Bayesian network for probability risk analysis of biomass boiler in renewable energy plant
The empty fruit bunches have remarkable potential for utilisation as solid fuel boilers in the production of energy. A well operated boiler with higher efficiency is vital for a good power generation plant. However, there are numerous safety and technical issues that may lead to a lower energy produ...
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| Format: | Conference or Workshop Item |
| Language: | English |
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EDP Sciences
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42338/ http://umpir.ump.edu.my/id/eprint/42338/1/Bayesian%20network%20for%20probability%20risk%20analysis%20of%20biomass.pdf |
| _version_ | 1848826584597790720 |
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| author | Nurul Ain Syuhadah, Mohammad Khorri Nurul Sa'aadah, Sulaiman |
| author_facet | Nurul Ain Syuhadah, Mohammad Khorri Nurul Sa'aadah, Sulaiman |
| author_sort | Nurul Ain Syuhadah, Mohammad Khorri |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The empty fruit bunches have remarkable potential for utilisation as solid fuel boilers in the production of energy. A well operated boiler with higher efficiency is vital for a good power generation plant. However, there are numerous safety and technical issues that may lead to a lower energy production rate. A simple yet complete probabilistic risk analysis is needed to predict those issues to ensure the biomass boiler operates at its maximum efficiency. In this work, a probabilistic risk assessment model for empty fruit bunch boiler using Bayesian network approach was developed. Bayesian network provides a clear probabilistic model of cause-effect relationships of the biomass boiler system. The conditional probability values were elicitated from experts' opinion to identify the most influential factors for efficient biomass boiler operation. A case study from Renewable Energy Plant in Pahang was applied. Prediction analysis and diagnostic analysis were performed and the results show that the most important biomass boiler failure factors are corrosion and overheating. These findings are in agreement with existing literature and expert judgement. Thus, the proposed model is useful in maintaining and helping the decision maker for biomass boiler operation as well as increasing its reliability. |
| first_indexed | 2025-11-15T03:47:09Z |
| format | Conference or Workshop Item |
| id | ump-42338 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:47:09Z |
| publishDate | 2021 |
| publisher | EDP Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-423382024-10-30T04:31:44Z http://umpir.ump.edu.my/id/eprint/42338/ Bayesian network for probability risk analysis of biomass boiler in renewable energy plant Nurul Ain Syuhadah, Mohammad Khorri Nurul Sa'aadah, Sulaiman QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology The empty fruit bunches have remarkable potential for utilisation as solid fuel boilers in the production of energy. A well operated boiler with higher efficiency is vital for a good power generation plant. However, there are numerous safety and technical issues that may lead to a lower energy production rate. A simple yet complete probabilistic risk analysis is needed to predict those issues to ensure the biomass boiler operates at its maximum efficiency. In this work, a probabilistic risk assessment model for empty fruit bunch boiler using Bayesian network approach was developed. Bayesian network provides a clear probabilistic model of cause-effect relationships of the biomass boiler system. The conditional probability values were elicitated from experts' opinion to identify the most influential factors for efficient biomass boiler operation. A case study from Renewable Energy Plant in Pahang was applied. Prediction analysis and diagnostic analysis were performed and the results show that the most important biomass boiler failure factors are corrosion and overheating. These findings are in agreement with existing literature and expert judgement. Thus, the proposed model is useful in maintaining and helping the decision maker for biomass boiler operation as well as increasing its reliability. EDP Sciences 2021-07-06 Conference or Workshop Item PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42338/1/Bayesian%20network%20for%20probability%20risk%20analysis%20of%20biomass.pdf Nurul Ain Syuhadah, Mohammad Khorri and Nurul Sa'aadah, Sulaiman (2021) Bayesian network for probability risk analysis of biomass boiler in renewable energy plant. In: E3S Web of Conferences. 2021 International Conference on Process Engineering and Advanced Materials, ICPEAM2020 , 13 - 15 July 2021 , Kuching. pp. 1-5., 287 (03008). ISSN 2555-0403 (Published) https://doi.org/10.1051/e3sconf/202128703008 |
| spellingShingle | QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Nurul Ain Syuhadah, Mohammad Khorri Nurul Sa'aadah, Sulaiman Bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| title | Bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| title_full | Bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| title_fullStr | Bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| title_full_unstemmed | Bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| title_short | Bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| title_sort | bayesian network for probability risk analysis of biomass boiler in renewable energy plant |
| topic | QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/42338/ http://umpir.ump.edu.my/id/eprint/42338/ http://umpir.ump.edu.my/id/eprint/42338/1/Bayesian%20network%20for%20probability%20risk%20analysis%20of%20biomass.pdf |