Energy Consumption Of Compressor Research Articles

Experimental assessment of using phase change materials in vapor compression refrigeration systems for condenser pre-cooling

The primary objective of this investigation is to empirically assess a new vapor compression cycle while employing phase change material (PCM) energy storage. During off-peak periods, the PCM undergoes charging, and during on-peak hours, it is discharged to cool the refrigerant entering the condenser, thereby enhancing the condenser's overall performance. In contrast to previous studies that exclusively examined the charging or discharging processes of air conditioning (AC) units, this research delves into the entire 24-h charge and discharge cycle. The proposed system is subjected to testing under identical conditions, both without PCM (conventional mode) and with a PCM storage tank, throughout a 24-h period. The PCM storage tank, which utilizes water as its phase change medium, possesses a volume of approximately 300 L and starts at an initial temperature of 25 °C. The outcomes of the study demonstrate notable improvements when incorporating the PCM storage tank. Specifically, the daily coefficient of performance (COP) increases by approximately 7 %, rising from 2.17 to 2.33. Additionally, it leads to a reduction in both the daily available cooling load and daily compressor energy consumption, with decreases of approximately 3.7 % (from 58.4 to 56.2 kWh) and 10.3 % (from 26.9 to 24.1 kWh), respectively.

A novel heat recovery VRF system: Principle and cooling performance analysis

Faced with an increasingly severe energy problem and increased demand for indoor comfort, heat recovery VRF systems have demonstrated remarkable potential for energy conservation. However, traditional heat recovery VRF systems need more suboptimal energy efficiency in terms of cooling and dehumidification and fail to guarantee precise temperature and humidity control in specific environments. In this study, a novel heat recovery VRF system that can achieve highly efficient cooling while ensuring precise humidity control is proposed. It can realize four primary operation modes by changing the function of indoor heat exchangers. The results showed that the optimal volume ratio of the two compressor cylinders was 1.4, which was used as the design parameter for the system. Compared to the single-evaporation temperature system, the proposed double-evaporation temperature system has better energy efficiency and dehumidification capacity in the cooling mode. The cooling coefficient of performance (COP) can be increased by 3.18 %–6.05 %, the compressor energy consumption can be reduced by 3.07 %–7.03 %, and the moisture extraction rate and latent heat ratio of the system can be increased by 5.6 %–33.3 %.

Experimental correlations to predict the cooling performance and compressor energy consumption of domestic refrigerator with phase change material

ABSTRACT The phase change material technology is continuously evolving in commercial market. In order to develop/design PCM modules of different capacity and different PCM material, it is necessary to develop the equation which will provide required information for development of PCM module. This paper establishes correlations to predict backup time and equivalent energy consumption of domestic refrigerator based on experimental data of testing of PCMs, potassium chloride (KCl), sodium chloride (NaCl), and sodium fluoride (NaF) dissolved in water for PCM masses varying between 0 and 1.75 kg in both closed door and door opening conditions. This paper investigates the impact of incorporating a phase-change material (PCM) on the cooling performance and energy consumption of a domestic refrigerator. The PCM module, filled with potassium chloride (KCl), sodium chloride (NaCl), and sodium fluoride (NaF) dissolved in water, is strategically placed on the contact surface of the refrigerator’s evaporator coil. The utilization of PCM aims to mitigate cabin temperature fluctuations and reduce overall energy consumption. Through experimental evaluation, the study assesses the effectiveness of different phase-change materials in enhancing the cooling performance and energy efficiency of the domestic refrigerator. The findings reveal that the incorporation of phase-change material significantly improves the cooling performance, as evidenced by the extended backup time during power cut-off and a reduction in compressor energy consumption. The tested phase-change materials, including KCl, NaCl, and NaF, demonstrate varying impacts on performance parameters. The backup time for closed-door tests is 1.83 – 1.03 times longer than that for door opening tests, respectively. The backup time of door opening tests is shortened due to heat entered to the cabin for all masses, between 45.45% and 3.23%. The backup time is increased by the masses of PCM KCl between 14% and 155%, the masses of PCM NaCl between 18% and 159%, and the masses of PCM NaF between 27% and 182%. The equivalent energy consumption for door opening tests is 1.12–1.02 times larger than that for the closed-door tests. The correlation equation established based on the existing experimental data can be used to accurately predict the backup time and equivalent energy consumption of refrigerator for tested PCM of any mass within the interval of 0 Kg to 1.75 Kg with ± 10% accuracy.

