Central Air Conditioning System Research Articles

Numerical analysis for the design of a central air conditioning system for center of excellence

The temperature of the towing tank floor of the Center of Excellence in Marine and Offshore Engineering, Rivers State University needs regulation due to the high level of humidity during experiments. Since vapor evaporates from the towing-tank liquid column, the need for central air conditioning is timely. With the temperature regulated, it will help enhance the comfort of the students and improve the working condition of the staff and equipment. This is achieved by conducting a cooling load analysis, consistent with ASHRAE Guidelines, on the floor plan of the Laboratory building that provided useful information on the proper selection and sizing of fit-for-purpose air-conditioning equipment. Analysis revealed a total sensible and latent heat load of 59195.74 W with system capacity of 16.91TR (tons). Duct design utilizing the Velocity Reduction method resulted in a main supply duct length of 57m and flow are of 0.72m2 incorporating four branched ducts measuring 6m each in length and flow area of 1.05m2

Design of a Novel Creel Conditioning System for Circular Knitting Machines

Maintaining a certain humidity level of humidity in yarn packages and removing fibre fly from the environment is very important for production efficiency and product quality in textile mills. Humidity has a significant effect on the quality and efficiency of fabrics, especially those made from hydrophilic fibres. The high temperature and low humidity level in the factory environment results in lower yarn strength and higher yarn breakage. The solution is to provide optimum temperature and humidity levels through a central air conditioning system. However, conditioning the entire factory environment is an expensive approach and does not facilitate satisfactory production efficiency. In this study, a new creel system for circular knitting machines was designed to provide excellent yarn package conditioning. More efficient production and economical conditioning were achieved with this new creel system.

Short-term cooling load prediction for central air conditioning systems with small sample based on permutation entropy and temporal convolutional network

In air-conditioning load predicting, the mainstream methods mainly include mechanism model based methods and data-driven prediction methods. The mechanism model can achieve accurate prediction, but are normally used to predict non-existent buildings and by default have to make several assumptions regarding post occupancy. This study proposes a novel data-driven short-term cooling load prediction method based on historical data of the existing buildings to improve the prediction performance when the size of the training sample datasets, including load data and air conditioning internal operation data, is small. Firstly, an improved version of variational mode decomposition (IVMD) technique is proposed to deconstruct complex historical cooling load signals. Then, the permutation entropy and Savitzky-Golay (PE-SG) smoothing method is developed to extract singles with distinct characteristics and high regularity, which comprises multiple single-component amplitude and frequency modulation (AFM) signals, thus mitigating the interference of noise in predictive accuracy. Subsequently, a data-driven model based on temporal convolutional network (TCN) is constructed, which effectively alleviates the risks of over-fitting and under-fitting. Finally, the efficacy of the proposed method is validated through two case studies, and the obtained results show that the issues confronted by small sample datasets are adeptly solved by this method. The precision of prediction is significantly enhanced according to the comparison to other traditional models, showcasing greater stability and generalization ability.

Energy saving and economic competitiveness of solar desiccant cooling technology – A case study of the Moroccan Kingdom

As many countries in the world, Morocco is vulnerable to climate change having direct impacts on its main economic sectors highly dependent on natural resources. This is exemplified, among other things, by long periods of drought, increase of average temperatures and successive heat waves. These circumstances have increased the demand for cooling energy, particularly in tertiary building where centralized air conditioning systems still predominant. Currently, the installed power capacity seems to be insufficient to respond effectively to this increasing demand. In this context, sustainable alternatives to traditional cooling systems such as solar air conditioning systems can be a suitable option for energy efficiency and security. Solar desiccant cooling systems present a great opportunity since they do not use any ozone-depleting coolants. This study discusses the techno-economic feasibility of solar desiccant systems in tertiary building, in particular a conference room, compared to a conventional air handling unit. Besides the use of air solar collectors to heat up the regeneration air, the particularity of the studied system is the use of exhaust air leaving the desiccant wheel and purge zone to preheat regeneration air. Accordingly, simulations were performed in three climatic regions with different enthalpy loads. The main result of this work is that solar desiccant cooling system can be an attractive investment if appropriate economic conditions are met with a substantial governmental support, since a significant energy-saving of about 77% can be achieved compared to conventional systems.

