Genetic Algorithm for Sudden Contaminant Source Identification in Ventilation System

Abstract:

This paper illustrated a source identification method in air duct system, aiming at detecting contaminant source within a short period of time after a bioterrorist attack. The method was based on genetic algorithm (GA) with minimal difference between calculated concentration and measured concentration as fitness function. We established a database of calculated concentration of sensors considering different releasing scenarios. Then we discussed the impact of the number of sensors, the location and measurement time of sensors, the capability of sensors, and the distance between nodes on the overall average relative error of inversion results (ξ). Results of a case study showed that the ξ decreased as more sensors were set in the ventilation system. The optimized number of sensors in this case was supposed three, considering both the decrement provided by each sensor and the high cost of each sensor. Meanwhile, the convergence generations were few while the convergence time was short. Then, the impact of sensor location and detection time on the ξ was coupled. The inversed source location x0 is sensitive to the time interval of feedback data but nonsensitive to the detecting error of sensors. Finally, there existed an appropriate number of nodes distance in air duct system, which gave consideration to lower calculated load and global optimization.

Key words:ventilation system; source identification; sudden contaminant; sensor