A combined system of chilled ceiling,displacement ventilation and desiccant dehumidification

Different types of heating, ventilation, and air-conditioning (HVAC) systems consume different amounts of energy yet they deliver similar levels of acceptable indoor air quality (IAQ) and thermal comfort. It is desirable to provide buildings with an optimal HVAC system to create the best IAQ and thermal comfort with minimum energy consumption. In this paper, a combined system of chilled ceiling, displacement ventilation and desiccant dehumidification is designed and applied for space conditioning in a hot and humid climate. IAQ, thermal comfort, and energy saving potential of the combined system are estimated using a mathematical model of the system described in this paper. To confirm the feasibility of the combined system in a hot and humid climate, like China, and to evaluate the system performance, the mathematical model simulates an office building in Beijing and estimates IAQ, thermal comfort and energy consumption. We conclude that in comparison with a conventional all-air system the combined system saves 8.2% of total primary energy consumption in addition to achieving better IAQ and thermal comfort. Chilled ceiling, displacement ventilation and desiccant dehumidification respond consistently to cooling source demand and complement each other on indoor comfort and air quality. It is feasible to combine the three technologies for space conditioning of office building in a hot and humid climate.     

Evaluation of distributed environmental control systems for improving IAQ and reducing energy consumption in office buildings

Conventional heating, ventilation, and air conditioning (HVAC) systems are incapable of providing control over individual environments or adjusting fresh air supply based on the dynamic occupancy of individual rooms in an office building. This paper introduces the concept of distributed environmental control systems (DECS) and shows that improvement in indoor air quality (IAQ) and energy efficiency can be achieved by providing required amounts of fresh air directly to the individual office spaces through distributed demand controlled ventilation (DDCV). In DDCV, fresh air is provided to each micro-environment (room or cubicle) based on input from distributed sensors (CO₂, VOC, occupancy, etc.) or intelligent scheduling techniques to provide acceptable IAQ for each occupant, rather than for groups or populations of occupants. In order to study DECS, a numerical model was developed that incorporates some of the best available models for studying building energy consumption, indoor air flow, contaminant transport and HVAC system performance. The developed model was applied to a DECS in a model office building equipped with a DDCV system. By implementing DECS/DDCV and intelligent scheduling techniques it is possible to achieve an improvement in IAQ along with a reduction in annual energy consumption compared to conventional ventilation systems.     

Identifying decaying contaminant source location in building HVAC system using the adjoint probability method

Building heating, ventilation and air-conditioning (HVAC) system can be potential contaminant emission source. Released contaminants from the mechanical system are transported through the HVAC system and thus impact indoor air quality (IAQ). Effective control and improvement measures require accurate identification and prompt removal of contaminant sources from the HVAC system so as to eliminate the unfavourable influence on the IAQ. This paper studies the application of the adjoint probability method for identifying a dynamic (decaying) contaminant source in building HVAC system. A limited number of contaminant sensors are used to detect contaminant concentration variations at certain locations of the HVAC ductwork. Using the sensor inputs, the research is able to trace back and find the source location. A multi-zone airflow model, CONTAM, is employed to obtain a steady state airflow field for the studied building with detailed duct network, upon which the adjoint probability based inverse tracking method is applied. The study reveals that the adjoint probability method can effectively identify the decaying contaminant source location in building HVAC system with few properly located contaminant concentration sensors.     

Double cooling coil model for non-linear HVAC system using RLF method

The purpose of heating, ventilating and air conditioning (HVAC) system is to provide and maintain a comfortable indoor temperature and humidity. The objective of this work is to model building structure, including equipments of HVAC system. The hybrid HVAC model is built with physical and empirical functions of thermal inertia quantity. Physical laws are used to build the sub-model for subsystems that have low thermal inertia while the empirical method is used to build the sub-model for subsystems with high thermal inertia. The residential load factor (RLF) is modeled by residential heat balance (RHB). RLF is required to calculate a cooling/heating load depending upon the indoor/outdoor temperature. The transparency, functionality of indoor/outdoor temperatures and simplicity of RLF makes it suitable for modeling. Furthermore, the parameters of the model can be calculated differently from room to room and are appropriate for variable air volume (VAV) factor. Nowadays, a VAV system is universally accepted as means of achieving both energy efficiency and comfortable building environment. In this research work, a pre-cooling coil is added to humidify the incoming air, which controls the humidity more efficiently inside conditioned space. The model presented here is verified with both theoretical and numerical methods.     

