Air-conditioning Water System Research Articles

Variable Pressure Difference Control Method for Chilled Water System Based on the Identification of the Most Unfavorable Thermodynamic Loop

A variable pressure differential fuzzy control method is proposed based on the online identification method for key parameters and the fuzzy subset inference fuzzy control method of the chilled water system network model. Firstly, a phase plane fuzzy identification method is proposed for the most unfavorable thermal loop. The study focuses on analyzing the trend of room temperature deviation and deviation change in different quadrants in the phase plane. Furthermore, we establish a chilled water pipe network model that recalculates flow variation in both the main pipe and each branch pipe section to eliminate the most unfavorable thermal loop. Finally, the test platform for the fan coil variable flow air conditioning water system was designed and constructed to meet the requirements of energy-saving regulation. Additionally, the network monitoring system for the test platform was completed. The calibration and debugging results demonstrate that the monitoring error is within ±5.0%, ensuring precise control of room temperature at the end of the branch within ±0.5 °C. Results demonstrate that our novel method exhibits superior stability in room temperature control compared to traditional linear variable pressure differential set point controls while achieving energy saving ranging from 4.7% to 6.5%.

A global optimization method for data center air conditioning water systems based on predictive optimization control

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems.

Performance improvement for chilled water air conditioning cooling coils with various dimple fin geometries

The present research inspected the thermal and flow behavior of finned coiled tube heat exchangers in crossflow for chilled water air conditioning systems. The investigation variables cover the impacts of several variables, such as fin dimple shape, the number of fins, and the Reynolds number of water and air sides on the thermal-hydraulic aspects of a finned cooling coiled tube heat exchanger. The goal is to enhance this famous finned cooling coiled tube heat exchanger using dimples concerning the same coiled tube heat exchanger without dimples. Six test specimens were fabricated, three with fin dimple geometries as sharp-sided triangle dimples, curved-sided triangle dimples, and teardrop dimples. The other specimens with three different fin numbers 140, 155, and 170. The test runs were experimented in a transparent air duct with air Reynolds numbers ranging from 1540 to 6205, while the chilled water flow side has experimented with Reynolds numbers ranging from 3350 to 16250. The findings revealed that a higher Nusselt number and flow area goodness factor is achieved by a heat exchanger (D) with a curved-sided triangle fin dimple shape. The energy efficiency ratio for a cooling coil with a fin number of 170 is higher than that of cooling coils with fin numbers 140 and 155 by approximately 24.68% and 12.94%, respectively. Correlations of energy efficiency ratio, effectiveness, flow area goodness factor, and friction factor are extracted according to the experimental results with deviations.

Variable Differential Pressure Control Strategy for Variable Water Flow Air Conditioning Systems

In large-scale air conditioning water systems, variable water flow (VWF) control strategies are frequently utilized to conserve energy. This paper presents a variable differential pressure (DP) set-point control strategy for VWF air conditioning systems based on the pipeline characteristic curve. This strategy bifurcates the most unfavorable loop into two segments: the equivalent main pipe (EMP) and the most unfavorable terminal branch pipe (MUTBP). Initially, the impedance of the EMP is obtained by curve fitting the measured values of the water supply and return main pipes (WSRMP), as well as the MUTBP. Subsequently, by calculating the disparity between the DP of the actual pipeline and the DP of the EMP, and comparing it with the DP of the MUTBP, the optimal working condition point for pipeline operation can be identified. Finally, a theoretical calculation is conducted on a typical air conditioning water system. This adjustment strategy achieves an energy-saving rate of 15.27%, 12.10%, and 11.50%, respectively, under the three adjustment conditions of closing the nearest terminal, the middle terminal, and the most unfavorable terminal, as compared with the constant DP set-point control strategy of WSRMP. This strategy boasts fewer control devices, a simple control system, and better operability and engineering applicability than other strategies.

