Hasil untuk "Heating and ventilation. Air conditioning"

Elif Çinar, Tayfun Abut

Heating, ventilation, and air conditioning (HVAC) systems are control systems that ensure indoor temperature and air quality meet desired conditions. In this study, a novel control strategy is proposed for an HVAC system operating under two distinct environmental zones with variable flow rates, addressing control challenges arising from external disturbances such as ambient temperature and humidity changes. In the system design, mathematical models were obtained, including the heat losses of two zones to the outdoor environment, as well as the heat transfer dynamics in the cooling unit, fans, and air ducts. For system control, considering ambient temperature, humidity, and variable flow rate, the required airflow was achieved by controlling the dampers placed in the indoor air inlet ducts. The core novelty of this work lies in the development and comparison of advanced control algorithms, including the Linear Quadratic Regulator (LQR), a Particle Swarm Optimization (PSO)-based LQR, and a newly designed PSO-based Fuzzy LQR (FLQR) controller. Comfort conditions were achieved by cooling the temperatures of two different regions from the ambient temperature to approximately 7 °C. The proposed FLQR controller combines the adaptability of fuzzy logic with the optimization capabilities of PSO to enhance system responsiveness and occupant comfort. Simulation results show that the FLQR method improves comfort performance by 90.4 % for Zone-1 and 88.1 % for Zone-2 compared to conventional LQR. The effectiveness of the proposed method (FLQR) is demonstrated through a comprehensive performance evaluation using Mean Squared Error (MSE) metrics, confirming its potential for intelligent HVAC applications.

Piotr Narowski, Dariusz Heim, Maciej Mijakowski

This article proposes new values and geospatial models of winter and summer external design temperatures for designing buildings’ heating, ventilation, and air-conditioning (HVAC) systems. The climatic design parameters applicable in Poland for the sizing of these installations are approximately 50 years old and do not correspond to Poland’s current climate. New values of climatic design parameters were determined following the methods described in European standards and the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Handbook of Fundamentals. The determined climatic design parameters, particularly the winter and summer external design temperatures, were compared with those currently in force by law in Poland. The external air design dry-bulb temperatures presented in the article were developed based on meteorological and climatic data from the years 1991–2020 from two data sources: synoptic data from the Institute of Meteorology and Water Management (IMWM) in Poland and reanalysis models of the ERA5 database of the European Centre for Medium-Range Weather Forecasts (ECMWF). According to ASHRAE, with 99.6% and 0.4% frequency of occurrence, external air design dry-bulb temperatures for winter and summer were used to develop mathematical geospatial models of external design temperatures for the Central Europe area with Poland’s territory in the centre part. Scattered data from 667 meteorological stations were interpolated to 40,000 uniform mesh points using a biharmonic spline interpolation method to develop these models. Linear regression and ANOVA analysis for the ERA5-generated data from 900 checkpoint data items were used to estimate the correctness of these models. Verified models were used to calculate winter and summer external design temperature isolines presented together with colour space representation on Mercator projected maps of Central Europe.

Joo Seung Lee, Malini Mahendra, Anil Aswani

Mechanical ventilation is a critical life support intervention that delivers controlled air and oxygen to a patient's lungs, assisting or replacing spontaneous breathing. While several data-driven approaches have been proposed to optimize ventilator control strategies, they often lack interpretability and alignment with domain knowledge, hindering clinical adoption. This paper presents a methodology for interpretable reinforcement learning (RL) aimed at improving mechanical ventilation control as part of connected health systems. Using a causal, nonparametric model-based off-policy evaluation, we assess RL policies for their ability to enhance patient-specific outcomes-specifically, increasing blood oxygen levels (SpO2), while avoiding aggressive ventilator settings that may cause ventilator-induced lung injuries and other complications. Through numerical experiments on real-world ICU data from the MIMIC-III database, we demonstrate that our interpretable decision tree policy achieves performance comparable to state-of-the-art deep RL methods while outperforming standard behavior cloning approaches. The results highlight the potential of interpretable, data-driven decision support systems to improve safety and efficiency in personalized ventilation strategies, paving the way for seamless integration into connected healthcare environments.

Qirui Huang, Marc Syndicus, Jérôme Frisch et al.

