Hasil untuk "Heating and ventilation. Air conditioning"

K. Chua, S. Chou, Wenming Yang et al.

S. Dimitroulopoulou, M. Dudzińska, L. Gunnarsen et al.

Buildings are constructed and operated to satisfy human needs and improve quality of life. Good indoor air quality (IAQ) and thermal comfort are prerequisites for human health and well-being. For their provision, buildings often rely on heating, ventilation, and air conditioning (HVAC) systems, which may lead to higher energy consumption. This directly impacts energy efficiency goals and the linked climate change considerations. The balance between energy use, optimum IAQ and thermal comfort calls for scientifically solid and well-established limit values for exposures experienced by building occupants in indoor spaces, including homes, schools, and offices. The present paper aims to appraise limit values for selected indoor pollutants reported in the scientific literature, and to present how they are handled in international and national guidelines and standards. The pollutants include carbon dioxide (CO2), formaldehyde (CH2O), particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and radon (Rn). Furthermore, acknowledging the particularly strong impact on energy use from HVAC, ventilation, indoor temperature (T), and relative humidity (RH) are also included, as they relate to both thermal comfort and the possibilities to avoid moisture related problems, such as mould growth and proliferation of house dust mites. Examples of national regulations for these parameters are presented, both in relation to human requirements in buildings and considering aspects related to energy saving. The work is based on the Indoor Environmental Quality (IEQ) guidelines database, which spans across countries and institutions, and aids in taking steps in the direction towards a more uniform guidance for values of indoor parameters. The database is coordinated by the Scientific and Technical Committee (STC) 34, as part of ISIAQ, the International Society of Indoor Air Quality and Climate.

G. Correia, Lisa Rodrigues, M. A. D. Silva et al.

Summary The world is facing a pandemic of unseen proportions caused by a corona virus named SARS-CoV-2 with unprecedent worldwide measures being taken to tackle its contagion. Person-to-person transmission is accepted but WHO only considers aerosol transmission when procedures or support treatments that produce aerosol are performed. Transmission mechanisms are not fully understood and there is evidence for an airborne route to be considered, as the virus remains viable in aerosols for at least 3 h and that mask usage was the best intervention to prevent infection. Heating, Ventilation and Air Conditioning Systems (HVAC) are used as a primary infection disease control measure. However, if not correctly used, they may contribute to the transmission/spreading of airborne diseases as proposed in the past for SARS. The authors believe that airborne transmission is possible and that HVAC systems when not adequately used may contribute to the transmission of the virus, as suggested by descriptions from Japan, Germany, and the Diamond Princess Cruise Ship. Previous SARS outbreaks reported at Amoy Gardens, Emergency Rooms and Hotels, also suggested an airborne transmission. Further studies are warranted to confirm our hypotheses but the assumption of such way of transmission would cause a major shift in measures recommended to prevent infection such as the disseminated use of masks and structural changes to hospital and other facilities with HVAC systems.

Yujiao Chen, Zhe-ming Tong, Yang Zheng et al.

Abstract Advanced control strategies are central components of smart buildings. For model-based control algorithms, the quality of the model that represents building systems and dynamics is essential to guarantee satisfactory performance of smart building control and automation. For the model predictive control of the heating, ventilation, and air conditioning systems in buildings coupled with natural ventilation, a high-fidelity model is necessary to reliably predict the thermal responses of the building under various environmental and operational conditions. This task can be accomplished by using a deep neural network, which can capture the dynamics of complicated physical processes, such as natural ventilation. Training a deep neural network requires the collection of a large amount of data; however, in practice, the target building may not have enough operational data available. This study demonstrates how transfer learning could help with this dilemma. By freezing most layers of a deep neural network model with 42,902 parameters that are pre-trained on multi-year data from a source room in Beijing, the model can be re-trained with only 200 trainable parameters on only 15 days of data from the target room in Shanghai that has entirely different floor area, building material, and window size. The proposed transfer learning model achieves high accuracy predicting both indoor air temperature and relative humidity for a time horizon from 10 minutes to 2 hours, showing the mean squared error almost one magnitude smaller than the comparison model that is only trained on source data or target data. This methodology can be applied to the design of the control system in a new building which reduces the required amount of data for the training of the model, thus saving costs in control system design and commissioning.

