Optimization Based Partitioning Selection for Improved Contaminant Detection Performance

Alexis Kyriacou, Stelios Timotheou, Vasso Reppa, Francesca Boem, Christos Panayiotou, Marios Polycarpou, Thomas Parisini

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance.

Original languageEnglish
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
ISBN (Electronic)9781538613955
Publication statusPublished - 2019
Externally publishedYes
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: 17 Dec 201819 Dec 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States


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