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Appraisal of methane production and anaerobic fermentation kinetics of livestock manures using artificial neural networks and sinusoidal growth functions

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Abstract

This study aimed to perform a comparative analysis of the performance of five models (Gompertz, logistic, Richards, the first-order, artificial neural networks) in predicting methane production rate from anaerobic digestion of livestock manures. The input variables were fermentation time, digestion temperature, biogas temperature, ambient temperature, pH, and specific biogas production rate. The physicochemical compositions of cow manure and sheep manure showed that volatile solid (VS) contents were close to each other in manure compositions (77.6% and 64.7%, respectively), while the potential of methane production from cow manure (673.44 mL CH4/g VS) was greater than that from sheep manure (320.32 mL CH4/g VS). The determination coefficients (R2) for logistic function, Gompertz, Richards, the first-order, and ANN models were obtained as 0.968, 0.967, 0.975, 0.825, and 0.995 for the cow manure, respectively. In case of the sheep manure, the R2 values obtained from these models were 0.976, 0.979, 0.981, 0.968 and 0.991, respectively. Although the determination coefficients of all models were in satisfactory agreement with the experimental data, the ANN model showed competitive lower RMSE values of 0.111 and 0.164 for cow and sheep manure data sets, respectively, indicating its superior performance than other models.

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Acknowledgements

The authors would like to thank the providers of the livestock manures used as the substrates in this study. The authors wish to extend their special thanks to the Center of Studies and Research on Renewable Energy (CERER) for the use of measuring instruments during the experimental study.

Funding

This AILARA project has been supported by the French Embassy in Mauritania (Project Number: 2018–2019).

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Authors and Affiliations

  1. Department of Physics, University of Nouakchott Al Aasriya (UNA), Nouakchott, Mauritania

    Mohamed Mahmoud Ali & Mamoudou Ndongo

  2. Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, Esenler, 34220, Istanbul, Turkey

    Kaan Yetilmezsoy, Majid Bahramian & Bülent İlhan Goncaloğlu

  3. Department of Mechanical Engineering, Ecole Supérieur Polytechnique (ESP), Nouakchott, Mauritania

    Boudy Bilal

  4. Center of Studies and Research On Renewable Energies (CERER), Cheikh Anta Diop University, Dakar, Senegal

    Issakha Youm

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  1. Mohamed Mahmoud Ali
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Contributions

All authors contributed to the study conception and design. Preparation of the materials, collection of the experimental data, and analyses were conducted by MMA. MMA and KY coordinated and supervised the collaboration, developed the models, and analyzed the data. MMA, KY, and MB improved the theoretical and computational frameworks of the research. MN, BB, IY, and BİG contributed to the interpretation of the results and helped shape the investigation. The first draft of the manuscript was written by MMA, KY, and MB. The authors read and approved the final manuscript.

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Correspondence to Mohamed Mahmoud Ali or Kaan Yetilmezsoy.

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The authors declare that there are no conflicts of interest including any financial, personal, or relationships with other people or organizations.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Ali, M.M., Ndongo, M., Yetilmezsoy, K. et al. Appraisal of methane production and anaerobic fermentation kinetics of livestock manures using artificial neural networks and sinusoidal growth functions. J Mater Cycles Waste Manag 23, 301–314 (2021). https://doi.org/10.1007/s10163-020-01130-2

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