Hua Yihuai, Li Qiuying, Cheng Hao et al.

Ortho-para hydrogen conversion in the hydrogen liquefaction process is significant for the long-term storage and long-distance transportation of liquid hydrogen. This paper outlines the differences in the properties of orthohydrogen and parahydrogen, reviews the research progress on the physical mechanisms and reaction kinetic models of the ortho-para hydrogen catalytic conversion process, and summarizes the performance of common catalysts. Finally, three mainstream schemes for ortho-para hydrogen conversion are compared. Research on the internal physical mechanisms and reaction kinetic models explores the conversion process from microscopic and macroscopic perspectives, respectively. Owing to the lack of experimental data, scholars have not yet formed a unified explanation for the surface characteristics of catalysts, which must be quantitatively validated. Furthermore, although nickel-based catalysts have higher catalytic efficiency, iron hydroxides and oxide catalysts are the main catalyst choices for ortho-para hydrogen conversion, considering the preparation, activation, and deactivation of catalysts and the characteristics of the liquefier. Among the three mainstream ortho-para hydrogen conversion schemes, the hydrogen liquefaction process with continuous conversion has the lowest energy consumption and is the future direction. Relevant research in China is still in its early stages and has great potential for development. This study provides theoretical guidance for the design and construction of ortho-para hydrogen catalytic conversion test benches.

Zixin Jiang, Zhipeng Deng, Xuezheng Wang et al.

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85 % accuracy using a one-person offset. The accuracy of 15 min of the ahead prediction could reach 95 % with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2 % for 93 % of the day. This OBMPC model could reduce the coil load by 52.44 % and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9 % to 26 % difference in demand ventilation, a 0.3 °C to 2.4 °C difference in zone temperature, a 28.5 % to 44.5 % difference in outdoor airflow rate, and a 10.7 % to 28.2 % difference in coil load.

M. Andamon, P. Rajagopalan, J. Woo

School classrooms are often reported as having insufficient ventilation with elevated indoor CO2 concentrations. This paper reports on pre-pandemic field measurements of CO2 concentration levels conducted for an academic year in 10 classrooms from four primary and a secondary school in Victoria, Australia. Measured CO2 concentrations across the 10 classrooms which were operated with a mix of intermittent natural ventilation and air-conditioning for cooling or heating, on average ranged between 657 ppm and 2235 ppm during school hours with median over 1000 ppm in 70% of classrooms. All 10 classrooms in the study exceeded the Australian recommended limit of 850 ppm. Using average peak CO2 concentrations from year-long measurements, estimated ventilation rate (VR) of 4.08 Ls-1 per person show under-performing classrooms where 60% had VRs 35–40% lower than the 10-12 Ls−1 per person Australian recommendation. Estimated VR range of 1.24–2.07 Ls-1 per person using peak maximum CO2 levels were 19–30% lower than ASHRAE recommendation of 6.7 Ls-1 per person. These VRs translate to a range of air change rates on average between 0.52 and 0.88 h−1 ± 0.26–0.59, well below the 6.0 h−1 recommendation for good indoor ventilation by the World Health Organisation in the context of COVID-19 pandemic. Characterisation of ventilation and indoor air quality in current Australian classroom stock is critical for the improvement of classroom design, induction on room operating practices, understanding of the school community on the relevance of building ventilation on school performance and health, and development of appropriate ventilation and indoor air quality guidelines for schools. © 2023 The Authors

Ivan Panfilov, Alexey N. Beskopylny, Besarion Meskhi

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver’s blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport.

