Operation Of Air Conditioning Systems Research Articles

From fault-detection to automated fault correction: A field study

A fault detection and diagnostics (FDD) tool, as addressed by this study, is a tool that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Although FDD tools can inform operators of building operational faults, currently an action is always required to correct the faults to generate energy savings. Fault auto-correction integrating with commercial FDD technology offerings can close the loop between the passive diagnostics and active control, increase the savings generated by FDD tools, and reduce the reliance on human intervention. This paper presents the field study of seven fault auto-correction algorithms implemented in commercial FDD platforms. Implementation includes software changes in the FDD tools and additional controls hardware or software changes in the BAS that were required to enable the execution of different types of auto-correction algorithms in real buildings. The routines successfully and automatically correct faults and improve the operation of large built-up Heating, Ventilation, and Air Conditioning (HVAC) systems, common in most commercial buildings. The auto-correction algorithms are tested across four buildings and three different building automation systems, following a rigorous procedure to make sure they work properly and do not negatively impact the system and building occupants. Technology benefits, market drivers, and scalability changes are drawn from the implementation effort and test results, to drive future research and industry engagement.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Inactivation of airborne microbial contaminants by a heat-pump-driven liquid-desiccant air-conditioning system

The COVID-19 pandemic has led to increasing interest in controlling airborne virus transmission during the operation of air-conditioning systems. Therefore, beyond an examination of the ability of liquid-desiccant material itself to inactivate microbes, a heat-pump-driven liquid-desiccant air-conditioning system was proposed and constructed to experimentally investigate the effect of liquid-desiccant solution on the inactivation of airborne bacteria and fungi in various air-conditioning processes. The proposed system comprises a liquid-desiccant unit to dehumidify or humidify process air using a desiccant-solution and heat-pump unit to cool or heat it and accommodate solution thermal loads. The decrease in the concentration of airborne bacteria and fungi before and after passing through the system (i.e., inactivation efficiency) were compared for the base, summer, and winter operating modes. The results indicated that airborne fungi were less inactivated than bacteria because they possess more stress-resistant cellular structures that resist inactivation. During the air-conditioning processes in both the summer and winter operating modes, the bacterial and fungal inactivation efficiencies improved compared to the base mode owing to the contact with desiccant solution. The higher solution flow rate and solution temperature improved the bacterial inactivation efficiency by 27% for the winter compared to the summer mode. Conversely, because of possible growth of fungi in the heated and humidified supply air in the winter, the fungal inactivation efficiency improved by only 1.5% for the winter compared to the summer mode. In conclusion, the proposed system can contribute to control the airborne transmission of microbial contaminants while operating air-conditioning systems.

Flow measurement uncertainty quantification for building central cooling systems with multiple water-cooled chillers using a Bayesian approach

Measurement uncertainty has significant negative impacts on the operation and control of heating, ventilation and air conditioning systems. It is a big challenge and should be solved urgently. Existing studies focus on reducing the impacts of measurement uncertainty by developing uncertainty tolerant methods without quantifying the measurement uncertainties themselves. They therefore fail to fundamentally solve them. This study aims to directly quantify the measurement uncertainties of water flow meters in multiple water-cooled chiller systems using a Bayesian approach. A measurement uncertainty quantification strategy is proposed based on Bayesian inference and energy balance models, and the Markov chain Monto Carlo method is used to achieve the strategy. The site data collected from a chiller system are used to test the strategy. Four simulation tests with different levels of measurement uncertainty are conducted to further test and systematically validate the strategy. Test results show that the measurement uncertainties (both systematic and random uncertainties) of the water flow meters in the chiller systems can be quantified effectively and with acceptable accuracy. The strategy performs very well in quantifying random uncertainties of flow meters, and the relative errors range from 0% to 12.8%. The performance of the strategy in quantifying systematic uncertainties is also satisfactory, and the relative errors range from 0.1% to 36.57%. The proposed strategy is able to quantify measurement uncertainties and can be used to optimize the control of chiller systems and improve the reliability of chiller systems.

