Method for optimizing energy consumption related to defrosting a cold battery

Abstract

Method for optimizing the energy consumption related to the defrosting of a chilled water coil (10) of a refrigeration system (1), comprising: - a learning phase (100) during which data relating to the cooling production and defrosting phases of the chilled water coil (10) are collected, this data including at least the energy consumption associated with each defrosting phase, the times of completion of flow (105) of the water from the defrosting of the chilled water coil, and the actual times required for defrosting, the quantity of water (106) from the defrosting of the chilled water coil; - an optimization phase (200) during which the data collected are used to determine an optimal operating profile taking into account at least the energy impact of the defrosting phases or an overall cooling COP; the defrosting phases being adapted in terms of number, type of defrosting, and duration of defrosting. Figure for the abridged version: Fig. 4

Description

Title of the invention: Method for optimizing energy consumption related to defrosting a cold storage unit. Technical field

[0001] The invention relates to a method for optimizing energy consumption related to defrosting a cold battery.

[0002] It finds application in various sectors such as: - commercial and industrial refrigeration, where it is crucial to maintain constant temperatures for the preservation of food, beverages or perishable products; - 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; - chemical and biological storage facilities, where specific temperatures are required to maintain the stability of the compounds; and - industrial processes that rely on refrigeration for chemical reactions or manufacturing operations, such as in the food or chemical industries. State of the art

[0003] 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 in the room to be cooled. Such a chiller coil 1 is schematically illustrated in [Fig. 1] and is generally formed of 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 exchange surface area. The chiller coil 1 can also be coupled to air handling means, such as one or more fans 4, configured to circulate the room air 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 [Fig. 1]), configured to collect and drain the condensate from the coil.

[0004] The room in which the cold storage unit is located may be intended for the storage of food products that must be stored at negative or slightly positive temperatures, requiring a heat transfer fluid at negative temperatures. The opening of doors and the temperature of products entering the room necessarily induce variations in the levels of temperature, humidity, and Airflow velocity. These variations encountered by the cooling coil during its operation are factors responsible for the formation and growth of frost on the coil's heat exchange surfaces, such as the tubes and / or fins. The layer of frost reduces the efficiency of heat transfer between the air to be cooled and the fluid circulating within the cooling coil. Furthermore, frost accumulation on the cooling coil obstructs the airflow, leading, over time and for a given fan speed, to a decrease in circulating airflow 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.

[0005] Thus, the accumulation of frost reduces the energy performance, and in particular the cooling capacity, of the cooling coil, and leads to an increase in the system's energy consumption. It is therefore necessary to plan for one or more defrosting cycles during the operation of the cooling coil.

[0006] Various methods for detecting frost on the surfaces of the cold battery combined with defrosting means have been developed.

[0007] Examples of ice formation detection techniques include: - visual detection by an operator; - the detection of an uncontrolled temperature drift of the room relative to a setpoint; - measuring the evolution of the electrical power consumed by the air handling system; - the implementation of various sensors, such as temperature sensors, humidity sensors combined with control algorithms, specific frost detectors; - measuring the opening time of the refrigerant valves; etc.

[0008] De-icing means include, for example: - the intervention of an operator to detach the frost manually; - the use of electric heating elements; - the pulsation of hot air towards the cold battery; - the application of vibrations to detach frost from the surface of the cold battery; - the circulation of a hot fluid in adjacent tubes; - the injection of hot fluid (hot water or hot gas) into the cold coil; - modulate the circulating airflow to limit frost formation; etc.

[0009] However, these detection and defrosting methods necessarily entail significant costs related to energy consumption, whether electrical or thermal. Furthermore, all these solutions are implemented without These methods must take into account potential variations in environmental conditions or the operational specifics of the equipment, which can lead to unnecessary or insufficient defrosting cycles, thus affecting energy efficiency and equipment lifespan. They also do not account for potential malfunctions of the cooling coil during operation, which could lead to increased energy consumption. Furthermore, some of these defrosting methods, which require cooling coil downtime, result in significant energy consumption to reach the set temperature when the cooling coil is restarted.

[0010] There is therefore a need to optimize the energy consumption of defrosting at least one cold battery. Description of the invention

[0011] 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. The heat exchange surfaces of the cooling coil susceptible to ice formation include, for example, those of the fluid circulation tubes and / or those of the fins.

[0012] 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 the 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.

[0013] 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.

[0014] The invention thus makes it possible to implement, based on the operating profile of the cooling coil, corrective actions consisting of adjusting the parameters of cooling production by the cooling coil (adaptation of the temperatures of the heat exchange surfaces of the cooling coil, adaptation of the operation of the fan(s), etc.) and / or defrosting parameters (number and time of defrosting, defrosting methods, etc.), in order to reduce the overall energy consumption related to the operation (cooling production and defrosting) of the cooling coil, and / or related to the defrost cycles of the cold battery, while ensuring that the cold battery operates with a good overall coefficient of performance (COP).

