Method for optimizing the energy consumption related to the defrosting of a cooling battery

Abstract

Disclosed is a method for optimizing the energy consumption related to the defrosting of a cooling battery (10) of a cooling system (1), comprising: - a learning phase (100) during which data relating to phases in which the cooling battery (10) produces cooling and phases in which it is defrosted are collected, these data comprising at least: the energy consumption associated with each defrosting phase; the instants marking the end of flow (105) of the water resulting from the defrosting of the cooling battery and the actual durations necessary for the defrosting; the amount of water (106) resulting from the defrosting of the cooling battery; - an optimization phase (200) during which the collected data are used to determine an optimal operating profile taking into account at least the energy impact of the defrosting phases or of an overall cold COP; the defrosting phases being adapted in terms of their number, the type of defrosting, and the duration of the defrosting.

Description

[0001] DESCRIPTION

[0002] Method for optimizing energy consumption related to defrosting a cold battery

[0003] technical field

[0004] The invention relates to a method for optimizing energy consumption related to the defrosting of a cold battery.

[0005] It finds application in various sectors such as:

[0006] - commercial and industrial refrigeration, where it is crucial to maintain constant temperatures for the preservation of food, beverages or perishable products;

[0007] - the cold chain for the transport and storage of pharmaceutical products, where the temperature must be strictly controlled to guarantee the integrity of medicines and vaccines;

[0008] - chemical and biological storage facilities, where specific temperatures are required to maintain the stability of the compounds; and

[0009] - industrial processes that rely on refrigeration for chemical reactions or manufacturing operations, such as in the food or chemical industries.

[0010] State of the art

[0011] There is a wide variety of technological solutions for producing cold. For example, to cool a room or space, one solution is to position a chiller coil within the area to be cooled. Such a chiller coil 1 is schematically illustrated in Figure 1 and is generally formed from a set of tubes 2 or coiled ducts through which a cooling medium (glycol water or refrigerant) circulates, coupled to fins 3 to increase the heat exchange surface area. The chiller coil 1 can also be coupled to air handling devices, such as one or more fans 4, configured to circulate air from the room through the chiller coil. The air in contact with the coil walls is cooled and then blown into the room. The chiller coil is also equipped with a condensate collection tray (not shown in Figure 1), configured to collect and drain the condensate from the coil.

[0012] The room housing the chiller may be used for storing food products that must be kept at sub-zero or slightly above-freezing temperatures, requiring a refrigerant fluid at sub-zero temperatures. Door openings and the temperature of products entering the room inevitably cause variations in temperature, humidity, and airflow velocity. These variations encountered by the chiller during its operation are responsible for the formation and growth of frost on its heat exchange surfaces, such as the tubes and / or fins. The frost layer reduces the efficiency of heat transfer between the air being cooled and the fluid circulating within the chiller.Furthermore, frost buildup on the cooling coil obstructs airflow, leading to a decrease in airflow over time and at a given fan speed, and therefore a reduction in cooling capacity. To compensate for this decrease, the fans are sometimes forced to operate at higher speeds, resulting in increased power consumption.

[0013] Thus, frost buildup reduces the energy efficiency, particularly the cooling capacity, of the cooling coil, and leads to an increase in the system's energy consumption. It is therefore necessary to schedule one or more defrost cycles during the cooling coil's operation.

[0014] Various methods for detecting frost on the surfaces of the cold battery combined with defrosting means have been developed.

[0015] Examples of ice formation detection techniques include: - visual detection by an operator; - detection of an uncontrolled temperature drift in the room relative to a setpoint;

[0016] - measuring the evolution of the electrical power consumed by the air handling system;

[0017] - the implementation of various sensors, such as temperature sensors, humidity sensors combined with control algorithms, specific frost detectors;

[0018] - measuring the opening time of the refrigerant valves; etc.

[0019] De-icing methods include, for example:

[0020] - the intervention of an operator to detach the frost manually;

[0021] - the use of electric heating elements;

[0022] - the pulsation of hot air towards the cold battery;

[0023] - the application of vibrations to detach frost from the surface of the cold battery;

[0024] - the circulation of a hot fluid in adjacent tubes;

[0025] - the injection of hot fluid (hot water or hot gas) into the cold coil;

[0026] - modulate the circulating airflow to limit frost formation; etc.