Energy-saving optimization of air-conditioning water system based on data-driven and improved parallel artificial immune system algorithm

As the air-conditioning water system is designed according to the maximum load, the system will deviate from its optimum state while operating under partial load. Therefore, it is critical that the numerous operating parameters of the various equipments in the system are dynamically adjusted in an effective and timely manner to maximize the operational energy efficiency of the system. To this end, an improved parallel artificial immune system (IPAIS) algorithm is proposed to determine the optimal operating parameters of the system under different loads. Before optimization, the power consumption model is developed using generalized regression neural network (GRNN) combined with mechanism model for each kind of equipment in the system. Afterwards, the optimal control problem is described with the objective of minimizing the total power consumption of all equipments and considering the relevant constraints. Subsequently, the IPAIS is developed to solve the problem by introducing four improvement strategies. Finally, a simulation experiment is conducted using an actual case of an air-conditioning water system. The results show that the developed power consumption model performs well in accuracy, robustness and generalization ability, and the total system energy consumption is reduced by 15.19% after optimization. Meanwhile, the IPAIS is extended to five variants to confirm the functionality and effectiveness of each improved strategy. Furthermore, the optimization performance of IPAIS in the actual system is comprehensively verified and analyzed using an experimental platform. Compared with the comparison algorithms, IPAIS is able to achieve superior optimization results and presents significant advantages in convergence, robustness and computational complexity.

Experimental investigations into thermal performance of phase change emulsion in a fin-and-tube heat exchanger

AbstractPhase change emulsions (PCMEs) are always identified as potential working fluids that could be used to reduce circulating pump energy consumption in chill water air conditioning systems. But how PCME behaves in a fin-and-tube heat exchanger is still unclear, which limited the application of such material. The paper focused on experimental studies of performance of a novel PCME, named as PCE-10, in a fin-and-tube heat exchanger. The research analyzed heat transfer and flow behavior in fin-and-tube heat exchangers and the experimental results are compared with numerical studies published. Both studies showed that PCE-10 had its advantages as a cold storage medium, as PCE-10 did help to improve the heat transfer rate of heat exchanger by factor of 1.1–1.3 at the same flow rate compared with water.

An investigation of the seasonal relationships between meteorological factors, water quality, and sporadic cases of Legionnaires' disease in Washington, DC.

Since the discovery of Legionnaires' disease (LD), limited progress has been made in understanding the epidemiology of sporadic cases of LD. Outbreaks have confirmed that air conditioning and potable water systems can be sources of community-acquired LD. However, studying the association between water quality and LD incidence has been challenging due to the heterogeneity of water systems across large geographic areas. Furthermore, although seasonal trends in incidence have been linked to increased rainfall and temperatures, the large geographic units have posed similar difficulties. To address this issue, a retrospective ecological study was conducted in Washington, DC, from 2001 to 2019. The study identified aseasonal pattern of LD incidence, with the majority of cases occurring between June and December, peaking in August, October, and November. Increased temperature was found to be associated with LD incidence. In surface water, higher concentrations of manganese, iron, and strontium were positively associated with LD, while aluminum and orthophosphate showed a negative association. Intreatment plant water, higher concentrations of total organic carbon, aluminum, barium, and chlorine were positively associated with LD, while strontium, zinc, and orthophosphate showed a negative association. The results for orthophosphates and turbidity were inconclusive, indicating the need for further research.

Design and energy analysis of 100% fresh air based hybrid liquid desiccant air conditioning system – A case study of a thermal power plant TG building

Turbine generator (TG) buildings in thermal power plants require chilled water air conditioning systems (CWAS) to control the temperature and humidity of heat dissipated rooms, which are energy-intensive and non-eco-friendly. As an alternative, a novel refrigerant-free 100% fresh air (FA)-based hybrid liquid desiccant air conditioning system (HLDAS) is proposed and presented the methodology to design it for a case study of TG building of 2X660 MW power plant in the most humid climate. The cooling and heating requirements for HLDAS are met with a cooling tower and potential waste heat from the plant. To assess the saving potential of the proposed system, annual energy consumption for the HLDAS, 10% FA-based CWAS (CWAS-1) and 100% FA-based CWAS (CWAS-2) are quantified and compared. The results revealed that HLDAS consume 55% and 31% of CWAS-1 and CWAS-2 power consumption, respectively. Low-grade potential waste heat from the plant is estimated and found to be sufficient for solution regeneration. The proposed system is extremely energy efficient for 100% FA applications where low-grade waste heat is available. In addition to this, since there are no ODP gases associated with the proposed system, the proposed HLDAS is recommendable in view of environmentally friendly technology and indoor air quality (since it is a 100% FA-based system). This work will support HVAC engineers and researchers in designing the LDAS for a given air conditioning application.

