Development of ANN models for optimization of methane yield from floating dome digester

  • Authors

    • S Sathish
    • A Parthiban
    • R Balakrishna
    • R Anandan
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.12393
  • Substrate loading rate, temperature, agitation time, methane.

  • Abstract

    The development of methane generation is mainly based on a desirable combination of operating parameters. The essential objective of this analysis systematically analyzes the prediction of methane yield with different operating parameters. Current work is to analyse the reaction of Temperature (T), Agitation time (AT), pH, value and Substrate Loading Rate (SLR) which are all considered to be the different factors. Artificial Neural Network (ANN) is the modern method aid to solve complex issues that could not be addressed by conventional methods. In this work examine the study employ the ANN as a tool for prediction of methane from floating dome anaerobic digester with press mud. The result showed that ANN model is found the value of methane yields much closed to theoretical methane yield. It is obtained the percentage of predicted value of methane is 58% and theoretical value of methane is 62 % with the temperature of 45ºC and agitation time of 20 min, pH value of 7.2 and substrate loading rate of 120 kg.

     

  • References

    1. [1] Angeli SD, Turchetti L, Monteleone G & Lemonidou AA, “Catalyst development for steam reforming of methane and model biogas at low temperatureâ€, Applied Catalysis B: Environmental, Vol.181, (2016), pp.34-46.

      [2] Bora BJ & Saha UK, “Experimental evaluation of a rice bran biodiesel–biogas run dual fuel diesel engine at varying compression ratiosâ€, Renewable energy, Vol.87, (2016), pp.782-790.

      [3] Collet P, Flottes E, Favre A, Raynal L, Pierre H, Capela S & Peregrina C, “Techno-economic and Life Cycle Assessment of methane production via biogas upgrading and power to gas technologyâ€, Applied energy, Vol.192, (2017), pp.282-295.

      [4] Hagos K, Zong J, Li D, Liu C & Lu X, “Anaerobic co-digestion process for biogas production: Progress, challenges and perspectivesâ€, Renewable and Sustainable Energy Reviews, Vol.76, (2017), pp.1485-1496.

      [5] Kiesel A & Iris L, “Miscanthus as biogas substrate–Cutting tolerance and potential for anaerobic digestionâ€, Gcb Bioenergy, Vol.9, No.1, (2017), pp.153-167.

      [6] Sathish S & Vivekanandan S, “Experimental investigation on biogas production using industrial waste (press mud) to generate renewable energyâ€, Int. J. Innov. Res. Sci. Eng. Technol, Vol.4, No.2, (2015), pp.388-392.

      [7] Sathish S & Vivekanandan S, “Parametric optimization for floating drum anaerobic bio-digester using Response Surface Methodology and Artificial Neural Networkâ€, Alexandria Engineering Journal, Vol.55, No.4, (2016), pp.3297-3307.

      [8] Sathish S, Chandrasekaran M & Parthiban A, “Effect of Co-Digestion Agricultural-Industrial Residues: Various Slurry Temperaturesâ€, International Journal of Ambient Energy, (2017)

      [9] Xia ZM, Li XS, Chen ZY, Li G, Yan KF, Xu CG, Lv QN & Cai J, “Hydrate-based CO2 capture and CH4 purification from simulated biogas with synergic additives based on gas solventâ€, Applied Energy, Vol.162, (2016), pp.1153-1159.

      [10] Zou S, Wang X, Chen Y, Wan H & Feng Y, “Enhancement of biogas production in anaerobic co-digestion by ultrasonic pretreatmentâ€, Energy Conversion and Management, Vol.112, (2016), pp.226-235.

  • Downloads

  • How to Cite

    Sathish, S., Parthiban, A., Balakrishna, R., & Anandan, R. (2018). Development of ANN models for optimization of methane yield from floating dome digester. International Journal of Engineering & Technology, 7(2.21), 316-318. https://doi.org/10.14419/ijet.v7i2.21.12393

    Received date: 2018-05-03

    Accepted date: 2018-05-03

    Published date: 2018-04-20