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Arbogast J.E.,Delaware Research and Technology CenterAmerican Air Liquide200 wark | Oktem U.G.,Risk Management and Decision Processes Center
AIChE Journal | Year: 2016

A method of designing model-predictive safety systems that can detect operation hazards proactively is presented. Such a proactive safety system has two major components: a set of operability constraints and a robust state estimator. The safety system triggers alarm(s) in real time when the process is unable to satisfy an operability constraint over a receding time-horizon into the future. In other words, the system uses a process model to project the process operability status and to generate alarm signals indicating the presence of a present or future operation hazard. Unlike typical existing safety systems, it systematically accounts for nonlinearities and interactions among process variables to generate alarm signals; it provides alarm signals tied to unmeasurable, but detectable, state variables; and it generates alarm signals before an actual operation hazard occurs. The application and performance of the method are shown using a polymerization reactor example. © 2016 American Institute of Chemical Engineers. Source

Pariyani A.,University of Pennsylvania | Oktem U.G.,Risk Management and Decision Processes Center | Seider W.D.,University of Pennsylvania
AIChE Annual Meeting, Conference Proceedings | Year: 2010

A new methodology involving near-miss utilization and management for chemical plants is introduced to help identify an escalation in the probability of the occurrence of incidents - permitting the system to alert the operator(s) of a potential major problem likely to occur in the near future. Also, the system can detect the onset and/or presence of inherent faults, or specialcauses, likely to lead eventually to incidents. Using this new methodology, the operators can be alerted up to several hours before potential undesirable events are likely to occur. Thereafter, as the special-causes progress, with the potential for trips and possibly accidents increasing, the frequency of alerts increases. To demonstrate this technique, graphs of the dynamic frequency of abnormal events for the constituent (high-, medium-, and low-priority) variables associated with a fluid-catalytic-cracking unit (FCCU) at a major petroleum refinery, are presented. Then, indicators are identified that would have projected trips that occurred during the selected time period. Source

Ahooyi T.M.,Drexel University | Soroush M.,Drexel University | Arbogast J.E.,Air Liquide | Seider W.D.,Drexel University | Oktem U.G.,Risk Management and Decision Processes Center
AIChE Journal | Year: 2014

This work addresses the problem of estimating complete probability density functions (PDFs) from historical process data that are incomplete (lack information on rare events), in the framework of Bayesian networks. In particular, this article presents a method of estimating the probabilities of events for which historical process data have no record. The rare-event prediction problem becomes more difficult and interesting, when an accurate first-principles model of the process is not available. To address this problem, a novel method of estimating complete multivariate PDFs is proposed. This method uses the maximum entropy and maximum likelihood principles. It is tested on mathematical and process examples, and the application and satisfactory performance of the method in risk assessment and fault detection are shown. Also, the proposed method is compared with a few copula methods and a nonparametric kernel method, in terms of performance, flexibility, interpretability, and rate of convergence. © 2014 American Institute of Chemical Engineers. Source

Ahooyi T.M.,Drexel University | Arbogast J.E.,Air Liquide | Oktem U.G.,Risk Management and Decision Processes Center | Seider W.D.,University of Pennsylvania | Soroush M.,Drexel University
Industrial and Engineering Chemistry Research | Year: 2014

This paper presents a method of estimating discrete multivariate probability distributions from scarce historical data. Of particular interest is the estimation of the probabilities of rare events. The method is based on maximizing the information entropy subject to equality constraints on the moments of the estimated probability distributions. Two criteria are proposed for optimal selections of the moment functions. The method models nonlinear and nonmonotonic relations with an optimal level of model complexity. Not only does it allow for the estimation of the probabilities of rare events, but, together with Bayesian networks, it also provides a framework to model fault propagation in complex highly interactive systems. An application of this work is in risk assessment and fault detection using Bayesian networks, especially when an accurate first-principles model is not available. The performance of the method is shown through an example. © 2014 American Chemical Society. Source

Kleindorfer P.,INSEAD | Oktem U.G.,Risk Management and Decision Processes Center | Oktem U.G.,Near Miss Management LLC | Pariyani A.,Near Miss Management LLC | Seider W.D.,University of Pennsylvania
Computers and Chemical Engineering | Year: 2012

This paper describes the potential contribution of near-miss management systems to improving company profitability and reducing the frequency and severity of major industrial accidents. The near-miss concept has long been understood in different industries, as examples in this paper illustrate. However, what has been largely missing is the integration of near-miss management into the culture and day to day operations in a manner that underlines the critical connections between near-misses and behavior. Often, near-miss management has played an ex post forensic role in risk management rather than an alerting one, summarizing leading indicators and precursors of hazardous conditions. This paper describes several strands of recent research that aim to correct this and to make near-miss management an organic element of Enterprise Risk Management. In this respect, a new concept, "potential safety profit loss", is introduced to calculate the potential monetary losses due to unexpected shutdowns and accidents. © 2012 Elsevier Ltd. Source

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