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Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system

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  • Zareei, Samira
  • Khodaei, Jalal

Abstract

This study was focused on the prediction and optimization of biogas production from cow manure with maize straw under various total solid content (TS), Carbon to Nitrogen (C/N) ratio and stirring intensity. This research used full-scale (1200 L) batch reactor under mesophilic condition. An adaptive neuro-fuzzy interference system (ANFIS) was utilized to predict and optimize biogas production from anaerobic digestion. C/N ratio, TS and stirring intensity of substrates, each of them in three levels, were considered as input variables and biogas production was regarded as the output variable of the model. The coefficient of determination (R2) between observed and predicted biogas production values was 0.99 which showed good match and accuracy of the model. Highest biogas production was achieved from C/N ratio 26.76, TS 9% and moderate stirring. Biogas production increased about 8% with optimal conditions suggested by the ANFIS model.

Suggested Citation

  • Zareei, Samira & Khodaei, Jalal, 2017. "Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 114(PB), pages 423-427.
    • Handle: RePEc:eee:renene:v:114:y:2017:i:pb:p:423-427
    DOI: 10.1016/j.renene.2017.07.050
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    References listed on IDEAS

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    1. Zhang, Quanguo & Hu, Jianjun & Lee, Duu-Jong, 2016. "Biogas from anaerobic digestion processes: Research updates," Renewable Energy, Elsevier, vol. 98(C), pages 108-119.
    2. Gelegenis, John & Georgakakis, Dimitris & Angelidaki, Irini & Mavris, Vassilis, 2007. "Optimization of biogas production by co-digesting whey with diluted poultry manure," Renewable Energy, Elsevier, vol. 32(13), pages 2147-2160.
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    Cited by:

    1. KeChrist Obileke & Golden Makaka & Nwabunwanne Nwokolo, 2022. "Efficient Methane Production from Anaerobic Digestion of Cow Dung: An Optimization Approach," Challenges, MDPI, vol. 13(2), pages 1-11, October.
    2. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    3. Yazan, Devrim Murat & Fraccascia, Luca & Mes, Martijn & Zijm, Henk, 2018. "Cooperation in manure-based biogas production networks: An agent-based modeling approach," Applied Energy, Elsevier, vol. 212(C), pages 820-833.
    4. Abdullah Nsair & Senem Onen Cinar & Ayah Alassali & Hani Abu Qdais & Kerstin Kuchta, 2020. "Operational Parameters of Biogas Plants: A Review and Evaluation Study," Energies, MDPI, vol. 13(15), pages 1-27, July.
    5. Olatunji, Kehinde O. & Ahmed, Noor A. & Madyira, Daniel M. & Adebayo, Ademola O. & Ogunkunle, Oyetola & Adeleke, Oluwatobi, 2022. "Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction," Renewable Energy, Elsevier, vol. 189(C), pages 288-303.

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