Accurate occupancy information helps to improve building energy efficiency and occupant comfort. Occupancy detection methods based on CO2 sensors have received attention due to their low cost and low intrusiveness. In naturally ventilated buildings, the accuracy of CO2-based occupancy detection is generally low in related studies due to the complex ventilation behavior and the difficulty in measuring the actual air exchange through windows. In this study, we present two novel features for occupancy detection based on the spatial distribution of the CO2 concentration. After a quantitative analysis with Support Vector Machine (SVM) as classifier, it was found that the accuracy of occupancy state detection in naturally ventilated rooms could be improved by up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1 score 0.84) without any ventilation information. With ventilation information, the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy quantity detection was significantly improved by up to 25.3 percentage points versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44 occupants, using only CO2-related features. Additional ventilation information further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By incorporating spatial features, the model using only CO2-related features revealed similar performance as the model containing additional ventilation information, resulting in a better low-cost occupancy detection method for naturally ventilated buildings.

Chaobo Zhang, Yazhou Zhao, Yang Zhao et al.

Andrea Lozoya-Peral, Carlos Pérez-Carramiñana, Antonio Galiano-Garrigós et al.

This research explores the energy behaviour of a traditional house on the Mediterranean coast of south-eastern Spain. The objective of the work is to determine the optimal passive strategies for rehabilitating a traditional house, improving its energy savings and comfort, considering the characteristics of the warm semi-arid Mediterranean climate. The main novelty of this article is that it demonstrates that the limits imposed by current regulations, based on globalised climate strategy approaches, undermine the energy efficiency capacity that passive solutions in vernacular architecture already employed. The methodology used consists of a systematised multi-objective study of various energy rehabilitation strategies. Four strategies were studied: raising the thermal insulation of enclosures, improving thermal insulation and solar control glazing with movable shading devices, increasing the size of windows and introducing the use of natural ventilation enhanced by ceiling fans. The results show that simultaneous improvement of these parameters reduces cooling and heating requirements by up to 87%, reducing the energy consumption of air conditioning systems. Indoor temperatures are also maintained within the comfort limits set by regulations for 91% of hours per year without the need for air conditioning systems. This results in a passive energy-efficient and comfortable house almost all year round. This work offers an alternative solution to the comfort standards of current Spanish regulations and demonstrates the need to adapt Fanger’s analytical method for comfort estimation. The research concludes that the comfort criteria of current energy regulations should be modified to better adapt the design criteria to the dry Mediterranean climate.

Alexander Pretty, Ian M. Griffiths, Zechariah Lau et al.

We incorporate local ventilation effects into a spatially dependent generalisation of the Wells--Riley model for airborne disease transmission. Aerosol production and removal through ventilation, biological deactivation, and gravitational settling as well as transport around a recirculating air-conditioning flow and turbulent mixing are modelled using an advection--diffusion--reaction equation. The local ventilation model, motivated by air purifiers, is compared with the global ventilation model for a weak purifier (CADR = 140 m$^3$h$^{-1}$) and a strong purifier (CADR = 1,000 m$^3$h$^{-1}$). We find that, as expected, increasing the distance of the infectious person from the purifier reduces the aerosol concentration. Moreover, the concentration is generally lowest when the infectious person is upstream of the purifier, located in regions where the airflow streamlines are directed into the purifier inlet. For these infectious source locations, the global ventilation model significantly overestimates the concentration throughout the room. For infectious sources outside of these regions, there is generally good agreement between the models, particularly for the weak purifier. We also studied, for fixed distance from the purifier, how the infection risk to a susceptible person varies as the infectious person changes location. The infection risk is greatest when the susceptible person is directly downstream of the infectious person. There is better agreement between local and global ventilation models for the weak purifier than the strong purifier.

Ata Nazari, Changchang Wang, Ruichen He et al.

Airborne transmission of SARS-CoV-2 via virus-laden aerosols in enclosed spaces poses a significant concern. Elevators, commonly utilized enclosed spaces in modern tall buildings, present a challenge as the impact of varying heating, ventilation, and air conditioning (HVAC) systems on virus transmission within these cabins remains unclear. In this study, we employ computational modeling to examine aerosol transmission within an elevator cabin outfitted with diverse HVAC systems. Using a transport equation, we model aerosol concentration and assess infection risk distribution across passengers' breathing zones. We calculate particle removal efficiency for each HVAC design and introduce a suppression effect criterion to evaluate the effectiveness of the HVAC systems. Our findings reveal that mixing ventilation, featuring both inlet and outlet at the ceiling, proves most efficient in reducing particle spread, achieving a maximum removal efficiency of 79.40% during the exposure time. Conversely, the stratum ventilation model attains a mere removal efficiency of 3.97%. These results underscore the importance of careful HVAC system selection in mitigating the risk of SARS-CoV-2 transmission within elevator cabins.

