Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production
This paper proposed a framework to model and optimises a biogas production using artificial neural networks and genetic algorithms. The intelligence computation was applied to achieve a better model and optimisation compared to a mathematical modeling. Two training approaches were used to train a se...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2013
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28367/ http://umpir.ump.edu.my/id/eprint/28367/1/Implementing%20artificial%20neural%20networks%20and%20genetic%20algorithms.pdf |
| _version_ | 1848823035018084352 |
|---|---|
| author | Abdul Sahli, Fakharudin Md Nasir, Sulaiman Jailani, Salihon Norazwina, Zainol |
| author_facet | Abdul Sahli, Fakharudin Md Nasir, Sulaiman Jailani, Salihon Norazwina, Zainol |
| author_sort | Abdul Sahli, Fakharudin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper proposed a framework to model and optimises a biogas production using artificial neural networks and genetic algorithms. The intelligence computation was applied to achieve a better model and optimisation compared to a mathematical modeling. Two training approaches were used to train a set of neural networks design. The trained networks model predictions were used to generate a maximum biogas output assisted by genetic algorithms optimisation. The result showed that modeling accuracy with low error will not give a better yield. It also reported a 0.44% increase of maximum biogas yield from the published result. |
| first_indexed | 2025-11-15T02:50:44Z |
| format | Conference or Workshop Item |
| id | ump-28367 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:50:44Z |
| publishDate | 2013 |
| publisher | Universiti Utara Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-283672021-01-22T04:14:06Z http://umpir.ump.edu.my/id/eprint/28367/ Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production Abdul Sahli, Fakharudin Md Nasir, Sulaiman Jailani, Salihon Norazwina, Zainol QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This paper proposed a framework to model and optimises a biogas production using artificial neural networks and genetic algorithms. The intelligence computation was applied to achieve a better model and optimisation compared to a mathematical modeling. Two training approaches were used to train a set of neural networks design. The trained networks model predictions were used to generate a maximum biogas output assisted by genetic algorithms optimisation. The result showed that modeling accuracy with low error will not give a better yield. It also reported a 0.44% increase of maximum biogas yield from the published result. Universiti Utara Malaysia 2013-08 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28367/1/Implementing%20artificial%20neural%20networks%20and%20genetic%20algorithms.pdf Abdul Sahli, Fakharudin and Md Nasir, Sulaiman and Jailani, Salihon and Norazwina, Zainol (2013) Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production. In: Proceedings of the 4th International Conference on Computing and Informatics (ICOCI 2013) , 28-30 August 2013 , Kuching, Sarawak. pp. 121-126. (088). (Published) http://www.icoci.cms.net.my/proceedings/2013/PDF/PID88.pdf |
| spellingShingle | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Abdul Sahli, Fakharudin Md Nasir, Sulaiman Jailani, Salihon Norazwina, Zainol Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| title | Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| title_full | Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| title_fullStr | Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| title_full_unstemmed | Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| title_short | Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| title_sort | implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production |
| topic | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/28367/ http://umpir.ump.edu.my/id/eprint/28367/ http://umpir.ump.edu.my/id/eprint/28367/1/Implementing%20artificial%20neural%20networks%20and%20genetic%20algorithms.pdf |