Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm

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 al...

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Main Author:	Fakharudin, Abdul Sahli
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Biogas Artificial intelligence Neural networks (Computer science)
Online Access:	http://psasir.upm.edu.my/id/eprint/68747/1/FSKTM%202018%208%20-%20IR.pdf
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id	my-upm-ir.68747
record_format	uketd_dc
spelling	my-upm-ir.687472019-06-11T02:07:50Z Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm 2017-07 Fakharudin, Abdul Sahli 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. Biogas Artificial intelligence Neural networks (Computer science) 2017-07 Thesis http://psasir.upm.edu.my/id/eprint/68747/ http://psasir.upm.edu.my/id/eprint/68747/1/FSKTM%202018%208%20-%20IR.pdf text en public doctoral Universiti Putra Malaysia Biogas Artificial intelligence Neural networks (Computer science)
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Biogas Artificial intelligence Neural networks (Computer science)
spellingShingle	Biogas Artificial intelligence Neural networks (Computer science) Fakharudin, Abdul Sahli Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
description	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.
format	Thesis
qualification_level	Doctorate
author	Fakharudin, Abdul Sahli
author_facet	Fakharudin, Abdul Sahli
author_sort	Fakharudin, Abdul Sahli
title	Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
title_short	Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
title_full	Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
title_fullStr	Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
title_full_unstemmed	Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
title_sort	modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
granting_institution	Universiti Putra Malaysia
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/68747/1/FSKTM%202018%208%20-%20IR.pdf
_version_	1747812626773049344

Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm

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