Room Air-Conditioning Operating as a Filling Box

The air temperature variation of a closed room, well insulated, during the initial time of operation of air-conditioning systems up to temperature stabilization, is simulated by a two-dimensional integral model as a quasi-steady-state phenomenon. The model equipped with a conservation equation for tracer concentration or relative temperature, including the stratification parameter, is well qualified. The flow leaving the air conditioning device forms an inclined buoyant jet which bends over and meets the room floor, where it spreads sideways forming a layer with jet temperature. A sequence of layers, which affect the jet temperature through entrainment, are produced by a novel bottom-up technique. The layer air temperatures are calculated through the bulk dilution of a near bottom jet cross-section, which feeds each new layer. The model simulated a real case and predicted the transient variation of room air and buoyant jet temperatures up to stabilisation. It also predicted the time needed for stabilisation, the cooling rates of the room and jet air temperatures, the Brunt-Väisälä frequency occurring during the temperature transitions, and more. The results are promising as they agree with observations. Thus, the model could be used to evaluate the effectiveness of relevant HVAC systems operating in such rooms.

A pressure and temperature cycling test stand with hot-gas bypass control for evaluation of adhesive joints in HVAC&R applications

The formation of joints is critical to the long-term reliability with leak-free operation of heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems. Leakages commonly occur due to fatigue failure developing in joined materials from continuous pressure and temperature cycling with mechanical vibration and refrigerant under pressure inside the system. In particular, rapid pressure and temperature changes happen frequently (e.g., multiple times per day) when the system is switched on and off. Therefore, it is important to have an automatically controlled fast-response pressure and temperature cycling (PTC) test stand available to test the performance of refrigeration joints to evaluate new bonding technologies. An innovative PTC test stand with hot-gas bypass control was designed, built, and demonstrated that eliminated the need for an evaporator and ensures rapid transition between different operating conditions. Tests were performed to demonstrate test stand functionality using R410A as the refrigerant to provide pressure and temperature cycles from 600 to 4500 kPa and 5 to 80 °C. A 50-cycle, 5-hour demonstration test was performed with both adhesively bonded and brazed joints following standardized joint testing guidelines. Both joint types survived the test without leaking, suggesting that the adhesive joints have sufficient thermal fatigue resistance along with the conventional brazed joints. Throughout the demonstration, the test stand accurately controlled the setpoint temperatures and pressures while switching the test section between these conditions. The test stand serves as a new approach for pressure and temperature cyclic fatigue testing of joints in HVAC&R systems.

Research on the Prediction of Energy-saving Potential of Traffic Tunnel Based on BP Neural Network

Accurate grasp of underground building environmental temperature and humidity and cooling and heating loads is the basis for energy-saving analysis, ventilation and air conditioning system design and operation control. In order to come up with a method that can quickly and accurately predict the environmental temperature and humidity and energy-saving potential of underground power station traffic tunnel, this paper takes Xiangyou pumped storage power station traffic tunnel as the research object and designs a BP neural network model for traffic tunnel temperature and relative humidity prediction with respect to the main environmental influencing factors. Comparing with the test data, it is verified that the model has high prediction accuracy. At the same time, the prediction model was used to calculate the energy saving potential of traffic tunnel ventilation. The results showed that the relative error of predicting the energy saving potential of traffic tunnel in summer was less than 2% and in winter was less than 0.3%. This prediction method can obtain the traffic tunnel air temperature and humidity and ventilation energy saving potential simply and accurately, so that the design can be developed in the direction favorable to the energy saving of underground buildings.

Data Reconstruction of Wireless Sensor Network and Zonal Demand Control in a Large-Scale Indoor Space Considering Thermal Coupling

An indoor high and open space is characterized by high mobility of people and uneven temperature distribution, so the conventional design and operation of air conditioning systems makes it difficult to regulate the air conditioning system precisely and efficiently. Thus, a Wireless Sensor Network was constructed in an indoor space located in Hong Kong to monitor the indoor environmental parameters of the space and improve the temperature control effectively. To ensure the continuity of the measurement data, three algorithms for reconstructing temperature, relative humidity and carbon dioxide data were implemented and compared. The results demonstrate the accuracy of support vector regression model and multiple linear regression model is higher than Back Propagation neural network model for reconstructing temperature data. Multiple linear regression is the most convenient from the perspective of program complexity, computing speed and difficulty in obtaining input conditions. Based on the data we collected, the traditional single-input-single-output control, zonal temperature control and the proposed zonal demand control methods were modeled on a Transient System Simulation Program (TRNSYS) control platform, the thermal coupling between the subzones without physical partition was taken into account, and the mass transfer between the virtual boundaries was calculated by an external CONTAM program. The simulation results showed the proposed zonal demand control can alleviate the over-cooling or over-heating phenomenon in conventional temperature control, thermal comfort and energy reduction is enhanced as well.

Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand

Occupancy behaviour in buildings can impact the energy performance and operation of heating, ventilation and air-conditioning (HVAC) systems. HVAC, which uses conventional control strategies or “fixed” setpoint schedules, could not adjust to the conditioned spaces' actual requirements, resulting in building spaces being over or under-conditioned. While the unintended opening of windows can lead to substantial heat loss and consequently raises energy consumption. To optimise building operations, it is necessary to employ solutions such as demand-driven controls, which can monitor the utilisation of indoor spaces and provide the actual thermal comfort requirements of occupants. This study presents a novel vision-based deep learning framework for occupancy activity detection and recognition including the manual window operations in buildings. A region-based Convolutional Neural Network (R-CNN) model was trained and deployed to a camera for real-time detection and recognition. Based on the field experiments conducted within a case study University building, overall accuracy of 85.63% was achieved for occupancy activity detection and 92.20% for window operation detection. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable HVAC systems to respond to dynamic changes within indoor spaces. Results showed that the proposed approach could reduce the over-or under-estimation of occupancy heat gains compared with the use of “fixed” or static profiles. In addition, the approach can help alert building users or managers about windows left open unintentionally, which can reduce unnecessary ventilation heat losses. Furthermore, the approach can also predict the room CO2 concentration and advise occupants about a suitable natural ventilation strategy. The study highlighted the potential of the multi-purpose detection approach, but further development is necessary, including optimisation of the deep learning model, full integration with HVAC controls and further model training and field testing.

Coloured low-emissivity films for building envelopes for year-round energy savings

Buildings are responsible for over 40% of total US energy use, of which about 40% is directly related to the operation of heating, ventilation and air-conditioning (HVAC) systems. Saving energy to heat and cool buildings would contribute substantially to sustainability. Here we propose a category of coloured low-emissivity films for building walls that constitute the main component of the building thermal envelope. We demonstrate high reflectance (~90%) in infrared wavelength range and selective reflectance in the visible light wavelength range for desired colours. These films can help minimize radiative heat exchange between the indoor and outdoor environments, thus saving energy for all-year cooling and heating while satisfying the required aesthetical effect. Simulations show that these films can help reduce heat gain and loss by up to 257.6 MJ per installation wall area annually. In the case of a typical midrise apartment building, the HVAC system can save up to 124.46 GJ (equal to 9.87% of the building’s HVAC energy consumption). By rough estimation, a global CO2 emission reduction of 1.14 billion metric tons annually could be achieved. Our work provides insights for innovative energy-saving building envelope materials that can help achieve global carbon neutrality and sustainability.

Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

A coupled deep learning-based internal heat gains detection and prediction method for energy-efficient office building operation

Occupants' behaviour and the use of electrical equipment can significantly impact the building energy demand. Accurate occupancy and equipment usage information are key to improving the performance of demand-driven control, which can automatically adjust the heating, cooling and ventilation system operation. Employing static schedules is commonly used for the operation of heating, ventilation and air-conditioning systems, while it cannot satisfy the actual requirements due to the dynamic variations within the conditioned spaces. This study introduces a coupled real-time occupancy and equipment usage detection and recognition approach using deep learning and computer vision techniques for efficient building energy controls. The experimental results presented an overall equipment detection and occupancy activity detection accuracy of 78.39% and 93.60%. To investigate the influence of the implementation of the approach on building energy demand, a case study office building was selected to conduct experimental tests and modeled using a building energy simulation tool. Four scenarios with different occupancy and equipment profiles were defined and evaluated. The simulation results showed that heat gains, when employing static profiles were larger than the heat gains predicted when using the deep learning influenced profiles. Up to 53.95% lower heat gains were estimated when using both occupancy and equipment detection approaches than static schedules solely. The results highlighted the importance of monitoring real-time occupancy and electrical equipment usage and the advantages of using deep learning detection techniques to provide data for demand-driven controls, optimising building energy efficiency while maintaining a comfortable indoor environment.

Modified efficiency-NTU method (m-ε-NTU) for calculating air coolers in dehumidifying or frost conditions. Part III

A new calculation method that is applicable to air coolers operating in «dry» (without dehumidification and frosting), «wet» (with dehumidification or frosting on the entire surface), or combined (with dehumidification or frosting on part of the surface) conditions has been developed for counterflow and parallel-flow air coolers without phase transition of the cooling fluid and for cases with phase transition. The advantages of the developed method include the following: its versatility; consideration of the effect of dehumidification and frosting on the heat exchange process; the possibility of application to design and verification calculations; and low algorithmic complexity (due to the absence of the need to divide the heat exchanger into separate segments for calculation, as well as the absence of iterative calculations to determine the proportion of the dry heat exchange surface in the combined operating mode), resulting in the high speed execution of calculation programs.
 The third part of the article describes a method for calculating air coolers operating in «wet» and combined conditions. The transition criterion from the «wet» operating mode of the air cooler to the combined mode is provided. In the case of the combined operating mode, for counterflow air coolers, the proportion of the dry heat exchange surface can be directly expressed, which helps to avoid iterative calculations and reduce the execution time of calculation programs.
 BACKGROUND: A universal method for calculating air coolers that is applicable to design and verification calculations is necessary. The method considers the influence of dehumidification and frosting on the heat exchange process and allows the quick performance of a large number of calculations to simulate the operation of refrigeration and air conditioning systems without significant loss of accuracy. A calculation method that addresses all the above-mentioned criteria is unavailable in domestic and foreign literature.
 AIMS: This study aims to develop a universal method for calculating air coolers that is applicable to design and verification calculations. This method considers the influence of dehumidification and frosting on the heat exchange process and allows the quick performance of a large number of calculations to simulate the operation of refrigeration and air conditioning systems without significant loss of accuracy.
 MATERIALS AND METHODS: The developed method of calculating air coolers is based on the classical approach of ε-NTU (efficiency – the number of heat transfer units) and is its adaptation, allowing to consider the influence of dehumidification and frosting on the heat exchange process and perform the calculation (including the combined operating mode of the air cooler) without dividing the heat exchanger into separate segments. The estimation of the error of calculations performed using the developed method was conducted by comparing the calculated values of the thermal power of the device with the same values calculated using the segmented division method for a variety of operating modes (including combined).
 RESULTS: Comparison with the segmented division method of the heat exchanger demonstrated good convergence of the calculation results with multiple reductions in their execution time. The deviation value of the calculated value of the thermal power computed using the developed method from the same value computed using the segmented division method averaged 3.23% modulo and did not exceed 4.5% modulo. When the heat exchanger is divided into 40 segments, the execution time of the calculation programs increases approximately 18 times compared to using the developed method, which can be called a significant advantage of the latter.
 CONCLUSION: The division of the heat exchanger into segments for calculation does not lead to a significant increase in their accuracy compared to the new method. Therefore, the developed m-ε-NTU method can be widely used for the selection of air coolers, their verification and design calculations.

Mechanisms and ways of transmission of SARS-CoV-2 coronavirus infection

The relevance of the problem. A new coronavirus infection caused by the SARS-CoV-2 virus has created an emergency situation of international importance. The incidence of it is characterized by a rapid spread across all countries, high morbidity rates, severe complications and mortality. Despite the fact that there are currently vaccines, it is not yet possible to stop the infection. Of particular importance as a preventive measure is the impact on the second link of the epidemic process. In this connection, obtaining objective data on the mechanisms and ways of spreading the SARS-CoV-2 virus will allow for more rational preventive measures and significantly reduce the risk of transmission of infection. The aim is to analyze current data on the mechanisms and ways of spreading the COVID-19 pathogen from available literature sources and to consider possible measures to influence the second link of the epidemiological chain in order to reduce the risks of infection. Materials and methods. The selection of peer-reviewed literature published from 2019 to 2021 was carried out. in the PubMed database and e-Library.ru, as well as links in articles. To obtain statistical data, the Internet resources of the World Health Organization (WHO), open data of Rospotrebnadzor, the Ministry of Health of the Russian Federation, Rosstat were used. Results. Analysis of scientific sources containing information on the mechanisms and ways of transmission of the new coronavirus infection COVID-19 indicates that the authors in their publications mention almost all the mechanisms and ways of transmission of this disease, but to varying degrees of their significance. The main mechanism of the spread of the SARS-CoV-2 virus is aspiration with airborne and airborne dust transmission routes. At the same time, the effect of contact, fecal-oral mechanisms is not excluded, the probability of the implementation of the vertical mechanism of transmission of the pathogen from the mother to the child, as well as the genital tract, is being studied. An important measure for the prevention of a new infection is the impact on the second link of the epidemic process, especially ensuring the highly efficient operation of ventilation and air conditioning systems in medical institutions and public places with a massive crowd of people, as well as compliance with personal preventive measures and disinfection measures.

