Accident modelling and analysis in process industries Ali Al-shanini a, b , Arshad Ahmad a, b, * , Faisal Khan c a Institute of Hydrogen Economy, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia b Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia c Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada article info Article history: Received 25 July 2014 Accepted 30 September 2014 Available online 5 October 2014 Keywords: Accident modelling Dynamic sequential accident models Dynamic risk assessment Precursor data abstract Accident modelling is a methodology used to relate the causes and effects of events that lead to acci- dents. This modelling effectively seeks to answer two main questions: (i) Why does an accident occur, and (ii) How does it occur. This paper presents a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs). DSAMs are sequential models with a systematic procedure to utilise precursor data to estimate the posterior risk profile quantitatively. DSAM also offers updates on the failure prob- abilities of accident barriers and the prediction of future end states. Following a close scrutiny of these methodologies, several limitations are noted and discussed, and based on these insights, future work is suggested to enhance and improve this category of models further. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction The chemical process industry (CPI) is a highly complex system with diverse equipment, control schemes and operating pro- cedures. It is also common for plants in this industry to utilise a variety of hazardous materials as raw materials and/or products. The interactions among these components, human factors, and management and organisational (M&O) issues make CPI suscepti- ble to process deviations, which, in turn, may lead to failures if not properly managed (Khan and Abbasi, 1998c, Papazoglou et al., 1992). As illustrated by Fig. 1 , when process failures occur, some may be recovered from, while others escalate into minor or major accidents and losses. To maintain the plant economy at desired levels, process plants are often equipped with a comprehensive process control system to ensure smoothness of operation and to prevent accidents. The system provides protection through varying degrees of automation, facilitated by human intervention and shielded by additional layers of protection as mitigating measures should the system fail. Nevertheless, despite all these measures, accidents still continue to happen. Examples of recent accidents in the CPI, along with some key information, are shown in Table 1 . An efficient means of combating accidents is to formulate suit- able preventive measures targeting the right plant components. However, this is difficult to realise unless accidents can be antici- pated and are thoroughly understood, such that the failed component can be identified prior to the occurrence of an accident. Such efforts fall within the realm of accident modelling, which relates the causes and effects of events that lead to accidents. Effectively, accident modelling seeks to answer two main ques- tions: (i) why does an accident occur, and (ii) how does it occur. The development of these methodologies can be traced back to 1941, when Heinrich introduced the domino theory (Qureshi, 2007). Accident models can be classified in many ways. Qureshi (2007) has proposed a reasonably comprehensive classification by dividing the models into two broad categories, i.e., traditional and modern: the traditional approach is further categorised into sequential (SAMs) and epidemiological (EAMs), while the modern approach includes systematic (SyAMs) and formal (FAMs). This classification can be further extended by introducing a third category within the modern approach, called the dynamic sequential accident model (DSAM) (see Fig. 2). DSAM is a precursor-based technique that in- cludes two modelling schemes: (i) process hazard prevention ac- cident models (Kujath et al., 2010; Rathnayaka et al., 2011a); and (ii) dynamic risk assessment (DRA) models. Some of the most common accident models based on this categorisation are shown in Fig. 2. The accuracy, capability, and limitation of accident models vary significantly, depending on their purpose and focus (Rathnayaka et al., 2011a). Brief descriptions of these AMs (except the DSAMs because they will be extensively reviewed in this article), as well as their limitations regarding their use in the CPI, are summarised in Table 2. One major problem with these models is that they are * Corresponding author. Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia. E-mail address: arshad@cheme.utm.my (A. Ahmad). Contents lists available at ScienceDirect Journal of Loss Prevention in the Process Industries journal homepage: www.elsevier.com/locate/jlp http://dx.doi.org/10.1016/j.jlp.2014.09.016 0950-4230/© 2014 Elsevier Ltd. All rights reserved. Journal of Loss Prevention in the Process Industries 32 (2014) 319e334