With the development of cold chain Internet of Things technology, real-time temperature monitoring and data sharing have become important means of improving the efficiency of chilled meat supply chain management. In this paper, a time-temperature coordination optimization strategy based on cold chain Internet of Things is proposed to enhance the operational efficiency of the chilled meat supply chain. First, based on predictive microbiology and system reliability theory, the effects of time and temperature on the quality of chilled meat were analyzed, and a quality change model was constructed.
Next, through experimental analysis, an energy consumption model for the cold fresh meat supply chain was developed, and the preservation costs of each stage were quantified. It was found that there is an optimal freshness level in the chilled meat supply chain that maximizes supply chain benefits. Further analysis revealed that when the freshness level at a stage deviates from this optimal level, the supply chain benefits can still be maximized by adjusting the time and temperature in subsequent stages. Finally, the chilled chicken supply chain was used as a case study to explore the time-temperature coordination optimization strategy based on cold chain Internet of Things, providing a reference for improving the management efficiency of chilled meat supply chains.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Abstract Current fault diagnosis (FD) methods for heating, ventilation, and air conditioning (HVAC) systems do not accommodate for system reconfigurations throughout the systems’ lifetime. However, system reconfiguration can change the causal relationship between faults and symptoms, which leads to a drop in FD accuracy. In this paper, we present Fault-Symptom Brick (FSBrick), an extension to the Brick metadata schema intended to represent information necessary to propagate system configuration changes onto FD algorithms, and ultimately revise FSRs. We motivate the need to represent FSRs by illustrating their changes when the system reconfigures. Then, we survey FD methods’ representation needs and compare them against existing information modeling efforts within and outside of the HVAC sector. We introduce the FSBrick architecture and discuss which extensions are added to represent FSRs. To evaluate the coverage of FSBrick, we implement FSBrick on (i) the motivational case study scenario, (ii) Building Automation Systems’ representation of FSRs from 3 HVACs, and (iii) FSRs from 12 FD method papers, and find that FSBrick can represent 88.2% of fault behaviors, 92.8% of fault severities, 67.9% of symptoms, and 100% of grouped symptoms, FSRs, and probabilities associated with FSRs. The analyses show that both Brick and FSBrick should be expanded further to cover HVAC component information and mathematical and logical statements to formulate FSRs in real life. As there is currently no generic and extensible information model to represent FSRs in commercial buildings, FSBrick paves the way to future extensions that would aid the automated revision of FSRs upon system reconfiguration.
Zhao Shouzheng, Zhu Zongsheng, Zhao Songsong
et al.
The rapid development of fresh food distribution is facing significant pressure to reduce global carbon emissions. Reducing carbon emissions from last-mile distribution is important for energy conservation, environmental protection, and economic benefits. In this study, six typical cities in China are selected to analyze and evaluate the carbon emissions from ice storage and photovoltaic refrigeration in fresh food distribution using the entire life cycle method. When the design temperature in the delivery box is -5 °C, the results show that the carbon emission of six cities in the production stage for the photovoltaic refrigeration mode is higher than that of the ice storage mode. In the case of a 20-year life cycle, the total carbon emissions of the photovoltaic refrigeration mode in each city were reduced by 97.95%–98.78% compared with the total carbon emissions of the ice storage mode, and the emission reduction effect was significant. Among them, the carbon emissions from the use stage of the ice storage distribution mode contribute the most, and the carbon emissions from the production stage of the photovoltaic refrigeration distribution mode account for the most. Emission reduction benefits can be obtained in the decommissioning stage. When the design temperature in the distribution box increases from -5 °C to 0 °C, the carbon emissions of each city in the photovoltaic refrigeration mode are reduced by 17.74%–19.31%, whereas the carbon emissions in the ice storage mode are reduced by 13.21–18.79%. When the design temperature in the distribution box is increased from 0 °C to 5 °C, the carbon emissions of each city in the photovoltaic refrigeration mode are reduced by 17.03%–18.24%, whereas the carbon emissions in the ice storage mode are reduced by 15.22%–19.71%.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
AbstractOver the past three years, regulations have been implemented to combine natural ventilation (NV) and air conditioning to mitigate the risk of disease transmission, particularly in response to the COVID-19 outbreak. As we know, simultaneous use of NV and air conditioning can make it challenging to achieve indoor thermal comfort. This paper aims to analyze the effect of NV on the air conditioning`s cooling and heating load in a classroom through simulation. A simulation model was developed using EnergyPlus software with an OpenStudio interface software. Simulation results demonstrate that continuous use of NV alongside an air conditioner increases the cooling load from 1.06 to 1.75 times during summer and a 1.54 to 9.49 times heating load increase during winter. On the other hand, intermittent NV every hour results in a cooling load increase from 1.05 to 1.46 times in summer and a heating load increase from 1.13 to 4.63 times in winter. Moreover, employing NV based on the outside air temperature can reduce the cooling load at the air conditioner with set-point 26℃—28℃ from 0.94 to 0.88 times. The outcomes of this study are expected to serve as a reference for determining strategies that effectively combine NV and air conditioning to meet various needs without causing a significant increase in energy consumption. Additionally, the results are expected to be useful for reducing AC energy consumption in extremely hot and cold weather with some strategies of NV application.
Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research.
In this study, based on fins of four conventional shapes that are applied in microchannels, four novel fins are proposed using compound fins with cavities. Numerical simulations were conducted to investigate the flow and heat transfer characteristics of microchannels with conventional fins and those integrating fins with cavities. The mechanism by which the cavities influence the flow and heat transfer performance was analyzed, and performance evaluation criteria (PEC) was adopted as a criterion to compare the overall performance. The results showed that as the fin was compounded with cavities, the width of the fin decreased; thus, the separation zone downstream of the fin was reduced. In addition, the main flow induced a cyclic flow in the cavities. The velocity gradient at the liquid-liquid interface between the main flow and the cyclic flow was lower than that at the liquid-solid interface between the main flow and the fin surface, resulting in lower flow friction. Therefore, the pressure drop of the flow associated with fins with cavities was lower than that for conventional fins. However, the cavities reduced the disturbance of the main flow, leading to a deteriorated heat transfer enhancement performance. The influence of the cavities on the flow and heat transfer depended on the fin shape. Compared with conventional fins, the overall performance of fins compounded with cavities was reduced owing to heat-transfer deterioration.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
This study investigates the atomizing law of the nucleator nozzles, which are an important atomizing device for domestic outdoor snow-making units. Air and water are used as the medium, and the Sauter mean diameter, atomization flux, and spray angle of the domestic KBJD-1 nucleator nozzle are studied by a high-speed camera and laser particle size analyzer. The results show that the Sauter mean diameter decreases exponentially with an increase in air supply pressure but increases exponentially with an increase in water supply pressure. Moreover, the rate of change of the particle size gradually decreases with an increase in the gas-liquid pressure ratio. At the same experimental condition of water supply pressure at 1.0 MPa, when varying the air supply pressure from 0.3 MPa to 0.8 MPa, the atomization flow rate approximately decreases linearly with the air supply pressure. The spray angle first increases and then decreases with an increase in the air supply pressure. When the air supply pressure reaches 0.55 MPa, the spray angle reaches its maximum value.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
The nature and airborne dispersion of the underestimated biological agents, monitoring, analysis and transmission among the human occupants into building environment is a major challenge of today. Those agents play a crucial role in ensuring comfortable, healthy and risk-free conditions into indoor working and leaving spaces. It is known that ventilation systems influence strongly the transmission of indoor air pollutants, with scarce information although to have been reported for biological agents until 2019. The biological agents’ source release and the trajectory of airborne transmission are both important in terms of optimising the design of the heating, ventilation and air conditioning systems of the future. In addition, modelling via computational fluid dynamics (CFD) will become a more valuable tool in foreseeing risks and tackle hazards when pollutants and biological agents released into closed spaces. Promising results on the prediction of their dispersion routes and concentration levels, as well as the selection of the appropriate ventilation strategy, provide crucial information on risk minimisation of the airborne transmission among humans. Under this context, the present multidisciplinary review considers four interrelated aspects of the dispersion of biological agents in closed spaces, (a) the nature and airborne transmission route of the examined agents, (b) the biological origin and health effects of the major microbial pathogens on the human respiratory system, (c) the role of heating, ventilation and air-conditioning systems in the airborne transmission and (d) the associated computer modelling approaches. This adopted methodology allows the discussion of the existing findings, on-going research, identification of the main research gaps and future directions from a multidisciplinary point of view which will be helpful for substantial innovations in the field.
Buildings account for 40% of total primary energy consumption and 30% of all CO2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building’s electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.
This paper presented a review of the literature on the human thermal comfort model, which can be employed to predict the response of a human towards the environmental surroundings. An important premise of this paper is that governments in tropical regions have taken proactive action in minimizing energy consumption by air-conditioning through elevated room temperature. However, would such an action worsen the quality of interior conditions, particularly the thermal comfort? To answer this question, developing a human thermal comfort model under stratum ventilation mode can become a reference model for air-conditioning system design in all tropical buildings and indirectly reduce the emission of carbon dioxide (CO2) from heating, ventilation, and air-conditioning (HVAC) system that caused a warmer environment. For this purpose, there are two critical processes to identify the role of human thermal comfort, namely human reaction towards the thermal ambient (thermoregulation) and the heat transfer and air movement that occur in the enclosed space due to natural and forced convection.
Abstract Occupancy is a key input variable for sizing heating, ventilation and air-conditioning (HVAC) in buildings. However, HVAC designers typically estimate occupancy data based on assumptions which rarely reflect the actual situation. Consequently, these assumptions might lead to under- or oversized HVAC systems that either provide too low or too high peak loads or ventilation airflows than actually required to satisfy indoor environmental quality (IEQ) requirements during building operation. To address these issues, existing studies suggest various methods for collecting and analysing occupancy, however mostly in single office spaces or at an overall building level. The objective of the present study was to evaluate the suitability of using passive-infrared (PIR) sensors mounted below occupants’ desks for collecting long-term occupancy data in open-plan and single office spaces. The method was tested in two office buildings for seven months. It determined occupant presence and count with an accuracy of 87.5% compared to manual observations. Furthermore, the study demonstrated that occupancy data could be used to (1) generate occupancy schedules for input in building simulation models, (2) potentially reduce design ventilation airflows for HVAC sizing and (3) evaluate decisions to change the office space layout (e.g. number of desks) for more efficient space-use.
