Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production

Biogas production from waste is a valuable renewable energy and with better process design, maximum biogas yield can be obtained from the same amount of waste. Modelling and optimisation are widely used in biological and chemical process domain to increase and to improve the efficiency of this proce...

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Main Authors:	Abdul Sahli, Fakharudin, Md Nasir, Sulaiman, Norwati, Mustapha
Format:	Article
Language:	English
Published:	JATIT 2017
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/17428/ http://umpir.ump.edu.my/id/eprint/17428/ http://umpir.ump.edu.my/id/eprint/17428/1/Modelling%20of%20Biogas%20Production%20from%20Banana%20Stem.pdf

id	ump-17428
recordtype	eprints
spelling	ump-174282017-07-20T02:55:07Z http://umpir.ump.edu.my/id/eprint/17428/ Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production Abdul Sahli, Fakharudin Md Nasir, Sulaiman Norwati, Mustapha QA75 Electronic computers. Computer science Biogas production from waste is a valuable renewable energy and with better process design, maximum biogas yield can be obtained from the same amount of waste. Modelling and optimisation are widely used in biological and chemical process domain to increase and to improve the efficiency of this process. In recent years, intelligence computation is applied to design a better process model and optimised biogas yield. This paper presents a comparative study of several neural networks learning (back-propagation, resilient propagation, Lavenberg-Marquardt and particle swarm optimisation) algorithms for process modelling and optimisation and its relation with the optimisation result. The result shows an improvement of around 10% of biogas production and 8% more from the engineering mathematical optimisation. Two main complications were identified, first one is the high accuracy modelling is not a guarantee for optimised production and the second is a false solution with high optimised production may happen. To clarify this situation, a solution is suggested using factor deviation percentage. JATIT 2017-01-31 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17428/1/Modelling%20of%20Biogas%20Production%20from%20Banana%20Stem.pdf Abdul Sahli, Fakharudin and Md Nasir, Sulaiman and Norwati, Mustapha (2017) Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production. Journal of Theoretical and Applied Information Technology, 95 (2). pp. 285-291. ISSN 1992-8645(print); 817-3195(online) http://www.jatit.org/volumes/ninetyfive2.php
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Abdul Sahli, Fakharudin Md Nasir, Sulaiman Norwati, Mustapha Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production
description	Biogas production from waste is a valuable renewable energy and with better process design, maximum biogas yield can be obtained from the same amount of waste. Modelling and optimisation are widely used in biological and chemical process domain to increase and to improve the efficiency of this process. In recent years, intelligence computation is applied to design a better process model and optimised biogas yield. This paper presents a comparative study of several neural networks learning (back-propagation, resilient propagation, Lavenberg-Marquardt and particle swarm optimisation) algorithms for process modelling and optimisation and its relation with the optimisation result. The result shows an improvement of around 10% of biogas production and 8% more from the engineering mathematical optimisation. Two main complications were identified, first one is the high accuracy modelling is not a guarantee for optimised production and the second is a false solution with high optimised production may happen. To clarify this situation, a solution is suggested using factor deviation percentage.
format	Article
author	Abdul Sahli, Fakharudin Md Nasir, Sulaiman Norwati, Mustapha
author_facet	Abdul Sahli, Fakharudin Md Nasir, Sulaiman Norwati, Mustapha
author_sort	Abdul Sahli, Fakharudin
title	Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production
title_short	Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production
title_full	Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production
title_fullStr	Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production
title_full_unstemmed	Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production
title_sort	modelling of biogas production from banana stem waste with neural networks learning strategies to optimse the production
publisher	JATIT
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/17428/ http://umpir.ump.edu.my/id/eprint/17428/ http://umpir.ump.edu.my/id/eprint/17428/1/Modelling%20of%20Biogas%20Production%20from%20Banana%20Stem.pdf
first_indexed	2023-09-18T22:24:03Z
last_indexed	2023-09-18T22:24:03Z
_version_	1777415847753547776

Modelling Of Biogas Production From Banana Stem Waste With Neural Networks Learning Strategies To Optimse The Production

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