David Meyer, Robert Schoetter, Maarten van Reeuwijk

Heat pumps (HPs) have emerged as a key technology for reducing energy use and greenhouse gas emissions. This study evaluates the potential switch to air-to-air HPs (AAHPs) in Toulouse, France, where conventional space heating is split between electric and gas sources. In this context, we find that AAHPs reduce heating energy consumption by 57% to 76%, with electric heating energy consumption decreasing by 6% to 47%, resulting in virtually no local heating-related CO$_{2}$ emissions. We observe a slight reduction in near-surface air temperature of up to 0.5 °C during cold spells, attributable to a reduction in sensible heat flux, which is unlikely to compromise AAHPs operational efficiency. While Toulouse's heating energy mix facilitates large energy savings, electric energy consumption may increase in cities where gas or other fossil fuel sources prevail. Furthermore, as AAHPs efficiency varies with internal and external conditions, their impact on the electrical grid is more complex than conventional heating systems. The results underscore the importance of matching heating system transitions with sustainable electricity generation to maximize environmental benefits. The study highlights the intricate balance between technological advancements in heating and their broader environmental and policy implications, offering key insights for urban energy policy and sustainability efforts.

E. Png, S. Srinivasan, Korkut Bekiroglu et al.

Scalability of control algorithms used for savings energy in commercial building Heating, Ventilation and Air-Conditioning (HVAC) system and their implementation on low cost resource constrained hardware is a challenging problem. This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software, networking, and their integration) along with their interactions. A detailed description of the different components, hardware design, deployment issues, and their integration with legacy systems as well as cloud-connectivity is presented. In addition, simple modifications required for transforming the optimization models to an active control technique is also presented. While scalability is provided by the decentralized control, recursive zone thermal model identification, prediction occupant’s thermal sensation, and embedding them within the optimization models enhances the smartness. Consequently, due to the implementation of Smart-TBSA using IoT devices, an otherwise centralized control architecture of the legacy building automation system is transformed to a more scalable and smart decentralized one. The proposed Smart-TBSA and IoT prototype are illustrated on a pilot building in Nanyang Technological University, Singapore having 85 zones. Our results shows that by combining IoT with decentralized control, energy savings up to 20% can be derived. Moreover, we show that legacy building automation system can be transformed into a more smart, adaptable, scalable, and decentralized control by deploying IoT devices without incurring significant costs.

Yuting Huang, Chao Li

Abstract Accurate prediction of the building load is crucial to ensure the energy saving and improve the operational efficiency of the heating, ventilation, and air conditioning (HVAC) system. In this study, the heating load (HL) and cooling load (CL) of buildings are analyzed using the Spearman method considering eight influencing factors: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. The ant colony optimization (ACO) method is used to optimize the ability of a wavelet neural network (WNN) to predict the HL and CL values of residential buildings. The linearly decreasing inertia weight and self-adaptive mutation operator are introduced to improve the optimizing capability of the ACO. An improved ACO-WNN (I-ACO-WNN) model is proposed to achieve a high-precision building load forecasting, and the formulas including the influence factors of the building load, are proposed. The regression coefficient values of the proposed forecasting model of HL and CL are 0.9714 and 0.9783, respectively. Compared to the traditional WNN model, the root mean square error values of HL and CL predictions by the I-ACO-WNN model are decreased by 66.01% and 73.28%, respectively; while the mean absolute error values are decreased by 82.44% and 84.82%, respectively; also, the mean absolute percentage error values are reduced by 81.21% and 85.31%, respectively; lastly, the mean square error values are reduced by 88.44% and 92.86%, respectively. The proposed prediction model can be used as a reliable tool for HL and CL estimation in future intelligent urban planning.

A. Shajahan, C. Culp, Brandon Williamson

The indoor environment of a mechanically ventilated hospital building controls infection rates as well as influences patients' healing processes and overall medical outcomes. This review covers the scientific research that has assessed patients' medical outcomes concerning at least one indoor environmental parameter related to building heating, ventilation, and air conditioning (HVAC) systems, such as indoor air temperature, relative humidity, and indoor air ventilation parameters. Research related to the naturally ventilated hospital buildings was outside the scope of this review article. After 1998, a total of 899 papers were identified that fit the inclusion criteria of this study. Of these, 176 papers have been included in this review to understand the relationship between the health outcomes of a patient and the indoor environment of a mechanically ventilated hospital building. The purpose of this literature review was to summarize how indoor environmental parameters related to mechanical ventilation systems of a hospital building are impacting patients. This review suggests that there is a need for future interdisciplinary collaborative research to quantify the optimum range for HVAC parameters considering airborne exposures and patients' positive medical outcomes.

