Prediction and Identification of Sudden Pollution Source

Such as submarines，manned spacecraft and other closed micro-environment，cabin air pollution has become a hazard to the safety of staff with the residence time extend. There is an urgent need for fast and accurate prediction of pollution concentration and location identification of a sudden source to improve the closed environment active control ability for unexpected pollution. Dynamic cabin concentration prediction and pollution sources identified are a key to achieve real-time air quality forecast. A concept of lumped source and a variable structure concentration model were built to realize concentration prediction together using Kalman filtering and least-squares algorithm. In addition，a source location method was studied because it is a key link for source identification. The contaminant source location method based on multi-hypothesis source position was established and attempt to solve the source location problems. This method realizes source identification by comparing the similarity between the sensor-measured concentration distribution and the multiple hypothetical concentration distributions calculated at the monitoring point based on multi-hypothesis source position. The proposed method is capable of identifying a source position，estimating its initial emission time and approximate strength.