Wei-Han Chen, Fengqi You

A novel model predictive control (MPC) framework is proposed to optimize the energy management of integrated rooftop greenhouses and buildings, aiming to reduce control costs and the likelihood of climate violations. The centralized intelligent control approach employed for both the integrated rooftop greenhouse and the building ensures optimal conditions for crops and occupants. The integrated rooftop greenhouse utilizes waste heat and air from the building, resulting in reduced energy and CO2 consumption. The nonlinear dynamic models of temperature, humidity, and CO2 concentration for integrated rooftop greenhouse climate and building are first constructed. An integrated optimization problem is then formulated to acquire the optimal control decisions. The proposed MPC framework is implemented to regulate temperature, humidity, and CO2 level via controlling fans, pad cooling, shades, heating, ventilation and air conditioning systems, CO2 injection, and lighting systems. The indoor climate of an integrated rooftop greenhouse on a building in Brooklyn, New York, is controlled for the case study to show the advantages of the proposed nonlinear model predictive control framework. The results show that the average energy savings from the building to the integrated rooftop greenhouse amount to 15.2 % with the integration of the i-RTG and the host building under the proposed MPC framework.

E. Kamel, S. Sheikh, Xueqing Huang

Abstract Data-driven models can estimate the buildings’ energy consumption using machine learning algorithms. This approach works based on the correlation between energy consumption and various inputs such as weather data, occupancy schedules, heating, air conditioning, and physical properties of buildings. Seasonal changes affect buildings’ energy use. Hence, the required data for data-driven models (DDMs) during the heating and cooling days could be different. Selecting the most impactful inputs can help to choose the type and quantity of sensors for deployment that improve the model’s accuracy and minimize the costs. This paper performs feature selection for heating, cooling, hot water, and ventilation loads in residential buildings under the mixed-humid climate zone. Filter method, wrapper backward elimination, wrapper recursive feature elimination, Lasso regression, linear regression, and Extreme Gradient Boosting (XGBoost) regression are adopted for heating and cooling days, separately. We use twenty-five outputs from a computer model, and the results show that the key features for a DDM are different for heating and cooling days, and XGBoost provides the most accurate forecast. The findings of this paper are useful for selecting proper models, sensors, and inputs for model-predictive control systems during the heating and cooling seasons.

Sanjiban Sekhar Roy, Pijush Samui, Ishan Nagtode et al.

John Kaiser Calautit, Hassam Nasarullah Chaudhry

The built environment sector is responsible for a significant proportion of the final global energy consumption [...]

Saad Dilshad, A. Kalair, N. Khan

Refrigerants bearing high global warming potential (GWP) and ozone depletion potential (ODP) were outlawed or facing time‐beared permission under the Montreal (1987), Kyoto (1997) protocols, F‐Gas law (2015), Paris Accord (2016), and recent Kigali Amendment to Montreal Protocol (2019). In order to modify followed by the paradigm shift of existing heating and cooling systems, American Society of Heating, Refrigeration, and Air‐Conditioning Engineers (ASHRAE) envisaged natural and synthetic refrigerants (Synrefs) are under investigation globally. Carbon dioxide (CO2) is a popular natural refrigerant (Natref) replacing Synrefs used in commercial heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems globally. A rampant rise is observed in markets of Asia (Japan and China), North America (the United States and Canada), the Australian continent, and Africa (South Africa) by reaching 20 000 CO2‐stores around the globe. The European markets are leading the utilization of CO2 in the heat pump, and refrigeration, CO2 based markets estimate 14% of the total food retail stores (400 m2). Japan is the second leading market of CO2 heat pump and refrigeration with more than 10 200 CO2 condensing units. CO2 transcritical systems have a share of more than 10% in only European market (large stores); however, their share is less than 10% of total stores in other major markets of the world. New pump and compressor‐driven transcritical CO2 systems integrate ejectors, condensers, and booster systems to reduce energy consumptions, enhance efficiency, efficacy, and coefficient of performance. This article reports a critical review of the CO2 based heating, cooling, and refrigeration system and presents updated literature along with barriers and challenges on commercial use of Natref‐based heating and cooling applications worldwide.