Abstract ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating, Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30% energy under ideal simulation environments. However, HVAC systems are susceptible to various types of faults in a real building operation. There are no existing studies that pertain to a comprehensive fault impact analysis of the high-performance control sequences suggested by ASHRAE Guideline 36 for HVAC systems. How these sequences handle and adapt to the various types of faults is still largely unknown. In this context, a comprehensive fault impact analysis and robustness assessment of the high-performance control sequences is conducted. A Modelica-based medium office virtual testbed is developed following the air-side and the plant-side SOO. A total of 359 fault scenarios in three different seasonal operating conditions (cooling, shoulder, and heating seasons) are injected into the baseline model. The evaluated key performance indexes (KPIs) include the operational cost, source energy, site energy, control loop quality, thermal comfort, ventilation, and power system metrics. The faults of the most negative impact are identified for different seasonal operating conditions over all the KPIs. The results also show that high-performance control sequences are well adapted for the vast majority (∼90%) of all the fault scenarios over all the KPIs in this study.
Buildings account for a large proportion of the total energy consumption in many countries and almost half of the energy consumption is caused by the Heating, Ventilation, and air-conditioning (HVAC) systems. The model predictive control of HVAC is a complex task due to the dynamic property of the system and environment, such as temperature and electricity price. Deep reinforcement learning (DRL) is a model-free method that utilizes the “trial and error” mechanism to learn the optimal policy. However, the learning efficiency and learning cost are the main obstacles of the DRL method to practice. To overcome this problem, the hybrid-model-based DRL method is proposed for the HVAC control problem. Firstly, a specific MDPs is defined by considering the energy cost, temperature violation, and action violation. Then the hybrid-model-based DRL method is proposed, which utilizes both the knowledge-driven model and the data-driven model during the whole learning process. Finally, the protection mechanism and adjusting reward methods are used to further reduce the learning cost. The proposed method is tested in a simulation environment using the Australian Energy Market Operator (AEMO) electricity price data and New South Wales temperature data. Simulation results show that 1) the DRL method can reduce the energy cost while maintaining the temperature satisfactory compared to the short term MPC method; 2) the proposed method improves the learning efficiency and reduces the learning cost during the learning process compared to the model-free method.
The construction and conversion of ordinary homes into “smart homes” has seen a tremendous rise in recent years. This can be ascribed to technologies such as the Internet of Things, sensors, smart phones, smart appliances, cloud computing, and digital assistants such as Amazon Alexa, Google Home, Google Assistant, Apple Siri, and Microsoft Cortana. At the outset, smart homes were built to enhance the quality of life for ordinary nondisabled persons. Impressively, we have seen smart home residents reaping the benefits of security, energy saving, and the ability to control their lighting, HVAC (heating, ventilation, and air conditioning), door locks, and coffee makers while they are in their space of comfort, for example in bed or siting on a couch. However, most smart home devices are not designed with people with disabilities and limited range of movement in mind. Of course, being able to control home devices using smart technology could be a tremendous benefit to people with physical disabilities and the older persons. This paper presents a system that uses smart plugs, smart cameras, smart power strips and a digital assistant such as Amazon Alexa, Google Home, Google Assistant, Apple Siri, or Microsoft Cortana to capture voice commands, from a person with physical disabilities, spoken in a much more natural way to control ordinary home electrical appliances in order to turn them on or off, with minimal exertion.
Abstract Phase change materials (PCM) can be outfitted in building envelopes to not only provide thermal comfort for occupants but also trim heating, ventilation and air conditioning (HVAC) loads. However, the efficacy of PCMs depends highly on its thermo-physical properties and climatic condition. In this regard, a multi-objective optimization technique is adopted to unearth the optimal type and location of PCM that can minimize heating and cooling loads considering five cities of Iran namely Tehran, Tabriz, Bandar Abas, Shiraz and Yazd with distinctive climates. Then, the optimal PCMs are environmentally and economically assessed. The study showed that the PCM with a melting temperature of 25 °C outperforms in terms of cooling load while the PCM with a melting temperature of 21 °C favors the heating performance. Moreover, the utilization of PCM results in electricity saving of 4.5–5.5% for all the climates. On average, the annual carbon footprint is reduced by 1297 kg, 1420 kg, 2040 kg, 1027 kg, and 1248 kg for Tehran, Tabriz, Bandar Abas, Shiraz, and Yazd, respectively. The payback period was found to be more than 70 years for all the cities considering current economic conditions. However, the energy subsidies are projected to fall in the near future that may make PCM integration economically feasible.