Modeling the causal mechanism in process safety management (PSM) systems from historical accidents

Abstract

Process safety management (PSM) underpins reducing major accidents in the process industries. For decades, PSM systems have been implemented globally. However, few studies have modeled the complexities within the PSM system, leading to some underlying chronic problems within organizations that go unnoticed. Here, we investigate causal mechanisms among PSM elements from historical accidents to deliver a practical tool for safety intervention formulation. Specifically, based on the Center for Chemical Process Safety's (CCPS) PSM framework, 100 chemical accident reports derived from the U.S. Chemical Safety and Hazard Investigation Board (CSB) were analyzed and converted into structured information stored in 22 PSM elements. Then, employing two causal structure learning algorithms, the causal structures among 22 elements were learned from the processed accident information. Finally, the causal effects among PSM elements were estimated by extending Rubin's counterfactual causal framework to an approach applicable to multi-treatment scenarios. We demonstrate that the causal structures and effects learned from accident reports reveal the causal mechanism within the PSM system. A total of 20 significant causal relationships among single elements, nine significant causal relationships among element combinations, and several interesting PSM failure paths were identified. According to these causal patterns, the PSM effort will shift from the traditional "what to do" to "how to do it."