Supermarket Refrigeration Systems Research Articles

Waste heat utilization: Energy and economic benefits from multi-ejector chiller sub-cooling R744 supermarket refrigeration systems

This study investigated the energy and economic benefits of a novel sub-cooling technique using a heat-driven multi-ejector chiller (HDEC) in R744 supermarket refrigeration systems. Two configurations were evaluated: a conventional booster system with HDEC (CBEC) and a parallel compression system with HDEC (PCEC). Among various natural refrigerants, R717 was identified as most suitable for the HDEC. Various multi-ejector combinations were examined to assess the potential for waste heat utilization and energy savings in the R744 refrigeration system. The CBEC system provided effective operation only at ambient temperatures above 31 °C, whereas the PCEC system was effective above 24 °C. Performance of the proposed configurations were compared with a conventional R744 booster system (CB) and a parallel compression system (PC) both devoid of HDEC. The PCEC system showed improvements in the coefficient-of-performance ranging from 4.2 % to 23.9 % at ambient temperatures between 28 °C and 40 °C compared to the baselines. In various warm-climatic zones, including India, the Middle East, Thailand, and the USA, energy savings of approximately 2.1 %–9.5 % were observed compared to the PC system. Economic analysis indicated a reasonable payback period of 2.1–2.7 years for the additional investment required for deploying the PCEC system in Phoenix.

High-fidelity model development of CO2 booster refrigeration systems in supermarkets using field measurements

Supermarket refrigeration systems adopting traditional refrigerants with high global warming potential (GWP) have impacts on global warming for indirect and direct greenhouse gases (GHG) emissions. CO2 is a popular low-GWP alternative. The transcritical operation of CO2 systems worsens their energy performance, but provides recoverable heat as a heat source to reduce gas consumption. To evaluate operation performance, data-driven models, trained by historical data, are weak in implementation with datasets outside the scope of training data; in contrast, theoretical models have better extrapolation ability to calculate all operation conditions of CO2 systems at supermarket. Existing theoretical modeling approaches often lack validation against the limited public-access data, which reduces model reliability for further applications, and adopt oversimplified inference methods for unmeasured variables, which increases the risks of breaking thermodynamic laws and lowering model accuracy. This study therefore develops a steady-state theoretical model for CO2 booster refrigeration systems validated against field measurements from three UK supermarkets. The available measurements are utilized to the best level to ensure model accuracy and physical interpretability. Proposed methods to infer missing variables in CO2 systems include condenser outlet temperature, evaporating temperature, compressor isentropic efficiency and compressor mass flow rate. Results show that proposed inference methods enhance the abilities of the proposed modelling approach to ensure data integrity, avoid breaking thermodynamic laws, and improve model accuracy by reflecting real-time actual values of unmeasured variables rather than rough assumptions. The proposed modeling approach provides satisfactory accuracy validated using high-resolution measurements across the whole year from three real supermarkets.

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.

Transcritical CO2 system with two-stage throttling for supermarkets: Field tests, energy and emission performance assessment in China

Transcritical CO2 system is eco-friendly for the application of refrigeration and cooling in supermarket. To evaluate the applicability of transcritical CO2 supermarket refrigeration system in China with different climate regions, this study focuses on the field tests of a transcritical CO2 two-stage throttling system in a supermarket in Suzhou, Jiangsu Province, China. Furthermore, based on the tested data, the system energy and emissions performance models of four system configurations are also developed and validated, including the system integrated with an internal heat exchanger, a parallel compression, and mechanically assisted subcooling. In order to improve the usability of the system in different climatic zones, 40 cities were selected for calculation in China. Five representative cities (Beijing, Harbin, Xiamen, Chongqing, and Kunming) located in different climate regions of China are selected, and the annual performance factor and life cycle climate performance (LCCP) are determined to evaluate the application potential of the CO2 refrigeration systems in different climate conditions. The tested results show the operation mode of transcritical and subcritical changes occurs at an ambient temperature of about 27 °C, at which the coefficient of performance (COP), compressor discharge pressure and discharge temperature change dramatically. The simulation results demonstrate that the mechanical subcooling system shows the best performance. The annual performance factor can be improved by 18.69 % when the mechanical subcooling is adopted. For the case of the system used in Xiamen by using mechanical subcooling, the carbon emissions can be reduced by 19.3 %.

Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding

Supermarket refrigeration systems are integral to food security and the global economy. Their massive scale, characterized by numerous evaporators, remote condensers, miles of intricate piping, and high working pressure, frequently leads to problematic leaks. Such leaks can have severe consequences, impacting not only the profits of the supermarkets, but also the environment. With the advent of Industry 4.0 and machine learning techniques, data-driven automatic fault detection and diagnosis methods are becoming increasingly popular in managing supermarket refrigeration systems. This paper presents a novel leak-detection framework, explicitly designed for supermarket refrigeration systems. This framework is capable of identifying both slow and catastrophic leaks, each exhibiting unique behaviours. A noteworthy feature of the proposed solution is its independence from the refrigerant level in the receiver, which is a common dependency in many existing solutions for leak detection. Instead, it focuses on parameters that are universally present in supermarket refrigeration systems. The approach utilizes the categorical gradient boosting regression model and a thresholding algorithm, focusing on features that are sensitive to leaks as target features. These include the coefficient of performance, subcooling temperature, superheat temperature, mass flow rate, compression ratio, and energy consumption. In the case of slow leaks, only the coefficient of performance shows a response. However, for catastrophic leaks, all parameters except energy consumption demonstrate responses. This method detects slow leaks with an average F1 score of 0.92 within five days of occurrence. The catastrophic leak detection yields F1 scores of 0.7200 for the coefficient of performance, 1.0000 for the subcooling temperature, 0.4118 for the superheat temperature, 0.6957 for the mass flow rate, and 0.8824 for the compression ratio, respectively.

Performance Analysis of an R744 Supermarket Refrigeration System Integrated with an Organic Rankine Cycle

The energy and economic performance of a transcritical R744 booster supermarket refrigeration system with and without parallel compression and integrated with an organic Rankine cycle (ORC) was investigated. The results obtained were compared with those of a transcritical R744 booster supermarket refrigeration system with and without parallel compression and those of a conventional R404A direct expansion (DX) system. Nine different locations, namely Copenhagen (Denmark), Paris (France), Athens (Greece), New Delhi (India), Phoenix and Miami (US), Madrid (Spain), Bangkok (Thailand) and Riyadh (Saudi Arabia), were considered. It was discovered that the ORC is effective only at ambient temperatures higher than 27 °C when operating without parallel compression and 28 °C when operating with parallel compression. By using the heat recovered from the gas cooler to fuel the ORC, the latter was found to be capable of covering between 4% and 24% of the electricity demand of the R744 system in warm and hot climates (without parallel compression). The simple payback period of the additional investment associated with the ORC was found to be between 1.4 and 2.5 years in warm climate locations, while the same was found to be less than about 0.5 years in locations experiencing hot climatic conditions.

A fault-tolerant control strategy to estimate and compensate the temperature sensor bias in supermarket refrigeration systems

The paper proposes a data-driven fault-tolerant control (FTC) strategy to construct and accommodate the bias on ambient temperature measurements in supermarket refrigeration systems. The bias, which is caused by direct or indirect exposure of the sensor to the sun, can have a significant impact on the refrigeration system’s energy consumption. Based on analysis of the real data a comprehensive model of the bias is developed and then used to generate realistic scenarios for testing the proposed FTC method. The FTC method uses a feed forward Neural Network (NN) as a black box model. The model is trained by active injection of perturbation signals during the night operations. During the Monte-Carlo tests, the strategy was implemented in a Plug & Play manner, demonstrating that substantial energy savings can be achieved during summer periods.