Energy-Saving Control Method of Building Central Air Conditioning Based on Genetic Algorithm

In order to solve the problem of huge energy consumption of building central air conditioning, the water system of building central air conditioning was taken as the research object and the genetic algorithm of energy-saving operation and control of building central air conditioning based on MATLAB was designed, so as to build the energy-saving control system model of central air conditioning. On the one hand, the feasibility of the optimized algorithm to solve the energy-saving problem of central air conditioning was proved through the optimization of genetic algorithm. On the other hand, the energy-saving effect was demonstrated through the operation parameters and the variation of air-conditioning energy consumption before and after the experiment. The feasibility and rationality of genetic algorithm to improve the energy-saving effect of central air conditioning was verified by the comparison of the experiment. The results showed that in the design load range of 30%–70%, the average energy-saving rate of central air-conditioning compressor was 16.8%. The maximum energy-saving efficiency was 37.4%, and the minimum energy-saving efficiency was 15.5%. The average energy-saving rate of water system was 26.8%. Therefore, the genetic algorithm had fast convergence speed and high solving accuracy, which was beneficial to improve the energy-saving ability of central air-conditioning water system.

Leakage Diagnosis of Air Conditioning Water System Networks Based on an Improved BP Neural Network Algorithm

Compared with traditional pipe networks, the complexity of air conditioning water systems (ACWSs) and the alternation of cooling and heating are more likely to cause pipe network leakage. Pipe leakage failure seriously affects the reliability of the air conditioning system, and can cause energy waste or reduce human comfort. In this study, a two-stage leakage fault diagnosis (LFD) method based on an Adam optimization BP neural network algorithm, which locates leakage faults based on the change values of monitoring data from flow meters and pressure sensors in air conditioning water systems, is proposed. In the proposed LFD method, firstly, the ACWS network’s hydraulic model is built on the Dymola platform. At the same time, a cuckoo algorithm is used to identify the pipe network’s characteristics to modify the model, and the experimental results show that the relative error between the model-simulated value and the actual values is no more than 1.5%. Secondly, all possible leakage conditions in the network are simulated by the model, and the dataset is formed according to the change rate of the observed data, and is then used to train the LFD model. The proposed LFD method is verified in a practical project, where the average accuracy of the first-stage LFD model in locating the leaking pipe is 86.96%; The average R2 of the second-stage LFD model is 0.9028, and the average error between the predicted location and its exact location with the second-stage LFD model is 6.3% of the total length of the leaking pipe. The results show that the proposed method provides a feasible and convenient solution for timely and accurate detection of pipe network leakage faults in air conditioning water systems.

Data-driven fault detection and diagnosis for centralised chilled water air conditioning system

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Application of machine learning and its improvement technology in modeling of total energy consumption of air conditioning water system.

Accurate energy consumption model is the basis of energy saving optimal control of air conditioning system. The existing energy consumption model of air conditioning water system mainly focuses on a certain equipment or a part of the cycle. However, the coupling between water system equipment will affect the setting of optimal energy consumption of equipment. It is necessary to establish the energy consumption model of water system as a whole. However, air conditioning water system is a highly nonlinear complex system, and its precise physical model is difficult to establish. The main goal of this paper is to develop an accurate machine learning modeling and optimization technique to predict the total energy consumption of air conditioning water system by using the actual operation data collected. The main contributions of this work are as follows: (1) Three commonly used machine learning techniques, artificial neural network (ANN), support vector machine (SVM) and classification regression tree (CART), are used to build prediction models of air conditioning water system energy consumption. The results show that all the three models have fast training speed, but the ANN model has better performance in cross-validation. (2) The improved differential evolution algorithm was used to optimize the parameters (initial weights and thresholds) of the ANN, which solved the problem that the ANN is easy to fall into the local optimal solution. The simulation results show that the root mean square error (RMSE) of the improved model decreases by 20.5%, the mean absolute error (MAE) decreases by 30.2%, and the coefficient of determination (R2) increases from 0.9227 to 0.9512. (3) Sensitivity analysis of the established optimization model shows that chilled water flow, chilled water outlet temperature and air conditioning load are the main factors affecting the total energy consumption.

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.

Experimental study on desiccant evaporative combined chilled air/chilled water air conditioning systems

• A desiccant evaporative combined chilled air/chilled water systems was proposed. • The dynamic characteristics was presented by experimental investigation. • The temperature of the chiller air and the chiller water is below 20℃ and 18℃. • Regeneration temperature and air–water ratio have great impacts on performance. • 70 °C regeneration temperature was recommended in high humidity regions. Evaporative cooling technology is limited by the environmental wet-bulb temperature and is not suitable for humid climates. Desiccant wheels adsorb water vapour, thereby converting high-humidity air into low-humidity air, and they can be regenerated by solar energy or waste heat. In this study, a desiccant evaporative combined chilled air/chilled water air-conditioning system is proposed. The proposed system integrates a desiccant wheel air conditioning system and a direct-evaporative cooler, which can be used for temperature and humidity independent control systems in humid climates. An experimental setup was built and used to test the system performance and important system parameter effects under moderate temperature and humidity (32 °C, 60% RH), moderate temperature and high humidity (32 °C, 80% RH), and high temperature and humidity (36 °C, 80% RH). The results indicated that for the system at a regeneration temperature of 70 °C, the chilled air temperature was lower than 20 °C, the chilled air humidity ratio was lower than 13 g/kg, and the chilled water temperature was lower than 18 °C, which meets the temperature and humidity independent control requirements.

Operating optimization of air-conditioning water system in a subway station using data mining and dynamic system models

Energy-conservation potential in the air-condition water system for subway stations is huge due to its conservative design method. Also, for operation strategy of such systems, the operation modes are formulated with the fixed schedule. This paper presents a data-based optimization method to obtain optimal parameters of the system for feedforward control. The data mining models are established by using the data from energy consumption platform of the refrigerating system. The study utilized the box-plot method, kNN algorithm and k-means algorithm to process and repair original data. Then Artificial Neural Network (ANN) model is adopted to developed the forecasting model to assess load, performance and energy consumption of the system. The input features of the models are determined by the existed models and clustering analysis. The optimal parameters under the conditions of different load-ratio range and ambient thermal environments are calculated via Genetic Algorithm and trained equipment models. And the optimal parameters are applied to establish operation schedule based on feedforward control and response time. The optimal feedforward control method is verified by a validated TRNSYS model. When the parameters are optimized, the water system energy consumption can be save by 9.5% in a cooling season.

Optimal Design of Integrated Chiller Capacity with Ice Thermal Storage for Commercial Buildings through Cooling System Cascade Analysis

Chilled water air conditioning system is used to supply cooling systems in large capacity for industrial processes and commercial buildings. Air conditioners contribute more than 60 percent of electricity consumption in buildings. District Cooling System (DCS) technology comprises a central chiller plant which provides advantage compared to local air conditioning system. It has higher efficiency, uses less power in system operation, allows more usable space in buildings, and can be operated with minimum manpower while handling same amount of cooling load. The integration of a chiller with ice thermal storage (ITS) offers more operational flexibility while reducing space cooling expenses. This paper presents a systematic framework for design and operation of District Cooling Plant (DCP) comprising an integrated chiller and ice thermal storage system. The Cooling System Cascade Analysis (COSCA) based on pinch analysis is constructed to determine the chiller optimal size and ice thermal storage capacity. The District Cooling System configuration for this study comprises a cooling tower, chiller (centrifugal, variable centrifugal, glycol) and ice thermal storage system. The application of this technique to fulfil 66,284 refrigerant tonne hour (RTH) cooling load demand from commercial buildings reveals the optimal capacity of the chiller is 3068.91 refrigerant tonne (RT), ice tank rating at 989 refrigerant tonne (RT) and ice tank capacity is 9892.75 refrigerant tonne hour (RTH).

Optimization of Frequency Conversion Operation of Solar Coupled Ground Source Heat Pumps System

Taking an office building in Qingdao as the research object, the optimal operation of the solar coupled ground source heat pumps system using frequency conversion technology at load side and source side is studied, The possibility of variable flow operation of air conditioning water system and the control strategy of variable frequency pumps are analyzed. The selection of the equipmets is completed based on the hourly load of the office building through the year. Based on the dynamic simulation software, the simulation model of solar coupled ground source heat pumps is established. The results show that the energy saving rate of variable frequency pumps is 53.18%, the energy saving rate of the heat pumps unit is-12.91%, the total energy saving rate of the system is 9.00%, and the annual performance coefficient of the system is 3.86, and the overall energy saving effect is remarkable.

