Introduction to Dynamic Risk Analyses

Seider, Warren D.; Pariyani, Ankur; Oktem, Ulku; Moskowitz, Ian H.; Arbogast, Jeffrey E.; Soroush, Masoud

doi:10.1016/bs.mcps.2017.02.001

Cited by 4 publications

(2 citation statements)

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“…PSM includes key components such as inherently safer designs, fault diagnosis and detection (FDD), RA, consequence modeling and evaluation, auditing, inspection, and abnormal situation management (ASM). 10 Over the past few decades, research contributions in the field of FDD have grown significantly as they play a key role in PSM. FDD refers to a set of techniques used to detect, identify, isolate, and analyze faults in chemical processes.…”

Section: Overview Of Fault Diagnosis and Ra Methods In Literaturementioning

confidence: 99%

“…The process safety management (PSM) standard, as defined by the US Occupational Safety and Health Administration, is crucial to chemical and process industries that aims to protect life and property by ensuring the safe and reliable operation of industrial processes. PSM includes key components such as inherently safer designs, fault diagnosis and detection (FDD), RA, consequence modeling and evaluation, auditing, inspection, and abnormal situation management (ASM) 10 . Over the past few decades, research contributions in the field of FDD have grown significantly as they play a key role in PSM.…”

Section: Preliminariesmentioning

confidence: 99%

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Multivariate alarm systems to recognize rare unpostulated abnormal events

Sudarshan,

Seider,

Patel

et al. 2023

AIChE Journal

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Most chemical and manufacturing plants have safety/reliability systems in place that are well equipped to handle commonly occurring postulated abnormal events, but often prove to be ineffective in predicting highly unanticipated and randomly occurring unpostulated abnormal events. In this paper, an advisory system is developed to analyze and recognize such events, consisting of novel, multivariate alarm systems and response actions introduced using process modeling and path‐sampling for unpostulated abnormal events. It augments existing safety/reliability systems, suggesting actions when unanticipated abnormal events are approached. Forward‐flux sampling (FFS), developed to discover rare molecular dynamics pathways, is applied. With an approximate process model, for an exothermic continuous‐stirred tank reactor using a perturbed feed concentration, the FFS algorithm is applied to identify rare trajectories between high‐conversion and low‐conversion steady‐states, with key process variables saved at various “crossing points.” Then, committer probabilities, , are computed at each crossing point, yielding a mathematical model that expresses as a function of the key process variables; that is, the reactor temperature (T), cooling‐water flow rate (), and cooling‐water temperature (), selected as choices for the primary alarm variables. For these, alarm thresholds; that is, L—low, LL—low low, and LLL—low low low, are suggested by computing their critical ranges, given the ranges for every alarm threshold.

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Section: Overview Of Fault Diagnosis and Ra Methods In Literaturementioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%