Influence of subcooling in R-449A supermarket refrigeration system and screening of refrigerant mixtures for its energetic and environmental improvement

Current demands of the cooling sector are focused on using refrigerants with a low Global Warming Potential (GWP) and increasing energy efficiency in vapour compression installations. The main objectives of this paperwork arise from these needs, in which the use of subcooling in an R-449A commercial refrigeration system is evaluated. In addition, eco-friendly alternative refrigerants are proposed and studied. Firstly, the effect of an integrated type of subcooling is semi-empirically analysed with Engineering Equation Solver (EES). Then, the refrigeration system’s operation is simulated using low-GWP alternative refrigerants, R-454C and R-290, which have been scarcely studied in commercial refrigeration systems due to their safety classification. Moreover, new refrigerant mixtures are searched from the binary and ternary combination of pure refrigerants using the software REFPROP. The best refrigerant with safety classification A1, A2L, and A2/A3, for both medium temperature (MT) and low temperature (LT) systems is obtained from this search. After their determination, the supermarket’s operation is simulated with these refrigerants. Finally, an environmental assessment is performed. The highest energy efficiency is obtained with R-152a for the MT system and the R-290/R-1270 mixture for the LT system. However, the highest reduction of GHG emissions is achieved by R-290, with a reduction of 73.78 % with respect to the baseline system.

Dynamic model-based feature extraction for fault detection and diagnosis of a supermarket refrigeration system†

With the increasing concerns over climate change and carbon emissions, fault detection and diagnostics (FDD) of low–global warming potential (GWP) refrigerant supermarket refrigeration systems has gained great attention from academic and industrial sectors. Various FDD approaches have been developed to detect, identify, and diagnose faults to save energy, improve food quality, and protect the environment. To mitigate the difficulty of collecting high-quality steady-state operational data in field operations faced by most model-based FDD methods, this study developed dynamic models of a low–GWP refrigerant (CO2) supermarket refrigeration system. The model accuracy was validated using manufacturer data and experimental data. Simulations were conducted to predict the system dynamic response under two common operational faults—evaporator air path blockage fault and the display case door open fault—to identify fault patterns and define key dynamic behavior indexes for supporting FDD algorithm development.

CFD supported thermodynamic analysis of a CO2 pressure exchanger based refrigeration system for supermarkets

This manuscript presents a method to thermodynamically model a pressure exchanger device. CFD-based model of a rotary energy recovery device used in desalination process is developed and validated with published experimental data with a maximum deviation of 5.19%. The dimensionality of the validated model was reduced to investigate the potential of the pressure exchanger device operating with CO2 as the working fluid. Based on the data obtained from several numerical experiments, two correlations were developed, by means of non-linear regression analysis, for estimating the pressure lift generated by the device as a function of inlet velocity of high pressure fluid (0.5 – 3 m/s), rotor speed (500 – 3000 rpm), receiver pressure (25 – 40 bar) and gas-cooler pressure (90 – 120 bar). The correlations were found to be within acceptable error margin. The pressure lift generated by the pressure exchanger was found to be directly proportional to the inlet velocity of the high pressure fluid and inverse proportionality to the rotor speed. The developed correlations are used to evaluate the thermodynamic performance of a multi evaporator supermarket refrigeration system integrated with pressure exchanger to a conventional parallel compression system and a published multi-ejector system in the ambient temperature range of 30 to 40 °C. The pressure exchanger based system was found to outperform the parallel compression as well as the multi-ejector system and demonstrate significant energy savings.

