Comparative Evaluation of Two Algorithms for Locating Contaminant Ingress Points

A procedure involving Data Mining based on Flow Direction and shortest flow time (DMFD) is described, which consists of two components: (i) possible ingress nodes (PINs) identification and (ii) probability quantification. PINs identification is completed based on the flow information, i.e. the flow direction and time in each pipe. Through shortest time which is calculated by Dijksta algorithm, from one node to a specific sensor, ingress time in the node is obtained. A distance metric is described to quantify the probability of one node as PIN. A multi-stage response is described to decrease the elapsed time before a contaminant ingress event is identified and responded to, which is essential to minimize the risk from consumption of the hazard. The roles of two algorithms, namely a Data Mining method based on Injection and Detection information (DMID) and DMFD, are examined. A case study is employed in a network with 285 nodes and 5 sensors. The five sensors all alarm to the injection event. With DMID, the number of PINs is decreased from 44 in the 1^st stage to 18, 11, 11 and 11 subsequently; For DMFD, the number is reduced from 44 in the 1^st stage to 21, 21, 21 and 21 in the following stages; DMID identifies the true intrusion node 44 with the highest probability in the five stages, while DMFD identifies it with the highest probability in the 3^rd and 4^th stage; the run time of both DMFD and DMID is less than 2 min, which suggests the two are effective in guiding emergency response.