Open AccessDevelopment of ANN models for optimization of methane yield from floating dome digester
Author(s) -
S. Sathish,
A. Parthiban,
R Balakrishna,
R. Anandan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.21.12393
Subject(s) - methane , yield (engineering) , biogas , anaerobic digestion , work (physics) , anaerobic exercise , dome (geology) , substrate (aquarium) , artificial neural network , mathematics , process engineering , chemistry , engineering , materials science , waste management , computer science , mechanical engineering , geology , composite material , machine learning , organic chemistry , physiology , oceanography , biology , paleontology
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.