Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm
In this paper, artificial neural network had been implemented to model the biodelignification process of oil palm trunk core using Pleurotus Ostreatus. The generated model was used as the fitness function for the genetic algorithm to obtain the optimise lignin left percentage. The 4-10-5-2-1 network...
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Association for Computing Machinery (ACM)
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24700/1/36.%20Modelling%20and%20optimisation%20of%20oil%20palm%20trunk%20core%20biodelignification%20using%20neural%20network%20and%20genetic%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/24700/2/36.1%20Modelling%20and%20optimisation%20of%20oil%20palm%20trunk%20core%20biodelignification%20using%20neural%20network%20and%20genetic%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/24700/ https://doi.org/10.1145/3323716.3323737 |
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| author | Abdul Sahli, Fakharudin Norazwina, Zainol Zulsyazwan, Ahmad Khushairi |
| author_facet | Abdul Sahli, Fakharudin Norazwina, Zainol Zulsyazwan, Ahmad Khushairi |
| author_sort | Abdul Sahli, Fakharudin |
| building | UMPSA Library |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In this paper, artificial neural network had been implemented to model the biodelignification process of oil palm trunk core using Pleurotus Ostreatus. The generated model was used as the fitness function for the genetic algorithm to obtain the optimise lignin left percentage. The 4-10-5-2-1 network architecture had been used to model the process and 10 models were generated randomly. These models were used to find the optimised the network output using genetic algorithm search. The modelling results had improved the accuracy and error when using the artificial neural network modelling with training MSE of 0.0096 and testing MSE of 0.2108. The results also show an improved lignin left around 7.55% when the network output was optimised by the genetic algorithm. The application of neural network and genetic algorithm had improved the delignification process. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.24700 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2019 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | eprints |
| spelling | my.ump.umpir.247002019-08-26T02:33:29Z http://umpir.ump.edu.my/id/eprint/24700/ Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm Abdul Sahli, Fakharudin Norazwina, Zainol Zulsyazwan, Ahmad Khushairi QA76 Computer software TP Chemical technology In this paper, artificial neural network had been implemented to model the biodelignification process of oil palm trunk core using Pleurotus Ostreatus. The generated model was used as the fitness function for the genetic algorithm to obtain the optimise lignin left percentage. The 4-10-5-2-1 network architecture had been used to model the process and 10 models were generated randomly. These models were used to find the optimised the network output using genetic algorithm search. The modelling results had improved the accuracy and error when using the artificial neural network modelling with training MSE of 0.0096 and testing MSE of 0.2108. The results also show an improved lignin left around 7.55% when the network output was optimised by the genetic algorithm. The application of neural network and genetic algorithm had improved the delignification process. Association for Computing Machinery (ACM) 2019-03 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24700/1/36.%20Modelling%20and%20optimisation%20of%20oil%20palm%20trunk%20core%20biodelignification%20using%20neural%20network%20and%20genetic%20algorithm.pdf pdf en http://umpir.ump.edu.my/id/eprint/24700/2/36.1%20Modelling%20and%20optimisation%20of%20oil%20palm%20trunk%20core%20biodelignification%20using%20neural%20network%20and%20genetic%20algorithm.pdf Abdul Sahli, Fakharudin and Norazwina, Zainol and Zulsyazwan, Ahmad Khushairi (2019) Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm. In: 8th International Conference on Informatics, Environment, Energy and Applications 2019 , 16 - 19 March 2019 , Osaka, Japan. pp. 155-158.. ISBN 978-145036104-0 (Published) https://doi.org/10.1145/3323716.3323737 |
| spellingShingle | QA76 Computer software TP Chemical technology Abdul Sahli, Fakharudin Norazwina, Zainol Zulsyazwan, Ahmad Khushairi Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| title | Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| title_full | Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| title_fullStr | Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| title_full_unstemmed | Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| title_short | Modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| title_sort | modelling and optimisation of oil palm trunk core biodelignification using neural network and genetic algorithm |
| topic | QA76 Computer software TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/24700/1/36.%20Modelling%20and%20optimisation%20of%20oil%20palm%20trunk%20core%20biodelignification%20using%20neural%20network%20and%20genetic%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/24700/2/36.1%20Modelling%20and%20optimisation%20of%20oil%20palm%20trunk%20core%20biodelignification%20using%20neural%20network%20and%20genetic%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/24700/ https://doi.org/10.1145/3323716.3323737 |
| url_provider | http://umpir.ump.edu.my/ |