A stochastic programming approach for gas detector placement using CFD-based dispersion simulations

A quantitative assessment on the placement practices of gas detectors in the process industries

“…In order to determine an optimal gas detector placement while rigorously considering the amount of information and uncertainties mentioned above, the use of a stochastic programming formulation (SP), was initially proposed, developed and validated by Legg et al (2012aLegg et al ( , b, 2013. Results demonstrated the potential and suitability of numerical optimization to approach the gas detector placement problem.…”

“…The acknowledgment of this fact by the industry has increased interest regarding the opportunities provided by formal quantitative approaches supplemented by dispersion simulations (IEC, 2007;NORSOK, 2008;ISA, 2010). More recently, the use of stochastic programming formulations was proposed, developed and validated by Legg et al (2012aLegg et al ( , b, 2013 and Benavides-Serrano et al (2014) in order to take further advantage of the quantitative information provided by dispersion simulations. These formulations identify the gas detector layout that minimizes the expected value of an overall damage coefficient (i.e., the minimization of a risk metric) given a set of dispersion scenarios.…”

Optimal gas detector placement under uncertainty considering Conditional-Value-at-Risk

“…The mixed-integer linear programming formulation for the placement of gas detectors, presented in this paper as problem (SP), was initially described in Legg et al (2012). All key notation found in this paper is summarized in …”

“…Therefore, CFD software is needed to generate rigorous dispersion simulations for the wide range of process conditions, leak locations, and weather conditions possible. Legg et al (2012) presented a stochastic programming formulation that addressed the key concerns outlined for gas detector placement, and this formulation is extended in this paper. Rigorous simulation of gas dispersion using FLACS provided hundreds of scenarios across different leak locations and varying weather conditions.…”

Optimal spatial formation of swarm robotic gas sensors in odor plume finding

“…Legg et al (2012) presented a method that utilizes computational fluid dynamics (CFD) simulations to optimize gas sensor locations in order to maximize the likelihood of early detection of gas clouds in specific facilities. Miyata and Mori (2011) introduced another procedure for optimization of gas detector locations by using gas dispersion simulation tools in specific chemical plants.…”