A comprehensive risk analysis for cargo leakage pollution at tanker ship manifold under cloud modelling and Bayesian belief network approach

Abstract

Oil and chemical tankers play a vital role in global trade, but pose significant environmental risks from potential cargo spills. The manifold area, a critical connection point during loading and unloading operations, is particularly vulnerable to spillage incidents caused by equipment failure, improper hose handling and operator error. This paper assesses the pollution risks associated with cargo spills in the manifold section of tankers by identifying and analysing the key risk factors. A total of 15 risk factors contributing to cargo spillage are identified, including valve malfunction, hose deformation, incorrect gauge installation, inadequate hose support and vessel position shifts. The Cloud Model (CM) and Bayesian Belief Network (BBN) methods are used to quantify and assess these risks. The CM approach is used to deal with uncertainty in expert judgment, while the BBN is used to establish causal relationships between risk factors. Sensitivity analysis reveals that valve failure, hose deformation, incorrect gauge installation, inadequate hose support and vessel position shifts are the most critical contributors to leak incidents. The findings provide valuable insights into risk mitigation strategies and suggest safety measures to minimise pollution risks in tanker operations.