Growing concerns over the safety of the indoor environment have made the use of sensors
ubiquitous. Sensors that detect chemical and biological warfare agents can offer early warning
of dangerous contaminants. However, current sensor system design is more informed by
intuition and experience rather by systematic design. To develop a sensor system design
methodology, a proper indoor airflow modeling approach is needed. Various indoor airflow
modeling techniques, from complicated computational fluid dynamics approaches to simplified
multi-zone approaches, exist in the literature. In this study, the effects of two airflow modeling
techniques, multi-zone modeling technique and zonal modeling technique, on indoor air
protection sensor system design are discussed. Common building attack scenarios, using a
typical CBW agent, are simulated. Both multi-zone and zonal models are used to predict
airflows and contaminant dispersion. Genetic Algorithm is then applied to optimize the sensor
location and quantity. Differences in the sensor system design resulting from the two airflow
models are discussed for a typical office environment and a large hall environment.
The indoor air quality (IAQ) has an important impact on public health. Currently, the indoor air pollution, caused by gas, particle, and bio-aerosol pollutants, is considered as the top five environmental risks to public health and has an estimated cost of $2 billion/year due to medical cost and lost productivity. Furthermore, current buildings are especially vulnerable for chemical and biological warfare (CBW) agent contamination because the central air conditioning and ventilation system serve as a nature carrier to spread the released agent from one location to the whole indoor environment within a short time period. To assure the IAQ and safety for either new or existing buildings, real time comprehensive IAQ and CBW measurements are needed. With the development of new sensing technologies, economic and reliable comprehensive IAQ and CBW sensors become promising. However, few studies exist that examine the design and evaluation issues related to IAQ and CBW sensor network. In this paper, relevant research areas including IAQ and CBW sensor development, demand control ventilation, indoor CBW sensor system design, and sensor system design for other areas such as water system protection, fault detection and diagnosis, are reviewed and summarized. Potential research opportunities for IAQ and CBW sensor system design and evaluation are discussed.
During the past several years, many new biological and chemical sensors have been or are being developed for infrastructure and environment protection, such as protecting water, indoor and outdoor air quality. However, there is a lack of fundamental system level research that develops the methodologies to optimize such a sensor network to maximize the protection and minimize the system cost. This paper describes a preliminary study to address the above questions. In this study, the evaluation criteria for a sensor system that is used to protect a building from airborne hazards are identified. Common building attack scenarios are described and simulated for a small commercial building. Genetic Algorithm is applied for each attack scenario to optimize the sensor sensitivity, location, and amount to achieve the best system behavior while reduce the total system cost. Assuming that each attack scenario has the same occurrence possibility, optimal system designs that present the best behavior for all attacking scenario are obtained.
KEYWORDS: Control systems, Systems modeling, Fluctuations and noise, Actuators, Data modeling, Optimization (mathematics), Complex systems, Temperature metrology, Control systems design, Instrument modeling
An innovative approach to building operation, called the adaptive optimal operation methodology (AOOM), is developed and validated in this study. The AOOM, which resides in the building energy management and control system, estimates the building and heating, ventilating, and air conditioning (HVAC) system loads and parameters and supplies the local controllers the optimal set points that minimize the energy cost while maintaining occupant comfort. The AOOM uses the recursive least square method with an adaptive forgetting factor to estimate the parameters for the building zones and HVAC systems. A genetic algorithm optimizer together with a system model is then used to generate the optimal set points, such as the supply air temperature set point as well as the set points of minimum air flow rate and zone temperature for each zone. The system model is validated through different types of experiments. System level validation experiments conducted during the heating and cooling seasons indicate that the HVAC system operated under the AOOM consumes 3 to 10.8 % less heated water energy during the heating season and 1 to 4 % less electrical energy during the cooling season when compared with a commercial operation methodology.
Conference Committee Involvement (4)
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
8 March 2010 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
9 March 2009 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
10 March 2008 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
19 March 2007 | San Diego, California, United States
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