[0027] However, these detection and defrosting methods inevitably lead to significant energy consumption costs, both electrical and thermal. Furthermore, all these solutions are implemented without considering potential variations in environmental conditions or the specific operational characteristics of the equipment, which can lead to unnecessary or insufficient defrosting cycles, thus impacting energy efficiency and equipment lifespan. These methods also fail to account for potential malfunctions of the cooling coil during operation, which can lead to increased energy consumption. Moreover, some of these defrosting methods, which require cooling coil shutdown periods, result in significant energy consumption to reach the set temperature when the cooling coil is restarted.Therefore, there is a need to optimize the energy consumption of defrosting at least one cold battery.

[0028] Description of the invention

[0029] The invention thus proposes a solution for optimizing the overall energy consumption of an industrial site by optimizing the defrosting of the heat exchange surfaces of at least one cooling coil, particularly in terms of the number, duration, and methods of defrosting. Examples of the cooling coil's heat exchange surfaces susceptible to ice formation include the fluid circulation tubes and / or the fins.

[0030] The invention proposes in particular a solution for controlling the operation (or use) of a cooling battery including cooling production cycles and defrosting cycles which adapt dynamically to operational and environmental conditions, in order to optimize the energy consumption related to defrosting the cooling battery, while ensuring optimal energy performance of the cooling battery to achieve the cooling setpoints.

[0031] To achieve this, the invention proposes to implement a learning phase for a predefined time, during which data relating to the production of cold by the cooling battery, the conditions of frost formation, and the defrosting of the cooling battery are collected to determine or construct an operating profile of the cooling battery.

[0032] The invention thus makes it possible to implement, based on the operating profile of the cooling battery, corrective actions consisting of adjusting the parameters of cold production by the cooling battery (adaptation of the temperatures of the exchange surfaces of the cooling battery, adaptation of the operation of the fan(s), etc.) and / or defrosting parameters (number and time of defrosting, means of defrosting, etc.), in order to reduce the overall energy consumption related to the operation (production of cold and defrosting) of the cooling battery, and / or related to the defrosting cycles of the cooling battery, while ensuring that the cooling battery operates with a good overall coefficient of performance (COP).

[0033] The invention also enables the implementation of anomaly or malfunction detection phases for the cold storage unit that cannot be corrected through the unit's operation or control (which therefore requires human intervention), and provides an alert regarding the need for intervention to address the detected anomaly. These anomalies may relate to a problem with the frequency or duration of the cold storage unit door opening, a mechanical problem with the fluid valves, a mechanical problem with the ventilation system (fan), etc.

[0034] The invention also makes it possible to integrate qualitative data, such as information collected on site by operators, to build the optimized operating model of the cold battery.

[0035] The invention relates to a method for optimizing the energy consumption of a refrigeration system located in a space or room within an industrial site. The refrigeration system comprises at least one cooling coil capable of icing, coupled with an air circulation system and defrosting means. The method includes:

[0036] - a learning phase during which the cooling system is put into operation for a predefined time and following an initial operating profile including phases of cold production to reach and maintain cold setpoints and phases of defrosting the cooling battery, data relating to these production and defrosting phases being collected during this learning phase so as to collect at least the energy consumption (electrical and / or thermal) associated with each defrosting phase;

[0037] - an optimization phase during which the collected data is used to determine (through simulations) an optimal operating profile for the refrigeration system, taking into account at least the energy impact of the defrosting phases; and

[0038] - an operational phase during which the refrigeration system is implemented according to the optimal operating profile. According to the invention, the following data are also collected for each defrosting phase during the learning phase and taken into account in the optimization phase:

[0039] - identification of the point at which the water flow from the defrosting of the cold storage unit ends (or ceases), and determination of the actual time required for defrosting; and

[0040] - quantification (volume or weight) of the water from the defrosting of the cold battery.

[0041] According to the invention, determining the optimal operating profile includes adapting the defrosting phases in terms of the number of defrosts, the type of defrosting (air, heat input), and the duration of each defrost, resulting in reduced energy consumption compared to the initial profile or an optimal overall cooling COP.

[0042] Thus, the solution of the invention adapts the defrosting phases (number, type, duration) to the actual needs for defrosting the cooling coil. Indeed, in prior art solutions, defrosting times are generally fixed and are sometimes not adapted to the actual amount of frost formed on the cooling coil. The defrosting time may be oversized relative to the actual amount of frost formed on the cooling coil, leading to unnecessary energy overconsumption.

[0043] The defrosting phase data collected during the training phase allows us to determine, for each defrosting phase, the actual time required, the actual amount of water trapped on the cooling coil, and the operating conditions of the cooling system that led to frost formation on the coil. This data feeds into a simulation to determine the necessary defrosting frequency, the type of defrosting best suited to the amount of trapped water and meeting a predefined key criterion, such as the energy consumption cost (electrical and / or thermal) of defrosting, or the overall cooling COP of the cooling system. In practice, we distinguish between two types of cooling COP: the cooling COP during production and the overall cooling COP.

[0044] The cooling COP during production corresponds to the "standard" cooling COP associated with the cooling coil, i.e., when the cooling coil is in operation and during which it may frost over. This cooling COP during production represents the cooling energy delivered to the space (to be cooled) divided by the energy consumption, particularly electrical, of the various components of the cooling system to produce the required cooling. These components include, for example:

[0045] - the cooling battery's aerodynamic system, in particular the fan;

[0046] - the distribution system for conveying cold from the machine room (SDM) to the cooling coil, for example pump systems;

[0047] - other production equipment present in the machine room (SDM).

[0048] The overall cooling COP corresponds to the cooling COP that incorporates the energy consumption related to the defrosting phases. In other words, this overall cooling COP corresponds to the overall efficiency of the cooling system over a complete cooling and defrosting cycle.

[0049] Thus, according to one embodiment, the learning phase may further include:

[0050] - continuous estimation of the cooling COP during each cooling phase of the cooling cycle, and / or continuous measurement of the cooling capacity (kWh) supplied by the cooling system, and / or the electrical power consumed by the cooling system; and

[0051] - The defrosting phase of the cooling coil is triggered when the measured production cooling COP is lower than a predefined theoretical production cooling COP, or when the measured cooling capacity is lower than a predefined theoretical threshold value, or the measured electrical power consumption is higher than a predefined theoretical threshold value. Indeed, frost accumulation on the cooling coil reduces its heat exchange surface area and obstructs airflow. To maintain the room temperature at the setpoint within acceptable high and low temperature tolerances, the rotational speed of the ventilation system's fan(s) increases, resulting in higher electrical consumption.The accumulation of frost on the cold battery generally leads to an actual cooling capacity (kW) lower than a theoretical cooling capacity and / or an increase in the actual electrical power consumed, and therefore to a degraded cooling COP.

[0052] Depending on one implementation, the optimization phase may include:

[0053] - the identification of at least one corrective action applicable to the cooling system, without impact on the production of cold for the predefined setpoint tolerances, and presenting a lower energy consumption compared to the energy consumption of the defrosting phases of the initial profile.

[0054] These corrective actions may include, for example:

[0055] - Adapting the operating mode of the refrigeration system over time to limit frost formation while ensuring that cooling setpoints can be met, without degrading the cooling COP during production. For example, this adaptation could consist of:

[0056] • an adjustment of the average temperature of the heat exchange surfaces of the cooling coil to limit the amount of frost formed on the cooling coil over time, taking into account acceptable high and low temperature tolerances, to reduce the amount of frost formed on the cooling coil over time and maintain or improve the overall cooling COP of the cooling system compared to the initial profile; and / or • an adaptation of the fan operating mode (in terms of duration and / or speed), and / or

[0057] . an adaptation of the opening and / or closing cycles of the cold fluid distribution valves, or the adaptation of the operating time of the cooling coil, etc., and / or

[0058] - adapting the environment in which the cooling battery is placed, for example, adjusting the temperature and / or humidity of the room, for example by installing an air renewal system; and / or

[0059] - the adaptation of defrosting phases, in terms of recurrence, and / or time of start of defrosting (for example, waiting for the formation of a sufficient quantity of frost to initiate a defrosting phase), and / or duration, and / or means of defrosting (for example use of air depending on the temperature of the room, use of hot fluid to be injected into the coil, use of electric resistance, etc.).

[0060] In practice, the solution may allow for an increase in the overall energy consumption of the cooling system, provided this increase enables the system to meet cooling needs while optimizing the overall cooling COP. Specifically, the solution aims to find a balance between maintaining or improving the cooling COP during production, which can be degraded by the presence of frost, and optimizing energy consumption or the cost of defrosting, which can be increased by unsuitable defrosting cycles.

[0061] The corrective action(s) are thus chosen to reduce the amount of frost formed on the cooling battery over time and / or to use one or more more suitable defrosting methods, so that the energy consumption of the cooling system does not induce a degradation of the overall cooling COP.

[0062] Advantageously, the optimal profile can be determined by taking into account qualitative maintenance data provided by an operator; this qualitative data relates, for example:

[0063] . to a visual detection of the presence of frost; and / or

[0064] . to unforeseen heat gains in the room (entry of products altering the room temperature); and / or

[0065] . to a quality of defrosting.

[0066] In practice, during the operational phase, anomaly detection is also implemented; these anomalies include, for example:

[0067] - opening a door for a predefined time that could cause the room temperature to vary outside of the high and low temperature tolerances;

[0068] - a mechanical problem with the hydraulic system of the refrigeration system (problem with the filters, or the valves, etc.);

[0069] - a mechanical problem with the air handling system of the refrigeration system (problem with the fans).

[0070] When an anomaly is detected, an alarm can be generated for human intervention on said anomaly.

[0071] Brief description of the drawings

[0072] The present invention and its advantages will become more apparent from the following description of several embodiments given by way of non-limiting examples, with reference to the accompanying drawings, in which:

[0073] [Fig 1] is a schematic representation of a cold battery coupled with fans, according to one embodiment;

[0074] [Fig 2] is a schematic representation of a cooling system consisting of cooling batteries each coupled to air and hydraulic systems, according to one embodiment;

[0075] [Fig 3] is a simplified flowchart of the steps in the optimization process, according to one implementation method;

[0076] [Fig 4] is a simplified flowchart of the operations implemented during the learning phase, according to a method of implementation;

[0077] [Fig 5] is a graph illustrating the evolution of the COP and cooling capacity, without defrosting optimization;

[0078] [Fig 6] is a graph illustrating the evolution of frost thickness and air volume flow rate, without defrosting optimization;

[0079] [Fig 7] is a graph illustrating the evolution of the static pressure and dynamic pressure of the cold battery, without defrosting optimization;

[0080] [Fig 8] is a graph illustrating the evolution of the fluid temperatures of the cold battery, without defrosting optimization; [Fig 9] is a graph illustrating the evolution of the COP and cooling capacity, with defrosting optimization according to an embodiment;

[0081] [Fig 10] is a graph illustrating the evolution of frost thickness and air volume flow rate, with defrosting optimization according to one embodiment;

[0082] [Fig 11] is a graph illustrating the evolution of the static and dynamic pressure of the cooling battery, with defrosting optimization according to one embodiment; and

[0083] [Fig 12] is a graph illustrating the evolution of the temperatures of the fluids in the cold battery, with optimization of defrosting according to an embodiment.

[0084] Description of the implementation methods

[0085] A method for optimizing energy consumption related to defrosting a cold battery located in a space or room of an industrial site according to an embodiment is described below.

[0086] The cooling system 1 illustrated in Figure 2 comprises two coils 10, 11, each incorporating a fan. At least one 10 is a cooling coil, while the other 11 can be either a cooling coil incorporating a fan or a heating coil. For example, a first coil 10 can be configured for cooling, and a second coil 11 can be configured for defrosting the cooling coil. The cooling system 1 further includes a system 5 for recovering condensate during the defrosting phase, as well as a hydraulic system assembly 6, 7 comprising, for example, valves 60, 70 and fluid injection pipes for the coils 10, 11.

[0087] This cooling system is generally configured to operate in a mode adapted to meet cooling setpoints and maintain the room temperature within predefined high and low temperature tolerances. Depending on the conditions, it is generally necessary to schedule defrost cycles for the cooling coil. To optimize the energy cost associated with the defrosting phases of the cooling coil, the process, according to an embodiment, some steps of which are illustrated in Figures 3 to 4, first includes a learning phase, which can be implemented for a predefined learning period, for example, one day, one week, one month, etc. The learning phase consists, in particular, of collecting data relating to the operation of the cooling coil during this learning period.This data includes information on cooling production phases and defrosting phases of the cooling coil. This data allows for the construction, during an optimization phase (200), of an optimal operating model or optimal operating profile for the cooling system, which takes into account the energy impact of the cooling coil defrosting cycles. The optimal operating model or profile defines, in particular, the cooling production phases along with their corresponding operating parameters and conditions, and the defrosting phases along with their corresponding defrosting parameters and conditions. Once the optimal profile is determined, an operational phase (300) is implemented, and the cooling system is operated according to this optimal profile. In practice, the learning phase (100) can be implemented during the operational phase to continuously adjust the optimal operating profile.

