, the 1-step ahead with the retraining method showed the potential of achieving accurate predictions using a single input parameter, pH, which was considerably less labor-intensive to monitor than the other parameters often required in AD models (e.g., VSS). As such, the 1-step ahead with retraining method is suitable for efficient real-time prediction of AD performance in real-world operations.

Suggested Citation

  • Park, Jun-Gyu & Jun, Hang-Bae & Heo, Tae-Young, 2021. "Retraining prior state performances of anaerobic digestion improves prediction accuracy of methane yield in various machine learning models," Applied Energy, Elsevier, vol. 298(C).
  • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s030626192100670x
    DOI: 10.1016/j.apenergy.2021.117250
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192100670X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.117250?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. Smrekar, J. & Potočnik, P. & Senegačnik, A., 2013. "Multi-step-ahead prediction of NOx emissions for a coal-based boiler," Applied Energy, Elsevier, vol. 106(C), pages 89-99.
    2. Appels, Lise & Lauwers, Joost & Degrève, Jan & Helsen, Lieve & Lievens, Bart & Willems, Kris & Van Impe, Jan & Dewil, Raf, 2011. "Anaerobic digestion in global bio-energy production: Potential and research challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4295-4301.
    3. Gaida, Daniel & Wolf, Christian & Bongards, Michael, 2017. "Feed control of anaerobic digestion processes for renewable energy production: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 869-875.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Farzin, Farzad & Moghaddam, Shabnam Sadri & Ehteshami, Majid, 2024. "Auto-tuning data-driven model for biogas yield prediction from anaerobic digestion of sewage sludge at the south-tehran wastewater treatment plant: Feature selection and hyperparameter population-base," Renewable Energy, Elsevier, vol. 227(C).

    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. Alejandro Moure Abelenda & Kirk T. Semple & George Aggidis & Farid Aiouache, 2022. "Circularity of Bioenergy Residues: Acidification of Anaerobic Digestate Prior to Addition of Wood Ash," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    2. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    3. Lane, Blake & Kinnon, Michael Mac & Shaffer, Brendan & Samuelsen, Scott, 2022. "Deployment planning tool for environmentally sensitive heavy-duty vehicles and fueling infrastructure," Energy Policy, Elsevier, vol. 171(C).
    4. Palakodeti, Advait & Azman, Samet & Rossi, Barbara & Dewil, Raf & Appels, Lise, 2021. "A critical review of ammonia recovery from anaerobic digestate of organic wastes via stripping," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    5. Mohamed A. Hassaan & Antonio Pantaleo & Francesco Santoro & Marwa R. Elkatory & Giuseppe De Mastro & Amany El Sikaily & Safaa Ragab & Ahmed El Nemr, 2020. "Techno-Economic Analysis of ZnO Nanoparticles Pretreatments for Biogas Production from Barley Straw," Energies, MDPI, vol. 13(19), pages 1-26, September.
    6. Kerstin Nielsen & Christina-Luise Roß & Marieke Hoffmann & Andreas Muskolus & Frank Ellmer & Timo Kautz, 2020. "The Chemical Composition of Biogas Digestates Determines Their Effect on Soil Microbial Activity," Agriculture, MDPI, vol. 10(6), pages 1-20, June.
    7. Ao, Tianjie & Chen, Lin & Chen, Yichao & Liu, Xiaofeng & Wan, Liping & Li, Dong, 2021. "The screening of early warning indicators and microbial community of chicken manure thermophilic digestion at high organic loading rate," Energy, Elsevier, vol. 224(C).
    8. Tan, Peng & He, Biao & Zhang, Cheng & Rao, Debei & Li, Shengnan & Fang, Qingyan & Chen, Gang, 2019. "Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory," Energy, Elsevier, vol. 176(C), pages 429-436.
    9. Edwards, Joel & Othman, Maazuza & Burn, Stewart, 2015. "A review of policy drivers and barriers for the use of anaerobic digestion in Europe, the United States and Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 815-828.
    10. Zheng, Wei & Wang, Chao & Yang, Yajun & Zhang, Yongfei, 2020. "Multi-objective combustion optimization based on data-driven hybrid strategy," Energy, Elsevier, vol. 191(C).
    11. Mohammadpour, Hossein & Cord-Ruwisch, Ralf & Pivrikas, Almantas & Ho, Goen, 2022. "Simple energy-efficient electrochemically-driven CO2 scrubbing for biogas upgrading," Renewable Energy, Elsevier, vol. 195(C), pages 274-282.
    12. Saha, Chayan Kumer & Nandi, Rajesh & Akter, Shammi & Hossain, Samira & Kabir, Kazi Bayzid & Kirtania, Kawnish & Islam, Md Tahmid & Guidugli, Laura & Reza, M. Toufiq & Alam, Md Monjurul, 2024. "Technical prospects and challenges of anaerobic co-digestion in Bangladesh: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    13. Tan, Peng & Xia, Ji & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2016. "Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method," Energy, Elsevier, vol. 94(C), pages 672-679.
    14. Gahyun Baek & Jaai Kim & Jinsu Kim & Changsoo Lee, 2018. "Role and Potential of Direct Interspecies Electron Transfer in Anaerobic Digestion," Energies, MDPI, vol. 11(1), pages 1-18, January.
    15. Safieddin Ardebili, Seyed Mohammad, 2020. "Green electricity generation potential from biogas produced by anaerobic digestion of farm animal waste and agriculture residues in Iran," Renewable Energy, Elsevier, vol. 154(C), pages 29-37.
    16. Ornelas-Ferreira, B. & Lobato, L.C.S. & Colturato, L.F.D. & Torres, E.O. & Pombo, L.M. & Pujatti, F.J.P. & Araújo, J.C. & Chernicharo, C.A.L., 2020. "Strategies for energy recovery and gains associated with the implementation of a solid state batch methanization system for treating organic waste from the city of Rio de Janeiro - Brazil," Renewable Energy, Elsevier, vol. 146(C), pages 1976-1983.
    17. Siwal, Samarjeet Singh & Zhang, Qibo & Devi, Nishu & Saini, Adesh Kumar & Saini, Vipin & Pareek, Bhawna & Gaidukovs, Sergejs & Thakur, Vijay Kumar, 2021. "Recovery processes of sustainable energy using different biomass and wastes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    18. Di Maria, Francesco & Micale, Caterina & Contini, Stefano, 2016. "Energetic and environmental sustainability of the co-digestion of sludge with bio-waste in a life cycle perspective," Applied Energy, Elsevier, vol. 171(C), pages 67-76.
    19. Awasthi, Mukesh Kumar & Ferreira, Jorge A. & Sirohi, Ranjna & Sarsaiya, Surendra & Khoshnevisan, Benyamin & Baladi, Samin & Sindhu, Raveendran & Binod, Parameswaran & Pandey, Ashok & Juneja, Ankita & , 2021. "A critical review on the development stage of biorefinery systems towards the management of apple processing-derived waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    20. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).

    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:appene:v:298:y:2021:i:c:s030626192100670x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.