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Air balancing is an important technique in heating, ventilation, and air conditioning (HVAC) applications to achieve accurate air supply for satisfactory indoor air quality and better energy saving performance. The existing air balancing methods are usually based on a hybrid duct system model, which includes a data-driven duct system model between terminal flow and static pressure, and an American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) damper model between static pressure and terminal damper angle. Most of the existing methods just focus on how to improve the accuracy of the former part (i.e., the data-driven duct system model), whereas neglecting the latter. However, it is found that the ASHRAE damper model cannot exactly reflect the characteristics of the actual dampers in practice and may lead to unavoidable modeling errors. To fix this issue, this article develops a full data-driven duct system (FD3S) model to directly reveal the relationship between the terminal flow and terminal damper angle in the whole duct system. As a result, the desired terminal damper angle under the design flow conditions can be more accurately predicted. Besides, since the proposed method directly models the relationship between terminal flow and terminal damper angle, it avoids installing extra pressure sensors and reduces the operating cost. Besides, the proposed FD3S model greatly reduces the model complexity compared with the previous hybrid model. The proposed FD3S model-based air balancing method is fully tested on three real duct systems, and the results proved the effectiveness and generality of the proposed FD3S model-based air balancing method. [ABSTRACT FROM AUTHOR]

Intelligent buildings are typically endowed with sensing devices that are able to measure the concentration of specific contaminants in relevant zones. The collected measurements are subsequently processed by intelligent algorithms in order to enable the prompt detection and isolation of contaminant sources inside the building. Unfortunately, in real-world conditions, these sensing devices may suffer from faults affecting the sensors or the embedded electronics. Such faults, generally result in perturbed or missed data in the acquired data-stream, that can induce false alarms (or possibly missed alarms) and compromise the contaminant detection and isolation ability. This paper proposes a three-layer cognitive monitoring system for the detection and isolation of both contaminants and sensor faults in intelligent buildings. The first two layers are designed for the prompt detection of small variations in the concentration of a specific contaminant, while reducing the possible occurrence of false alarms. At the third layer, a cognitive mechanism employing a propagation model for the contaminant, which is based on the airflows between the building zones, allows to isolate the source zone and discriminate between sensor faults and the presence of a contaminant source. The proposed method is validated using a realistic 14-zone building scenario. [ABSTRACT FROM PUBLISHER]

In recent years, the deterioration of indoor air quality (IAQ) became a big concern. Because indoors is the place where we spend most of our life, an IAQ is an important factor to be considered for comfortable and healthy living. In this study, we evaluated the products and decomposition process of hexadecane in small and large capacity spaces by using microplasma electrode. In the case of hexadecane removal, both small and large capacity microplasma process could reduce hexadecane by 83% and 89%, respectively. In the process, we found various hydrocarbons such as 1-hexene (C6H12), 1-heptene (C7H14), and 1-octene (C8H 16) as intermediate products. CO2 and N2O were also confirmed as final products of the microplasma treatment process. [ABSTRACT FROM PUBLISHER]

Indoor air quality was improved by controlling particulate matter (PM) consisting of components such as house dust and bacteria, using an atmospheric pressure plasma. Two types of electrode, which generate corona discharges, were placed inside a reactor and energized to generate a nonthermal plasma, i.e., a disc-type reactor and a needle-type reactor. A plasma can decompose harmful substances by various active species such as reactive radicals (e.g., N^\ast,\;O^\ast,\;\and\;OH^\ast) and ions, and a strong electric field. The particle collection rate depended on the discharge voltage, number of mesh filters, flow rate, and reactor characteristics. The dust-collecting rate in the case of the disc-type reactor was 95% and in the case of the needle-type reactor was 98%. Formaldehyde removal was also performed to confirm the improved performance of the nonthermal plasma and reduction of formaldehyde to below the permitted value of 0.08 ppm. [ABSTRACT FROM AUTHOR]

Microplasma has advantages of reducing the power and downsizing the entire plasma system. Decomposition of formaldehyde (HCHO) by a microplasma reactor was demonstrated for improving indoor air quality. One experimental investigation used a one-pass treatment (5 L/min), and the second used a large-volume treatment (1500 L/min). The removal ratio of HCHO (initial concentration of 0.8 ppm) in the one-pass treatment was 96% at a discharge voltage of 1.3 kV using a high-voltage (HV) amplifier and a Marx generator with MOSFET switches as pulsed-power supplies. In the case of using the Marx generator, there was no \NOx generation. The removal ratio of HCHO (initial concentration of 0.5 ppm) in the large-volume treatment after 60 min was 51% at 1.2 kV when using the HV amplifier and considering 41% natural decay ratio of HCHO. The removal ratio was 54% at 1.2 kV with the Marx generator, and the natural decay ratio was 44% after 60 min of treatment. [ABSTRACT FROM AUTHOR]