A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities. (January 2018) - Liu, Jianfeng; Laird, Carl D.
Abstract: Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem that requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This paper was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center. Highlights: Optimal gas detector placement is formulated as an MINLP. Multi-tree, outer-approximation approach is used for efficient solution. Globally optimal placements can be solved with low computational effort.