Mary Kay O'Connor Process Safety Center

Texas A&M Engineering Experiment Station

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Dispersion Analysis

List of Center Publications

About

Dispersion analysis is an evaluation of the predicted outcome from an incident and how it affects the surrounding equipment and people. It is one of the main components of risk assessment and can be used to optimize plant layout, reduce the risk from an unacceptable level by improving design, develop an emergency preparedness plan, and assess the mitigation system. By using consequence models, consequence analysis includes the prediction of the magnitude of potential jet and pool fire, Boiling Liquid Expanding Vapor Explosion (BLEVE), vapor dispersion, toxic chemical release, and explosion caused by incidental release. There are four types of models:

  • Workbook or correlation models: These models are based on mathematical relations built to estimate the consequence of a scenario. Typically, these models are based on a set of experiments.
  • Integral models: These models include the solutions of basic differential equations; however, these models tend to idealize atmospheric conditions and assume a flat terrain. More DNV and Canary by Quest are very good examples of these models.
  • Shallow layer models: These models have characteristics of Computational Fluid Dynamics models and integral models.
  • Computational Fluid Dynamics (CFD) models: These models solve the Navier Stokes equations. These models give good representation of the final consequences in a scenario as it considers the inclusion of obstruction. However, it is very sensitive to the scenario set up and the user’s level of experience.

Currently, the center is validating the likelihood to obtain Deflagration-to-Detonation Transition (DDT) on different scenarios by using the CFD model FLACS. So far, the validation has been carried out with data found in the literature. Moreover, using the current facilities available at the Turbomachinery Laboratory experiments are being performed to provide data to validate this CFD model.

Additionally, studies for multi-component toxic mixtures such as formaldehyde-water-methanol have been performed in order to determine the possible consequences of accidental releases at different scenarios and to provide necessary data to design a facility layout and to plan an emergency response.

A simple example of information gained from a simple consequence analysis

A release from a 1.5 inch leak and of 20 lb/sec toxic and flammable chemical at atmospheric stability classes of B and wind speed of 2 m/s, could affect 0.5 square miles zone and injure 20 people. Once ignited, it will create a 3 meter diameter pool fire with thermal radiation of 5 kW/m2 (threshold for human injury) in a 10 m radius.

At the MKOPSC, various studies related to consequence analysis has been conducted over the years. Studies for multi-component toxic mixtures such as formaldehyde-water-methanol have been performed in order to determine the possible consequences of accidental releases at different scenarios and to provide necessary data to design a facility layout and to plan an emergency response.

Modeling of Silane Releases

Questions such as if a semiconductor company has a silane leak how do they know if it will ignite or not, what is the volume of the explosive mixture, how many silane is involved for delayed ignitions, what if a wall, cabinet, or gas cylinder is in the path of the leak, how do we measure these quantities leads us to more exploration in this area.

List of Center Publications

  1. Sun, Yue, et al. “Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks.” Industrial & Engineering Chemistry Research 59.1 (2019): 464-474. https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b05032
  2. Safitri, Anisa, Xiaodan Gao, and M. Sam Mannan. “Dispersion modeling approach for quantification of methane emission rates from natural gas fugitive leaks detected by infrared imaging technique.” Journal of Loss Prevention in the Process Industries 24.2 (2011): 138-145. https://www.sciencedirect.com/science/article/abs/pii/S0950423010001506
  3. Gopalaswami, N., K. Kakosimos, L.N. Vechot, T. Olewski and M.S. Mannan, “Analysis of Meteorological Parameters for Dense Gas Dispersion Using Mesoscale Models,” Journal of Loss Prevention in the Process Industries, vol. 35, May 2015, pp. 145-156. Link
  4. Hansen, O.R., S. Davis and M.S. Mannan, “Assessing the Credibility of Major Incidents During a Process Hazards Analysis,” Proceedings of the 13th Annual Mary Kay O’Connor Process Safety Center Symposium – Beyond Regulatory Compliance: Making Safety Second Nature, College Station, Texas, October 26-28, 2010, pp. 808-816. PDF
  5. Roberts, M., W.J. Rogers, M.S. Mannan, and S.W. Ostrowski, “Prevention and Suppression of Metal Packing Fires,” Proceedings of the 5th Annual Mary Kay O’Connor Process Safety Center Symposium, Beyond Regulatory Compliance: Making Safety Second Nature, College Station, Texas, October 29-30, 2002, pp. 123-131. PDF
  6. Sposato, C.F., W.J. Rogers and M.S. Mannan, “Effects of Obstacle Geometry on Jet Mixing for Releases of Silane,” Proceedings of the 3rd Annual Mary Kay O’Connor Process Safety Center Symposium, Beyond Regulatory Compliance: Making Safety Second Nature, College Station, Texas, October 24-25, 2000, pp. 500-533. PDF
  7. Ahammad, M., T. Olewski, L.N. Véchot and M.S. Mannan, “A CFD-Based Model to Predict Film Boiling Heat Transfer of Cryogenic Liquids,” Journal of Loss Prevention in the Process Industries, vol. 44, November 2016, pp. 247-254. Link
  8. Benavides-Serrano, A.J., M.S. Mannan and C.D. Laird, “Optimal Placement of Gas Detectors: A P-Median Formulation Considering Dynamic Nonuniform Unavailabilities,Link” AIChE Journal, Vol. 62, No. 8, August 2016, pp. 2728-2739. Link
  9. Benavides-Serrano, A.J., S.W. Legg, R. Vazquez-Roman, M.S. Mannan and C.D. Laird, “A Stochastic Programming Approach for the Optimal Placement of Gas Detectors: Unavailability and Voting Strategies,” Industrial and Engineering Chemistry Research, vol. 53, no. 13, 2014, pp. 5355–5365. Link
  10. Liu, R., A.R. Hasan and M.S. Mannan, “Flow Rate and Total Discharge Estimations in Gas-Well Blowouts,” Journal of Natural Gas Science and Engineering, vol. 26, September 2015, pp. 438-445. Link
  11. Safitri A., and M.S. Mannan, “Analysis of methane gas visualization using infrared imaging system and evaluation of temperature dependence gas emissivity,” Industrial and Engineering Chemistry Research, vol. 49, no. 8, 2010, pp. 3926-3935. PDF
Mary Kay O’Connor Process Safety Center
Room 200, Jack E. Brown Building
Texas A&M University, 3122 TAMU
College Station, TX 77843-3122
E-mail: [email protected]
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