Cooling tower optimization control method, device, terminal and medium for central air conditioning system

Abstract

The present invention provides a cooling tower optimization control method, device, terminal and medium for a central air-conditioning system, the method comprising: obtaining the wet-bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air-conditioning system under the current environment; determining the control strategy to be optimized of the cooling tower corresponding to the lowest power consumption value of the cooling tower according to the wet-bulb approximation; optimizing the control strategy to be optimized by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and executing the optimized control strategy to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment. The present invention calculates the wet-bulb approximation by considering the influence of the relative humidity of air, the synergistic effect of wind speed and the dynamic change of the cooling tower load rate on the wet-bulb temperature, and determines the control strategy, and further optimizes and executes the control strategy by modulating the cooling tower model and the interaction information of the parallel cooling towers to improve and maintain the comprehensive energy efficiency of the central air-conditioning system.

Description

Technical Field

[0001] The invention belongs to the technical field of central air-conditioning automatic control, and in particular relates to a cooling tower optimization control method, device, terminal and medium of a central air-conditioning system. Background Art

[0002] In the prior art, the method for obtaining the wet-bulb approximation of a cooling tower is usually to subtract the outlet water temperature of the cooling tower from the wet-bulb temperature under the current environment. However, in the research and practice of this prior art, the inventors of the present application found that this method often ignores the actual environmental conditions of the operation of the cooling tower, such as the influence of factors such as the relative humidity of the air, wind speed, and changes in load rate on the calculation results. Due to the failure to fully consider these actual environmental conditions, it is impossible to accurately grasp the operating conditions of the cooling tower, and it is difficult to adjust the control strategy of the cooling tower in a timely and accurate manner according to these changes. This makes the operation of the cooling towers uncoordinated, and it is impossible to achieve the overall optimal control effect, and thus it is impossible to give full play to the performance of the cooling tower, which ultimately leads to the inability to effectively improve the energy efficiency of the central air-conditioning system.

[0003] Based on this, how to solve the above-mentioned technical problems in the prior art, optimize the control strategy of the cooling tower and execute it to improve and maintain the comprehensive energy efficiency of the central air-conditioning system has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the present application provides a cooling tower optimization control method, device, terminal and medium for a central air-conditioning system, which can calculate the wet-bulb approximation by considering the synergistic effect of air relative humidity, wind speed and the impact of dynamic changes in cooling tower load rate on wet-bulb temperature, and determine the control strategy, and further optimize and execute the control strategy by modulating the cooling tower model and the interactive information of parallel cooling towers to improve and maintain the comprehensive energy efficiency of the central air-conditioning system.

[0005] In a first aspect, the present application provides a cooling tower optimization control method for a central air conditioning system, the method at least comprising:

[0006] Obtain the wet bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air conditioning system under the current environment; wherein the wet bulb approximation is calculated based on the synergistic effect of the relative humidity of the air and the wind speed, and the influence of the dynamic change of the cooling tower load rate on the wet bulb temperature under the current environment;

[0007] Determining, according to the wet-bulb approximation, a control strategy to be optimized for the cooling tower corresponding to when the power consumption value of the cooling tower is the lowest;

[0008] The optimization of the control strategy to be optimized is completed by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and the optimized control strategy is executed to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment.

[0009] In a possible implementation manner, the wet bulb approximation of the cooling tower is determined according to the following formula:

[0010] ;

[0011] in, is the wet bulb approximation, is the outlet water temperature of the cooling tower, is the wet bulb temperature, is the relative humidity of air, is the actual wind speed, is the reference wind speed; and are the corresponding coefficients, which respectively reflect the influence of relative air humidity and actual wind speed on the wet-bulb approximation. When the relative air humidity increases, the influence of wet-bulb temperature on the approximation decreases, while when the actual wind speed increases, the approximation decreases accordingly due to the enhanced heat exchange; is the actual load of the cooling tower, is the maximum design load of the cooling tower; is the cooling tower load rate influence coefficient. When the cooling tower load rate decreases, Approaches 0, when the cooling tower load rate increases, The wet bulb approach will increase accordingly to reflect the change in cooling effect of the cooling tower under high load.

[0012] In a possible implementation manner, the optimization control strategy of the cooling tower corresponding to the lowest power consumption value of the cooling tower determined according to the wet-bulb approximation includes:

[0013] Selecting an initial wet-bulb approximation according to the wet-bulb approximation, and setting a positive optimal approximation value and a negative optimal approximation value in the positive and negative directions of the initial approximation value based on a preset optimal resolution;

[0014] The power consumption values ​​of the benchmark control strategy, the control strategy obtained by positive optimization and the control strategy obtained by negative optimization corresponding to the initial wet-bulb approximation are calculated, the wet-bulb approximation of the control strategy with the lowest power consumption value is defined as the new initial approximation, and the control strategy with the lowest power consumption value is determined as the control strategy to be optimized.

