Open AccessDevelopment of risk assessment model for biomass plant boiler using bayesian network
Author(s) -
Fares Ahmed Alaw,
Nurul Sa’aadah Sulaiman
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/991/1/012136
Subject(s) - boiler (water heating) , bayesian network , electricity , oil refinery , engineering , environmental science , waste management , process engineering , risk analysis (engineering) , environmental economics , computer science , business , economics , artificial intelligence , electrical engineering
Malaysia as the second-largest producer of crude palm oil has abundance of biomass residues from palm oil industries which can be converted to bio-chemicals to generate electricity. However, despite institutional arrangements of the biomass industry, there are several risks which may prone to reduce efficiency of biopower boiler especially empty fruit bunch as the fuel. Boiler is one of the primary equipment of power generation plants, in a significant role in converting biofuel to electricity. The main risk areas in biopower boiler are dearator, economizer, fuel preparation, and water cooling system. Available risk methodologies are not able to provide accurate results for a combination of risks. In this work, Bayesian network approach is introduced to determine and predict risk associated with biopower boiler. The predictive and diagnosis analyses of the Bayesian Network were performed to find the casual links which cause the failure and make a prediction of the control measures to reduce the rate of mistakes. Results revealed that dearator showed a significant effect when the system operates beyond the limits of its design. In conclusion, Bayesian Networks appear to be an assist for decision makers to decide when and where to take preventive or mitigate measures.