Hasil untuk "Heating and ventilation. Air conditioning"

Nicholas Andrew Harvey, Eziaku Onyeizu Rasheed

This paper attempts to find alternative ways in which heating, ventilation, air conditioning and refrigeration systems can be made more energy efficient and sustainable at a global level. Eight technologies or solutions that either passively or supplementarily reduce the heating or cooling load required by a structure are detailed. These technologies or solutions were then presented to heating, ventilation, air conditioning and refrigeration industry professionals in New Zealand to determine their viability and further establish market readiness towards integrating new, innovative, and sustainable solutions in New Zealand. A literature review was conducted to establish the performance of the selected solutions and understand their operational principles and the efficiency they provided. Qualitative research and data collected via semi-structured interviews provided the data for assessing the viability of the selected technologies in the New Zealand market. Following a thematic and hybrid-thematic analysis of the data, the technologies were ranked, and suggestions were made to help improve innovation and energy efficiency in the heating, ventilation, air conditioning, and refrigeration industry in New Zealand. Of the technologies selected, airtightness, heat recovery ventilation retrofits, materials and design principles, and photovoltaic hot water heating were identified as the most viable. The New Zealand market was deemed not to be in a good position to adopt new or alternative solutions. The main issues affecting New Zealand’s market readiness to assimilate innovative and energy-efficient solutions are a lack of new technologies, poor standards of education throughout the industry, a lack of regulation, and a lack of government incentives.

Islam El Jaddaoui, Kayo Denda, Hassan Ghazal et al.

Molds readily grow on wet books, documents, and other library materials where they ruin them chemically, mechanically, and aesthetically. Poor maintenance of libraries, failures of Heating, Ventilation, and Air Conditioning (HVAC) systems, roof leaks, and storm damage leading to flooding can all result in accelerated fungal growth. Moreover, when fungal spores are present at high concentrations in the air, they can be linked to severe respiratory conditions and possibly to other adverse health effects in humans. Climate change and the accompanying storms and floods are making the dual potential of fungi to biodegrade library holdings and harm human health more common. This essay is intended for microbiologists without much background in mycology who are called in to help librarians who are dealing with mold outbreaks in libraries. Our goal is to demystify aspects of fungal taxonomy, morphology, and nomenclature while also recommending guidelines for minimizing mold contamination in library collections.

Aliya Banu, Namra Mir, Muftah H. El‐Naas et al.

ABSTRACT This article presents a thorough life cycle assessment (LCA) study on carbon capture and utilization (CCU) systems for low‐carbon fuel production. The process involves capturing carbon dioxide (CO2) from indoor environments using an integrated heating, ventilation, and air conditioning (HVAC)—direct air capture (DAC) unit, a technology crucial for mitigating climate change (CC). Integrating DAC with HVAC systems is highlighted for its potential to enhance energy efficiency and indoor air quality. Electrochemical reduction of CO2 to formic acid (FA) and Fischer–Tropsch processes are studied for carbon utilization. A sensitivity analysis was performed on the adsorbent type, electricity source, and water source. The environmental impacts were found to be 1.80 kg CO2 eq, 9.04 × 10−4 kg PM2.5 eq, 1.04 × 10−5 kg P eq, 2.95 × 10−3 kg SO2 eq, 0.36 kg 1,4 DB eq. for CC, fine particulate matter, freshwater eutrophication, terrestrial acidification, and terrestrial ecotoxicity, respectively, per kg FA produced. Using renewable energy can significantly lower the environmental impacts; the lowest value was obtained from integration with nuclear energy at 0.496 kg CO2 eq/kg FA. A specific Qatar case study was also performed for FA production with CO2 utilized from DAC‐HVAC. The paper highlights the environmental benefits of CCU, emphasizing its dual purpose of addressing CC and sustainable fuel production. This study represents a significant contribution to global initiatives for a more sustainable and carbon‐neutral future.

Jingfan Hu, Wandong Zheng, Sirui Zhang et al.

Reza Andika Setyadi, Rudy Setiabudy

Good power quality is required in health facilities since the increasing use of microprocessor-based equipment. Poor power quality in the electric power system can cause medical equipment in health care centers to malfunction and give incorrect medical diagnoses. Since 2020 we are facing a new type of Virus (Covid-19), this virus requires special laboratory construction with specific air conditioning systems (temperature, humidity, and pressure) to process specimens. This paper presents measurement results of harmonic distortion characteristic by Variable Frequency Drive (VFD) of Heating Ventilation Air Conditioning (HVAC) system in PCR Laboratory at Clinic A. The VFD in this system is used to adjust the rotation of the exhaust fan and outdoor unit. The measured voltage, current and power are used to assess power quality. The main power quality problems found in medical facilities are voltage flicker, neutral currents, and total harmonic distortion (THD) values. The measurement of total harmonic distortion (THD) is used to find the source of the harmonics. Potential problems can be identified within the facility. The results of this study can be used to develop, test, and validate the system that has been used.