Modeling the resiliency of energy‐efficient retrofits in low‐income multifamily housing

Residential energy efficiency and ventilation retrofits (eg, building weatherization, local exhaust ventilation, HVAC filtration) can influence indoor air quality (IAQ) and occupant health, but these measures’ impact varies by occupant activity. In this study, we used the multizone airflow and IAQ analysis program CONTAM to simulate the impacts of energy retrofits on indoor concentrations of PM_2.5 and NO₂ in a low‐income multifamily housing complex in Boston, Massachusetts (USA). We evaluated the differential impact of residential activities, such as low‐ and high‐emission cooking, cigarette smoking, and window opening, on IAQ across two seasons. We found that a comprehensive package of energy and ventilation retrofits was resilient to a range of occupant activities, while less holistic approaches without ventilation improvements led to increases in indoor PM_2.5 or NO₂ for some populations. In general, homes with simulated concentration increases included those with heavy cooking and no local exhaust ventilation, and smoking homes without HVAC filtration. Our analytical framework can be used to identify energy‐efficient home interventions with indoor retrofit resiliency (ie, those that provide IAQ benefits regardless of occupant activity), as well as less resilient retrofits that can be coupled with behavioral interventions (eg, smoking cessation) to provide cost‐effective, widespread benefits.     

Particle loading rates for HVAC filters, heat exchangers, and ducts

The rate at which airborne particulate matter deposits onto heating, ventilation, and air-conditioning (HVAC) components is important from both indoor air quality (IAQ) and energy perspectives. This modeling study predicts size-resolved particle mass loading rates for residential and commercial filters, heat exchangers (i.e. coils), and supply and return ducts. A parametric analysis evaluated the impact of different outdoor particle distributions, indoor emission sources, HVAC airflows, filtration efficiencies, coils, and duct system complexities. The median predicted residential and commercial loading rates were 2.97 and 130 g/m(2) month for the filter loading rates, 0.756 and 4.35 g/m(2) month for the coil loading rates, 0.0051 and 1.00 g/month for the supply duct loading rates, and 0.262 g/month for the commercial return duct loading rates. Loading rates are more dependent on outdoor particle distributions, indoor sources, HVAC operation strategy, and filtration than other considered parameters. The results presented herein, once validated, can be used to estimate filter changing and coil cleaning schedules, energy implications of filter and coil loading, and IAQ impacts associated with deposited particles. PRACTICAL IMPLICATIONS: The results in this paper suggest important factors that lead to particle deposition on HVAC components in residential and commercial buildings. This knowledge informs the development and comparison of control strategies to limit particle deposition. The predicted mass loading rates allow for the assessment of pressure drop and indoor air quality consequences that result from particle mass loading onto HVAC system components.     

Sick building syndrome—A case study in a multistory centrally air-conditioned building in the Delhi City

Recently, airtight envelope system has become popular in the design of office buildings to reduce heating and cooling loads. Maintaining allowable indoor air quality (IAQ) for such airtight buildings totally depends on mechanical ventilation systems. Subsequently, poor operation of the ventilation system in such office buildings causes ineffective removal of polluted indoor air, and displays a sign of “sick building syndrome” (SBS). User's perception is an important parameter for evaluating IAQ. A questionnaire study was carried out to investigate the prevalence of the SBS at a multistory centrally air-conditioned Airport Authority of India (AAI) building in the New Delhi city. Quantification of the perceptions of the users regarding IAQ was done by converting their responses to a SBS score. The quantified answers were then subjected to statistical analysis. Qualitative analysis of the questionnaire was carried out to evaluate relationships between SBS score and carbon dioxide (CO₂) and other parameters related to building and work environment. Quantitative analysis of IAQ was also conducted by monitoring indoor concentrations of four pollutants, namely, nitrogen dioxide (NO₂), sulphur dioxide (SO₂), suspended particulate matter (SPM) and carbon monoxide (CO). Concentrations of pollutants were complying with IAQ standards as given by ASHRAE and WHO. The SBS was higher on the third floor as compared to other floors and the control tower. The main symptoms prevailing were headache (51%), lethargy (50%), and dryness in body mucous (33%). The third floor and the control tower were affected by infiltration, mainly from entrance doors. A direct relation between the average SBS score and CO₂ concentration was found, i.e., the average SBS score increased with CO₂ concentration and vice versa, clearly signifying the usefulness of SBS score in IAQ.     

