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020			_aTHE0009880 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aFTKKP .F37 2024 r Thesis
100	0		_aFares Ahmed Qaid Alaw, _eauthor.
245	1	0	_aDevelopment of risk assessment model for biopower plant boiler using bayesian network / _cFares Ahmed Qaid Alaw
264		1	_aKuantan, Pahang : _bUMPSA, _c2024
264		4	_c© 2024
300			_axii, 75 pages : _billustration ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Chemical and Process Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2024
504			_aIncludes bibliographical references
520	3		_aThe growing popularity of the biomass power generation process is connected to its label of green and cheap renewable energy. The enhancement of empty fruit bunch (EFB) is increasing following its usage for power generation; however, there are several serious risks and health and safety hazards related to EFB biopower boilers that cannot be ignored. Multiple steam boiler incidents are still being reported from different industries year by year. The consequences of boiler failure are very fatal and mostly leads to economic loss and lost of many engineers’ lives. According to data from reliable sources, it is understood that avoidable accidents do occur due to lack of knowledge of boiler hazards. A risk-based inspection serves as a pursuit of security and economy for unified system management ideas and techniques that can effectively estimate equipment risk and reduce operational cost. Typical risk assessment methods tend to have lack of uncertainty and time consuming. Thus, this study developed a condition and safety relationship framework of biomass power generation boilers using the Bayesian network (BN), which is an inexorably famous technique for demonstrating uncertain and complex systems compared with typical risk assessment methods. The conditional probability values were elicited to determine important factors that must be considered to mitigate boiler failure. A case study from FTJ Biogas Power Plant in Jengka, Pahang was applied. The main risk areas in biopower boilers are deaerator, economizer, fuel preparation, and water-cooling system were investigated. The software used for constructing the network are GeNIE 3.0 and Hugin Expert. The next step was to specify the states and to input values for a Conditional Probability Table (CPT) of each node using historical data and experts’ opinion. The prior probability result obtained from the proposed Bayesian Networks model indicates that oxygen attack with over 60% risk value causing 50% probability of boiler failure. The predictive and diagnosis analysis of the Bayesian network were performed to identify the casual links causing the boiler failure. From the diagnosis analysis, it was found that the oxygen attack node had the highest risk value with over 70% when the boiler failure probability is set at 100%. The result shows that the main factor affecting the boiler failure is oxygen attack from the dearator. This is in line with the research whereby the presence of oxygen in hot water is very crucial to be monitored cause high percentage of oxygen will lead to scale formation and corrosion in steam boilers. It may results in damaging the metal of the boiler and eventually boiler explosion if left overlooked. Lastly, Bayesian sensitivity analysis was performed, and it was found that the probability changes simultaneously with each change in the percentage of probability showing a reliable framework. Thus, it is believed that this framework may support decision-makers to locate certain risks based on quantitative and qualitative information that is used to build the network and decide when and where to take preventive or mitigate measures in the risk management process for biomass power plants.
610	2	0	_aFaculty of Chemical and Process Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS