Variable Refrigerant Flow Air Conditioning Research Articles

Efficiency and adaptability: a study on variable refrigerant flow (VRF) air conditioning systems in Sri Lanka

PurposeThe Heating, Ventilation and Air Conditioning (HVAC) system is a significant energy consumer in built environments, and the building energy consumption could be minimised by optimising HVAC controls. Hence, this paper aims to investigate the applicability of Variable Refrigerant Flow (VRF) air conditioning systems for optimising the indoor comfort of buildings in Sri Lanka.Design/methodology/approachTo address the research aim, the quantitative approach following the survey research strategy was deployed. Data collected through questionnaires were analysed using descriptive statistical tools, including Mean Rating (MR), Relative Important Index (RII) and Standard Deviation (SD).FindingsThe findings revealed that VRF systems are popularly used in Sri Lankan apartment buildings. Furthermore, energy efficiency and comfort were recognised as the most significant top-ranked benefits, while ventilation issues and initial cost were recognised as significant challenges. Moreover, the allocation of trained technicians and provision of proper ventilation through a Dedicated Outdoor Air System (DOAS) were highlighted as applicable mitigation strategies for the identified challenges in VRFs.Practical implicationsThe study recommends VRF systems as a suitable technology to ensure energy efficiency, reduce GHG emissions and achieve climate performance within the built environment. The opportunities for adopting VRF systems for developing countries could be explored based on the research findings. The identified challenges would assist the design engineers and facilities professionals to devise suitable strategies to mitigate issues of VRF systems in developing countries.Originality/valueThis research provides empirical proof of the energy efficiency and comfort aspects of VRFs. The study has explored and recommended VRF technology as a beneficial application to overcome the persistent energy crisis in developing countries.

A hybrid control strategy for frequency regulation with variable refrigerant flow air conditioning system

Heating, ventilation and air conditioning (HVAC) systems of buildings are abundant sources for the Frequency Regulation (FR) ancillary service, whose actuating devices are controlled to follow the real-time power reference signals with the contractual capacity. For HVAC practice, model-based FR controls are hindered by expensive model acquisition. This paper presents a two-layer control strategy for HVAC systems to perform energy efficient FR service, with a rather low model requirement. At the lower level, the FR operation with a selected actuator is realized with a sliding mode control which is robust to power map variation. At the higher level, system efficiency is optimized in real time via a model-free constrained extremum seeking control (CESC). A conceptual variable refrigerant flow (VRF) system is used as the illustrative application, for which the outdoor unit fan offers the FR power reference tracking with SMC. At the outer loop, the compressor discharge pressure is adjusted by a CESC to minimize the total power consumption while the zonal temperature regulation is reinforced as equality constraint. The proposed strategy is evaluated with simulations on a Modelica simulation, and the results validate its effectiveness. The Net Energy Cost can reduce by 15% to 23% under different load levels.

Super‐heating degree responsive control strategy of variable refrigerant flow air conditioning system for energy saving and stable operation

AbstractThe control strategy is important for variable refrigerant flow (VRF) air conditioning system to realize energy saving and stable operation. In this work, the super‐heating degree (SHD) responsive control strategy was developed to dynamically adjust the respective SHD target value (ΔTe,tar) of indoor units so as to adapt to the variable cooling load. The capacity code (Ecode) is introduced into the control model to quantify the reference target value of SHD. To responsively adapt to variable cooling load, the correction target values of SHD based on discharge temperature and deviation of the outlet air temperature are introduced in the SHD model for operation protection of the compressor and the consistency of refrigerant distribution. The enthalpy difference analogy method is developed to estimate the performance of the VRF system including cooling capacity, electric power, and energy efficiency ratio (EER). The performance was tested under the conditions of different ΔTe,tar. Experimental results demonstrate that the ΔTe,tar is an important factor to determine the operation frequency, which affects cooling capacity, power, EER, and operation status of the system. The cooling capacity and electric power at the ΔTe,tar of 9°C was about 55.2% and 63.5% lower than those at the ΔTe,tar of 1°C, and the corresponding EER was improved by 22.6%. To further verify the SHD control strategy, the VRF system was tested by the responsive control model under the condition that Ecode and ΔTe,tar were constantly automatically adjusted and changed with the actual indoor ambient temperature. Experimental results demonstrate that the VRF system can achieve a good and uniform cooling effect and realize energy saving and stable operation according to the responsive super‐heating degree control.

Operation characteristics based on a novel performance model based on capacity utilization rate of a variable refrigerant flow air conditioning system

In the context of a large market share, enhancing the actual performance of variable refrigerant flow (VRF) air-conditioning systems is of considerable importance. The operating states of indoor units (IUs) significantly influence the energy efficiency of a VRF system with multiple indoor terminals. Therefore, it is challenging to address accurate and concise representation of the operational characteristics of IUs for analyzing their impact on system performance. This study explored a novel performance index called capacity utilization rate (CUR) to illustrate the effect of IUs on system performance and developed a performance model based on this index. Simulation and experimental results demonstrated that energy efficiency and evaporating temperature decrease with a reduction in CUR under specific ambient temperatures and part-load rates. The proposed performance model was applied to real buildings in field tests to examine the operational characteristics of the VRF system. This research offers valuable insights for system design, performance simulation, and performance evaluation of VRF systems.

Variable refrigerant flow air conditioning system applicant company selection using PROMETHEE method

Variable refrigerant flow air conditioning system applicant company selection using PROMETHEE method

Super-Heating Degree Responsive Control Strategy of Variable Refrigerant Flow Air Conditioning System for Energy Saving and Stable Operation

Super-Heating Degree Responsive Control Strategy of Variable Refrigerant Flow Air Conditioning System for Energy Saving and Stable Operation

Alternative variable refrigerant flow (VRF) air conditioning systems with rational distribution of thermal load

One of the most attractive reserves of enhancing the energetic efficiency of air conditioning systems (ACS) is to provide operation of compressors in closed to nominal modes by choosing the rational design refrigeration capacities and their distribution according to current thermal loading to provide closed to maximum annual refrigeration energy generation. Generally, the overall thermal load band of any ACS comprises the unstable load range, corresponding to ambient air precooling with significant load fluctuations, and a comparatively stable load part for further air conditioning from a threshold temperature to a target value. The stable thermal load range can be covered by operation of conventional compressor in closed to nominal mode, meantime ambient air precooling needs load modulation by applying a variable speed compressor. A proposed ACS enables a wide range of refrigerant flow variation without heat flux drop in air coolers and can be considered as advanced alternative to variable refrigerant flow systems.

New fault diagnostic strategies for refrigerant charge fault in a VRF system using hybrid machine learning method

Fault diagnosis of a refrigerant charge is significant for the stable operation of a variable refrigerant flow (VRF) air conditioning system. At present, machine learning methods are widely used to diagnose faults associated with the model training. However, new faults that are not associated with the process are rarely considered for diagnosis. On this basis, this study presents two strategies using hybrid back propagation neural network (BPNN) and decision tree (DT) methods, namely, distinguish threshold-based and vector similarity principle-based strategies. This study aims to construct a diagnostic model that can diagnose new faults for a VRF system. The data of the refrigerant charge amount fault are employed to verify the strategies and are divided into training, validating, and testing datasets. The training dataset simply constituting known faults is first employed to train the proposed hybrid model. The validating dataset is employed to evaluate the diagnostic performance of a hybrid model. In a well-trained hybrid model, the BPNN is responsible for diagnosing known and new faults from the testing dataset. Meanwhile, the DT is responsible for diagnosing the specific fault type from the known faults. Results show that the hybrid model using the proposed strategies possesses good diagnostic performance. The hit rates of the new faults can yield 94.47% and 97.44%. The correct rates of the testing dataset can yield 95.2% and 96.63%.

Assessing the energy performance of VAV and VRF air conditioning systems in an office building located in the city of Florianópolis

Abstract The objective of this study is to analyze the energy performance of two types of water-cooled air conditioning systems, variable air volume (VAV) and variable refrigerant flow (VRF), in terms of their cooling energy use through building simulation. These systems were designed to operate in an office building located in the city of Florianópolis, Brazil. The analysis involved the application of two building use schedules: a) constant and b) variable. Moreover, an analysis of the coefficient of performance (COP) and partial load ratio (PLR), and the percentage of operating hours for each range of cooling COP and PLR for each air conditioning system, allowed the system cooling efficiency to be assessed and the results to be related to the annual energy consumption. The coefficient of performance of VAV and VRF is 6.7 and 5.0, respectively, but the VRF system presented the lowest energy consumption for both schedules. The difference in the cooling energy consumption values for the VRF and VAV systems, for the variable schedule compared with the constant schedule, is mainly influenced by the partial load performance during the hottest period of the year.

An online compressor liquid floodback fault diagnosis method for variable refrigerant flow air conditioning system

Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the Classification and Regression Tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability.

An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems

This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.

Control strategy of compressor and sub-cooler in variable refrigerant flow air conditioning system for high EER and comfortable indoor environment

The contradiction between increasing demand of variable refrigerant flow (VRF) air conditioning system and high energy consumption has stimulated great interest in energy saving without sacrificing the environmental comfort and reliability. This work greatly aimed at achieving enhanced energy conservation, comfortable environment and reliable operation by the coordination control of the compressor and sub-cooler in VRF system. The dynamic control model of the compressor output capacity was developed on the base of the static model, in which the average gas pipe temperature in the indoor units was used to calibrate the dynamic reference pressure control target. The influence of the static and dynamic control model on the cooling performance of VRF system with sub-cooler was comparatively tested and analyzed. The experimental results show that when one and two indoor units were thermo-on, the EERs by the dynamic control model were respectively 1.1–9.4% and 7.9–32.6% higher than those by the static model, and the air outlet temperatures can be controlled at 13.5–15 °C, within the suitable range of the air outlet temperature. The investigation results have proved that the control strategy can be considered as an efficient technology to achieve high EER under part load condition, comfortable thermal environment and assure reliable operation of the system.

Case study of an advanced integrated comfort control algorithm with cooling, ventilation, and humidification systems based on occupancy status

In this study, we propose a new control algorithm based on occupancy status for indoor environmental devices to maintain thermal comfort while saving energy of residential buildings. The algorithm consists of two parts: an environmental data driven model for the occupancy status detection and an integrated comfort algorithm for operating the indoor devices. To detect the occupancy status, a multinomial logistic regression model is developed based on the data obtained from passive infrared (PIR) sensors, a door sensor, CO2 concentrations, and lighting electricity consumption. The occupancy status is categorized into three classes: away, active, and inactive (sleep).The new control algorithm integrates and operates air-conditioning, ventilation, and humidification systems by considering the outdoor environment, and ensuring indoor thermal comfort and energy savings at each occupancy status. The control algorithm is evaluated at different outdoor conditions with a testbed that consists of a variable refrigerant flow (VRF) air conditioning system, humidifier, and ventilation system.The performance of the proposed control algorithm is experimentally compared with that of a conventional individual control method in terms of the thermal comfort and energy consumption. The results show that the advanced integrated comfort control system improves the thermal comfort and decreases the energy consumption. Specifically, Case 1 (active, hot, and mild humidity) exhibits a 6.3% energy reduction and 64% improvement in the thermal comfort. Case 2 (active, hot, and low humidity) shows a 21% energy reduction and 72% improvement in the thermal comfort. Case 3 (inactive, comfort indoors) displays a 7% energy increase and 31% improvement in the thermal comfort. These results confirm that the proposed advanced integrated comfort control algorithm could be useful for designing better space environmental control to ensure thermal comfort and energy conservation.

Outdoor air thermal plume simulation of layer-based VRF air conditioners in high-rise buildings

Variable refrigerant flow (VRF) air conditioning system is widely used in commercial buildings for space cooling and heating. However, some VRF systems used in high-rise buildings cannot work efficiently or even stop working because of the relatively high ambient air temperature, caused by the thermal plume effect of exhaust heat from outdoor units. In this paper, the thermal plume air flow of the layer-based VRF systems is investigated through computational fluids dynamics (CFD) simulation. Moreover, an illustrative example of practical VRF system in a 30-storey office building in Shenzhen is analyzed to optimize the layout of the outdoor units. Preliminary results show that the exhaust heat of outdoor units can cause ascending thermal plume flow, leading to higher inlet temperatures for VRF air conditioners on upper floors, even exceeding the warning upper threshold value. It also indicates that enlarging the distance between outdoor units on different floors is an effective way to impair the thermal plume effect for VRF outdoor units and improve the thermal performance of the whole system. For the studied case, the average inlet temperatures can be decreased by 22% for VRF outdoor units with floor interval. This work can provide guidance for the optimization layout design of practical VRF air conditioning systems used in high-rise buildings.

A machine learning bayesian network for refrigerant charge faults of variable refrigerant flow air conditioning system

An intelligent fault diagnosis network for variable refrigerant flow air conditioning system is proposed in this study. The network is developed under the foundation of bayesian belief network theory, which comprises two main elements: the structure and parameters. The structure obtained by machine learning and experts’ experiences illustrates the relationships among faults and physical variables from the qualitative prospective, and its parameters (including prior probability distribution and conditional distribution) describe the uncertainty between them quantitatively. Once the structure and parameters are determined, the posterior probability distribution which can be used to complete fault diagnosis and isolation will be calculated by some algorithms. In comparison with other fault diagnosis approaches, the proposed approach can make full use of performance information. Moreover, it is more reasonable and precise to express the relationship between faults and variables rather than Boolean variables. Evaluation was conducted on a variable refrigerant flow air conditioning system, which demonstrated that this strategy is effective and efficient.