[0015] The invention also makes it possible to implement phases for detecting anomalies or malfunctions in the cold storage unit that cannot be corrected through the unit's operation or control (which therefore requires human intervention), and to alert the system to the need for intervention to address the detected anomaly. These anomalies may relate to a problem with the frequency or duration of the cold room door opening, a mechanical problem with the fluid valves, a mechanical problem with the ventilation system (fan), etc.

[0016] 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.

[0017] The invention thus relates to a method for optimizing the energy consumption of a refrigeration system located in a space or room of an industrial site, the refrigeration system comprising at least one cooling coil capable of icing, coupled to an air circulation system and defrosting means. The method comprises: - 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; - an optimization phase during which the data collected are used to determine (by simulations) an optimal operating profile of the cooling system taking into account at least the energy impact of the defrosting phases; and - an operational phase during which the cooling system is implemented according to the optimal operating profile.

[0018] 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: - 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 - quantification (volume or weight) of the water from the defrosting of the chilled water coil.

[0019] According to the invention, determining the optimal operating profile includes adapting the defrosting phases in terms of the number of defrost cycles, the type of defrosting (air, heat input), and duration of each defrosting, resulting in reduced energy consumption compared to the initial profile or an optimal overall cooling COP.

[0020] 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 overconsumption of energy.

[0021] The defrosting phase data collected during the training phase make it possible to determine, for each defrosting phase, the actual time required for defrosting, the actual amount of water trapped on the cooling coil, and the operating conditions of the cooling system that led to the formation of frost on the cooling coil. This data can be used to run a simulation to determine the required defrosting frequency, the type of defrosting appropriate to the amount of water trapped and meeting a predefined key criterion, such as the cost related to the energy consumption (electrical and / or thermal) of defrosting, or the overall cooling COP of the cooling system.

[0022] In practice, two types of cold COP are distinguished, namely the cold COP in production, and the overall cold COP.

[0023] The cooling COP in production corresponds to the "standard" cooling COP associated with the cooling coil, that is, when the cooling coil is in the cooling production phase and during which it may frost over. This cooling COP in production corresponds to the cooling energy transferred to the space (to be cooled) relative to the energy consumption, particularly electrical, of the various components of the cooling system for the required cooling output. These components are, for example: - the cooling battery's aerodynamic system, in particular the fan; - the distribution system for conveying cold from the machine room (SDM) to the cooling coil, for example pump systems; - other production equipment present in the machine room (SDM).

[0024] The overall cooling COP corresponds to the cooling COP incorporating 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.

[0025] Thus, according to one embodiment, the learning phase may further include: - 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 - the triggering of the defrosting phase of the cold battery when the measured production cold COP is less than a predefined theoretical production cold COP, or when the measured cooling power supplied is less than a predefined theoretical threshold value, or the measured electrical power consumed is greater than a predefined theoretical threshold value.

[0026] 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 power consumption. Frost accumulation on the cooling coil generally leads to an actual cooling capacity (kW) lower than the theoretical cooling capacity and / or an increase in actual power consumption, and therefore a degraded cooling COP.

[0027] According to one embodiment, the optimization phase may include: - 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.

[0028] These corrective actions may, for example, include: - 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: • 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's operating mode (in terms of duration and / or speed), and / or . 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 - 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 - 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.).

[0029] In practice, the solution may allow an increase in the overall energy consumption of the cooling system, provided that this increase enables the system to meet the cooling demand while optimizing the overall cooling COP. The solution aims in particular to find a balance between maintaining or improving the cooling COP during production, which may be degraded by the presence of frost, and optimizing the energy consumption or cost of defrosting, which may be increased by unsuitable defrosting phases.

[0030] 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 means, so that the energy consumption of the cooling system does not induce a degradation of the overall cooling COP.

[0031] Advantageously, the optimal profile can be determined by taking into account qualitative maintenance data provided by an operator; this qualitative data is relative, for example: . to a visual detection of the presence of frost; and / or . to unforeseen heat gains in the room (entry of products altering the room temperature); and / or . to a quality of defrosting.

[0032] In practice, during the operational phase, anomaly detection is also implemented; these anomalies include, for example: - 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 (problem with the filters, or the valves, etc.); - a mechanical problem with the air handling system of the refrigeration system (problem with the fans).

