Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production
In a polyhydroxyalkanoates (PHA) production, optimized fermentation process helps in reducing overall cost by increasing productivity. Dissolved oxygen (DO) concentration influences growth rate which in turn affect the PHA production rate. Data driven technique using artificial neural network (ANN)...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/69133/ http://psasir.upm.edu.my/id/eprint/69133/1/Optimization%20of%20neural%20network%20architecture%20using%20particle%20swarm%20algorithm%20for%20dissolved%20oxygen%20modelling%20in%20a%20200L%20bioreactor%20PHA%20production.pdf |
| _version_ | 1848856320975831040 |
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| author | Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak |
| author_facet | Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak |
| author_sort | Mamat, Nor Hana |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In a polyhydroxyalkanoates (PHA) production, optimized fermentation process helps in reducing overall cost by increasing productivity. Dissolved oxygen (DO) concentration influences growth rate which in turn affect the PHA production rate. Data driven technique using artificial neural network (ANN) is beneficial as process data based on real conditions are used. In this paper, we propose the use of particle swarm optimization (PSO) method in artificial neural network (ANN) model to determine the optimal number of neurons in hidden layer for modelling dissolved oxygen (DO) concentration in PHA fermentation process. The neural network is modelled using real production data from a pilot scale 200L fed-batch bioreactor. A comparison between the proposed ANN-PSO and ANN is provided. Simulation result shows that ANN-PSO eliminates the need for time consuming repeated runs and able to obtain similar number of optimal hidden neuron with improved model accuracy. |
| first_indexed | 2025-11-15T11:39:47Z |
| format | Conference or Workshop Item |
| id | upm-69133 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:39:47Z |
| publishDate | 2018 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-691332020-05-20T03:14:47Z http://psasir.upm.edu.my/id/eprint/69133/ Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak In a polyhydroxyalkanoates (PHA) production, optimized fermentation process helps in reducing overall cost by increasing productivity. Dissolved oxygen (DO) concentration influences growth rate which in turn affect the PHA production rate. Data driven technique using artificial neural network (ANN) is beneficial as process data based on real conditions are used. In this paper, we propose the use of particle swarm optimization (PSO) method in artificial neural network (ANN) model to determine the optimal number of neurons in hidden layer for modelling dissolved oxygen (DO) concentration in PHA fermentation process. The neural network is modelled using real production data from a pilot scale 200L fed-batch bioreactor. A comparison between the proposed ANN-PSO and ANN is provided. Simulation result shows that ANN-PSO eliminates the need for time consuming repeated runs and able to obtain similar number of optimal hidden neuron with improved model accuracy. IEEE 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69133/1/Optimization%20of%20neural%20network%20architecture%20using%20particle%20swarm%20algorithm%20for%20dissolved%20oxygen%20modelling%20in%20a%20200L%20bioreactor%20PHA%20production.pdf Mamat, Nor Hana and Mohd Noor, Samsul Bahari and Che Soh, Azura and Ab Rashid, Ahmad Hazri and Jufika Ahmad, Nur Liyana and Mohd Yusuff, Ishak (2018) Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production. In: 2018 IEEE 16th Student Conference on Research and Development (SCOReD), 26-28 Nov. 2018, Selangor, Malaysia. . 10.1109/SCORED.2018.8711233 |
| spellingShingle | Mamat, Nor Hana Mohd Noor, Samsul Bahari Che Soh, Azura Ab Rashid, Ahmad Hazri Jufika Ahmad, Nur Liyana Mohd Yusuff, Ishak Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production |
| title | Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production |
| title_full | Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production |
| title_fullStr | Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production |
| title_full_unstemmed | Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production |
| title_short | Optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200L bioreactor PHA production |
| title_sort | optimization of neural network architecture using particle swarm algorithm for dissolved oxygen modelling in a 200l bioreactor pha production |
| url | http://psasir.upm.edu.my/id/eprint/69133/ http://psasir.upm.edu.my/id/eprint/69133/ http://psasir.upm.edu.my/id/eprint/69133/1/Optimization%20of%20neural%20network%20architecture%20using%20particle%20swarm%20algorithm%20for%20dissolved%20oxygen%20modelling%20in%20a%20200L%20bioreactor%20PHA%20production.pdf |