Application of an unconventional thermal and mechanical energy storage coupled with the air conditioning and domestic hot water systems of a residential building

This paper proposes the application on microscale of an innovative trigeneration system with micro CAES (Compressed Air Energy Storage) – TES (Thermal Energy Storage) and the integration of renewable energy production, focusing on the potential use for air conditioning and domestic hot water systems. The system allows storing mechanical energy in the form of elastic and thermal potential of compressed air through two thermal storage units, HTTES (High Temperature Thermal Energy Storage) and LTTES (Low Temperature Thermal Energy Storage). The proposed system is tested on a single-family building in a warm climate.The analysis is carried out for integrated and independent operating modes. In integrated mode, the LTTES refrigerant charge and the vapor compression chiller operate simultaneously to cover the building's thermal load. In this case the energy expenditure is only related to the power supply of the chiller. In independent mode, the cold storage and the chiller work alternately during evening operation to limit the absorption of energy from the distribution network.The results show that, in integrated operating mode with partial recirculation and internal temperature conditions of 26 °C and 50% of relative humidity, the system reaches, in relation to the peak thermal load, a maximum operating time of 236 min. The minimum operating time of 26 min is reached using the full external air operating mode with an internal temperature of 26 °C and relative humidity of 40%. In independent operation mode, it is possible to sustain the evening thermal load for up to a maximum of 5 h without electricity absorption from the distribution network.

Effect of hybrid and single nanofluids on the performance characteristics of chilled water air conditioning system

In this work, the performance characteristics of a vapor compression refrigeration system associated with a chilled water air conditioning unit utilized nanofluids (Al2O3, TiO2, and a hybrid of Al2O3/TiO2 based H2O) were experimentally investigated.The tested parameters were; nanoparticle concentration, nanoparticle type, nanofluid flow rate, nanoparticle hybridization ratios, and air velocity. The study incorporated different concentration ratios of nanoparticles 0.05, 0.1, 0.2, 0.3, 0.4, 0.6 and 1% for Al2O3/H2O whereas 0.1, 0.3, 0.4, 0.6 and 1% for TiO2/H2O. Five different concentrations of hybrid nanoparticles Al2O3/TiO2 were employed by 0.1/0.1, 0.2/0.1, 0.3/0.1, 0.3/0.2 and 0.3/0.3 wt%. Experiments were done at air velocity ranging 0.61–1.9 m/s whilst the fluid flow rate was varied by 0.12–0.3 m3/s. Experimental results indicated that the single nanofluid of Al2O3/H2O contributed a higher coefficient of performance and a lower elapsed time for cooling the fluid of a chiller system. Higher values of the VCRS coefficient of performance were obtained at lower values of air velocity of air conditioner and higher values of the nanofluid flow rate of the chiller unit. The Al2O3/H2O provided lower values of compression ratio and higher values of the refrigeration effect in comparison with TiO2/H2O by approximately 4.1% and 5.3%, respectively.

Pipe-work optimization of water flow window

Water flow window has been demonstrated an energy-efficient product that benefits both air conditioning and hot water systems. Its thermal performance was found affected much by the design geometry and system configuration. For those based on buoyant-driven flow, liquid water is at very slow movement; the magnitude depends on the overall flow-path friction loss and hence the connecting-pipe arrangement. In this study, the impacts of such recirculating pipe-work have been examined extensively. Firstly, experiment tests were executed to evaluate the system performances with and without distribution headers. Then their thermal and flow characteristics were compared using CFD analysis. Further on, the impacts of connecting-pipe dimensions were examined through the use of a validated FORTRAN program. The optimal pipe size was determined accordingly. It was found that without headers, more uniform temperature distribution close to the glazing surfaces could be achieved. The system thermal efficiency could be improved by around 5%. On the other hand, the variation in room heat gain was found around 1%, as a result of the self-regulating nature of the buoyant-driven flow. For the given window system, an optimal pipe size of 20–25 mm is recommended.