Field Study of a Liquid-Desiccant Central Air-Conditioning System for an Apartment Building

Field Study of a Liquid-Desiccant Central Air-Conditioning System for an Apartment Building

Energy efficiency optimization analysis for a cooling composite air conditioning system for internet data centers

In this study, a validated mathematical model for a composite air conditioning system was established based on onsite test data. The system consists of a refrigerant pump driven two-phase cooling loop (RPTCL) and vapor compression refrigeration (VCR) cycles. The system can be switched to refrigerant pump (RP) mode, compressor mode, or hybrid mode, according to the outdoor temperature. By analyzing, the inadequacy of the existing Internet Data center (IDC) air conditioning system operation strategy was confirmed, and subsequently, five optimization strategies were proposed. The optimization strategies took two identical composite air conditioning systems as optimization unit, and under the premise of meeting the demand for cooling capacity, maximizing the operating time of RP and hybrid mode, which with higher energy efficiency, through complementary cooling capacity, to reduce the energy consumption. Meanwhile, when the cooling supply was higher than the demand, paired with optimization unit’s ON/OFF ratio control, the unit’s energy consumption was further reduced. The most optimization strategy provided 525.6 MWh of cooling energy to meet demand while consuming a minimum of 182.5 MWh. The optimization strategy also achieved a minimum of 12.1% and a maximum of 52.5% energy savings for IDCs in other climate zones of China.

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

Pentagon Found Daily, Metagenomic Detection of Novel Bioaerosol Threats to Be Cost-Prohibitive: Can Virtualization and AI Make It Cost-Effective?

In 2022, the Pentagon Force Protection Agency found threat agnostic detection of novel bioaerosol threats to be "not feasible for daily operations" due to the cost of reagents used for metagenomics, cost of sequencing instruments, and cost of labor for subject matter experts to analyze bioinformatics. Similar operational difficulties might extend to many of the 280,000 buildings (totaling 2.3 billion square feet) at 5,000 secure US Department of Defense military sites, 250 Navy ships, as well as many civilian buildings. These economic barriers can still be addressed in a threat agnostic manner by dynamically pooling samples from dry filter units, called spike-triggered virtualization, whereby pooling and sequencing depth are automatically modulated based on novel biothreats in the sequencing output. By running at a high average pooling factor, the daily and annual cost per dry filter unit can be reduced by 10 to 100 times depending on the chosen trigger thresholds. Artificial intelligence can further enhance the sensitivity of spike-triggered virtualization. The risk of infection during the 12- to 24-hour window between a bioaerosol incident and its detection remains, but in some cases it can be reduced by 80% or more with high-speed indoor air cleaning exceeding 12 air changes per hour, which is similar to the rate of air cleaning in passenger airplanes in flight. That level of air changes per hour or higher is likely to be cost-prohibitive using central heating ventilation and air conditioning systems, but it can be achieved economically by using portable air filtration in rooms with typical ceiling heights (less than 10 feet) for a cost of approximately $0.50 to $1 per square foot for do-it-yourself units and $2 to $5 per square foot for high-efficiency particulate air filters.

Research on energy-saving optimization method for central air conditioning system based on multi-strategy improved sparrow search algorithm

Aiming at the problem of high energy consumption caused by the nonlinear, strong coupling, and large hysteresis characteristics of central air conditioning (CAC) system, an energy-saving optimization method based on WTD–CNN–LSTM and multi-strategy improved sparrow search algorithm (MISSA) is proposed, which works to minimize the total energy consumption during the operation of the CAC system. Firstly, minimize the sum of energy consumption of chillers, freezing pumps, cooling pumps, and cooling towers as the objective function, and use the range of operating parameters of each equipment as the basic constraint conditions to establish an energy-saving optimization model for the CAC system. Then, WTD–CNN–-LSTM is used to predict the future cooling load, and the predicted results are used as key constraint condition to achieve on-demand cooling. Finally, MISSA is proposed to improve the initialization, update and convergence stages of the CAC system operation parameter optimization process, accurately obtaining the optimal operating parameters. Compared with manual experience, MISSA reduces energy consumption by 15.32 % and improves energy efficiency ratio by 20.76 %. Meanwhile, compared with other optimization algorithms, MISSA reduces energy consumption by 4.85–13.26 % and improves energy efficiency ratio by 6.53–16.33 % after optimizing the CAC system. The experiment verifies that MISSA is more energy efficient when applied to the CAC system and has the advantages of accuracy, fast convergence, strong global search capability, and the ability to jump out of local optimization.

Radon levels and dose assessment at the basement workplaces of hospitals in different regions of Taiwan

The basements of large hospitals in Taiwan are usually working areas for image radiometry, radiation therapy, and nuclear medicine, where the radon concentration can be accumulated. This study investigated radon concentrations in the basement working environments of 30 hospitals in different regions with various geology of Taiwan, aiming to explore the relationship between geological conditions and radon concentrations and estimate the radiation dose received by the working staff. Portable MR107+ semiconductor radon detectors calibrated with a radon chamber were used for radon measurement at the hospital basement working areas according to the protocol of MAH-2019 instrument placement recommendations from the US EPA. The excess lung cancer risk (ELCR) as well as the lung cancer cases per million persons (LCC) were further derived from the radiation dose.The basement working areas investigated are located on the first to sixth underground floors of hospitals in Taiwan mainland and islands, Penghu and Kinmen. The 222Rn concentration higher than 100 Bq/m3 was observed at CH-1 (122.1 Bq/m3) in Changhua City and CY-1 (114.7 Bq/m3) in Chiayi City. This is attributed to that the central air conditioning systems of these hospitals were turned off during off-duty hours. The radon concentrations of all hospitals were lower than the action level, 150 Bq/m3, regulated by the US EPA. No significant relationship was found between radon concentration and the geological characteristics of the measured sites. That is, the ventilation of the basement working areas in hospitals is more important than local geological characteristics on indoor radon concentration, and therefore proper ventilation is crucial for reducing radon gas concentration in the hospital basements. Estimated annual effective dose from 222Rn for the hospital staff was higher than 1 mSv/y at YL (1.24 mSv/y), CH-1 (1.64 mSv/y), CY-1 (1.54 mSv/y), CY-2 (1.34 mSv/y), and PH-2 (1.14 mSv/y), and that for the rest sites was <1 mSv/y. The maximum mean ELCR was found to be 0.63% at CH-1, and the related LCC was estimated to be 29.5 per million persons.

The impact of the new EPBD “Green Houses” in the Italian building context: Technical-economic analysis on improving the energy class of existing buildings

The new European directive “Green Houses” aims at the decarbonization of the existing building stock. In this work, three building types widespread in Italy are analysed, virtually positioned in Palermo, Naples, and Milan. Considering that almost 60% of Italian residential buildings fall within the G and F energy classes, three energy retrofit measures are proposed (thermal insulation of vertical walls and roofs; replacement of the methane gas boiler for building heating and domestic hot water with a high temperature air-to-water heat pump; installation of a photovoltaic system), in order to reach at least D energy class based on Italian energy performance certification. A simulation-based approach is used. A declining tendency is noted in the normalized prices of the interventions when moving from detached house to terraced house and from terraced house to apartment in a tower building. Shifting from an independent to a centralized air conditioning system results in the same savings. Considering equal building features, hotter and milder climate zones reach higher energy classes; this also results in more difficult energy efficiency improvement in colder climates. Potential inequalities result from the implementation of the new European directive. Economic incentives to support the energy efficiency transition should be tailored to more specific conditions.

Energy saving control method for central air conditioning systems in public buildings based on improved particle swarm optimisation

Energy saving control method for central air conditioning systems in public buildings based on improved particle swarm optimisation

Comprehensive and quantitative evaluation for selecting a suitable scheme of central air-conditioning during the design phase

As for routinely selecting the number and size of chillers in central air-conditioning system, the conventional scheme in which the total cooling capacity is allocated in an equal or empirical way is generally suboptimal. A few factors are brought into consideration, and the time domain of evaluation is too short. The low suitability between the cooling load of building and the scheme selected results in an inefficient state of system during the most of operation time. Some novel methods provide long-term evaluations for selecting an efficient scheme by simulating the cooling loads then calculating the cost and performance of the scheme. However, the schemes are formed without correlating actual plants which is difficult to be applied to an actual project, and the long-term environmental impact is rarely considered. The evaluation method with a high engineering value is required. But the more complicated the method is, the more work is required. The relationship between the benefits and the costs is unknown. Therefore, this study proposes three evaluation methods at different levels. From simple to complex, the three methods are corresponding the cooling load curve of building calculated through EnergyPlus to the performance curves of chillers, system simulation using TRNSYS and co-simulation based on BCVTB respectively. A case study using a hospital building located in Zhejiang province, China combined with equipment list was conducted to demonstrate the evaluation process. The results calculated by different methods and the various indexes of different schemes were compared to illustrate how to optimize the scheme selection during the design phase in order to select a suitable scheme for a certain building reducing the energy consumption and carbon emission.

Power saving in a central air conditioning system by using multiple PCMs integrated with fresh air path

Decreasing the fresh air temperature before entering the air conditioning (AC) system will have a significant influence on reducing domestic power usage. To obtain a low fresh air temperature, the current work highlights a numerical investigation of an air-phase change materials (PCM) heat exchanger (H.EX.) with AC systems using ANSYS software. The heat exchanger was divided into several cells filled with multiple PCMs (RT-22, RT-24, RT-26). Throughout the day, the outside air passes through the system to decrease its temperature. Also, the PCMs are discharged by the low ambient temperature during the night. A numerical model was established and validated with experimental results from the literature and got a good match. The influences of multiple types of PCMs, outlet air temperature from the system, and the reduction in the cooling coil power were investigated. The outcomes concluded that multiple PCMs have a significant impact in decreasing the fresh air temperature before entering the AC system, compared to a single PCM. The fresh air temperature decreased from 45, 42, and 39 °C to roughly the values of 33, 32, and 31 °C, respectively, by using the air-PCM H.EX. The cooling power decreased by 11.76, and 11.17 % at the fresh air temperature of 45, and 39 °C, respectively, compared to the system without air-PCM H.EX. The proposed design is suggested to integrate with the AC systems.

The Review of the Application of the Heat Pipe on Enhancing Performance of the Air-Conditioning System in Buildings

An air-conditioning system (ACS), which consumes large amounts of high-grade energy, is essential for maintaining the indoor thermal environment of modern buildings. However, an ACS consumes almost half of the total energy of the building. Therefore, it is necessary to reduce the energy consumption of the ACS to promote energy conservation and emission reduction in the building sector. In fact, there is an abundance of waste heat and low-grade energies with the potential to be utilized in ACS in nature, but many of them are not utilized efficiently or cannot be utilized at all due to the low efficiency of thermal energy conversion. Known as a passive thermal transfer device, the application of a heat pipe (HP) in the ACS has shown explosive growth in recent years. HPs have been demonstrated to be an effective method for reducing building cooling and heating demands and energy consumption in ACS with experimental and simulation methods. This paper summarizes the different HP types applied in the ACS and provides brief insight into the performance enhancement of the ACS integrated with HP. Four types of HPs, namely tubular HP (THP), loop HP (LHP), pulsating HP (PHP) and flat HP (FHP), are presented. Their working principles and scope of applications are reviewed. Then, HPs used in natural cooling system, split air conditioner (SAC), centralized ACS (CACS) and cooling terminal devices are comprehensively reviewed. Finally, the heat transfer characteristics and energy savings of the above systems are critically analyzed. The results show that the performance of the HP is greatly affected by its own structure, working fluid and external environmental conditions. The energy saving of ACS coupled with HP is 3–40.9%. The payback period of this system ranges from 1.9–10 years. It demonstrates that the HP plays a significant role in reducing ACS energy consumption and improving indoor thermal comfort.

Load rebound suppression strategy and demand response potential of thermal storage HVAC systems: An experimental and simulation study

The problem of power imbalance caused by the large power consumption of heating, ventilation and air-conditioning (HVAC) units in the summertime can be alleviated by building demand response (DR) strategies. However, after DR, load rebound will occur due to the sudden increase in demand. Meanwhile, there is a general lack of information on effective DR strategies. To study the DR potential and load rebound suppression strategies of HVAC systems, nine operation strategies were devised and applied experimentally. The physical experiments were conducted on a central air-conditioning system to investigate control strategies such as regional temperature reset, active energy storage, cold source unit activation and deactivation, as well as pre-cooling. Additionally, a simulation model was developed utilizing TRNSYS. DR evaluation indices, such as duration, power reduction capability, system operation cost, etc. were applied. The experimental results show that after a water storage tank is added as a buffer tank, the start and stop times of the air source heat pump were reduced from 46 to 6. Utilizing the thermal inertia of building and HVAC system, while ensuring personnel's thermal comfort, the DR participation time can be extended to 40–50 min. This will result in an 8.9 %–12.6 % reduction in power consumption. The simulation results show that with active energy storage added, HVAC system's DR participation time can be increased to about 120 min, the maximum power consumption can be reduced by 23.4 %, and the maximum operating cost can be reduced by 21.7 %. The study shows that a slow recovery strategy and the extension of DR control time can limit load rebound effectively. From the result of load rebound suppression effect, if the load rebound suppression strategy is not adopted, the power after DR will be 3.7 % higher than that pre-DR; the load rebound suppression strategy is adopted, which is 3.6 % lower than that pre-DR. By implementing the proposed DR strategies, the issue of frequent activation and deactivation of refrigeration units can be effectively mitigated. This will greatly alleviate the power supply and demand contradiction, while also effectively suppressing the load rebound effect to prevent a secondary peak in power consumption.

A hybrid model of commercial building cooling load prediction based on the improved NCHHO-FENN algorithm

Accurate building cooling load prediction is one of the key conditions for optimal operation, energy efficiency and sustainability studies of large central air-conditioning systems. In this paper, a novel model based on nonlinear chaotic mapping Harris Hawks Optimization (HHO) optimized Fully Elman Neural Network (FENN), called NCHHO-FENN, is proposed for building cooling load prediction. First, the entropy weight method is combined with the grey correlation method to generate evaluation indexes for feature extraction. Then, parameters of the Fully Elman Neural Network prediction model were optimized using the nonlinear chaotic Harris hawk optimization (NCHHO) algorithm, and the hybrid model for cooling load prediction was constructed. Finally, cool loading data from a sizable commercial building in Shaanxi Province was adopted as the research sample for validation. The experimental results show that the Root Mean Square Error (RMSE) and coefficient of determination (R-square) of the NCHHO-FENN prediction model are reduced and improved by 54.82%–6.64%, 49.20%–4.81% and 11.72%–0.46% respectively, compared with the three improved Fully Elman Neural Network models, which explains that harris hawks algorithm can not only be used to optimize the parameters of fully elman neural network but also has superior prediction effect. At the same time, in the case of a few training samples, NCHHO-FENN can still proficiently predict the cooling load in various months, which also shows that it has high generalization. It demonstrates that the proposed method is efficient and reasonable, and can be applied to building cooling load prediction, contributing to theoretical justification for building energy-saving operation and management.

Modeling the Impacts of Filter Fouling on Energy Consumption

The use of higher efficiency particulate air filters in central heating, ventilating, and air-conditioning (HVAC) systems to improve indoor air quality (IAQ) in homes is continuing to increase. However, as filters are loaded with collected particulate matter and dust, the pressure drop will increase and lead to a variety of direct and indirect energy impacts. The magnitude of energy impacts on fouled, or dirty, filters is not yet known. Therefore, this paper explores the energy impacts of fouled filters on new energy-efficiency homes in multiple climates around the United States by performing whole building energy simulations using BEopt and EnergyPlus. Relevant inputs include filter pressure drop, total pressure drop, HVAC airflow rate, fan efficiency, heating and cooling nominal capacity, and rated airflow rate. The results indicate that annual energy consumption generally increases for permanent split capacitor (PSC) and electronically commutated motor (ECM) fans as the filter gets loaded or dirty over time, although the energy impacts are smaller for ECMs.

Active and passive methods for cooling load reduction in a tropical building: A case study

In the present study, energy saving potential and economical viability of implementing various active and passive cooling methods to a hostel building was accessed for tropical climate. The transmitted load for the building envelope was reduced by proposed Energy conservation measures (ECMs) including Gypsum-based cow dung plaster at the walls, double glazing glass for the windows and cool paint in conjunction with Photovoltaic panel for the roof. The experimentally calculated U-values for proposed plastered wall, double glazing window and cool painted with Photovoltaic shaded roof were 1.85 W/m2 K, 3.45 W/m2 K and 1.1 W/m2 K respectively. The ventilation load for the proposed centralized air conditioning system was reduced by connecting an Earth Air Heat Exchanger (EAHE) to the outlet vent port. The sizing of EAHE system was done using a MATLAB code validated with own experimental data. By parametric analysis, EAHE system of 16 pipe configuration with each of length 100 m and diameter 0.4 m was selected as a good compromise between effectiveness of heat transfer and pressure drops. Using eQuest simulation software, the total peak cooling load calculated for base and proposed case was 285 TR and 186 TR respectively. For the proposed case, the ventilation load was reduced by 18 TR and transmission load was reduced by 81 TR. The energy saving, payback time, and Greenhouse house gas mitigation for the proposed case with ECMs and EAHE was 344 MWh/year, 3.2 year, and 378 t CO2/year respectively.

A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers

At present, the energy consumption of the parallel chillers system accounts for about 60% of the whole energy consumption of central air conditioning system and 25– 40% of the total energy consumption. Unreasonable load distribution brings huge energy consumption and carbon emissions during building operation. In order to reduce energy consumption and carbon emissions of the parallel chillers system in large-scale refrigeration system, we proposed an improved sparrow search algorithm (TMSSA). This algorithm initializes the population using Tent chaotic map to increase population diversity, which can enhance the possibility of TMSSA to obtain the optimal solution before the optimization iteration, and speed up the convergence process. Besides, a Levy flight mechanism is applied to improve the position update of the producer, enhancing the randomness and local search ability of this algorithm. Moreover, Gaussian mutation method is utilized to perturb the position of scroungers, strengthening the ability of the algorithm to escape local optima to improve robustness of TMSSA. To evaluate the proposed algorithm, 12 benchmark functions were chosen, and the results showed that it overcomes the limitations of traditional SSA regarding local optima trapping. The algorithm is also used to solve the optimal chiller loading (OCL) problem, which has 3 typical cases, and comparisons were made with other algorithms. The results further demonstrate that TMSSA is highly accurate, fast-converging, and robust with excellent energy-saving performance.