Wang Moran, Dong Bin, Liang Kunfeng et al.

The driving range of pure electric vehicles (PEVs) significantly decreases in winter. In this study, a mathematical model of vehicle driving range considering the heat production of the battery and the load of the air conditioning was established to simulate and analyze the influence of heat pump heating, ambient temperature (AT) and cabin temperature (CT) on the driving range and the vehicle battery during the cold start under three driving conditions, namely, highway fuel economy test (HWFET)、new European driving cycle(NEDC)、China light-duty vehicle test cycle for passenger car (CLTC-P). Compared with the measured data, the simulation and experiment match well. The results show that the driving range decreases gradually with the decrease of AT and the increase of CT under three driving conditions. When the AT is 0 ℃ and the CT is 15 ℃, 20 ℃, and 25 ℃, the driving range of the China CLTC-P standard decreases by 21.46%, 27.74%, and 33.19%, respectively. The influences of three heat distribution strategies on driving range are different during the cold start of PEVs. During the cold start, when all the heat of heat pump is used to heat the battery, the battery power can be recovered properly. Under the driving conditions of NEDC and CLTC-P, the maximum state of charge (SOC) of the battery increases by 1.52% and 2.03% compared with the initial SOC. The driving range can be increased to a certain extent by using the heat pump.

Xiaochen Liu, Xiaohua Liu, Tao Zhang

Abstract Air infiltration is common in large space buildings during heating and cooling seasons, which leads to thermal discomfort and high energy consumption. However, there still lacks a valid theoretical model to analyze its mechanism and influence factors. In this study, a theoretical model of buoyancy-driven air infiltration in large space buildings is established with the factors of heating, ventilation and air-conditioning (HVAC) systems. The prediction results agree well with the field measurement and the numerical simulation. This model is used to analyze the influences of HVAC systems on air infiltration (i.e., thermal stratification and mechanical fresh/exhaust air). A dimensionless buoyancy-driven force of air infiltration (CT) is defined to quantify the thermal stratification. Compared with other typical terminal devices, the radiant floor has the lowest CT in the heating and cooling conditions, which results in the lowest air infiltration rate. Besides, the air infiltration rate can theoretically decrease to zero with a sufficient mechanical fresh air rate of 1.4 times of the initial air infiltration rate, which is too large to realize in real buildings. This model helps to understand the air infiltration in large space buildings, which is beneficial to energy-efficient design and operation of HVAC systems.

Xiaozhou Wu, Jie Gao, Pin Lv et al.

A mixing ventilation (MV) system integrated with a chilled ceiling (CC) cooling system will be a potential advanced heating, ventilation and air conditioning (HVAC) system in the modern buildings. This paper presented an experimental study concerning the effect of CC on the indoor air distribution with MV when the internal and external sensible cooling loads changed. The vertical distributions of indoor air temperature, air velocity and contaminant (CO2) concentration were evaluated by the vertical air temperature difference, turbulence intensity and contaminant removal effectiveness, respectively. The results showed that when chilled ceiling surface temperature ranged from 17.0 °C to 26.0 °C, the average vertical air temperature difference, turbulence intensity and contaminant removal effectiveness were 0.2 °C–0.4 °C, 30%–36% and 0.6–0.84 when both internal and external sensible cooling loads were 41.5 W/m2. Moreover, the ranges of these evaluation indices varied slightly when the internal or external sensible cooling load increased from 41.5 W/m2 to 69.5 W/m2. Hence, the chilled ceiling had a slight impact on the indoor air distribution in a room with mixing ventilation. It is interestingly found that the small vertical air temperature difference coincided with the large turbulence intensity and contaminant removal effectiveness with different internal or external cooling loads.

Yin Zheng, Guiqiang Wang, Guohui Feng et al.

Jing Lv, Guo Li, Tang fuyi Xu et al.

Zuo Cheng, Baolong Wang, Wenxing Shi et al.

Zhaoyi Zhuang, Jian Song, Jun Xu et al.