Distributed Contaminant Detection and Isolation for Intelligent Buildings

The automatic preservation of the Indoor Air Quality is an important task of the intelligent building design in order to ensure the health and safety of the occupants. The indoor air quality, however, is often compromised by various airborne contaminants that penetrate the indoor environment as a result of accidents or planned attacks. In this work, we provide the detailed analysis, implementation and evaluation of a distributed methodology for detecting and isolating multiple contaminant events in large-scale buildings. Specifically, we consider the building as a collection of interconnected subsystems and we design a contaminant event monitoring software agent for each subsystem. Each monitoring agent aims to detect the contaminant and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighbouring agents. For configuring the subsystems, we implement both exact and heuristic partitioning solutions. A main contribution of this work is the investigation of the impact of the partitioning solution on the performance of the distributed Contaminant Detection and Isolation scheme with respect to the detectability and isolability of the contaminant sources. The performance of the proposed distributed contaminant detection and isolation methodology is demonstrated using models of real building case studies created on CONTAM.A. Kyriacou, M. P. Michaelides, V. Reppa, S. Timotheou, C. G. Panayiotou and M. M. Polycarpou, "Distributed Contaminant Detection and Isolation for Intelligent Buildings," in IEEE Transactions on Control Systems Technology, vol. 26, no. 6, pp. 1925-1941. doi: 10.1109/TCST.2017.2754986 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works