Dynamic evaluation of a vapor compression refrigeration cycle combined with a PCM storage tank for improving condenser performance

In this study, a vapor compression refrigeration cycle integrated with a phase change material (PCM) storage tank has been dynamically simulated over a 24-h period. The primary objective of this system is to reduce electric energy consumption during on-peak hours (12:00–19:00) and shift it to off-peak hours (1:00–10:00). During off-peak hours, the vapor compression refrigeration system stores cooling energy in the PCM storage tank. The stored cooling energy is then reused during on-peak hours to pre-cool the condenser inlet refrigerant, enhancing the cooling system's performance and reducing electric consumption during on-peak hours. Oleic acid, eutectic mixtures of 45 % capric acid and 55 % lauric acid by weight (CL), a commercial blend of salt hydrate and paraffin wax (SP224A), and CaCl2.6H2O have been selected as the PCM mediums. The effects of weather conditions and PCM storage tank parameters on the system's performance have been investigated. The results indicate that SP224A performs the best in most weather conditions. The maximum electric peak load shaving occurred under Ramsar city weather conditions, achieving 98.85 %. Additionally, the compressor's electric energy consumption is reduced by about 23.38 %. Moreover, increasing the length of PCM storage pipes leads to a reduction in compressor energy consumption during on-peak hours, as well as electric peak load shaving, with an improvement of up to 156 %.

Cryogenic-membrane separation process for helium extraction and ethane co-production from natural gas

In order to address the defects of high energy consumption and low efficiency in the single cryogenic separation process for helium extraction, this paper integrate post-expansion nitrogen cycle refrigeration with membrane separation to establish a novel natural gas cryogenic-membrane helium recovery process. Based on four mainstream processes for ethane recovery from natural gas, an optimal ethane recovery method is selected and incorporated into the proposed cryogenic-membrane helium recovery process. The process is then simulated using ASPEN HYSYS software. The effects of operating parameters on critical equipment energy consumption, helium product concentration and ethane recovery were evaluated. The simulation results indicate that in the proposed helium recovery process, the concentration of refined helium reaches 99.6 %, and the ethane recovery rate is 90.13 %. Compared to the single helium extraction process, the total compressor energy consumption in our process is reduced by 23.10 %, and the integrated energy consumption is reduced by 20.4 %.

Data-driven modeling of the refrigeration load in supermarkets — A case study on three European supermarkets

When investigating supermarket refrigeration systems, it is essential to consider the refrigeration load as it directly affects the required compressor capacity and energy consumption. Therefore, it is important to have knowledge of the refrigeration load and its main influencing factors.Direct usage of data for the refrigeration load in a whole-year energy simulation can present significant challenges. This is partly due to long lead times for data collection and the challenges posed by missing data. As a result, often there is no complete data set of the refrigeration load for the whole-year. Therefore, a mathematical model can serve as a tool to interpolate and extrapolate the refrigeration load over the course of a year. The model is not intended for transfer to other locations.This paper begins with a overview of correlations found in the literature, followed by the development of an improved model that accounts for the impact of ambient temperature and time on the refrigeration load. Additionally, a novel approach using a neural network is proposed as a second model. Both models are then applied to data from three supermarkets in Europe and the model performance is evaluated.Both models performed acceptably in representing the refrigeration load of all three supermarkets. Especially including the influence of time of day into the first model significantly improves the prediction accuracy. The neural network has the highest prediction performance and can improve the prediction performance by 40% over the improved literature model.

