Bayesian Network for Probability Risk Analysis of Biomass Boiler in Renewable Energy Plant

Abstract

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.