用于空调系统设计的全年双负荷曲线分析法

为了更好地节省空调系统能耗和获得更好的室内空气质量，基于建筑全年模拟分析的观点，提出了一种简单实用的建筑双负荷曲线分析法，它不仅能给出最大的空调冷热负荷     

Is ventilation duct cleaning useful? A review of the scientific evidence

Ventilation duct cleaning (DC) is widely advocated to provide good indoor air quality (IAQ), health benefits, cost savings, and enhance ventilation system performance. The aim of the present review is to evaluate the scientific evidence as shown in the literature. There is evidence that under normal operating conditions, ventilation ducts can be contaminated with dusts and serve as reservoirs for microbials to proliferate. While controlled experiments noted that contaminants resuspension can elevate exposure levels indoors, no field studies have correlated poor IAQ with duct contamination. Despite high efficiencies of contaminant removal within the ducts during cleaning, reductions for different indoor air pollutants vary widely, where, post-cleaning air pollutants concentrations can be higher than pre-cleaning levels. Further, there are health concerns in the use of biocides, sealants and encapsulants. There is inadequate evidence to show that DC can improve airflow in ducts and reduce energy consumption. Although epidemiological studies indicate suggestive evidence that improperly maintained ducts are associated with higher risks of symptoms among building occupants, this review finds insufficient evidence that DC can alleviate occupant's symptoms. In summary, the need for duct cleanliness has to be properly balanced by the probable generation of indoor pollution resulting from DC and subsequent potential health risks. PRACTICAL IMPLICATIONS: Existing evidence is insufficient to draw solid conclusions regarding positive impact of duct cleaning on IAQ, health benefits, cost savings and HVAC performance. Maintaining duct cleanliness has to be properly balanced by the probable generation of indoor pollution and potential health risks.     

HVAC air-quality model and its use to test a PM2.5 control strategy

This paper presents a MATLAB^® Simulink air-quality model of a commercial building with a heating, ventilation, and air conditioning (HVAC) system in Fairbanks, Alaska. Outdoor and indoor real-time fine particulate matter (PM_2.5) levels were measured at this building during a summer wild-fire smoke episode and then during a winter period. The correlation coefficient between the model-predicted and the measured indoor concentrations was 0.99 for the summer and 0.98 for the winter, justifying the usability of the model for further studies. An HVAC control algorithm was developed that reduces the indoor PM_2.5 levels. The algorithm was tested using the HVAC Simulink model and the outdoor PM_2.5 data from the summer smoke episode. The average indoor PM_2.5 level with this control algorithm was 65% lower than with the regular control. Thanks to the PM_2.5 control strategy being automatically engaged only during episodes, it was shown to have the potential of significantly reducing the indoor PM_2.5 levels without significantly compromising the purpose of the original control strategy.     

Modeling of the heating system in small building for control

Model identifications for the heating system of small building are presented. Basic problems of data acquisition and preprocessing are detailed. Specially designed wire-less data collection and control system to conduct real-world experiments are described. The model structure choice based on analysis of weather condition influences on the system performance are presented. Finally, the general structure of two-layered heating control strategy for the heating system is proposed. Instead of heating, ventilating and air conditioning (HVAC) standard, the proposed strategy controls only indoor temperature and changes the set point according to additional measurements of indoor humidity to keep thermal comfort. Non-linear compensation of outdoor temperature and wind speed is also introduced.     

