A Stochastic Programming Approach for Gas Detector Placement in Process Facilities

Given hundreds of potential gas detector locations, a stochastic programming formulation is developed for determining the optimal placement of these sensors for detecting gas release events in petrochemical facilities. Using a rigorous dispersion model with actual geometry from the process facility, hundreds of different scenarios are simulated using FLACS with different leak locations, process conditions, and weather properties. Pyomo, a python-based optimization package, is used to formulate the multi-scenario, mixed-integer programming problem. Using CPLEX to solve the formulations, different objective functions are explored. Optimal results are presented for the minimum number of sensors required to detect all scenarios, the minimum expected time to detect events using a fixed number of sensors, and a robust formulation that minimizes the maximum time to detection across all scenarios. In all these examples, the formulation can be solved efficiently for real, large-scale problems.