Liu Xiangzeng, Feng Wei, Zhang Gangan et al.

Experiments were conducted to study the condensation heat transfer and pressure drop characteristics of refrigerant R32 in aluminum herringbone tubes, ripple tubes, and smooth tubes. The experimental refrigerant mass flow rate is 100-350 kg/(m2·s). The saturation temperature is 308 K, 313 K, and 318 K, respectively, while the vapor quality is 0.2-0.8. The experimental results show that the herringbone tube has the highest coefficient of heat transfer, followed by the ripple tube, while the smooth tube is the worst. The friction pressure drop is highest for the ripple tube, and the pressure drop of the herringbone tube is higher than that of the smooth tube. The surface coefficient of heat transfer and friction pressure drop decrease with an increasing saturation temperature and increase with an increasing mass flow rate. The performance evaluation factor, PEF, was introduced to evaluate the heat transfer performance of the heat transfer tube. The herringbone tube has the best heat transfer performance with PEF values ranging from 2.07 to 2.72, while the ripple tube has PEF values ranging from 0.68 to 0.90. A new heat transfer correlation was proposed following the form of classical heat transfer correlations, and the error of the new correlation formula was within ±20%.

Nan Gao, Wei Shao, M. Rahaman et al.

HVAC (Heating, Ventilation and Air Conditioning) system is an important part of a building, which constitutes up to 40% of building energy usage. The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage. Besides, thermal comfort is also crucial for well-being, health, and work productivity. Recently, data-driven thermal comfort models have got better performance than traditional knowledge-based methods (e.g. Predicted Mean Vote Model). An accurate thermal comfort model requires a large amount of self-reported thermal comfort data from indoor occupants which undoubtedly remains a challenge for researchers. In this research, we aim to tackle this data-shortage problem and boost the performance of thermal comfort prediction. We utilise sensor data from multiple cities in the same climate zone to learn thermal comfort patterns. We present a transfer learning based multilayer perceptron model from the same climate zone (TL-MLP-C*) for accurate thermal comfort prediction. Extensive experimental results on ASHRAE RP-884, the Scales Project and Medium US Office datasets show that the performance of the proposed TL-MLP-C* exceeds the state-of-the-art methods in accuracy, precision and F1-score.

P. Tien, S. Wei, J. Calautit et al.

Abstract This paper introduces a vision-based deep learning approach that enables the detection and recognition of occupants’ activities within building spaces. The data can feed into building energy management systems through the establishment of occupancy heat emission profiles, which can help minimise unnecessary heating, ventilation, and air-conditioning (HVAC) energy loads and effectively manage indoor conditions. The proposed demand-driven method can enable HVAC systems to adapt and make a timely response to dynamic changes of occupancy, instead of using “static” or fixed occupancy operation schedules, historical load, and time factor. Based on a convolutional neural network, the model was developed to enable occupancy activity detection using a camera. Training data was obtained from online image sources and captured images of various occupant activities in office spaces. Tests were performed by real-time live detection and predictions of occupancy activities in buildings. Initial activities response includes sitting, standing, walking, and napping. Average detection accuracy of 80.62% was achieved. The detection formed the real-time occupancy heat emission profiles known as the Deep Learning Influenced Profile. Along with typical ‘scheduled’ office occupancy profiles, a building energy simulation (BES) tool was used to further assess the framework. An office space in Nottingham, UK was selected to test the proposed method and modelled using building simulation. Using the deep learning detection method, the results showed that the occupancy heat gains could be represented more accurately in comparison to using static office occupancy profiles. The accurate detection of occupants and their activities can also be used to effectively estimate CO2 concentration. The information can be useful for modulating ventilation systems leading to better indoor environmental quality. Overall, this initial approach of the study showed the capabilities of this framework for detecting occupancy activities and providing reliable predictions of building internal gains.

Mariam Elnour, N. Meskin, M. Al-Naemi

Abstract The Heating, Ventilation, and Air conditioning (HVAC) system is a major system in buildings for conditioning the indoor environment. Sensor data validation and fault diagnosis for HVAC systems are essentially important to secure a reliable and efficient operation since sensor measurements are vital for the HVAC closed-loop control system. The aim of this work is to address this matter by developing a data-driven approach using the system's normal operation data and without the need for the knowledge of the mathematical model of the system. It is based on an Auto-Associative Neural Network (AANN) that is structured and trained to construct an input-output mapping model based on data dimensionality reduction that is capable of validating sensor measurements in terms of sensor error correction, missing data replacement, noise filtering, and inaccuracy correction. It can be used for both single and multiple sensor faults diagnosis by monitoring the consistency between the actual and the AANN-estimated sensor reading. The validation of the proposed method is demonstrated on data obtained from a 3-zone HVAC system simulated in TRNSYS. The evaluation results show the effectiveness of the proposed approach and an improvement in terms of data validation and diagnostic accuracy when compared with a PCA-based method.