Real-time determination of optimal indoor-air condition for thermal comfort,air quality and efficient energy usage

For building, “surroundings” that effect on indoor-air condition change with respect to the time. Without proper determination of the desired indoor-air condition to heating, ventilating and air-conditioning (HVAC) system, it may not be feasible to provide simultaneously occupants with thermal comfort and acceptable air quality with efficient energy consumption all the time. This paper presents an alternative methodology of real-time determination of optimal indoor-air condition for HVAC system in order to achieve such total requirements. Predicted mean vote (PMV), CO₂ concentration and cooling/heating load are used as parameter indices for thermal comfort, indoor-air quality and energy consumption respectively. The performance index of the HVAC system is then defined by summation in terms of square errors between those actual parameter indices and their desired values. This performance index is to be systematically minimized by a gradient-based technique in order to yield optimal indoor-air condition for HVAC system. A case study was chosen in 24 h operating HVAC system of a single-storey building by determining indoor-air temperature, indoor-air humidity, indoor-air velocity, and air-ventilation rate. The experiment results show that the proposed methodology can be efficiently implemented in the real-time determination of indoor-air condition to HVAC system that maintains PMV and CO₂ concentration close to the desired levels with less energy consumption when compared to those from the conventional approach.     

确定长江流域供暖空调能耗指标的边界条件

讨论了影响长江流域住宅供暖空调能耗水平的诸因素,包括热环境质量、室内空气品质和新风量、供暖空调系统和比和性能系数、供暖期空调期除湿期天数及建筑热工特性等,给出了这些因素的建议值。     

Machine learning and statistical models for predicting indoor air quality

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario‐based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty‐seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM_2.5 and PM₁₀) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.     

Comparison of Data from an IAQ Test House with Predictions of an IAQ Computer Model

One of the objectives of EPA's indoor air quality (IAQ) program is to provide guidance on the impact of indoor sources on IAQ. A computer model, EXPOSURE, was developed to assist in this effort. EXPOSURE calculates pollutant concentration as a function of time for each room of the building. EXPOSURE includes effects of sources, sinks, room-to-room air movement, and air exchange with the outdoors. Several experiments designed to evaluate the impact of indoor sources on IAQ are described. Measured concentrations are compared with concentrations predicted by an IAQ model. The measured concentrations are in excellent agreement with the predictions. The model predictions and the experimental data demonstrate the importance of sinks in determining long-term IAQ.     

The effectiveness of stand alone air cleaners for shelter-in-place

Stand-alone air cleaners may be efficient for rapid removal of indoor fine particles and have potential use for shelter-in-place (SIP) strategies following acts of bioterrorism. A screening model was employed to ascertain the potential significance of size-resolved particle (0.1-2 microm) removal using portable high efficiency particle arresting (HEPA) air cleaners in residential buildings following an outdoor release of particles. The number of stand-alone air cleaners, air exchange rate, volumetric flow rate through the heating, ventilating and air-conditioning (HVAC) system, and size-resolved particle removal efficiency in the HVAC filter were varied. The effectiveness of air cleaners for SIP was evaluated in terms of the outdoor and the indoor particle concentration with air cleaner(s) relative to the indoor concentration without air cleaners. Through transient and steady-state analysis of the model it was determined that one to three portable HEPA air cleaners can be effective for SIP following outdoor bioaerosol releases, with maximum reductions in particle concentrations as high as 90% relative to conditions in which an air cleaner is not employed. The relative effectiveness of HEPA air cleaners vs. other removal mechanisms was predicted to decrease with increasing particle size, because of increasing competition by particle deposition with indoor surfaces and removal to HVAC filters. However, the effect of particle size was relatively small for most scenarios considered here. PRACTICAL IMPLICATIONS: The results of a screening analysis suggest that stand-alone (portable) air cleaners that contain high efficiency particle arresting (HEPA) filters can be effective for reducing indoor fine particle concentrations in residential dwellings during outdoor releases of biological warfare agents. The relative effectiveness of stand-alone air cleaners for reducing occupants' exposure to particles of outdoor origin depends on several factors, including the type of heating, ventilating and air-conditioning (HVAC) filter, HVAC operation, building air exchange rate, particle size, and duration of elevated outdoor particle concentration. Maximum particle reductions, relative to no stand-alone air cleaners, of 90% are predicted when three stand-alone air cleaners are employed.     