Effects of sub-cooler on cooling performance of variable refrigerant flow air conditioning system

The contradiction between increasing demand of variable refrigerant flow (VRF) air conditioning system and some technical bottlenecks, such as low energy efficiency and high noise, stimulated great interest in studying related technologies. This work attempted to adopt the sub-cooling technology to improve energy efficiency, decrease noise, and ensure reliable operation. The effects of sub-cooler on the cooling capacity, energy efficiency ratio, sub-cooling degree and discharge super-heated degree were tested and qualitatively analyzed. The results show that the reasonable adjustment of the sub-cooling electronic expansive valve opening can improve the energy efficiency ratio, increase the sub-cooling degree and ensure the safe discharge super-heated degree. The maximum energy efficiency ratio was 3.3%, 3.1% and 3.0% higher than those without the sub-cooler when one, two and four indoors were thermo-on, respectively. And the maximum sub-cooling degree was 25.6°C, 21.9°C and 17.6°C higher than those without the sub-cooler, respectively. The experimental results were analyzed by the pressure-enthalpy diagram and thermodynamic theory. Furthermore, the control model of the sub-cooling electronic expansive valve was built, in which a certain sub-cooling degree was used to control the electronic expansive valve opening, and the discharge super-heated degree was involved as the control restriction condition for the reliable operation. The investigation results provide theoretical guidance for the application of the sub-cooler in variable refrigerant flow air conditioning system.

Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining

This paper presents the optimized back propagation neural network (BPNN) method for fault diagnosis of the variable refrigerant flow air conditioning (VRF) system in the heating mode. A feature variable set optimization approach of diagnosis models is proposed based on data mining method. First, the correlation analysis method is used to eliminate redundant variables. Then, the association rule mining method is used to optimize the feature set (FS) selection. Five FSs (FS1-FS5) are obtained by optimized feature variable selection. FS1 is the original set. FS2 is the set obtained by correlation analysis and FS3-FS5 are the sets obtained by the association rule mining. The fault diagnosis models with different FSs are evaluated using four fault experiments which include outdoor unit heat exchanger air-side fouling, four-way reversing valve fault, refrigerant undercharge and refrigerant overcharge faults. The results show that the correlation analysis method can effectively eliminate redundant variables and the association rule mining method is feasible to optimize the FSs for fault diagnosis. The BPNN-FS5 model shows the best fault diagnosis performance of the VRF system in the heating mode, whose fault diagnosis correct rate has increased from 88.71% to 96.40% and hit rates of four faults are higher than 90%.

Development of a virtual variable-speed compressor power sensor for variable refrigerant flow air conditioning system

This paper proposed a universal virtual variable-speed compressor power (VVCP) sensor for VRF system based on 20-coefficient model. Compressor power can be obtained by the VVCP sensor using three input parameters (frequency, condensing temperature and evaporation temperature) which are measured by system itself. The performance of the proposed VVCP sensor is evaluated using experiments data. Experimental conditions include cooling, heating and non-standard refrigerant charge levels. The result shows that the mean square percentage errors (MSPE) are 9.9% under cooling conditions and 7.96% under heating conditions. The MSPE are 9.88% at steady state and 9.75% at dynamic state when the VVCP sensor is applied under nine different refrigerant charge levels. It demonstrated that the proposed VVCP sensor can obtain compressor power under cooling, heating and non-standard refrigerant charge levels, which could be applied to do operational monitoring and fault detection and diagnosis for VRF system at low cost.

Investigation on output capacity control strategy of variable refrigerant flow air conditioning system with multi-compressor

A set of 14 HP variable refrigerant flow air conditioning system (VRF AC) with multi-compressor has been designed, and the output capacity control strategy of compressor(s) including the switching control model of standard compressor has been built. In the output capacity control model, a certain suction pressure is used as the pressure control target to adjust the output capacity of compressors, and a little pressure fluctuation is taken into account to amend the target pressure. Furthermore, in the compressor switching control model, the most favorable operation frequency region is determined on base of the energy efficiency characteristic and noise characteristic of the compressor. And, in order to solve the large fluctuation problem of the system running and frequent ON-OFF action of the standard compressor, the equal output capacity switching principle can be used to determine the thermo-on and thermo-off switched frequency points, and control the switching process of the compressor. Experiments demonstrate the feasibility of this control strategy to ensure the stability and reliability, improve the energy efficiency and reduce the compressor noise.

Extending the virtual refrigerant charge sensor (VRC) for variable refrigerant flow (VRF) air conditioning system using data-based analysis methods

A proper refrigerant charge amount (RCA) prediction algorithm is important to air conditioning systems. In variable refrigerant flow (VRF) systems, the traditional virtual refrigerant charge (VRC) sensor models perform well at undercharge situations but produce large prediction errors at overcharge situations. When the refrigerant charge level (RCL) is over 90%, the correlation coefficient data-based method was introduced to select the additional input variables to modify the VRC models. Two data-based algorithms, multiple linear regression (MLR) and non-linear support vector regression (SVR), were used to improve the prediction performance. The prediction performance of the pure SVR model was also compared. Results reveal that the overall prediction errors for SVR based modified VRC model (SVR-VRC) is 5.53%, the minimum among the four models. The SVR-VRC model improves the VRC models and extends the application in the VRF system when only the system self-provided sensor measurements are used.