Yunjae Hwang, Laura, Kwong et al.

According to UNICEF, pneumonia is the leading cause of death in children under 5. 70% of worldwide pneumonia deaths occur in only 15 countries, including Bangladesh. Previous research has indicated a potential association between the incidence of pneumonia and the presence of cross-ventilation in slum housing in Dhaka, Bangladesh. The objective of this research is to establish a validated computational framework that can predict ventilation rates in slum homes to support further studies investigating this correlation. To achieve this objective we employ a building thermal model (BTM) in combination with uncertainty quantification (UQ). The BTM solves for the time-evolution of volume-averaged temperatures in a typical home, considering different ventilation configurations. The UQ method propagates uncertainty in model parameters, weather inputs, and physics models to predict mean values and 95% confidence intervals for the quantities of interest, namely temperatures and ventilation rates in terms of air changes per hour (ACH). The model predictions are compared to on-site field measurements of air and thermal mass temperatures, and of ACH. The results indicate that the use of standard cross- or single-sided ventilation models limits the accuracy of the ACH predictions; in contrast, a model based on a similarity relationship informed by the available ACH measurements can produce more accurate predictions with confidence intervals that encompass the measurements for 12 of the 17 available data points.

I. Romani de Oliveira, T. Matsumoto, E. C. Pinto Neto

The large public interest in Advanced Air Mobility (AAM) will soon lead to congested skies overhead cities, analogously to what happened with other transportation means, including commercial aviation. In the latter case, the combination of large distances and demanded number flights is such that a system with centralized control, with most of the decisions made by human operators, is safe. However, for AAM, it is expected a much higher demand, because it will be used for people's daily commutes. Thus, higher automation levels will become a requirement for coordinating this traffic, which might not be effectively managed by humans. The establishment of fixed air routes can abate complexity, however at the cost of limiting capacity and decreasing efficiency. Another alternative is the use of a powerful central system based on Artificial Intelligence (AI), which would allow flexible trajectories and higher efficiency. However, such system would require concentrated investment, could contain Single-Points-of-Failure (SPoFs), would be a highly sought target of malicious attacks, and would be subject to periods of unavailability. This work proposes a new technology that solves the problem of managing the high complexity of the AAM traffic with a secure distributed approach, without the need for a proprietary centralized automation system. This technology enables distributed airspace allocation management and conflict resolution by means of trusted shared data structures and associated smart contracts running on a blockchain ecosystem. This way, it greatly reduces the risk of system outages due to SPoFs, by allowing peer-to-peer conflict resolution, and being more resilient to failures in the ground communication infrastructure. Furthermore, it provides priority-based balancing mechanisms that help to regulate fairness among participants in the utilization of the airspace.

Gail M. Thornton, B. Fleck, Emily Kroeker et al.

Aerosol transmission has been a pathway for virus spread for many viruses. Similarly, emerging evidence regarding SARS-CoV-2, and the resulting pandemic as declared by WHO in March 2020, determined aerosol transmission for SARS-CoV-2 to be significant. As such, public health officials and professionals have sought data regarding the effect of Heating, Ventilation, and Air Conditioning (HVAC) features to control and mitigate viruses, particularly coronaviruses. A systematic review was conducted using international standards to identify and comprehensively synthesize research examining the effectiveness of ventilation for mitigating transmission of coronaviruses. The results from 32 relevant studies showed that: increased ventilation rate was associated with decreased transmission, transmission probability/risk, infection probability/risk, droplet persistence, virus concentration, and increased virus removal and virus particle removal efficiency; increased ventilation rate decreased risk at longer exposure times; some ventilation was better than no ventilation; airflow patterns affected transmission; ventilation feature (e.g., supply/exhaust, fans) placement influenced particle distribution. Some studies provided qualitative recommendations; however, few provided specific quantitative ventilation parameters suggesting a significant gap in current research. Adapting HVAC ventilation systems to mitigate virus transmission is not a one-solution-fits-all approach but instead requires consideration of factors such as ventilation rate, airflow patterns, air balancing, occupancy, and feature placement.

