Citation
Fakharudin, Abdul Sahli
(2017)
Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm.
Doctoral thesis, Universiti Putra Malaysia.
Abstract
In recent years, several researchers have actively pursued the application of
machine learning to biogas production processes. The application of artificial
neural network (ANN) to generate the production model is used to improve the
modelling accuracy. The model output optimisation by genetic algorithm (GA)
produces higher biogas production compared to the optimisation using
statistical methods.
This study utilised the evolutionary artificial neural network (EANN) modelling
to improve the model accuracy. The EANN modelling was used to represent
the biogas production process. One of the issues of ANN implementation is to
correctly select the output activation function in achieving higher output. The
EANN used a modified activation function to meet the optimisation requirement.
To evaluate the EANN model, 19 samples of experimental data from Zainol on the
regression modelling of biogas production from banana stem waste were selected.
Thirteen samples were used for training (70%) and six samples were used for
testing (30%). The second dataset fromMahanty which consisted of 36 samples on
the modelling and optimisation of biogas production from industrial sludge were
divided into 25 training samples and 11 testing samples. Meanwhile, 34 samples
from Tedesco on the optimisation of mechanical pretreatment of Laminariaceae
spp. biomass for the production of biogas were divided into 24 training samples
and 10 testing samples. The last dataset from the domain expert containing 143
samples were divided into 100 training samples and 43 testing samples.
The model performance was evaluated using root mean square error (RMSE)
and coefficient of determination (R2) and the maximum output from the optimisation was compared to the mathematical modelling. The experiment was
conducted with 50 trial runs on each dataset and EANN method produced
better modelling results compared to the mathematical modelling. The model
output from the optimisation using GA also produced better results than the
mathematical model and able to limit the maximum output of the
back-propagation and Levenberg-Marquardt ANN models which used linear
function output.
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