[0015] In a possible implementation manner, the cooling tower model is determined according to the following formula:

[0016]

[0017] in, A set of cooling tower operating parameters. is the humidity coefficient of the cooling tower, is the cooling tower temperature coefficient, is the energy saving coefficient of the cooling tower itself, is the energy consumption control condition, B is the cooling tower connection instruction, and Set thresholds for average energy consumption and energy consumption of cooling towers respectively; , are the maximum and minimum values ​​of the cooling tower automatic monitoring protection mechanism coefficient, n is the cooling tower electrification authority, , are the adjustment factors of the original first-stage and second-stage cooling tower controllers, , They are the performance requirements of the original first-stage and second-stage cooling tower controllers. is the power factor for electrification of cooling towers, It is the change value of electrical engineering debugging quantity per unit time.

[0018] In a possible implementation manner, the optimization of the control strategy to be optimized is completed by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, including:

[0019] By adjusting the modulation coefficient of the cooling tower model, the energy efficiency corresponding to the set of operating parameters of the cooling tower under the control strategy to be optimized is greater than or equal to the energy efficiency before adjustment;

[0020] A distributed probability estimation algorithm is used to optimize the operating parameters of the parallel cooling towers through the control strategy to be optimized, so as to determine the operating parameter set corresponding to the highest energy efficiency, thereby completing the optimization of the control strategy to be optimized.

[0021] In a second aspect, the present application provides a cooling tower optimization control device for a central air-conditioning system, the device comprising at least:

[0022] The first calculation module is used to obtain the wet bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air conditioning system under the current environment; wherein the wet bulb approximation is calculated based on the synergistic effect of the relative humidity of the air and the wind speed, and the influence of the dynamic change of the cooling tower load rate on the wet bulb temperature under the current environment;

[0023] A second calculation module is used to determine, according to the wet-bulb approximation, a control strategy to be optimized for the cooling tower corresponding to when the power consumption value of the cooling tower is the lowest;

[0024] The optimization control module is used to optimize the control strategy to be optimized by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and execute the optimized control strategy to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment.

[0025] In a possible implementation manner, the wet bulb approximation of the cooling tower is determined according to the following formula:

[0026] ;

[0027] in, is the wet bulb approximation, is the outlet water temperature of the cooling tower, is the wet bulb temperature, is the relative humidity of air, is the actual wind speed, is the reference wind speed; and are the corresponding coefficients, which respectively reflect the influence of relative air humidity and actual wind speed on the wet-bulb approximation. When the relative air humidity increases, the influence of wet-bulb temperature on the approximation decreases, while when the actual wind speed increases, the approximation decreases accordingly due to the enhanced heat exchange; is the actual load of the cooling tower, is the maximum design load of the cooling tower; is the cooling tower load rate influence coefficient. When the cooling tower load rate decreases, Approaches 0, when the cooling tower load rate increases, The wet bulb approach will increase accordingly to reflect the change in cooling effect of the cooling tower under high load.

[0028] In a possible implementation manner, the cooling tower model is determined according to the following formula:

[0029]

[0030] in, A set of cooling tower operating parameters. is the humidity coefficient of the cooling tower, is the cooling tower temperature coefficient, is the energy saving coefficient of the cooling tower itself, is the energy consumption control condition, B is the cooling tower connection instruction, and Set thresholds for average energy consumption and energy consumption of cooling towers respectively; , are the maximum and minimum values ​​of the cooling tower automatic monitoring protection mechanism coefficient, n is the cooling tower electrification authority, , are the adjustment factors of the original first-stage and second-stage cooling tower controllers, , They are the performance requirements of the original first-stage and second-stage cooling tower controllers. is the power factor for electrification of cooling towers, It is the change value of electrical engineering debugging quantity per unit time.

[0031] In a third aspect, the present application provides a terminal, comprising a touch screen, a memory, a transceiver, one or more processors, multiple applications, and one or more programs; wherein the one or more programs are stored in the memory, and the transceiver is used to transmit or receive wireless signals; when the one or more processors execute the one or more programs, the terminal implements the cooling tower optimization control method of the central air-conditioning system as mentioned above.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the cooling tower optimization control method of the central air-conditioning system as described above.

[0033] In the cooling tower optimization control scheme for the central air-conditioning system provided in the embodiment of the present application, the wet-bulb approximation is calculated by considering the synergistic effect of air relative humidity, wind speed and the dynamic change of cooling tower load rate on the wet-bulb temperature, which can more accurately reflect the actual operating environment and provide a reliable basis for determining the control strategy. The control strategy to be optimized is determined with the lowest cooling tower power consumption as the goal, which can effectively reduce the operating cost. By adjusting the modulation coefficient of the cooling tower model and optimizing and executing the control strategy by considering the interaction information between the cooling tower and the adjacent cooling towers, the coordinated operation of multiple cooling towers can be achieved, the comprehensive energy efficiency of the central air-conditioning system can be improved, the comprehensive energy efficiency at the current moment can be maintained to be greater than or equal to the comprehensive energy efficiency at the previous moment, and the efficient and stable operation of the central air-conditioning system can be ensured.

