Growing popularity of biomass power generation process is connected to its label of green and cheap renewable energy. As the second-largest producer of crude palm oil, Malaysia has abundance of biomass residues from palm oil industries and other renewable organic matters which can be converted to bio-chemicals to generate electricity. However, despite institutional arrangements, policy frameworks, funding mechanisms and incentives to support the growth of the biomass industry, there are several risks which may prone to reduce efficiency of biopower boiler especially empty fruit bunch as the fuels that cannot be ignored. Boiler is one of the primary equipment of power generation plants, in a significant role in converting biofuel to electricity. With increasing numbers of potentials of biomass as raw materials for renewable energy uses, new risks may be found. Yet there has been very little research into these risks and how to prevent them. Lack of understanding of modern risk identification methods, such as HAZOP, with the biopower industry is one of the reasons for the industry’s slow growth. An industry evolves through life cycle stages and at each stage presents risk factors such as overheating, oxygen corrosion and clinker. This paper identifies several key risks associated with EFB fired biopower boiler in Malaysia. The overview of risks not only provide a perspective from which an industry’s viability can be evaluated but also help the operators to better understand key risks and improve boiler capacity as well as plan their risk mitigation strategies more effectively.

The third party damage also known as external factor that contributed to pipeline failure was an unexpected event that considered as threats to human safety. In this work, two risk assessment approaches namely qualitative and quantitative were adopted to predict and measure the causes of offshore pipeline failure due to the third party damages. Qualitative analysis involving causal and consequences of an event was developed using Bow-tie (BT) model to determine the contributing parameter of an event. The parameters obtained were then converted to Bayesian Network (BN) for a quantitative approach. Based on the qualitative analysis, the major threats identified in third party damage were categorized into “anchor impact” and “impact” meanwhile the consequences were human safety, marine life ecosystem and economical loss. Statistical data from PARLOC 2012 were utilized for BN model and transformed into conditional probability table (CPTs). the results generated indicate that the major contribution to pipeline damage was “trawling”. The utilization of Bow-tie and BN analysis may complement the risk analysis of offshore pipeline due to third party damage for more informed pipeline maintenance decisions.

The utilization of Empty fruit bunch (EFB) in energy production has increased in Malaysia over the last two decades. The EFB can be used as a solid fuel in a boiler system for heat and power generation. However, numerous safety and technical issues lead to a lower energy production rate. A holistic probabilistic risk analysis is developed using the Bayesian Belief Network (BBN) to reduce the risk in the boiler system. The Conditional Probability Table (CPT) indicates the influence strength between the parent node and child node in BBN. Due to scarcely available information on EFB boiler, elicitation from the expert’s opinion is vital. The formulation for boiler failures likelihood prediction that relies on experts’ perceptions was developed using the Weighted Sum Algorithm (WSA). A case study from BioPower Plant in Pahang was applied in this project. The model illustrates the relationship between the cause and the effect of the biomass boiler efficiency in a systematic way. Two types of analyses, prediction and diagnostic analysis, were performed. The results facilitated the decision-maker to predict and identify the influential underlying factors of the boiler efficiency, respectively. The result shows that the most important boiler failure factor is combustion stability. It agrees with experts’ experience that most biomass boiler failure was caused by EFB, which contains high moisture content that affects flame stability. The proposed formulation for expert opinions and perceptions conversion can be utilized for risk analysis to benefit the boiler and other infrastructure that relies on experts’ experience.

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.