470034 Using Resilience Principles for Prediction of Loss of Containment Events in Batch Operations

Thursday, November 17, 2016: 12:49 PM
Monterey I (Hotel Nikko San Francisco)
Prerna Jain1,2, Efstratios N. Pistikopoulos3 and Sam Mannan3, (1)Artie McFerrin Department of Chemical Engineering, Mary Kay O'Connor Process Safety Center, Texas A&M University, College Station, TX, (2)Energy Institute, Texas A&M University, College Station, TX, (3)Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX

Increasing process safety and risk management challenges in the process industry and change in the perception of public about hazards and risk globally have necessitated exploring tools for efficient risk management [3]. Two types of factors: 1) technical (e.g., equipment malfunction, process parameter variation), and 2) social (e.g., regulations/policy, human and organizational factors) are important in assessing and managing risk of a process system. This need calls for the development of a holistic and integrated systems framework for risk management. The application of the resilience engineering perspective is gradually being explored as an approach for considering the dynamics of socio-technical aspects based on systems theory [2,4]. The resilience methodology emphasizes non-linear dynamics, consideration of new types of threats, uncertainty, and recovery from upset or catastrophic situations [1].

This paper presents a novel framework for incorporating both technical and social factors in an integrated approach - Process Resilience Analysis Framework and its four aspects: Early Detection, Error Tolerant Design, Plasticity and Recoverability. A combined framework for predictability, survivability and recoverability dynamic analysis is introduced with resilience metrics [4,5]. This work establishes and presents typical scenarios of Loss of Containment (LoC) events and resilience metrics for batch plant operations. The paper concludes with a discussion of the proposed integrated methodology for prediction of LoC events based on a mathematical model that explicitly accounts for process variations and safety constraints [6]. A detailed case study is utilized to illustrate the key ideas and a design optimization approach to obtain safer operational region at maximum average profit.

References

[1] Hollnagel, E. (2006). Resilience: the challenge of the unstable.

[2] Hollnagel, E., Nemeth, C. P., & Dekker, S. (2008). Resilience Engineering Perspectives, Volume 1: Remaining sensitive to the possibility of failure.

[3] Steen, R., & Aven, T. (2011). A risk perspective suitable for resilience engineering. Safety science, 49(2), 292-297.

[4] Jackson, S. (2009). Architecting resilient systems: Accident avoidance and survival and recovery from disruptions (Vol. 66). John Wiley & Sons.

[5] Dinh, L. T., Pasman, H., Gao, X., & Mannan, M. S. (2012). Resilience engineering of industrial processes: principles and contributing factors. Journal of Loss Prevention in the Process Industries, 25(2), 233-241.

[6] Thomaidis, T. V., & Pistikopoulos, E. N. (1995). Optimal design of flexible and reliable process systems. Reliability, IEEE Transactions on, 44(2), 243-250.


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