Automatic building partitioning for effective contaminant detection and isolation

Intelligent buildings are responsible for ensuring the indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events due to accidental or malicious actions. An emerging environmental control application is monitoring the intelligent buildings against the presence of such events, by incorporating various sensing technologies and distributed detection and isolation algorithms. The needed simplicity, the improved scalability and fault tolerance are some of the main reasons for choosing distributed approaches over centralized ones. Hence, the effective partitioning of buildings into smaller sections for contaminant detection and isolation approaches is of great importance. In this paper, we present a heuristic algorithm for partitioning the building into smaller sections. The proposed algorithm is based on matrix clustering techniques and groups the building zones in order to form the different sections while ensuring (i) maximum decoupling between the various sections, (ii) strong connectivity between the zones of a section and (iii) fairness with respect to the number of allocated zones. The partitioning is achieved in near real time, providing the ability of repartitioning and adapting to the dynamic nature of the airflows. The main contribution of this work is the automatic partitioning of the building into sections, which enables the distributed simulation, modeling, analysis and management of the intelligent building while ensuring the effective detection and isolation of contaminants in the building interior.A. Kyriacou, M. Michaelides and C. Panayiotou, "Automatic building partitioning for effective contaminant detection and isolation," 2016 18th Mediterranean Electrotechnical Conference (MELECON), Lemesos, 2016, pp. 1-6. doi: 10.1109/MELCON.2016.7495469 © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works