Contaminant source identification within a building: Toward design of immune buildings

An improved particle swarm optimization method for locating time-varying indoor particle sources

The indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.

Multi-objective optimization for sensor placement against suddenly released contaminant in air duct system

When a chemical or biological agent is suddenly released into a ventilation system, its dispersion needs to be promptly and accurately detected. In this work, an optimization method for sensors layout in air ductwork was presented. Three optimal objectives were defined, i.e. the minimum detection time, minimum contaminant exposure, and minimum probability of undetected pollution events. Genetic algorithm (GA) method was used to obtain the non-dominated solutions of multiobjectives optimization problem and the global optimal solution was selected among all of the non-dominated solutions by ordering solutions method. Since the biochemical attack occurred in a ventilation system was a random process, two releasing scenarios were proposed, i.e. the uniform and the air volume-based probability distribution. It was found that such a probability distribution affected the results of optimal sensors layout and also resulted in different detect time and different probability of undetected events. It was discussed how the objective functions are being compatible and competitive with each other, and how sensor quantity affect the optimal results and computational load. The impact of changes on other parameters was given, i.e. the deposition coefficient, the air volume distribution and the manual releasing. This work presents an angle of air ductwork design for indoor environment protection and expects to help in realizing the optimized sensor system design for sudden contaminant releasing within ventilation systems.

A methodology for optimal placement of sensors in enclosed environments: A dynamical systems approach

Air quality has been an important issue in public health for many years. Sensing the level and distributions of impurities help in the control of building systems and mitigate long term health risks. Rapid detection of infectious diseases in large public areas like airports and train stations may help limit exposure and aid in reducing the spread of the disease. Complete coverage by sensors to account for any release scenario of chemical or biological warfare agents may provide the opportunity to develop isolation and evacuation plans that mitigate the impact of the attack. All these scenarios involve strategic placement of sensors to promptly detect and rapidly respond. This paper presents a data driven sensor placement algorithm based on a dynamical systems approach. The approach utilizes the finite dimensional Perron-Frobenius (PF) concept. The PF operator (or the Markov matrix) is used to construct an observability gramian that naturally incorporates sensor accuracy, location constraints, and sensing constraints. The algorithm determines the response times, sensor coverage maps, and the number of sensors needed. The utility of the procedure is illustrated using four examples: a literature example of the flow field inside an aircraft cabin and three air flow fields in different geometries. The effect of the constraints on the response times for different sensor placement scenarios is investigated. Knowledge of the response time and coverage of the multiple sensors aides in the design of mechanical systems and response mechanisms. The methodology provides a simple process for place sensors in a building, analyze the sensor coverage maps and response time necessary during extreme events, as well as evaluate indoor air quality. The theory established in this paper also allows for future work in topics related to construction of classical estimator problems for the sensors, real-time contaminant transport, and development of agent dispersion, contaminant isolation/removal, and evacuation strategies.

Inverse identification of the release location, temporal rates, and sensor alarming time of an airborne pollutant source

With an accidental release of an airborne pollutant, it is always critical to know where, when, and how the pollutant has been released. Then, emergency measures can be scientifically advised to prevent any possible harm. This investigation proposes an inverse model to identify the release location, the temporal rate profile, and the sensor alarming time from the start of a pollutant release. The first step is to implement the inverse operation to the cause-effect matrix to obtain the release rate profiles for discrete candidate scenarios with concentration information provided by one sensor. The second step is to interpret the occurrence probability of each solution in the first step with the Bayesian model by matching the concentration at the other sensor. The proposed model was applied to identify a single pollutant source in a two-dimensional enclosure using measurement data and in a three-dimensional aircraft cabin with simulated data. The results show that the model is able to correctly determine the pollutant source location, the temporal rate profile, and the sensor alarming time. The known conditions for input into the inverse model include a steady flow field and the valid temporal concentrations at two different locations.

PRACTICAL IMPLICATIONS

The proposed inverse model can tell where, when, and how a gaseous pollutant has been accidently released based on the monitoring concentrations measured by two sensors. This methodology can be useful for providing emergency protection to indoor occupants.

Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time

The sudden release of airborne hazardous contaminants in an indoor environment can potentially lead to severe disasters, such as the spread of toxic gases, fire, and explosion. To prevent and mitigate these disasters it is critical to rapidly and accurately identify the characteristics of the contaminant sources. Although remarkable achievements have been made in identifying a single indoor contaminant source in recent years, the issues related to multiple contaminant sources are still challenging. This study presents a method for identifying the exact locations, emission rates, and release time of multiple indoor contaminant sources simultaneously released at constant rates, by considering sensor thresholds and measurement errors. The method uses a two-stage procedure for rapid source identification. Before the release of contaminants, only a limited number of time-consuming computational fluid dynamics (CFD) simulations need to be conducted. After the release of contaminants, the method can be executed in real-time. Through case studies in a three-dimensional office the method was numerically demonstrated and validated, and the results show that the method is effective and feasible. The effects of sensor threshold, measurement error and total sampling time on the source identification performance were analysed, and the limitations and applicability of the method were also discussed.