R. Kamal, Francesca Moloney, C. Wickramaratne et al.

Abstract An operational strategy to optimize building operating energy costs for suppliers and consumers is an important challenge for electrical power utilities. There are various supply-side measures that utilities have to take to ensure continuous energy supply for building heating and air-conditioning. During peak energy demand, utilities are often forced to use more expensive and less efficient generation, thereby increasing the cost of energy. However, some demand-side management practices behind the consumer meter can help in meeting this challenge. One such measure is the use of thermal storage for heating, ventilation, and air-conditioning applications in commercial buildings. There is a gap of adequate knowledge of an optimal control strategy of cold storage operation in buildings adapting to applicable time of day tariffs to minimize annual energy use and annual energy cost of operation. There is also a need to use commercially available tools to avoid the use of complex mathematical models. This study demonstrates strategic controls with six operating modes for using thermal energy storage to shift peak electricity demand, using the time of day tariffs as a decision variable, and reducing operating costs, while also minimizing the size of the system. EnergyPlus was used to model a standard reference large office building for three thermal energy storage system cases: mixed chilled water storage, stratified chilled water storage, and ice storage. An annual average shifting of 25–78% of peak electricity was achieved from the simulation results. The strategy was able to achieve an annual 10–17% cost reduction for consumers using the time of use rates available from a local utility.

Nan Li, G. Calis, B. Becerik-Gerber

Angelette Terk, Jennifer Ormsby, Paula Conrad et al.

Background: In spring 2021, the infection prevention and control department at a pediatric academic medical center identified 3 oncology patients with concern for invasive Rhizopus spp infections. An in-depth investigation was conducted, but a common source of the fungus was not identified. In August 2021, an additional oncology patient with concern for invasive Rhizopus spp was identified, resulting in an extended investigation for possible sources of fungus. Methods: A multidisciplinary work group was assembled. The CDC Targeted Environmental Investigation Checklist for Outbreaks of Invasive Infections Caused by Environmental Fungi was used as a framework for conducting the investigation. Stakeholders were engaged throughout the process, including the hematology–oncology service, hospital leadership, environmental services, patient safety and quality, and facilities and engineering. The investigation included hospital incident command system (HICS) activation; visual inspection of patient rooms and common spaces; heating, ventilation, and air conditioning (HVAC) review; environmental sampling (surfaces, linen, and air); chart review; and process mapping. Results: By early October 2021, 2 environmental samples grew isolates (each at 1 CFU/m3) of the same species of Rhizopus as one of the affected patients. One sample was from a patient room, and the other from an outdoor garden space. No source of indoor amplification of Rhizopus was identified. The investigation revealed several opportunities for improvement: annual room maintenance schedules, use of gardens and outdoor spaces by at-risk patients, linen storage, construction and/or infection control risk assessment (ICRA) processes, and appliances used by families (eg, washing machines and refrigerators). Work streams were established to address each of these areas. Conclusions: No definite source was identified for the 4 invasive Rhizopus spp infections. This extensive investigation highlighted multiple opportunities for improvement; the changes implemented may prevent future invasive fungal infections in high-risk pediatric patients.

Hossein Saber, Mehdi Ehsan, Moein Moeini-Aghtaie et al.

Transactive energy (TE) provides joint market and control functionality to manage distributed energy resources (DERs) in distribution networks. This work develops a real-time TE management framework that allows residential customers to actively join in the real-time transactive market with fulfilling households’ preferences including comfort, economical energy consumption, and privacy-preserving. In this regard, first, a user-friendly algorithm is developed to calculate the real-time willingness to pay (bid) for electric vehicles (EVs) and heating, ventilation, and air conditioning (HVAC) units considering customers’ preferences and concerns. Then, to preserve the privacy of households, the centralized market-clearing problem to maximize social welfare is decomposed into several subproblems using the alternating direction method of multipliers (ADMM) approach. Also, closed-form solutions to all subproblems are derived to simplify implementation and mitigate the computational complexity instead of solving optimization subproblems directly. This model is then implemented in a case study with several numbers of smart homes. The numerical results illustrate that the proposed distributed transactive model not only satisfies households’ comfort preferences but also decreases the average charging cost of EV batteries by 40% compared to the uncontrolled charging model. Further, the results show that our proposed model significantly mitigates the computational burden of the transactive market clearing problem compared to the centralized approach and the distributed approach without closed-form solutions.

Yashen Lin, P. Barooah, Sean P. Meyn et al.