Miaohong Huang, Y. Liao

Office occupants spend most of their time in an enclosed indoor environment, controlled by heating, ventilation and air-conditioning (HVAC) systems especially in subtropical regions owing to the hot and humid climate. A reasonable indoor environment evaluation model is necessary to achieve the reliable control of HVAC systems that satisfies the occupants’ health and comfort needs. However, traditional HVAC systems are controlled based on a simple index that does not consider the synthesis of indoor air quality, thermal comfort and occupant preferences. In this paper, we develop a comprehensive evaluation model that encompasses these three aspects based on field survey. Field surveys were conducted to investigate indoor environmental conditions and preferences of the occupants. Collected data were then verified for model hypothesis rationality and reviewed to identify weighting factors using Pearson and regression analysis. Results showed that these parameters had significant correlations without noticeable collinearity and can be integrated using regression method. The weighting factors of each parameter were calculated using occupants’ sensation and expectation to reflect the subjective preferences in model. Finally, an evaluation model expressing the indoor thermal, air quality and occupant preferences was developed to provide an HVAC intelligent control system that is more responsive to occupant needs.

J. Rodríguez, N. Fumo

Zhanjun Guo, H. Moayedi, L. K. Foong et al.

Abstract This study pursues optima modification of heating, ventilating, and air conditioning (HVAC) systems embedded in residential buildings through predicting heating load (HL) and cooling load (CL). This purpose is carried out by employing four wise metaheuristic algorithms, namely wind-driven optimization (WDO), whale optimization algorithm (WOA), spotted hyena optimization (SHO), and salp swarm algorithm (SSA) synthesized with a multi-layer perceptron (MLP) neural work in order to overcome the computational shortcomings of this model. The used dataset consists of overall height, glazing area, orientation, relative compactness, wall area, glazing area distribution, roof area, and surface area as independent factors, and the HL and CL as target factors. The results indicated a high capability of all four metaheuristic ensembles for understanding the non-linear relationship between the mentioned factors. Meanwhile, a comparison between the used models revealed that SSA-MLP (ErrorHL = 1.9178 and ErrorCL = 2.1830) is the most accurate model, followed by WDO-MLP (ErrorHL = 1.9863 and ErrorCL = 2.2424), WOA-MLP (ErrorHL = 2.1921 and ErrorCL = 2.5390), and SHO-MLP (ErrorHL = 3.1092 and ErrorCL = 4.5930). Regarding the satisfying accuracy of the SSA-based ensemble, it can be a reliable tool for estimating the HL and CL for future smart city planning.

A. Giampieri, Zhiwei Ma, A. Smallbone et al.

In an effort to minimise electricity consumption and greenhouse gases emissions, the heating, ventilation and air-conditioning sector has focused its attention on developing alternative solutions to electrically-driven vapour-compression cooling. Liquid desiccant air-conditioning systems represent an energy-efficient and more environmentally friendly alternative technology for dehumidification and cooling, particularly in those cases with high latent loads to maintain indoor air quality and comfort conditions. This technology is considered particularly efficient in hot and humid climates. As a matter of fact, the choice of the desiccant solution influences the overall performance of the system. The current paper reviews the working principle of liquid desiccant systems, focusing on the thermodynamic properties of the desiccant solutions and describes an evaluation of the reference thermodynamic properties of different desiccant solutions to identify which thermodynamic, physical, transport property influences the liquid desiccant process and to what extent. The comparison of these thermodynamic properties for the commonly used desiccants is conducted to estimate which fluid could perform most favourably in the system. The economic factors and the effect of different applications and climatic conditions on the system performance are also described. The paper is intended to be the first step in the evaluation of alternative desiccant fluids able to overcome the problems related to the use of the common desiccant solutions, such as crystallization and corrosion to metals. Ionic liquids seem a promising alternative working fluid in liquid desiccant air-conditioning systems and their characteristics and cost are discussed.

P. Horve, Leslie G Dietz, M. Fretz et al.

Available information on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission by small particle aerosols continues to evolve rapidly. To assess the potential role of heating, ventilation, and air conditioning (HVAC) systems in airborne viral transmission, this study sought to determine the viral presence, if any, on air handling units in a healthcare setting where Coronavirus Disease 2019 (COVID-19) patients were being treated. The presence of SARS-CoV-2 RNA was detected in approximately 25% of samples taken from nine different locations in multiple air handlers. While samples were not evaluated for viral infectivity, the presence of viral RNA in air handlers raises the possibility that viral particles can enter and travel within the air handling system of a hospital, from room return air through high efficiency MERV-15 filters and into supply air ducts. Although no known transmission events were determined to be associated with these specimens, the findings suggest the potential for HVAC systems to facilitate transmission by environmental contamination via shared air volumes with locations remote from areas where infected persons reside. More work is needed to further evaluate the risk of SARS-CoV-2 transmission via HVAC systems and to verify effectiveness of building operations mitigation strategies for the protection of building occupants. These results are important within and outside of healthcare settings and may present a matter of some urgency for building operators of facilities that are not equipped with high-efficiency filtration.