[0088] The learning phase 100, according to an implementation method illustrated in Figure 4, may consist of performing the following operations during a predefined learning period:

[0089] 101: Cold production phase: control or control of the components of the cold system (valves, fans, etc.) to reach and maintain the room temperature at a setpoint temperature within high and low temperature tolerances;

[0090] 102: during the cold production phase, continuous measurement of parameters relating to the conditions of the room (room temperature, humidity level, temperature of the surfaces of the cold battery, fan rotation speed, etc.), continuous measurement of the energy consumption (electrical and / or thermal) associated with this cold production phase;

[0091] 103: during the cold production phase, detection of a need to defrost the cold coil; 104: initiation of a defrosting phase by a defrosting means, for example via the hot coil 11, if a need to defrost is detected;

[0092] 105: determination of the actual time of disappearance of frost on the cold battery, for example by determining the absence or end of the flow of condensed water from defrosting in the recovery system 5, and stopping the defrosting, and resuming the production of cold;

[0093] 106: Determination of the quantity of water from defrosting, for example by determining the weight of the condensed water from defrosting in the recovery system 5; and

[0094] 107: determination of the energy consumption associated with this defrosting phase.

[0095] Defrosting needs can be detected via continuous measurements of the cooling COP during each cooling phase. This detection can include continuous measurement of the cooling capacity (kW) supplied by the cooling system and / or the electrical power consumed by the cooling coil fan. The defrosting phase is triggered when the measured cooling capacity (kW) falls below a predefined theoretical threshold, or when the electrical power consumed exceeds a predefined theoretical threshold. These theoretical threshold values ​​represent a degraded cooling COP during production. As explained above, the cooling COP during production corresponds to the total amount of cooling produced by the cooling coil (in kWh), divided by the total energy consumption during the cooling phase.

[0096] Another way to detect a need for frost protection involves monitoring the evolution of the chilled fluid demand by the cooling coil, particularly via the opening time of the chilled fluid supply valve. Indeed, a valve remaining open for too long can indicate that the system is struggling to reach the set cooling temperatures due to ice buildup in the cooling coil. These operations during the learning phase are repeated until the end of the learning period in order to model or build an operating profile of the cooling system, the profile including:

[0097] - the cooling production phases, and for each cooling production phase: the operating modes of the cooling system to achieve the cooling setpoints, and the corresponding energy consumption (thermal and / or electrical); and

[0098] - the defrosting phases, and for each defrosting phase, the start and end times of defrosting, the defrosting method used, the quantity of water from defrosting, and the corresponding energy consumption (thermal and / or electrical).

[0099] The optimization phase (200) consists of estimating an optimal operating profile for the refrigeration system, based on the data collected during the learning phase, taking into account at least the energy impact of defrost cycles. Specifically, the optimization includes adapting the defrosting phases in terms of the number of defrost cycles, the type of defrosting (air, heat input), and the duration of each cycle, resulting in reduced energy consumption compared to the profile obtained during the learning phase. The optimization may also include adapting the refrigeration production phases.

[0100] Therefore, adapting the defrosting and / or cooling phases may involve applying one or more corrective actions to the cooling and / or defrosting conditions. These corrective actions aim to reduce energy consumption related to defrosting without impacting cooling production in terms of target temperatures or degrading the system's overall cooling COP. Determining the optimal profile thus requires running system simulations to identify the corrective action(s) to be applied. As explained above, the overall cooling COP corresponds to the total amount of cooling produced by the cooling coil (in kWh), divided by the total energy consumption during the production and defrosting phases.As an example, a corrective action could be to adjust the average surface temperature of the cooling coil throughout the cooling production phases to prevent or limit frost formation on its surfaces. For instance, when data collected during the learning phase shows that the amount of frost formed is such that a slight variation in the cooling coil's surface temperature is sufficient to trigger defrosting, the optimal profile could include, during one or more production phases, periods of reduced average surface temperature for a duration determined during the simulation, for example, by controlling the fluid injection valve 60 in the cooling coil. The average surface temperature of the coil must nevertheless remain within the acceptable or predefined tolerance range relative to the target cooling levels.Thus, this slight modification of the surface temperature of the cold battery makes it possible to achieve defrosting without using a dedicated defrosting method, for example an electric resistance or the hot battery, which consumes electrical and thermal energy.