Estimation of evaporator valve sizes in supermarket refrigeration cabinets

In many applications, e.g. fault diagnostics and optimized control of supermarket refrigeration systems, it is important to determine the heat demand of the cabinets. This can easily be achieved by measuring the mass flow through each cabinet, however that is expensive and not feasible in large-scale deployments. Therefore it is important to be able to estimate the valve sizes from the monitoring data, which is typically measured. The valve size is measured by an area, which can be used to calculate mass flow through the valve — this estimated value is referred to as the valve constant. A novel method for estimating the cabinet evaporator valve constants is proposed in the present paper. It is demonstrated using monitoring data from a refrigeration system in a supermarket in Otterup (Denmark), consisting of data sampled at a one-minute sampling rate, however it is shown that a sampling time of around 10–20 min is adequate for the method. Through thermodynamic analysis of a two stage CO2 refrigeration system, a linear regression model for estimating valve constants is developed using time series data. The linear regression requires that transient dynamics are not present in the data, which depends on multiple factors e.g. the sampling time. If dynamics are not modelled it can be detected by a significant auto-correlation of the residuals. In order to include the dynamics in the model, an Auto-Regressive Moving Average model with eXogenous variables (ARMAX) is applied, and it is shown how it effectively eliminates the auto-correlation and provides less bias and higher accuracy of the estimates. Furthermore, it is shown that the sample time has a huge impact on the estimates. Thus, a method for selecting the optimal sampling time is introduced. It works individually for each of the evaporators, by exploring their respective frequency spectrum. That way, a reliable estimate of the actual valve constant can be achieved for each evaporator in the system and it is shown that sampling times above 10 min are optimal for the analysed systems.

Choosing an optimized refrigeration system based on sustainability and operational scenarios applied to four supermarket architectures in three European countries

The growing global demand for refrigeration requires the design of more sustainable systems. However, despite environmentally promising technologies in refrigeration, there are still barriers to their widespread adoption. In this paper, a generic approach able to describe the multiple performances (energy, environmental, economic, social) of refrigeration systems is proposed to assess their potential of adoption. To propose a realistic study framework, four architectures of supermarket refrigeration systems are modelled and simulated using ground data with different climatic conditions and electricity mixes (France, Sweden, Spain). The overall results of these operational scenarios show that the electricity mix is the most influential parameter on cost and environmental impact. In addition, architecture using CO2 refrigerant shows interesting performances regardless of location and despite degraded regimes during heat peaks. However, the maintenance score can be a limiting factor for installing more energy efficient systems. Two other scenarios are studied: with photovoltaic panels; with financial support. Photovoltaic panels help improving cost and environmental performances, but also strongly depend on maintenance performances. Financial support helps facilities using low-global-warming-potential refrigerant to be more competitive than those using high-global-warming-potential refrigerant.

Integrated Design of a Supermarket Refrigeration System by Means of Experimental Design Adapted to Computational Problems

In this paper, an integrated design of a supermarket refrigeration system has been used to obtain a process with better operability. It is formulated as a multi-objective optimization problem where control performance is evaluated by six indices and the design variables are the number and discrete power of each compressor to be installed. The functional dependence between design and performance is unknown, and therefore the optimal configuration must be obtained through a computational experimentation. This work has a double objective: to adapt the surface response methodology (SRM) to optimize problems without experimental variability as are the computational ones and show the advantage of considering the integrated design. In the SRM framework, the problem is stated as a mixture design with constraints and a synergistic cubic model where a D-optimal design is applied to perform the experiments. Finally, the multi-objective problem is reduced to a single objective one by means of a desirability function. The optimal configuration of the power distribution of the three compressors, in percentage, is (50,20,20). This solution has an excellent behaviour with respect to the six indices proposed, with a significant reduction in time oscillations of controlled variables and power consumption compared with other possible power distributions.

Grey box modeling of supermarket refrigeration cabinets

Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigeration systems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) is presented. It is a non-linear model with two states: the cabinet temperature and the refrigerant mass in the evaporator. To demonstrate its applicability, data with one-minute sampling resolution from ten evaporators in a supermarket in Otterup (Denmark) was used. The model parameters were estimated using a Kalman filter and the maximum likelihood method. Since the dynamical properties of the cabinets constantly change as goods are added and removed, the parameters were re-estimated for each night, over a period of approximately 2.5 years. The model is validated through a statistical analysis of the residuals and the importance of the ongoing re-estimation of parameters is highlighted. Furthermore, the physical meaning of the estimated parameters is discussed and potential applications for characterization and classification of cabinets are demonstrated, by showing how they can be differentiated as either open- or closed cabinets or rooms, using only the estimated heat transfer coefficients and heat capacities. For a selected case it is shown that the estimated parameter values are close to physics derived values, and that the accuracy measured by the standard errors of the estimates is approximately ±10% relative to the estimated values. The analysis demonstrates that the model is robust, accurate and reliable in terms of estimating physically meaningful parameters and it is therefore appropriate for large-scale implementation.