[0033] When an anomaly is detected, an alarm can be generated for human intervention on said anomaly. Brief description of the drawings

[0034] 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:

[0035] [Fig. 1] is a schematic representation of a cold battery coupled to fans, according to one embodiment;

[0036] [Fig.2] is a schematic representation of a cooling system made up of batteries cold units each coupled to aerodynamic and hydraulic systems, according to an embodiment;

[0037] [Fig.3] is a simplified flowchart of the steps in the optimization process, according to a method of implementation;

[0038] [Fig.4] is a simplified flowchart of the operations implemented during the learning phase, according to a method of implementation;

[0039] [Fig.5] is a graph illustrating the evolution of the COP and the power refrigeration, without defrosting optimization;

[0040] [Fig.6] is a graph illustrating the changes in frost thickness and flow rate air volume, without defrosting optimization;

[0041] [Fig.7] is a graph illustrating the evolution of static pressure and the dynamic pressure of the cold battery, without defrosting optimization;

[0042] [Fig.8] is a graph illustrating the evolution of fluid temperatures in the cold battery, without defrosting optimization;

[0043] [Fig.9] is a graph illustrating the evolution of the COP and power refrigeration, with defrosting optimization according to an embodiment method;

[0044] [Fig. 10] is a graph illustrating the evolution of frost thickness and air volume flow rate, with defrosting optimization according to one embodiment;

[0045] [Fig. 11] is a graph illustrating the evolution of the static pressure and dynamic pressure of the cold battery, with defrosting optimization according to one embodiment; and

[0046] [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. Description of the implementation methods

[0047] 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.

[0048] The cooling system 1 illustrated in [Fig. 2] comprises two coils 10, 11, each incorporating a fan. At least one 10 is a cooling coil, the other 11 being 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 battery. The cooling system 1 further includes a system 5 for recovering condensed water during the defrosting phase, as well as a hydraulic system assembly 6, 7 including, for example, valves 60, 70 and fluid injection pipes for the batteries 10, 11.

[0049] 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.

[0050] To optimize the energy cost associated with the defrosting phases of the cooling coil, the process, according to an embodiment of which certain steps are illustrated in Figures 3 to 4, first comprises a learning phase 100, which can be implemented for a predefined learning period, for example, one day, one week, one month, etc. The learning phase 100 consists in particular of collecting data relating to the operation of the cooling coil during this learning period. This data includes data relating to cooling production phases and defrosting phases of the cooling coil. This data makes it possible to construct, in an optimization phase 200, an optimal operating model or optimal operating profile of the cooling system, which takes into account the energy impact of the defrosting cycles of the cooling coil.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.

[0051] The learning phase 100, according to an embodiment illustrated in [Fig. 4], may consist of performing the following operations, during a predefined learning time: 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; 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; 103: during the cold production phase, detection of a need to defrost the cold battery; 104: initiation of a defrosting phase by a defrosting means, for example via the hot battery 11, if a need for defrosting is detected; 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 resumption of the production of cold; 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 107: determination of the energy consumption associated with this defrosting phase.

[0052] Defrosting needs 103 can be detected by continuously measuring the cooling COP during each cooling production phase. Thus, defrosting needs can be detected by continuously measuring the cooling capacity (kW) supplied by the cooling system and / or the electrical power consumed by the cooling coil fan 10. The defrosting phase is triggered when the measured cooling capacity (kW) is below a predefined theoretical threshold value, or when the electrical power consumed is above a predefined theoretical threshold value. 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 production phase.

[0053] Another way to detect a need for frost protection can involve monitoring the evolution of the demand for chilled fluid by the cooling coil, particularly via the opening time of the chilled fluid supply valve. Indeed, a valve remaining in the open position for too long can indicate that the system is having difficulty reaching the set cooling temperatures due to ice buildup in the cooling coil.

[0054] These operations of the learning phase are repeated until the end of the learning time, so as to model or construct an operating profile of the cold system, the profile comprising: - the cooling production phases, and for each cooling production phase: the operating modes of the cooling system to reach the cooling setpoints, and the corresponding energy consumption (thermal and / or electrical); and - the defrosting phases, and for each defrosting phase, the start and end times of defrosting, the defrosting method used, and the quantity of water resulting from defrosting, and the corresponding energy consumption (thermal and / or electrical).

[0055] The optimization phase 200 consists, based on the data collected during the learning phase, of estimating an optimal operating profile for the refrigeration system, taking into account at least the energy impact of the defrost cycles. In particular, the optimization includes at least adapting the defrosting phases in terms of the number of defrost cycles, the type of defrosting (air, heat input), and the duration of each defrost 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.

[0056] Thus, adapting the defrosting and / or cooling production phases can involve applying one or more corrective actions to the cooling and / or defrosting conditions. These corrective actions aim, in particular, to reduce energy consumption related to defrosting without impacting cooling production in terms of the cooling setpoints to be achieved, nor degrading the overall cooling COP of the system. Determining the optimal profile therefore involves performing system operation simulations to determine 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.