The Optimization of Chillers Air-Conditioning in Thailand Supermarkets using a Retail Energy Management System (REMS)

Supermarkets in Thailand continue to grow and there will be more than 1500 branches by 2023 open 18-24 hours a day. In this research, we will present how to increase energy efficiency in retail stores by reducing the energy consumption of compressors of chiller air conditioning systems by using energy management systems in retail stores. Retail energy management systems in retail stores use the Internet of Things to connect to a device to store data and analyze and process from big data, then Artificial Intelligence controls and commands to make the air conditioning system energy efficient. Controlling 10 AHUs using central sensors at the sales area instead of return sensors allows us to save 25% of energy or 12,886 kWh/Year. As for the cold-water pump, it is controlled by an inverter. By using sensors to measure the water flow rate, we save 33% of energy or 19,755 kWh/Year. When controlling the water pump and AHU, the chiller compressor will save energy because when the air handling unit and the water pump have reduced work, the work of the compressor to cool water is reduced as well, resulting in energy savings of 12% or 25,211 kWh/Day. Using such a system reduces the overall energy consumption of the chiller air conditioning system. 17% or 57,852 per year has a payback period of 3 years and can also be extended to other systems within the store or other stores.

Reducing Energy Consumption of Refrigerator Compressor using Aluminum Oxide Nanoparticles

Reducing Energy Consumption of Refrigerator Compressor using Aluminum Oxide Nanoparticles

Co-optimization method for injection strategy of underground natural gas storage integrating aboveground and underground parts

Underground natural gas storage (UNGS) is an important infrastructure to overcome the imbalance between supply and demand of natural gas. During the gas injection period, gas is compressed aboveground and then transported to injection wells for injection into the underground reservoir. Reasonably formulating the compressor operation scheme and injection scheduling scheme is a crucial step. However, “Compressor-pipeline-injection well-reservoir" constitutes a synergistic whole. More efficient and practical operation schemes can only be proposed through the establishment of an optimization method that coordinates the entire gas injection process. A novel two-layer multi-objective collaborative optimization method is proposed to optimize the monthly operation scheme during the injection period. The upper-layer model focuses on optimizing injection volume allocation to minimize reservoir pressure deviation and total compressor energy consumption. The under-layer model optimizes the compressor operation scheme by considering the multi-stage compression (MC) and clearance adjustment (CA) equipped in reciprocating compressors. The synergistic relationship during the injection process is integrated into the traditional model through the interlayer iteration method. Furthermore, a high accuracy and efficiency proxy model based on machine learning algorithms (ML) is developed to predict compressor performance. The results of the case study indicate that, compared with traditional optimization method and the two-stage co-optimization method, the proposed method achieves an average energy consumption reduction of 9.54 × 106 kWh (10.79%) and 2.12 × 106 kWh (2.4%) respectively. The effective utilization of CA and MC can significantly alleviate the deviations between displacement and processing requirements, eliminating 22,120.5 × 104 Nm3(85.62%). The two-layer co-optimization method effectively integrates the aboveground and underground parts of UNGS, providing a solution for safe and efficient operation during the injection period.

ML- and LSTM-Based Radiator Predictive Maintenance for Energy Saving in Compressed Air Systems

Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%.

