Optimizing Sensor Placement for Enhanced Source Term Estimation in Chemical Plants

The leakage of hazardous chemical gases in chemical plants can lead to severe consequences. Source term estimation (STE) algorithms are effective in locating the leak source. The layout of the sensor network significantly affects the performance of the STE algorithm, yet the underlying mechanism remains unclear. In this study, we first applied computational fluid dynamics (CFD) to simulate 160 hazardous chemical gas leakage scenarios under multi-directional wind conditions in two hypothetic scenes with a natural convection environment, creating an accident dataset. Subsequently, a mathematical model for sensor placement optimization was developed and applied to the dataset to generate a series of sensor layout solutions. Based on these layouts, 12,216 STE cases were calculated. By analyzing the error distribution of these cases, the relationship between sensor placement and STE performance was systematically investigated, and the most effective sensor layout optimization strategies were discussed. This study found that in scenarios with complex obstacles, increasing the average measured concentration of the sensor network can significantly reduce the errors in the STE algorithm.