Stochastic type-2 fuzzy modelling on GHG emission prediction for gas-fired power plant
This paper demonstrates the implementation of type-2 fuzzy logic model in prediction of greenhouse gas emission for gas fuelled power plant. The data collection and analysis on the GHG emission had been conducted with emphasis on the industrial scale with many uncertainty factors. The develope...
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| Format: | Other |
| Language: | en |
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IOP
2021
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| Online Access: | http://eprints.uthm.edu.my/6656/1/P13698_84aca01dc338961b7687e158a601e75f.pdf http://eprints.uthm.edu.my/6656/ https://doi.org/10.1088/1742-6596/2053/1/012020 |
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| author | Zakaria, Sazalina R. Ahmad, Radin Diana Abbas, Ahmad Rosly Mohideen Batcha, Mohd Faizal Zanil, Mohd Fauzi |
| author_facet | Zakaria, Sazalina R. Ahmad, Radin Diana Abbas, Ahmad Rosly Mohideen Batcha, Mohd Faizal Zanil, Mohd Fauzi |
| author_sort | Zakaria, Sazalina |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper demonstrates the implementation of type-2 fuzzy logic model in
prediction of greenhouse gas emission for gas fuelled power plant. The data collection and
analysis on the GHG emission had been conducted with emphasis on the industrial scale with
many uncertainty factors. The developed model will be used as a prediction tools to evaluate
potential GHG emission for future study and mitigation planning. In this work, 100 sample
sizes had been used as training dataset in the model framework which serve as the input
information while 14 sample sizes are used for model validation to achieve model
generalization. A novel Karnik-Mendel (KM) algorithm had been proposed with the Genetic
Algorithm (GA) model optimization to achieve high performance and accuracy for the
respective predictive model. In conclusion, the result showed that the model able to give an
outstanding accuracy. On the validation analysis, the results showed developed model able to
give satisfactory prediction with R2 of 0.978 and 0.84968 for training and validation,
respectively. |
| format | Other |
| id | my.uthm.eprints-6656 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2021 |
| publisher | IOP |
| record_format | eprints |
| spelling | my.uthm.eprints-66562022-03-14T01:34:34Z http://eprints.uthm.edu.my/6656/ Stochastic type-2 fuzzy modelling on GHG emission prediction for gas-fired power plant Zakaria, Sazalina R. Ahmad, Radin Diana Abbas, Ahmad Rosly Mohideen Batcha, Mohd Faizal Zanil, Mohd Fauzi TJ Mechanical engineering and machinery This paper demonstrates the implementation of type-2 fuzzy logic model in prediction of greenhouse gas emission for gas fuelled power plant. The data collection and analysis on the GHG emission had been conducted with emphasis on the industrial scale with many uncertainty factors. The developed model will be used as a prediction tools to evaluate potential GHG emission for future study and mitigation planning. In this work, 100 sample sizes had been used as training dataset in the model framework which serve as the input information while 14 sample sizes are used for model validation to achieve model generalization. A novel Karnik-Mendel (KM) algorithm had been proposed with the Genetic Algorithm (GA) model optimization to achieve high performance and accuracy for the respective predictive model. In conclusion, the result showed that the model able to give an outstanding accuracy. On the validation analysis, the results showed developed model able to give satisfactory prediction with R2 of 0.978 and 0.84968 for training and validation, respectively. IOP 2021 Other NonPeerReviewed text en http://eprints.uthm.edu.my/6656/1/P13698_84aca01dc338961b7687e158a601e75f.pdf Zakaria, Sazalina and R. Ahmad, Radin Diana and Abbas, Ahmad Rosly and Mohideen Batcha, Mohd Faizal and Zanil, Mohd Fauzi (2021) Stochastic type-2 fuzzy modelling on GHG emission prediction for gas-fired power plant. IOP. https://doi.org/10.1088/1742-6596/2053/1/012020 |
| spellingShingle | TJ Mechanical engineering and machinery Zakaria, Sazalina R. Ahmad, Radin Diana Abbas, Ahmad Rosly Mohideen Batcha, Mohd Faizal Zanil, Mohd Fauzi Stochastic type-2 fuzzy modelling on GHG emission prediction for gas-fired power plant |
| title | Stochastic type-2 fuzzy modelling on GHG emission
prediction for gas-fired power plant |
| title_full | Stochastic type-2 fuzzy modelling on GHG emission
prediction for gas-fired power plant |
| title_fullStr | Stochastic type-2 fuzzy modelling on GHG emission
prediction for gas-fired power plant |
| title_full_unstemmed | Stochastic type-2 fuzzy modelling on GHG emission
prediction for gas-fired power plant |
| title_short | Stochastic type-2 fuzzy modelling on GHG emission
prediction for gas-fired power plant |
| title_sort | stochastic type-2 fuzzy modelling on ghg emission
prediction for gas-fired power plant |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.uthm.edu.my/6656/1/P13698_84aca01dc338961b7687e158a601e75f.pdf http://eprints.uthm.edu.my/6656/ https://doi.org/10.1088/1742-6596/2053/1/012020 |
| url_provider | http://eprints.uthm.edu.my/ |