Localization of indoor contaminant source based on residual-life-time of air in an indoor space

Abstract

Recent research and development related to smart home environments has focused on providing optimal environments for humans. IoT-based smart home technology can provide convenience and comfort to occupants by tracking their living patterns and requirements through sensors installed in rooms and using algorithms to control the internal condition of the indoor environment. Despite an optimal indoor air control strategy considering airflows, indoor air quality (IAQ) can vary depending on the release location of the contaminant. Thus, it is necessary to apply the ventilation strategy of recognizing the contaminant source location and handling it for better smart home. To identify release location of the contaminant source, inverse modeling has been used but it contains a computationally long optimization method and complex and unstable numerical process.

The purpose of this study is to investigate a method for identifying the location of indoor contaminant sources to link a smart home automation control system with ventilation system operation technology. This study focuses on local mean residual-life-time (LMR) to propose a simpler and cheaper method for localizing the contaminant source instead using the inverse modeling. First, the relationship among the average concentration of contaminant, the exhaust concentration of contaminant, ventilation rate, and LMR was mathematically derived. Next, to confirm the potential applicability of the mathematical relationship among the average concentration of contaminant, the exhaust concentration of contaminant, ventilation rate, and LMR, the experiments and numerical simulations were carried out. The contaminant concentration according to the location of the source was measured and the LMR at the source location was also measured using a tracer gas method. Since the number of measuring points is limited in the experiment, an additional analysis of the relationship between the LMR and the contaminant concentration depending on the source location was conducted using a computational fluid dynamics technique. Based on the outcomes, it was confirmed that the method for localizing the contaminant source using LMR has on average better accuracy and performance than the existing methods using inverse modeling. Lastly, with this verified mathematical equation, the process for detecting the contaminant source were proposed.

This study has the potential to play a crucial role in finding and controlling hazardous gases in factories, hospitals, laboratories, etc. In addition, the novel framework proposed in this study can be the basis for designing a smart ventilation algorithm to optimize an indoor environment according to contaminant source location.