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

  • Authors

    • S Sathish
    • A Parthiban
    • R Balakrishna
    • R Anandan
    https://doi.org/10.14419/ijet.v7i2.21.12393

    Received date: May 3, 2018

    Accepted date: May 3, 2018

    Published date: April 20, 2018

  • 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.

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  • 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 and Technology, 7(2.21), 316-318. https://doi.org/10.14419/ijet.v7i2.21.12393