Improved predictions of alarm and safety system performance through process and operator response‐time modeling

Abstract

Dynamic risk analysis (DRA) has been used widely to analyze the performance of alarm and safety interlock systems of manufacturing processes. Because the most critical alarm and safety interlock systems are rarely activated, little or no data from these systems are often available to apply purely‐statistical DRA methods. Moskowitz et al. (2015)1 introduced a repeated‐simulation, process‐model‐based technique for constructing informed prior distributions, generating low‐variance posterior distributions for Bayesian analysis,1 and making alarm‐performance predictions. This article presents a method of quantifying process model quality, which impacts prior and posterior distributions used in Bayesian Analysis. The method uses higher‐frequency alarm and process data to select the most relevant constitutive equations and assumptions. New data‐based probabilistic models that describe important special‐cause event occurrences and operators’ response‐times are proposed and validated with industrial plant data. These models can be used to improve estimates of failure probabilities for alarm and safety interlock systems. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3461–3472, 2016

Multivariate alarm systems to recognize rare unpostulated abnormal events

“…58,59 In addition, an approach was proposed for improving process models and introducing new probabilistic models that describe special-cause event occurrences and operator response times, resulting in more accurate alarm and safety system failure probability estimates. 60,61 More recently, neural network-based integrated DRAn approaches have been proposed to capture the complex nonlinear relationship between the process variables. 62,63 Note that while conventional risk analyses are important in quantifying performance, the precursor information pointing toward abnormal conditions are often overlooked and underutilized, because they reside in alarm databases associated with distributed control systems and emergency shutdown systems.…”

“…Further work was conducted on DRAn, wherein, improved consequence assessment approaches using Bayesian‐based failure mechanisms was proposed and applied on the BP Texas Refinery accident 58,59 . In addition, an approach was proposed for improving process models and introducing new probabilistic models that describe special‐cause event occurrences and operator response times, resulting in more accurate alarm and safety system failure probability estimates 60,61 . More recently, neural network‐based integrated DRAn approaches have been proposed to capture the complex nonlinear relationship between the process variables 62,63 …”