Operation of climate-adaptive building shells utilizing machine learning under sparse data conditions

Conventional machine learning (ML) techniques based on big data are difficult to integrate directly into building operations due to the “curse of dimensionality” caused by data sparsity . While the number of feature variables in buildings is considerable, in many cases, a reliable dataset is only obtained from the target building operation through unique features. This results in insufficient data for building an operable ML model. In this study, we proposed a methodology applying a robust algorithm and carefully selected feature variables based on building physics. An operation using climate-adaptive building shells is presented as a case study. Energy simulations utilizing a generic office building model equipped with electrochromic glasses were performed on 2016–2019 weather data from Tokyo and Fukuoka, Japan. The k -nearest neighbor algorithm was employed for the ML application because of its robustness regarding small datasets, and feature variables were prearranged and carefully chosen to set an adequate combination between the numbers of feature and objective variables. Without ML, the air conditioning system operation became unstable in intermediate seasons. The ML application successfully solved this problem; 95% of the undesired cooling operation was avoided. The results prove that a simple ML algorithm could become a better solution than a complex one in cases where building physics based on building engineers’ knowledge is effectively utilized. It expands the ML application in various building operations, even in cases that do not respond to the direct application of complex ML techniques that require large datasets, known as “big data.” • Machine learning is useful for improving the operation of climate adaptive building shells. • Conventional techniques based on big data are difficult to integrate into building operations. • The curse of dimensionality can easily occur owing to the unique features of individual buildings. • Machine learning with building physics-based variables improve electrochromic glass operations. • Building physics makes even a simple machine learning algorithm applicable to various tasks.

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

Reasonable distribution of cooling load between chiller and ice tank is the key to realize the economical and energy-saving operation of ice-storage air-conditioning (ISAC) system. A multi-objective optimization model based on improved firefly algorithm (IFA) was established in this study to fully exploit the energy-saving potential and economic benefit of the ISAC system. The proposed model took the partial load rate of each chiller and the cooling ratio of the ice tank as optimization variables, and the lowest energy consumption loss rate and the lowest operating cost of the ISAC system were calculated. Chaotic logic self-mapping was used to initialize population to avoid falling into local optimum, and Cauchy mutation was used to increase the population’s diversity to improve the algorithm’s global search ability. The experimental results show that compared with the operation strategy based on constant proportion, particle swarm optimization (PSO) algorithm, and firefly algorithm (FA), the optimal operation strategy based on IFA can achieve more significant energy-saving and economic benefits. Meanwhile, the convergence accuracy and stability of the algorithm are significantly improved. Practical application: The optimized operation strategy of the ice-storage air-conditioning system can reduce energy loss and operating costs. The traditional operation strategies have the problems of low optimization precision and poor optimization effect. Therefore, this study presents an optimal operation strategy based on IFA. The convergence accuracy and stability of the algorithm are increased after the algorithm is improved. The operation strategy can get the maximum energy-saving effect and economic benefit of the ISAC system.

Progressive Development and Challenges Faced by Solar Rotary Desiccant-Based Air-Conditioning Systems: A Review

