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...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
UUM College of Arts and Sciences, Universiti Utara Malaysia
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41311/1/41311.pdf http://psasir.upm.edu.my/id/eprint/41311/ http://www.icoci.cms.net.my/proceedings/2013/PDF/PID88.pdf |
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| Institution: | Universiti Putra Malaysia |
| Language: | English |
| Summary: | 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. |
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