Economical heat recovery dynamic control and business model for supermarket refrigeration system coupled with district heating system

Large amounts of waste heat during the cooling process of supermarket refrigeration systems (SRS) would be released. A heat recovery strategy potentially contributes to reducing the supermarket’s heating costs related to buying heat from a district heating system (DHS). This paper explores the techno-economic feasibility of heat recovery for a real SRS integrated with a heat recovery unit (HRU) in terms of designing a dynamic heat recovery control (HRC) and business models. A cost-effective HRC is firstly developed for HRU to optimally manipulate the amount of heat recovered, thereby minimizing the real-time heat recovery cost. Furthermore, the business models of heat recovery under a long-term operation are proposed based on two transactional strategies between the SRS and DHS. A field test of the dynamic heat recovery for a remote SRS in Copenhagen Nordhavn area is conducted which demonstrates the proposed HRC algorithm can have a benefit of 0.49€ from a 3-h heat recovery operation. Moreover, the one-year operation of the SRS is also simulated which proves the developed two business models of heat recovery can achieve significant savings of 93% and 41% in energy costs.

Fast frequency response provision from commercial demand response, from scheduling to stability in power systems

Reduced numbers of online conventional generators, due to increasing shares of renewable energy sources, are increasing the requirement for a fast frequency response capability from reserve providers when a large infeed/outfeed trips. The commercial demand-side sector, considering its size (MW/MWh) and existing communication and control infrastructure, makes it a potential valuable source of flexibility provision. Supermarket refrigeration systems are considered here as being representative of commercial end-uses, with individual devices stochastically simulated under different ambient conditions, supermarket occupancy, food categories, and refrigeration performance. An aggregated fleet of supermarket loads are then created to provide a system-wide reserve response. The all-island power system of Northern Ireland and Republic of Ireland, with a horizon of 2030, is considered as a representative case study. Three distinct demand-side frequency control methods, including static, droop, and hybrid control are presented and developed within a stability analysis platform. Year-long simulation results show a general improvement in the frequency nadir and (maximum) rate of change of frequency when supermarkets are providing fast frequency response services to the grid, with the likelihood of severe transients (frequency nadir <49 Hz) greatly reduced (82% improvement), depending on the FFR control design, and associated speed of response.

Semi-empirical analysis of HFC supermarket refrigeration retrofit with advanced configurations from energy, environmental, and economic perspectives

F-gas phase-down schedules will remove most of the HFCs (hydrofluorocarbons) used in refrigeration systems in the short term. Besides refrigerant substitution, configuration modifications can be considered in existing installations to increase energy efficiency. Basic cycle, direct injection, economiser, parallel compression, and cascade configurations with internal heat exchanger have been proposed to increase the energy efficiency of a supermarket refrigeration system when replacing R-404A with R-449A at low and medium temperatures. Both systems are based on indirect expansion and are equipped with a subcooler. Their regular operation was recorded using R-404A and R-449A during a representative period. Then, a semi-empirical approach is followed to determine R-449A energy performance with each proposed configuration. Only parallel compression (PC) and basic cycle with internal heat exchanger (IHX) benefit energy performance, highlighting the medium temperature (MT) system. However, this benefit is not extended to the environmental analysis because the R-449A charge in PC significantly increases. Moreover, additional PC components extend the expected payback period, and a drop-in replacement is financially more interesting. Therefore, using R-449A with minor modifications in the MT and LT R-404A refrigeration system decreases 52% and 60% carbon footprint, respectively. The payback period of this action is below one year in both circuits. This study provides a semi-empirical methodology for existing systems to predict alternative refrigerants' energy performance.