Yudong Ma, J. Matuško, F. Borrelli

Lan Wang, E. Lee, R. Yuen

Abstract Short-term load prediction, which forecasts a building’s thermal load with a lead time ranging from seconds to a few days, is essential for not only monitoring and controlling the system operation, but also on-line scheduling. Dynamic cooling load forecasting, which belongs to short-term load prediction, is both meaningful for monitoring the system or fuzzy on-line scheduling and crucial for solving the time-lag problem to meet the heating, ventilation and air-conditioning system’s time-varying cooling loads. Numerous studies have been carried out to develop dynamic load-forecasting models, and great achievements have been made. However, limitations in their applicability persist because most previous models are calendar- and time-based data-driven models that may fail when unexpected issues occur or special schedules are adopted. What’s more, the inputs that were selected passively from the source data pools at hand rather than via active exploration may be insufficient and impair the accuracy of forecasting models. This paper proposes a novel dynamic forecasting model for building cooling loads that combines an artificial neural network with an ensemble approach. Based on physical principles other than the available data source, the inputs are explored actively and are independent from both calendar and time indicators, which make the forecasting model being capable of dealing with irregular occasions and unexpected schedules with high accuracy. A benchmark is proposed that uses the current load Q ( t ) as a forecasted cooling load Q ^ ( t + i ) and gives the minimum accuracy requirement for a dynamic forecasting model. The benchmark not only can be used to evaluate dynamic forecasting models that are validated by various case studies, but also ensures that the proposed forecasting model can be applied immediately to heating, ventilation and air-conditioning systems to tackle the time-lag problem.

F. McGain, J. Burnham, R. Lau et al.

OBJECTIVE To use life cycle assessment to determine the environmental footprint of the care of patients with septic shock in the intensive care unit (ICU). DESIGN, SETTING AND PARTICIPANTS Prospective, observational life cycle assessment examining the use of energy for heating, ventilation and air conditioning; lighting; machines; and all consumables and waste associated with treating ten patients with septic shock in the ICU at BarnesJewish Hospital, St. Louis, MO, United States (US-ICU) and ten patients at Footscray Hospital, Melbourne, Vic, Australia (Aus-ICU). MAIN OUTCOME MEASURES Environmental footprint, particularly greenhouse gas emissions. RESULTS Energy use per patient averaged 272 kWh/day for the US-ICU and 143 kWh/day for the Aus-ICU. The average daily amount of single-use materials per patient was 3.4 kg (range, 1.0-6.3 kg) for the US-ICU and 3.4 kg (range, 1.2-8.7 kg) for the Aus-ICU. The average daily particularly greenhouse gas emissions arising from treating patients in the US-ICU was 178 kg carbon dioxide equivalent (CO2-e) emissions (range, 165-228 kg CO2-e), while for the Aus-ICU the carbon footprint was 88 kg CO2-e (range, 77-107 kg CO2-e). Energy accounted for 155 kg CO2-e in the US-ICU (87%) and 67 kg CO2-e in the Aus-ICU (76%). The daily treatment of one patient with septic shock in the US-ICU was equivalent to the total daily carbon footprint of 3.5 Americans' CO2-e emissions, and for the Aus-ICU, it was equivalent to the emissions of 1.5 Australians. CONCLUSION The carbon footprints of the ICUs were dominated by the energy use for heating, ventilation and air conditioning; consumables were relatively less important, with limited effect of intensity of patient care. There is large opportunity for reducing the ICUs' carbon footprint by improving the energy efficiency of buildings and increasing the use of renewable energy sources.

I. Beil, I. Hiskens, S. Backhaus

Varick L. Erickson, M. A. Carreira-Perpiñán, Alberto Cerpa

Zhang Rongrong, Zou Jiang, Sun Xiangli et al.

Direct refrigerant cooling has the advantages of low cost, high cooling efficiency, low weight, and high safety, and also problems of low evaporating temperature and uneven battery temperature in normal direct cooling refrigerant systems. In this study, the effectiveness of secondary throttling on the temperature regulation of a direct cooling plate was tested and verified. The results show that the direct cooling plate outlet pressure is increased while the superheat is reduced by adding a throttling device (fixed orifice device or a pressure-regulating valve with adjustable orifice) behind the cooling plate, thereby increasing the evaporation temperature and leading to better temperature uniformity. However, the throttle device with a fixed aperture cannot actively adjust the outlet pressure of the direct cooling plate, which increases as the thermal load increases. Therefore, it is difficult to control the battery temperature within an appropriate range when the thermal load changes. The throttle device with an adjustable aperture can adjust the direct cooling plate outlet pressure to a target value according to the operation load of the battery, which can not only avoid the evaporation temperature from being too low, but also improve the temperature uniformity.