Performance Assessment and Optimization of the Ultra-High Speed Air Compressor in Hydrogen Fuel Cell Vehicles

Air compressors in hydrogen fuel cell vehicles play a crucial role in ensuring the stability of the cathode air system. However, they currently face challenges related to low efficiency and poor stability. To address these issues, the experimental setup for the pneumatic performance of air compressors is established. The effects of operational parameters on energy consumption, efficiency, and mass flow rate of the air compressor are revealed based on a Morris global sensitivity analysis. Considering a higher flow rate, larger efficiency, and lower energy consumption simultaneously, the optimal operating combination of the air compressor is determined based on grey relational multi-objective optimization. The optimal combination of operational parameters consisted of a speed of 80,000 rpm, a pressure ratio of 1.8, and an inlet temperature of 18.3 °C. Compared to the average values, the isentropic efficiency achieved a 48.23% increase, and the mass flow rate rose by 78.88% under the optimal operational combination. These findings hold significant value in guiding the efficient and stable operation of air compressors. The comprehensive methodology employed in this study is applicable further to investigate air compressors for hydrogen fuel cell vehicles.

Intelligent pressure switching control method for air compressor group control based on multi-agent reinforcement learning

In order to effectively control the pressure and energy consumption of multiple air compressors within an acceptable range, an intelligent pressure switching control method for air compressor group control based on multi-agent RL is studied. This method uses sensors in the air compressor field control cabinet to collect data such as header pressure, air storage tank pressure, and air storage tank temperature and sends them to the edge data collector for integration. After integration, the main control cabinet sends them to the upper computer. Combined with the on-site collected data, a multi-agent-based air compressor group control model is designed to convert multiple air compressors in the air compressor group control problem into a multi-agent mode, facilitating unified switching control of the air compressor group. Then, using the intelligent pressure switching control method based on deep Q-learning, driven by a neural network controller, the frequency of the frequency converter is adjusted to control the pressure at the outlet of the air compressor terminal header within the set value range, completing the pressure intelligent switching control. After testing, this method has good application results in pressure control, energy saving, and other aspects after being used for intelligent pressure switching control of air compressor group control.

A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle’s Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively.

The Analysis of Next-Generation Refrigerants in Terms of Energy, Exergy, and LCCP Perspective

In this study, commonly used hydrofluorocarbon (HFC)-based refrigerants R404A and R410A, as well as hydrofluoroolefin (HFO)-based environmentally friendly next-generation refrigerant R1234yf with a low global warming potential (GWP), were analyzed in terms of energy, exergy, and life cycle climate performance (LCCP) in refrigeration and air conditioning systems. All three refrigerants were examined at four different evaporation temperatures (-30°C, -15°C, -5°C, 0°C) with a constant condenser temperature of 50°C using a simulation program. For different evaporation temperatures, the performance of the refrigerants was evaluated using the first and second laws of thermodynamics, and performance coefficients, exergy efficiency, and exergy destruction were calculated. Additionally, the amount of kgCO₂e equivalent was calculated using the LCCP method. In the study, it was found that the compressor energy consumption of R410A and R1234yf refrigerants was similar and approximately 7% lower than that of the R404A refrigerant. The highest coefficient of performance (COP) value was determined for R1234yf. It was observed that R1234yf refrigerant had the highest exergy efficiency starting from -15°C. The kgCO₂e equivalent emission values calculated using the LCCP method revealed that R404A had the highest CO₂ emissions, while R1234yf had the lowest. Furthermore, based on the simulation study and theoretical calculations, it was determined that R410A and R1234yf refrigerants could be considered as alternative choices to R404A refrigerants in systems where two refrigerants are used.

An example analysis of energy-saving improvement based on air compressor inlet pre-treatment

Briefly introduced the basic principles and characteristics of air compressor operation, and some commonly used air compressor energy-saving operation measures in industrial enterprises; The current situation of air compressor system of Ningbo cigarette factory was analysed, and the impact of different outdoor temperature and humidity conditions on air compressor energy consumption was analysed in winter and summer. Based on the existing conditions of the factory, the fresh air intake room of the air compressor is optimized and upgraded. A double-inlet air duct system is adopted. The indoor and outdoor air enthalpy value can be automatically calculated through the temperature and humidity sensor and central control system. The air compressor intake air is pre-treated according to the results, making full use of the outdoor low enthalpy air and the existing artificial cooling source in the factory area to improve the operating efficiency of the air compressor.