Annual energetic evaluation of multi-stage dedicated mechanical subcooling carbon dioxide supermarket refrigeration system in different climate regions of China using genetic algorithm

Supermarket is the main carbon producer due to the requirement of food refrigeration and freezing. In order to realize cleaner production, three configurations of carbon dioxide booster refrigeration system integrated with multi-stage dedicated mechanical subcooling (DMS) applied in commercial supermarkets in different climate regions of China are proposed in this study. Thermodynamic and cooling load models are established to study the energetic operation performance of proposed systems. The energy efficiency of the systems is optimized employing the method of genetic algorithm with the optimization variables of subcooling degree, discharge pressure and intermediate pressure. Meanwhile, the baseline, single-stage DMS and R404A refrigeration systems are compared with the multi-stage DMS systems. Results indicate that the coefficient of performance of the three-stage DMS system is 1.12–28.84% higher than the baseline system at the ambient temperature of 0–40 °C, and it also operates at the lowest discharge pressure. Furthermore, a switch parallel compression is added to get high energy efficiency in different ambient temperature ranges, which is compared with the R404A refrigeration system that is one of the extensively used in Chinese supermarkets. The annual performance factor of the three-stage DMS system combined with a switch parallel compression is 10.92% and 7.62% higher than the R404A refrigeration system and baseline system in Haikou, respectively. Consequently, a carbon dioxide booster refrigeration system integrated with a multi-stage DMS system can achieve a prominent performance especially for a three-stage DMS system, which can overcome the drawback of the lower efficiency of the baseline system in hot and warm regions. A multi-stage DMS system is a promising solution for commercial supermarket refrigeration and socially sustainable development.

ANN Modeling to Analyze the R404A Replacement with the Low GWP Alternative R449A in an Indirect Supermarket Refrigeration System

Artificial neural networks (ANNs) have been considered for assessing the potential of low GWP refrigerants in experimental setups. In this study, the capability of using R449A as a lower GWP replacement of R404A in different temperature levels of a supermarket refrigeration system is investigated through an ANN model trained using field measurements as input. The supermarket refrigeration was composed of two indirect expansion circuits operated at low and medium temperatures and external subcooling. The results predicted that R449A provides, on average, a higher 10% and 5% COP than R404A at low and medium temperatures, respectively. Moreover, the cooling capacity was almost similar with both refrigerants in both circuits. This study also revealed that the ANN model could be employed to accurately predict the energy performance of a commercial refrigeration system and provide a reasonable judgment about the capability of the alternative refrigerant to be retrofitted in the system. This is very important, especially when the measurement data comes from field measurements, in which values are obtained under variable operating conditions. Finally, the ANN results were used to compare the carbon footprint for both refrigerants. It was confirmed that this refrigerant replacement could reduce the emissions of supermarket refrigeration systems.

Optimal operation of an ice-tank for a supermarket refrigeration system

The increasing proportion of renewable energy sources in power grids leads to challenges concerning balancing production and consumption. One solution to this grid challenge is to utilize demand-side flexibility. To use the full potential of demand-side flexibility, dynamical models and optimal control methods must be used. This paper demonstrates how demand-side flexibility can be enabled for a refrigeration system using an add-on ice-tank module to actively curtail the refrigeration system and thereby leveraging time-varying power prices. The operation of the ice-tank is the solution to an optimal control problem that minimize the integrated electricity costs. This optimal control problem is solved numerically and the performance of the strategy is successfully tested in a real experiment where cost-savings of approximately 20% are observed (compared to not having an ice-tank available). The proposed optimal control strategy is tested in real-life experiments spanning over 24 h. The dynamical relation between the operation of the ice-tank and the power consumption (the compressor capacity) is modelled using stochastic differential equations. This differential equation model is calibrated on 13 h of training data using the continuous–discrete Kalman filter and the maximum likelihood framework.