The effect of combining a relative-humidity-sensitive ventilation system with the moisture-buffering capacity of materials on indoor climate and energy efficiency of buildings

Indoor moisture management, which means keeping the indoor relative humidity (RH) at correct levels, is very important for whole building performance in terms of indoor air quality (IAQ), energy performance and durability of the building. In this study, the effect of combining a relative-humidity-sensitive (RHS) ventilation system with indoor moisture buffering materials was investigated. Four comprehensive heat–air–moisture (HAM) simulation tools were used to analyse the performance of different moisture management strategies in terms of IAQ and of energy efficiency. Despite some differences in results, a good agreement was found and similar trends were detected from the results, using the four different simulation tools. The results from simulations demonstrate that RHS ventilation reduces the spread between the minimum and maximum values of the RH in the indoor air and generates energy savings. Energy savings are achieved while keeping the RH at target level, not allowing for possible risk of condensations. The disadvantage of this type of demand controlled-ventilation is that other pollutants (such as CO₂) may exceed target values. This study also confirmed that the use of moisture-buffering materials is a very efficient way to reduce the amplitude of daily moisture variations. It was possible, by the combined effect of ventilation and wood as buffering material, to keep the indoor RH at a very stable level.     

Coupling the multizone airflow and contaminant transport software CONTAM with EnergyPlus using co-simulation

Building modelers need simulation tools capable of simultaneously considering building energy use, airflow and indoor air quality (IAQ) to design and evaluate the ability of buildings and their systems to meet today’s demanding energy efficiency and IAQ performance requirements. CONTAM is a widely-used multizone building airflow and contaminant transport simulation tool that requires indoor temperatures as input values. EnergyPlus is a prominent whole-building energy simulation program capable of performing heat transfer calculations that require interzone and infiltration airflows as input values. On their own, each tool is limited in its ability to account for thermal processes upon which building airflow may be significantly dependent and vice versa. This paper describes the initial phase of coupling of CONTAM with EnergyPlus to capture the interdependencies between airflow and heat transfer using co-simulation that allows for sharing of data between independently executing simulation tools. The coupling is accomplished based on the Functional Mock-up Interface (FMI) for Co-simulation specification that provides for integration between independently developed tools. A three-zone combined heat transfer/airflow analytical BESTEST case was simulated to verify the co-simulation is functioning as expected, and an investigation of a two-zone, natural ventilation case designed to challenge the coupled thermal/airflow solution methods was performed.     

Influence of building parameters and HVAC systems coupling on building energy performance

The performance of different HVAC systems varies when coupled with different buildings. This paper examines the relationship between building heating and cooling load and subsequent energy consumption with different HVAC systems. Two common HVAC systems in use throughout the UK office building stock, variable air volume (VAV) system and fan coil (FC) with dedicated outside air system, have been coupled with a typical narrow plan office building with and without daylight control and for both cellular and open plan.The results presented in this paper clearly indicate that it is not possible to form a reliable judgment about building energy performance based only on building heating and cooling loads. For the two investigated systems, variable air volume system and fan coil with dedicated outside air system, the difference between system demand and building demand varied from over −40% to almost +30% for cooling and between −20% and +15% for heating. If a heat recovery unit is used, the difference in heating performance is even greater, rising to −70%.     

Uncertainty in indoor air quality and grey system method

Based on analysis of uncertainty, this paper presents grey system theory to handle the “grey” characteristic of IAQ. Grey comprehensive analysis of indoor air quality reveals that we should pay more attention to the air purification and humidity control in the design and maintenance of HVAC. In order to represent grey characteristic of IAQ system, the educed grey IAQ models can identify the variation intervals of key IAQ model parameters that are lack of directly measurable messages in practical situations. Furthermore, grey assessment is an effective multifactor comprehensive assessment method that can express the integrative influence of contamination indexes on indoor air quality. We can determine the IAQ grade and make comparison according to the grey incidence matrix R.