Bayesian networks based laboratory retrofitting towards inherent safety: A risk-based implementation framework

Abstract

Over the years, a number of high-profile laboratory accidents involving severe injuries, fatalities, and economic losses have been reported, prompting a significant increase in efforts towards laboratory safety. However, the dominant safety measures rely excessively on add-on safeguards such as sprinklers and respirators and pay little attention to reducing the hazardous factors at their sources. This study introduced the inherent safety concept to minimize laboratory hazards and developed a dedicated implementation tool called Generic Laboratory Safety Metric (GLSM). The Traditional Laboratory Safety Checklist (TLSC) was first used to represent the safety indicators, and then the Precedence Chart (PC) and Bayesian Networks (BN) methods were used to reconcile the safety indicators to develop the GLSM. The developed GLSM was subsequently demonstrated through a case study of a university laboratory. The results revealed that the safety level increased from 2.44 to 3.52 after the risk-based inherently safer retrofitting, thus creating laboratory conditions with a relatively satisfactory safety level. This work presented a set of generic solutions to laboratory retrofitting towards inherent safety with a novel GLSM as the implementation tool. The proposed GLSM would contribute to risk quantification and identification of key risk factors for assigning targeted and fundamental safety measures to achieve inherently safer laboratories.