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Predicting commercial-scale anaerobic digestion using biomethane potential

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  • van der Berg, David J.
  • Teke, George Mbella
  • Görgens, Johann F.
  • van Rensburg, Eugéne

Abstract

Standardized biomethane potential (BMP) tests are simple and cost-effective methods to evaluate the potential methane production from different organic materials. However, these methods are seldom reliable predictors for large-scale anaerobic digester (AD) performances due to scale-up effects from volume changes and different process conditions. Bench-scale BMP data was collected over 34 months from the feedstock for an industrial AD plant treating alcohol manufacturing wastewater to develop mathematical correlations for predictive purposes. The standardized BMP test data was converted into expected industrial plant performance using a dynamic model and an extrapolation method, and compared to actual plant data. Predictions of industrial plant performance using the dynamic model was more reliable using BMP input data, requiring a biogas production scaling factor of 0.92, as opposed to a 0.69 correction/scaling factor required for predictions using the extrapolation method. A combination of the dynamic model with the 0.92 scaling factor could, therefore, convert regular BMP measurements into expected industrial plant performance, based on variations in the daily organic feeds to the plant. Also, the sensitivity analysis performed based on model parameters was highly sensitive to changes, thus implies the model parameters impact the accuracy of predicting a full-scale industrial AD plant.

Suggested Citation

  • van der Berg, David J. & Teke, George Mbella & Görgens, Johann F. & van Rensburg, Eugéne, 2024. "Predicting commercial-scale anaerobic digestion using biomethane potential," Renewable Energy, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:renene:v:235:y:2024:i:c:s0960148124013727
    DOI: 10.1016/j.renene.2024.121304
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    References listed on IDEAS

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    1. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
    2. Jay N. Meegoda & Brian Li & Kush Patel & Lily B. Wang, 2018. "A Review of the Processes, Parameters, and Optimization of Anaerobic Digestion," IJERPH, MDPI, vol. 15(10), pages 1-16, October.
    3. Fiore, S. & Ruffino, B. & Campo, G. & Roati, C. & Zanetti, M.C., 2016. "Scale-up evaluation of the anaerobic digestion of food-processing industrial wastes," Renewable Energy, Elsevier, vol. 96(PA), pages 949-959.
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