A comparative study of optimization models for the gas detector placement in process facilities

Abstract Gas detector network is an important layer of protection in process facilities for prevention gas leakage accidents. But traditional standards just provide basic principles for the installation of detectors. In this study, three stochastic programming (SP) models are developed and contrasted, namely minimal detection time P-Median model (MDTP), minimal leakage concentration P-Median model (MLCP), and minimal individual risk P-median model (MIRP). Meanwhile all possible leak scenarios are identified based on the combination of wind field set and leakage sources. And clustering analysis is used to filter similar scenarios and select representative leak scenarios. The leak consequences are predicted by computational fluid dynamics (CFD) method and the results are served as the input data of these SP models. A case study is carried out in a diesel hydrogenation unit.