Development of map-based models for the performance characterization in a new prototype of Dual Source Heat Pump

This paper presents the development of accurate map-based models for characterizing a new Dual Source Heat Pump prototype. This unit includes three braze plate heat exchangers and a round tube fin heat exchanger, allowing the unit to select different operating modes such as heat pump, chiller, and domestic hot water production using as source the ground or air. Therefore, due to the hybrid typology of this unit and the possibility of reversing the cycle, this work covers the main heat pump and refrigeration equipment technologies currently available on the market (air source and ground source units). The modeling strategy selected has been to provide several polynomial expressions to predict the performance of these units, i.e., compressor energy consumption and condenser and evaporator capacities. This approach allows obtaining accurate, compact, and easy-to-implement models for developing dynamic models of more complex systems where this type of unit – the heat pump – is an integrated part of the system. Currently, a clear example of this modeling strategy can be found in characterizing one of the main components installed in these machines, the compressor. The AHRI-540 standard specifies a polynomial model as a function of evaporating and condensing temperatures. In this sense, for the characterization of heat pumps, the polynomials developed depend only on the unit’s external variables, so they can be useful in many scenarios, obtaining direct feedback on the heat pump performance when developing a dynamic model to optimize system control strategies or to develop techno-economic studies. In this case, the hybrid typology of this unit makes it particularly relevant to optimize the control to manage the type of source to be used (air or ground), allowing the development of a more efficient and sustainable technology by selecting the most adequate source in terms of performance. This study focuses on obtaining the polynomial expressions that minimize the number of terms while simultaneously minimizing prediction error. By carefully selecting the most significant terms and suitable transformations in the characterized variables, the goal is to prevent overfitting, minimize potential extrapolation or interpolation errors and obtain polynomial expressions that can be fitted with small experimental samples. For this purpose, a detailed model implemented in a commercial software for heat pump characterization has been used, with which a large number of simulation results were generated. These simulation results include a fine meshing working map of the unit that allowed us to analyze the relationships between the characterized and external variables.

Experimental and theoretical investigation on the energetic and economic performance of low-temperature cascade air-source heat pump considering the effects of cascade heat exchanger

Air-source heat pump (ASHP) is wide used for its carbon neutrality. In cold climates, to ensure good efficiency and stability, cascade ASHP is a promising technology for high-temperature water application, such as space heating boilers retrofit. Cascade heat exchanger is a key component in cascade ASHP. However, there are few experimental studies on the effects of its area on system energetic, economic, and environmental performance under low ambient temperature and high water-supply temperature, which is essential for system improvement and application. Focusing on this research gap, this paper carries out experiment of cascade ASHP system performance and conducts analysis on system suitability based on recurrent Neural Network. To fairly compare system performance in different cold climate cities with different cascade heat exchanger areas, conversion factor γ is proposed and used in the analysis. The results show total energy consumption of compressors decreases the most in New York (21498 MJ) and least in Shenyang (9414 MJ) as cascade heat exchanger areas increases from 0.6 to 1.6 m2. Similarly, the system in New York shows the most significant reductions in carbon emissions (2701 kg/year) and total annual cost (225$), while that in Shenyang shows the smallest drop of 1277 kg/year and 118$, indicating it’s more meaningful to increase cascade heat exchanger area in a city with longer heating period. The results also show the COP varies significantly across cities, strongly correlated with average ambient temperature. The increase in COP slows down when cascade heat exchanger area reaches 1.6 m2, indicating even larger area is not recommended. These findings provide a foundation for improving and applying cascade ASHP in cold climates.

