Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation. (May 2020)

• Record Type: Journal Article
• Authors: Sun, Lin; Chen, Xi; Zhang, Bo; Mu, Chao; Zhou, Cancan
• Is Part Of: Journal of loss prevention in the process industries. Volume 65(2020)
• Publication Date: 2020-05
• Subjects: Gas detection -- Leak scenario probability -- Detector reliability -- Markov model -- Computational fluid dynamics -- Stochastic programming; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2020.104131
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting. Highlights: A stochastic programming approach for the optimal placement of gas detectors is proposed. Complete accident scenario set (CASS) is built to calculate the scenario probability. Detector reliability is predicted by the Markov model and considered in optimization formulation. Two adaptive strategies are developed to address undetected scenarios due to failure detectors.