[0057] By way of 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 the coil surfaces. For example, when data collected during the learning phase show that the amount of frost formed is such that a slight variation in the surface temperature of the cooling coil is sufficient to trigger defrosting, the optimal profile could include, during one or more production phases, phases of reducing the average surface temperature of the cooling coil 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 range of acceptable or predefined tolerances relative to the cooling setpoints to be achieved.Thus, this slight modification of the surface temperature of the cold battery allows defrosting to occur without the need for a dedicated defrosting method, such as an electric heating element or the hot battery, which consumes electrical and thermal energy.

[0058] Another corrective action may be the programming of timed shutdowns of the cold battery 10, for example via the closing of the injection valve 60 of cold fluid. The cold battery is thus stopped for a determined duration during the simulation (for example for 5 minutes or for 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 exchange surface of the cold battery to avoid ice formation throughout the production of cold.

[0059] Depending on the room air temperature conditions, another corrective action may consist of varying the fan's rotation speed and operating time. The simulation thus involves applying several setpoint speeds to the fan and determining one or more optimal setpoint speeds.

[0060] On [Fig.6]: - the Frost curve corresponds to the evolution of the thickness of frost on the surface of the cold battery, 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.

[0061] On [Fig.7]: - the Ps tat 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 the liquid state to the gaseous state; - 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.

[0062] On [Fig.8]: - the Tff curve corresponds to the evolution of the temperature of the refrigerant fluid in the evaporator; - the Tsa curve corresponds to the evolution of the temperature on the surface of the fins of the cold battery; - the T s frost curve corresponds to the evolution of the temperature measured on the surface of the frost formed on the cold battery; - The Tce curve corresponds to the evolution of the condensation temperature at the evaporator, as a function of the pressure in the evaporator; and - 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.

[0063] Figures 9 to 12 illustrate simulations of the evolution of the above parameters, over the same predefined time, also over 350 minutes, during which an optimized defrosting according to the optimization process is programmed in such a way anticipated 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 optimized defrosting—in this case, anticipated defrosting—the frost layer is controlled and the cooling coil's performance is maintained at optimal levels.

[0064] On [Fig.9]: - The COP_opt curve (solid line) corresponds to the evolution of the coefficient of performance COP; and - the Qf _opt curve (in dotted lines) corresponds to the evolution of the cooling power of the cold battery.

[0065] On [Fig. 10]: - the Efrivre _opt curve corresponds to the evolution of the thickness of the frost on the surface of the cold battery, and in particular on the surface of the fins; - the Qv _opt curve corresponds to the evolution of the volume flow rate of the air coming from the fan coupled to the cold battery.

[0066] On [Fig. 11]: - the Pstat _opt 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 the liquid state to the gaseous state; - 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.

[0067] On [Fig. 12]: - the Tff_opt curve corresponds to the evolution of the temperature of the refrigerant fluid in the cooling battery; - the Tsa_opt curve corresponds to the evolution of the temperature on the surface of the fins of the cold battery; - the Tsgivre_opt curve corresponds to the evolution of the temperature measured on the surface of the frost formed on the cold battery; - 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, i.e. the actual temperature of the refrigerant fluid at the outlet of the evaporator, after heat exchange in the evaporator.

[0068] The present invention thus proposes a solution enabling: - to ensure the detection of a need to defrost a cold battery; - to implement corrective actions aimed at limiting the formation of ice on the surface of the cold battery, adapting the defrosting phases in terms of number, duration, triggering time, and defrosting method; - to optimize energy consumption (electrical and / or thermal) related to defrosting; - 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 refrigeration system (1) is put into operation for a predefined time and according to an initial operating profile comprising phases of cold production to reach and maintain cold setpoints and phases of defrosting of 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 cooling 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 a time at which the water from the defrosting of the cooling coil ends flow (105), and determination of the actual time required for defrosting; and - quantification of the water (106) from the defrosting of the cooling coil;and in that the determination of the optimal operating profile includes the adaptation of the defrosting phases in terms of the number of defrosts, the type of defrosting, and the 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 production COP by the cooling battery during each cold production phase; and - triggering the defrosting phase of the cooling battery when the measured cold production COP is less than a predefined theoretical cold production COP.

3. Optimization method according to claim 1, wherein the learning phase further comprises: - continuous measurement of the cooling power supplied by the cooling system and / or the electrical power consumed by the air handling system; - the defrosting phase of the cooling coil 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. Optimization method according to any one of claims 1 to 3, wherein the optimization phase includes: - 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 exhibiting 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 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; - an adaptation of the fan 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 data qualitative maintenance data provided by an operator relates to: . visual detection of frost presence; and / or . unexpected heat inputs in the room; and / or . defrosting quality.

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 include: - a door opening for a predefined time likely to cause the room temperature to vary outside the high and low temperature tolerances; - a mechanical problem in 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.