2,323 research outputs found

    Exergy analysis of cooling towers for optimization of HVAC systems

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    Conference theme: Green & Sustainable Technology Development (绿色科技与可持续发展)Exergy analysis is a useful thermodynamic technique for assessing and improving the efficiency of processes and systems. By examining the exergy destruction properties, it is possible to optimize the environmental and economic performance of the systems. This research applies exergy analysis technique to investigate and optimize the performance of evaporative cooling towers in HVAC (heating, ventilating and air conditioning) systems. The analysis is carried out in two steps. The first step considers the physical exergy of each component and process within a whole HVAC system for a typical office building. The building’s cooling load and cooling energy requirements are determined by building energy simulation software. Exergy destruction and exergy efficiency of the system are calculated from theoretical models. The result indicates that the exergy loss of cooling tower is quite significant, and the loss is affected by outdoor environment and condensing water temperature. The second step considers the chemical exergy of condensing water and outdoor air. A counter-flow wet cooling tower is investigated by experiments and theoretical models. The dry-bulb temperature and relative humidity of outdoor air are studied by an exergy approach in order to maximize the cooling tower performance. The distribution of exergy loss within the system has been shown and information to minimize exergy destruction and optimize the system efficiency can be determined.postprin

    On the optimal indoor air conditions for sars-cov-2 inactivation. An enthalpy-based approach

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    In the CoViD-19 pandemic, the precautionary approach suggests that all possible measures should be established and implemented to avoid contagion, including through aerosols. For indoor spaces, the virulence of SARS-CoV-2 could be mitigated not only via air changes, but also by heating, ventilation, and air conditioning (HVAC) systems maintaining thermodynamic conditions possibly adverse to the virus. However, data available in literature on virus survival were never treated aiming to this. In fact, based on comparisons in terms of specific enthalpy, a domain of indoor comfort conditions between 50 and 60 kJ/kg is found to comply with this objective, and an easy-to-use relationship for setting viable pairs of humidity and temperature using a proper HVAC plant is proposed. If confirmed via further investigations on this research path, these findings could open interesting scenarios on the use of indoor spaces during the pandemic

    PREDICTIVE CONTROL OF POWER GRID-CONNECTED ENERGY SYSTEMS BASED ON ENERGY AND EXERGY METRICS

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    Building and transportation sectors account for 41% and 27% of total energy consumption in the US, respectively. Designing smart controllers for Heating, Ventilation and Air-Conditioning (HVAC) systems and Internal Combustion Engines (ICEs) can play a key role in reducing energy consumption. Exergy or availability is based on the First and Second Laws of Thermodynamics and is a more precise metric to evaluate energy systems including HVAC and ICE systems. This dissertation centers on development of exergy models and design of model-based controllers based on exergy and energy metrics for grid-connected energy systems including HVAC and ICEs. In this PhD dissertation, effectiveness of smart controllers such as Model Predictive Controller (MPC) for HVAC system in reducing energy consumption in buildings has been shown. Given the unknown and varying behavior of buildings parameters, this dissertation proposes a modeling framework for online estimation of states and unknown parameters. This method leads to a Parameter Adaptive Building (PAB) model which is used for MPC. Exergy destruction/loss in a system or process indicates the loss of work potential. In this dissertation, exergy destruction is formulated as the cost function for MPC problem. Compared to RBC, exergy-based MPC achieve 22% reduction in exergy destruction and 36% reduction in electrical energy consumption by HVAC system. In addition, the results show that exergy-based MPC outperforms energy-based MPC by 12% less energy consumption. Furthermore, the similar exergy-based approach for building is developed to control ICE operation. A detailed ICE exergy model is developed for a single cylinder engine. Then, an optimal control method based on the exergy model of the ICE is introduced for transient and steady state operations of the ICE. The proposed exergy-based controller can be applied for two applications including (i) automotive (ii) Combined Heat and Power (CHP) systems to produce electric power and thermal energy for heating purposes in buildings. The results show that using the exergy-based optimal control strategy leads to an average of 6.7% fuel saving and 8.3% exergy saving compared to commonly used FLT based combustion control. After developing thermal and exergy models for building and ICE testbeds, a framework is proposed for bilevel optimization in a system of commercial buildings integrated to smart distribution grid. The proposed framework optimizes the operation of both entities involved in the building-to-grid (B2G) integration. The framework achieves two objectives: (i) increases load penetration by maximizing the distribution system load factor and (ii) reduces energy cost for the buildings. The results show that this framework reduces commercial buildings electricity cost by 25% compared to the unoptimized case, while improving the system load factor up to 17%

    Model predictive control for building automation

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    © 2018. The AuthorsWe propose a building HVAC system, integrating local energy production and storage, together with a model based controller. The heating system integrates several local heat production and storage devices and multiple fluid circuits at different temperatures to minimize entropy production through mixing. The controller uses a model of the System and predictive knowledge of demand and weather information to minimize electrical energy import, while maintaining thermal comfort by solving mixed integer optimization problems online. Time-varying and unknown system parameters are estimated and adapted online, using an unscented Kalman filter. The adaptation greatly reduces modeling effort and maintenance cost. The proposed setup is tested in a co-simulation, using a physical (modelica-) model of the building and energy system as well as realistic weather and demand data. Our system delivers nearly seven times more energy in the form of heat, than it needs to import (electrical) energy from external sources

    Control and Communication Protocols that Enable Smart Building Microgrids

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    Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility

    Reinforcement Learning Based Control for Heating Ventilation and Air-conditioning

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    Next-generation HVAC: Prospects for and limitations of desiccant and membrane-based dehumidification and cooling

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    Recently, next-generation HVAC technologies have gained attention as potential alternatives to the conventional vapor-compression system (VCS) for dehumidification and cooling. Previous studies have primarily focused on analyzing a specific technology or its application to a particular climate. A comparison of these technologies is necessary to elucidate the reasons and conditions under which one technology might outperform the rest. In this study, we apply a uniform framework based on fundamental thermodynamic principles to assess and compare different HVAC technologies from an energy conversion standpoint. The thermodynamic least work of dehumidification and cooling is formally defined as a thermodynamic benchmark, while VCS performance is chosen as the industry benchmark against which other technologies, namely desiccant-based cooling system (DCS) and membrane-based cooling system (MCS), are compared. The effect of outdoor temperature and humidity on device performance is investigated, and key insights underlying the dehumidification and cooling process are elucidated. In spite of the great potential of DCS and MCS technologies, our results underscore the need for improved system-level design and integration if DCS or MCS are to compete with VCS. Our findings have significant implications for the design and operation of next-generation HVAC technologies and shed light on potential avenues to achieve higher efficiencies in dehumidification and cooling applications

    Exergy-based Planning and Thermography-based Monitoring for energy efficient buildings - Progress Report (KIT Scientific Reports ; 7632)

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    Designing and monitoring energy efficiency of buildings is vital since they account for up to 40% of end-use energy. In this study, exergy analysis is investigated as a life cycle design tool to strike a balance between thermodynamic efficiency of energy conversion and economic and environmental costs of construction. Quantitative geo-referenced thermography is proposed for monitoring and quantitative assessment via continued simulation and parameter estimation during the operating phase
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