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008			260730t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010899 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKKP .A24 2025 r Bc.
100	0		_aAishah Umairah Binti Zuridi, , , , _eauthor.
245	1	0	_aBayesian decision making of palm oil biomass for power generation productivity and sustainability in Malaysia : _cAishah Umairah Binti Zuridi _benvironmental factor /
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axi, 59 pages : _billustrations ;
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Chemical and Process Engineering Technology
502			_aFinal Year Report (Bachelor of Technology Mechanical Engineering (Petroleum) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025
504			_aInclude bibliographical reference
520	3		_aThis research investigates the potential of palm oil biomass for sustainable power generation in Malaysia by developing a Bayesian decision-making framework. Recognizing the significant environmental challenges associated with biomass energy production, this study aims to provide a more robust and data-driven approach to decision-making. Traditional methods, such as FMEA, MCDA and CBA, have limitations in effectively dealing with the uncertainties and complex interdependencies inherent in environmental assessment. By leveraging the strength of Bayesian Networks, this research seeks to develop a framework that can integrate environmental factors, such as greenhouse gas (GHG) emissions, soil conditions and waste management considerations. This approach is in line with Malaysia's sustainability goals, contributing to a cleaner energy future and addressing the urgent need for sustainable waste management in the palm oil industry. The research explores the potential of palm oil biomass, including waste products such as Empty Fruit Bunches (EFB), as a renewable energy source while recognizing critical environmental factors such as GHG emissions, waste management, and land degradation. Furthermore, the research investigates the practical application of Bayesian Networks (BN) in specific contexts, such as evaluating productivity and sustainability for the environment in biomass power plants. By analyzing causal relationships between various factors, such as GHG emissions, waste management and soil conditions, BN can provide valuable insights to optimize productivity and sustainability. This research aims to provide a valuable tool for decision makers in the palm oil industry, enabling them to make more informed choices that balance environmental sustainability with economic viability and contribute to a more sustainable and resilient energy future for Malaysia.
610	2	0	_aFaculty of Chemical and Process Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
856			_uhttps://umpir.ump.edu.my/id/eprint/47874
942			_2lcc _cPSM
999			_c105355 _d105361