(700b) A Mixed-Integer Programming Framework for Combined Placement of Fire and Gas Detectors in Chemical Processing Facilities
AIChE Annual Meeting
Thursday, November 1, 2018 - 3:49pm to 4:08pm
The optimal gas detector placement problem was formulated by  as a stochastic program based on the p-median facility location problem and relied on scenarios generated from CFD simulations within a petrochemical facility. Their results showed that coupling coverage with expected detection time using a scenario-based method outperformed the existing coverage-only approaches, and has inspired formulations based on Conditional-Value-at-Risk , minimization of false positives and false negative alarms with voting schemes , maximum risk reduction of scenarios , and MINLP approaches for non-uniform failure probabilities .
But while gas detectors must be placed in the path of gas leaks for effective detection, fire detectors must be placed within unobstructed visual distance of flames. The optimal placement of visual fire detectors in processing plants can be particularly difficult due to the large number of physical obstructions (e.g. piping, valves, tanks). Most relevantly,  has proposed a basic formulation for placing visual fire detectors on the walls of a room with cylindrical objects, weighted by pre-determined hazard levels.
This work focuses on a mixed-integer optimization formulation that integrates placement of both fire (visual) and gas (point) detectors. The scenario-based, p-median approach demonstrated by  for gas detectors is combined with a maximum coverage model for fire detectors in a 3D space within a multi-objective framework. We show that this approach allows for the simultaneous placement of both sensor types for a fixed sensor budget while under uncertainty. We demonstrate the effectiveness of this formulation with fire and gas leak simulations from a chemical facility. We further discuss how the framework is readily scalable for larger problem sizes and amenable to various objectives.
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