[0101] Another corrective action could be to program timed shutdowns of the cooling coil 10, for example by closing the chilled fluid injection valve 60. The cooling coil is thus shut down for a duration determined during the simulation (for example, for 5 or 10 minutes), and at frequencies also determined during the simulation (for example, every 10 minutes or every hour), in order to control the temperature rise of the cooling coil's heat exchange surface and prevent icing throughout the cooling process.

[0102] Depending on the room's air temperature, another corrective action could be to vary the fan's rotation speed and operating time. The simulation therefore involves applying several setpoint speeds to the fan and determining one or more optimal setpoint speeds.

[0103] Figure 6:

[0104] - The Efrive curve corresponds to the evolution of the thickness of the frost on the surface of the cooling coil, and in particular on the surface of the fins; - The Qv curve corresponds to the evolution of the volume flow rate of the air coming from the fan coupled to the cooling coil.

[0105] Figure 7:

[0106] - the Pstat curve corresponds to the evolution of the pressure of the refrigerant fluid in the circuit during evaporation, and reflects the pressure of the refrigerant fluid absorbing heat and changing from a liquid to a gaseous state;

[0107] - The Pdyn curve corresponds to the evolution of the dynamic pressure of the refrigerant fluid and reflects the speed of movement of the refrigerant fluid through the evaporator coil.

[0108] Figure 8:

[0109] - the Tff curve corresponds to the evolution of the temperature of the refrigerant fluid in the evaporator;

[0110] - the Tsa curve corresponds to the evolution of the temperature on the surface of the fins of the cold battery;

[0111] - the Tsgivre curve corresponds to the evolution of the temperature measured on the surface of the frost formed on the cold battery;

[0112] - The Tce curve corresponds to the evolution of the condensation temperature at the evaporator, as a function of the pressure in the evaporator; and

[0113] - the Tes curve corresponds to the evolution of the condensation temperature at the surface of the evaporator, that is to say the actual temperature of the refrigerant fluid at the outlet of the evaporator, after heat exchange in the evaporator.

[0114] Figures 9 to 12 illustrate simulations of the evolution of the above parameters over the same predefined period of 350 minutes, during which an optimized defrost cycle, following the optimization process, is programmed in advance after 150 minutes of operation. During this simulation, the cooling coil's coefficient of performance (COP) remains at 12, the frost thickness remains below 1.5 mm, and the decrease in airflow from the fan does not exceed 16%. In other words, thanks to the defrost optimization—in this case, an early defrost cycle—the frost layer is controlled and the cooling coil's performance is maintained at optimal levels.

[0115] Figure 9:

[0116] - the COP_opt curve (solid line) corresponds to the evolution of the coefficient of performance COP; and

[0117] - the Qf_opt curve (dotted line) corresponds to the evolution of the cooling power of the cold battery.

[0118] Figure 10:

[0119] - the opt Frost curve corresponds to the evolution of frost thickness on the surface of the cold battery, and in particular on the surface of the fins;

[0120] - the Qv_opt curve corresponds to the evolution of the volume flow rate of the air coming from the fan coupled to the cooling coil.

[0121] Figure 11:

[0122] - the Pstat_opt curve corresponds to the evolution of the refrigerant pressure in the circuit during evaporation, and reflects the pressure of the refrigerant absorbing heat and changing from a liquid to a gaseous state;

[0123] - The Pdyn_opt curve corresponds to the evolution of the dynamic pressure of the refrigerant fluid and reflects the speed of movement of the refrigerant fluid through the evaporator coil.

