Normal view MARC view ISBD view

Bayesian decision making of palm oil biomass for power generation productivity and sustainability in Malaysia : Aishah Umairah Binti Zuridi environmental factor /

By:

Aishah Umairah Binti Zuridi, , , [author.]

Material type: Text

text

Media type:

unmediated

Carrier type:

volume

ISBN:

THE0010899 (Local)

Subject(s):

Online resources:

Click here to access online

Dissertation note: Final Year Report (Bachelor of Technology Mechanical Engineering (Petroleum) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025 Abstract: This 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings ( 1 )
Title notes ( 4 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	FTKKP .A24 2025 r Bc. (Browse shelf(Opens below))	Not for loan		T000004408

Faculty of Chemical and Process Engineering Technology

Final Year Report (Bachelor of Technology Mechanical Engineering (Petroleum) with Hons ) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025

Include bibliographical reference

This 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.