Implementing artificial neural networks and genetic algorithms to solve modeling and optimisation of biogas production

• Main Authors: Fakharudin, Abdul Sahli, Sulaiman, Md Nasir, Salihon, Jailani, Zainol, Norazwina
• Format: Conference or Workshop Item
• Language: English
• Published: UUM College of Arts and Sciences, Universiti Utara Malaysia 2013
• 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
• Institution: Universiti Putra Malaysia

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.