[0124] Figure 12:

[0125] - the Tff_opt curve corresponds to the evolution of the temperature of the refrigerant fluid in the cooling battery;

[0126] - the Tsa_opt curve corresponds to the evolution of the temperature on the surface of the fins of the cold battery;

[0127] - the Tsgivre opt curve corresponds to the evolution of the temperature measured on the surface of the frost formed on the cold battery;

[0128] - The Tce_opt curve corresponds to the evolution of the condensation temperature at the evaporator, as a function of the pressure in the evaporator; and - the Tcs_opt curve corresponds to the evolution of the condensation temperature at the surface of the evaporator, that is to say, the actual temperature of the refrigerant at the evaporator outlet, after heat exchange in the evaporator. The present invention thus proposes a solution enabling:

[0129] - to ensure the detection of a need to defrost a cold battery;

[0130] - to implement corrective actions aimed at limiting ice formation on the surface of the cold battery, adapting defrosting phases in terms of number, duration, triggering time, and defrosting method; - to optimize energy consumption (electrical and / or thermal) related to defrosting;

[0131] - while avoiding deviations from the expected cold conditions.

Claims

Demands 1. Method for optimizing the energy consumption of a refrigeration system (1) located in a space or room of an industrial site, the refrigeration system comprising at least one cooling coil (10) capable of icing, coupled to an air circulation system and defrosting means, the method comprising: - a learning phase (100) during which the cooling system (1) is put into operation for a predefined time and following an initial operating profile including phases of cold production to reach and maintain cold setpoints and phases of defrosting the cooling coil, data relating to these production and defrosting phases being collected during this learning phase (100) so as to collect at least the energy consumption associated with each defrosting phase; - an optimization phase (200) during which the collected data are used to determine an optimal operating profile of the refrigeration system (1), taking into account at least the energy impact of the defrosting phases; and - an operational phase (300) during which the cooling system (1) is implemented according to the optimal operating profile; characterized in that the following data are also collected for each defrosting phase during the learning phase and taken into account in the optimization phase: - identification of the end-flow time (105) of the water from the defrosting of the cold battery, and determination of the actual time required for defrosting; and - quantification of the water (106) from the defrosting of the cold battery; and in that the determination of the optimal operating profile includes the adaptation of the defrosting phases in terms of number of defrosts, type of defrosting, and duration of each defrost, responding to reduced energy consumption compared to the initial profile or to an optimal overall cooling COP.

2. An optimization method according to claim 1, wherein the learning phase further comprises: - continuous estimation of a cold COP in production by the cold battery during each phase of refrigeration production; and - the triggering of the defrosting phase of the cold battery when the measured cold COP in production is less than a predefined theoretical cold COP in production.

3. An optimization method according to claim 1, wherein the learning phase further comprises: - continuous measurement of the cooling capacity supplied by the refrigeration system and / or the electrical power consumed by the air handling system; - the defrosting phase of the cold battery is initiated when the measured cooling power supplied (kW) is less than a predefined theoretical threshold value, and / or when the electrical power consumed is greater than a predefined theoretical threshold value.

4. An optimization method according to any one of claims 1 to 3, wherein the optimization phase comprises: - the identification of at least one corrective action applicable to the cooling system, without impact on the production of cold for the predefined setpoint tolerances, and presenting a lower energy consumption compared to the energy consumption of the defrosting phases of the initial profile.

5. An optimization method according to claim 4, wherein a corrective action comprises one or a combination of the following actions: - an adjustment of the average temperature of the heat exchange surfaces of the cooling battery to limit the amount of frost formed on the cooling battery over time, taking into account acceptable high and low temperature tolerances, to reduce the amount of frost formed on the cooling battery over time and maintain or improve the overall cooling COP of the cooling system compared to the initial profile; - an adaptation of the fan's operating mode in terms of duration and / or speed; and - an adaptation of the opening and / or closing cycles of the cold fluid distribution valves.

6. An optimization method according to any one of claims 1 to 5, wherein the optimal profile is determined taking into account qualitative maintenance data supplied by an operator, such qualitative data being: . to visual detection of frost; and / or . to unexpected heat gains in the room; and / or . to a quality of defrosting.

7. An optimization method according to any one of claims 1 to 6, wherein, during the operational phase (300), an anomaly detection is also implemented, these anomalies including: - opening a door for a predefined time that could cause the room temperature to vary outside of the high and low temperature tolerances; - a mechanical problem with the hydraulic system of the refrigeration system; - a mechanical problem in the air handling system of the refrigeration system; and - the detection of an anomaly generating an alarm for human intervention on said anomaly.

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