Abstract

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.

Highlights

  • Intelligent buildings improve the comfort and productivity of occupants and ensure their health and safety by monitoring and controlling the building environment [1]

  • In order to enable the comparison of all possible partitioning solutions, an optimization algorithm is developed for extracting the observer gains that give the minimum detectable contaminant release rate in every zone, similar to [10], where an optimization methodology was developed for the design of a centralized observer-based, sensor fault detection scheme for a class of nonlinear systems

  • Indoor Air Quality monitoring is one of the most important properties of Intelligent Buildings that ensures the well-being of the occupants

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Summary

INTRODUCTION

Intelligent buildings improve the comfort and productivity of occupants and ensure their health and safety by monitoring and controlling the building environment [1]. In our previous works [4], [5], we have developed both a centralized and a distributed state-space model for describing the contaminant dispersion in the building interior along with the development of Contaminant Detection and Isolation (CDI) algorithms. We aim at improving the contaminant detection performance through the selection of the best partitioning solution that enables the detection of the smallest contaminant release rates. In order to enable the comparison of all possible partitioning solutions, an optimization algorithm is developed for extracting the observer gains that give the minimum detectable contaminant release rate in every zone, similar to [10], where an optimization methodology was developed for the design of a centralized observer-based, sensor fault detection scheme for a class of nonlinear systems. Sets are represented with calligraphic letters and the identity matrix with the symbol I

SYSTEM MODEL
DISTRIBUTED CONTAMINANT DETECTION
Contaminant Event Detection
DETECTABILITY ANALYSIS
Partitioning Effect on Detection Performance
DETECTABILITY BOUND OPTIMIZATION
Optimization Formulation
PERFORMANCE EVALUATION
CONCLUSIONS
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