A rotary desiccant-based air-conditioning system is a heat-driven hybrid system which combines different technologies such as desiccant dehumidification, evaporative cooling, refrigeration, and regeneration. This system has an opportunity to utilize low-grade thermal energy obtained from the sun or other sources. In this paper, the basic principles and recent research developments related to rotary desiccant-based cooling systems are recalled and their applications and importance are summarized. It is shown that with novel system configurations and new desiccant materials, there is great potential for improving the performance and consistency of rotary desiccant systems; at the same time, the use of solar energy for regeneration purposes can minimize the operating cost to a great extent. Some examples are presented to demonstrate how rotary desiccant air conditioning can be a promising solution for replacing traditional vapor-compression air-conditioning systems. Recent advances and ongoing research related to solar-powered hybrid rotary desiccant cooling systems are also summarized. The hybrid systems make use of a vapor-compression system in order to have better operational flexibility. These systems, although they consume electrical energy, use solar energy as the principal source of energy, and hence, significant savings of premium energy can be obtained compared to conventional vapor-compression systems. However, further research and development are required in order to realize the sustainable operation of solar rotary desiccant air-conditioning systems, as solar energy is not steady. Reductions in capital cost and size, along with improvements in efficiency and reliability of the system is still needed for it to become a player in the market of air conditioning.

Concentration profile and water level effect on the evaporation in the spent nuclear fuel storage pool using a diffusion approach

In the spent nuclear fuel storage pool (SFSP) of GA Siwabessy Multipurpose Reactor (GAS-MPR), there are three status of pool water level with unknown effect of water level on evaporation rate. This study aims to determine the effect of pool water level on the evaporation rate in the SFSP, so that it can be a consideration in minimizing the evaporation rate. A prediction of water evaporation rate can be explained as a diffusion model through a stagnant gas film. A numerical method, 4th order Runge-Kutta was used to describe the concentration profile of water and air over a SFSP column to the top of the pool. The effect of the water level can be explained then. The various distance of the water to the pool top at constant water temperature and constant air temperature shows a result that the evaporation rate is decreasing while the distance of the water to the pool top is increasing. The investigation results conclude that the closer of the liquid-water surface to the gas-air flow, increases the evaporation rate. This approach can be applied in a SFSP that has an abundant amount of water, the water temperature remained constant by operation of the water-cooling system, air temperature, pressure, and relative humidity remained constant as well by the operation of ventilation and air conditioning (VAC) system. Those constant conditions were stated as normal operation in SFSP of GAS-MPR. In the case of abnormal conditions such as the VAC not operating or the water-cooling system not operating, there will be dynamics of pool water and air temperatures which require further modeling research.

Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control

Precise and quick thermal load prediction for buildings is imperative in realizing the flexibility of building energy systems. Operation optimization based on the prediction results can effectively mitigate the energy consumption and operation cost. Here, an artificial neural network (ANN) model is built to predict the load demand and energy consumption of an office building. Thermal zones division is considered to improve the prediction accuracy. Measured data are collected to establish and validate the ANN model. The total number of samples for training, validation and testing are 13674, 3978 and 3977, respectively. A rule-based control optimization model is introduced and combined with ANN model to optimize the operation of heating, ventilation and air-conditioning system. Building energy flexibility is activated by optimization model in a time-of-use electricity price scenario. The results indicate that ANN model has high precision and the coefficient of variation of root mean squared errors corresponding to the load demand prediction and energy consumption prediction are 10.76% and 15.59%, respectively. Furthermore, the optimization results can reduce the operation cost by 39.22% and 44.41% in the heating and cooling season, respectively.

Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant

Recent studies have indicated a great potential for applying predictive methodologies to the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. Particularly for Centralized Chiller Plants with Thermal Energy Storage (TES) used for District Cooling, there is a substantial opportunity for cost savings when responding to variations in electricity price and ambient temperature. The present work addresses the problem of closed-loop scheduling of a large-scale chiller plant with TES tank under a Day-Ahead (DA) electricity price program. The main contributions include: (i) formulating the problem for a real large-scale complex system; (ii) comparing different dispatching policies with varying degrees of optimality, constraint satisfaction and operational complexity; and (iii) designing an optimization-based scheduling tool and describing its implementation and results. The proposed Mixed-Integer Linear Programming (MILP) formulation extends existing models by explicitly accounting for the effect of chilled water return temperature on the energy balances, considering real plant features, and performing pre-calculation of bounds on equipment and storage capacity. The study is performed with real process data from the central chiller plant located on the campus of the University of California, Davis. The comparison between different dispatching policies highlights the benefits of adopting an optimization-based operational strategy, indicating a cost reduction of up to 32% compared to other suboptimal policies. The study also demonstrates how additional constraints that reduce operational complexity affect the closed-loop performance of the optimization-based method, serving as a guideline for designing the optimization tool.