Process modelling and optimization of a 250 MW IGCC system: ASU optimization and thermodynamic analysis

Integrated gasification combined cycle (IGCC) underpins the advancement of pulverized coal combustion, yet the optimization of the air separation unit (ASU) in the IGCC system is still lacking, resulting in the limited reduction of energy consumption and efficiency improvement. Accordingly, a full-process IGCC model developed in our previous work (Energy, 2023,272: 127,040) is employed to optimize the ASU in a 250 MW IGCC power plant at an industrial scale. The feasibility of the ASU optimization was verified, and thermodynamic analysis evaluated the optimized IGCC system efficiency. The results show that the partial integration of ASU significantly reduces the compressor's energy consumption while the nitrogen re-injection method minimizes the use of saturated steam. Furthermore, reducing oxygen purity decreases the minimum separation work of the system. After optimization, the ASU uses only 53% as much power as the traditional system, and the total energy efficiency (44.9%) and exergy efficiency (43.6%) are improved by 2.2% and 1.8%, respectively. Consequently, the total energy consumption and exergy consumption decrease by 10.8% and 35.9%, respectively. The present work provides a comprehensive framework for optimizing other components of the IGCC system, which is expected to further reduce energy consumption and improve the system's efficiency.

Modeling the Energy Consumption of R600a Gas in a Refrigeration System with New Explainable Artificial Intelligence Methods Based on Hybrid Optimization.

Refrigerant gases, an essential cooling system component, are used in different processes according to their thermophysical properties and energy consumption values. The low global warming potential and energy consumption values of refrigerant gases are primarily preferred in terms of use. Recently, studies on modeling properties such as compressor energy consumption, efficiency coefficient, exergy, and thermophysical properties of refrigerants in refrigeration systems with artificial intelligence methods has become increasingly common. In this study, a hybrid-optimization-based artificial intelligence classification method is applied for the first time to produce explainable, interpretable, and transparent models of compressor energy consumption in a vapor compression refrigeration system operating with R600a refrigerant gas. This methodological innovation obtains models that determine the energy consumption values of R600a gas according to the operating parameters. From these models, the operating conditions with the lowest energy consumption are automatically revealed. The innovative artificial intelligence method applied for the energy consumption value determines the system's energy consumption according to the operating temperatures and pressures of the evaporator and condenser unit. When the obtained energy consumption model results were compared with the experimental results, it was seen that it had an accuracy of 84.4%. From this explainable artificial intelligence method, which is applied for the first time in the field of refrigerant gas, the most suitable operating conditions that can be achieved based on the minimum, medium, and maximum energy consumption ranges of different refrigerant gases can be determined.

Energy and Exergy Analysis of an Automobile Hybrid Ejector Refrigeration System Utilizing Its Exhaust Waste Heat

Abstract A major current focus of refrigeration and air-conditioning research is energy usage and environmental impact (e.g., the influence of refrigerants and air-conditioning systems on ozone layer depletion and global warming). The ejector-based refrigeration system is a technology that, it is hoped, can save power while using environment-friendly refrigerants to reduce any adverse effects on nature. The reduction of compressor dependency is the first and essential aim of this study; the second is to demonstrate the replacement of R134a with the new refrigerant R1234yf in motor vehicle air-conditioning systems, establishing the benefits of employing R1234yf in conjunction with a hybrid air-conditioning system. In such a system, the engine's exhaust gases are used to operate the ejector. A numerical model has been developed which estimates the ejector entrainment ratio at a specified spindle and primary nozzle exit position. A theoretical model using energy and exergy analysis illustrates the impact of hybrid systems on performance under different operating conditions (i.e., engine exhaust, ambient air, and evaporator temperatures). At a specified exhaust temperature, a detailed comparison has been conducted between a current air-conditioning system with R134a and the hybrid system with R1234yf. It was found that the R1234yf hybrid system reduced compressor energy consumption by 44.43% and operating exhaust heat levels by 12.79%. The coefficient of performance increased by 41.42%, while the exergetic efficiency reduced from 37.14% to 13.85%. Cooling capacity dropped by 12.73%. The hybrid air-conditioning system based on R1234yf demonstrated tremendous potential for improving vehicle air-conditioning system efficiency.