E3S Web Conf.
Volume 287, 2021International Conference on Process Engineering and Advanced Materials 2020 (ICPEAM2020)
|Number of page(s)||5|
|Section||Process Systems Engineering & Optimization|
|Published online||06 July 2021|
Bayesian Network for Probability Risk Analysis of Biomass Boiler in Renewable Energy Plant
1 College of Engineering, Department of Chemical Engineering, Universiti Malaysia Pahang, 26300, Gambang, Pahang, Malaysia
2 Faculty of Chemical and Process Engineering Technology, Department of Engineering Technology, Universiti Malaysia Pahang, 26300, Gambang, Pahang, Malaysia
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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