[0034] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0036] Figure 1 It is a flow chart of a cooling tower optimization control method for a central air-conditioning system provided by an embodiment of the present application;

[0037] Figure 2 It is a structural schematic diagram of a cooling tower optimization control device for a central air-conditioning system provided in one embodiment of the present application;

[0038] Figure 3 A schematic diagram of the hardware structure of a cooling tower optimization control device for a central air-conditioning system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Where there are descriptions involving “first”, “second”, etc. in the specification and claims of this application and the above-mentioned drawings, the descriptions of “first”, “second”, etc. are only used for descriptive purposes to distinguish different objects, rather than for describing a specific order, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features.

[0041] The terms "including" and "having" and any variations thereof as used herein are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units or modules is not limited to the listed steps or units or modules, but may optionally include steps or units or modules not listed, or may optionally include other steps or units or modules inherent to these processes, methods, products or devices.

[0042] For "and / or" or "and / or" appearing in the text, its meaning includes three parallel solutions. Taking "A and / or B" as an example, it includes Solution A, or Solution B, or a solution in which both A and B are satisfied.

[0043] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0044] The central air conditioning system consists of a cooling tower, cooling water pump, chilled water pump, central air conditioning host, air handling unit, fan coil, water collector, and water distributor, which are output to different air outlets through air ducts. The specific functions are as follows:

[0045] 1) The main function of a cooling tower is to dissipate heat. The cooling tower takes away the heat in the condenser through water circulation, and the heat is dissipated into the atmosphere through heat exchange between air and water.

[0046] 2) The task of the cooling water pump is to drive the cooling water to circulate in the cooling system. It connects the condenser and the cooling tower to ensure that the cooling water can flow and take away the heat in the condenser.

[0047] 3) The chilled water pump is responsible for circulating chilled water, which is the medium used to transport the cooling capacity of the refrigerant in the central air conditioning system. In the refrigeration unit, the refrigerant absorbs heat in the evaporator and becomes low-temperature and low-pressure steam, then flows to the condenser to release heat and condense into liquid.

[0048] 4) The central air conditioning host usually refers to the refrigeration unit, which is the core of the entire central air conditioning system and is responsible for generating cold or heat. The refrigeration unit has key components such as the compressor, condenser, evaporator and expansion valve, which achieve cooling or heating through the circulation of refrigerant.

[0049] 5) The air handling unit is an important component of the central air conditioning system. It is responsible for processing and regulating the amount of air delivered to the room.

[0050] 6) Fan coil unit is a terminal air handling equipment, usually installed on the ceiling or wall of the room.

[0051] 7) The water collector is mainly used to collect and distribute condensed water in the central air-conditioning system.

[0052] 8) The water distributor is a pipe distribution device in the central air-conditioning system. Its function is to distribute the chilled water or hot water from the central air-conditioning host to each air handling unit or fan coil.

[0053] See also Figure 1 , Figure 1 It is a flow chart of a cooling tower optimization control method for a central air-conditioning system provided in one embodiment of the present application.

[0054] In order to overcome the shortcomings of the existing optimization control method for cooling towers in central air-conditioning systems and achieve more precise, intelligent and flexible control, an embodiment of the present application provides an optimization control method for cooling towers in central air-conditioning systems.

[0055] S100, obtaining the wet bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air conditioning system under the current environment.

[0056] The wet-bulb approximation is calculated based on the synergistic effect of air relative humidity and wind speed, and the influence of the dynamic change of cooling tower load rate on the wet-bulb temperature in the current environment.

[0057] In a possible implementation manner, the wet bulb approximation of the cooling tower is determined according to the following formula:

[0058] ;

[0059] in, is the wet bulb approximation, is the outlet water temperature of the cooling tower, is the wet bulb temperature, is the relative humidity of air, is the actual wind speed, is the reference wind speed; and are the corresponding coefficients, which respectively reflect the influence of relative air humidity and actual wind speed on the wet-bulb approximation. When the relative air humidity increases, the influence of wet-bulb temperature on the approximation decreases, while when the actual wind speed increases, the approximation decreases accordingly due to the enhanced heat exchange; is the actual load of the cooling tower, is the maximum design load of the cooling tower; is the cooling tower load rate influence coefficient. When the cooling tower load rate decreases, Approaches 0, when the cooling tower load rate increases, The wet bulb approach will increase accordingly to reflect the change in cooling effect of the cooling tower under high load.

[0060] Wet bulb approach refers to the temperature difference between the outlet water temperature of the cooling tower and the wet bulb temperature of the air. The wet bulb temperature represents the limit of the heat that the air can take away through evaporation to reduce the water temperature. Therefore, the wet bulb approach directly affects the cooling capacity and energy consumption of the cooling tower, and is closely related to the selection of the cooling tower, the operating cost and the efficiency of the entire cooling system. A smaller wet bulb approach means that the cooling tower needs to cool the water to a temperature closer to the limit. However, due to the limited cooling capacity of the cooling tower, a larger cooling tower or other measures to enhance heat dissipation may be required to achieve a smaller wet bulb approach, which increases the cost of the equipment. For example, a standard 100-ton cooling tower can cool 100 tons of water when the wet bulb approach is 4°C; while at a wet bulb approach of 3°C, it can only cool 85 tons of water. The energy consumption of the cooling tower fan accounts for a certain proportion of the system energy consumption. In order to achieve a smaller wet bulb approach, the fan may need to operate at a higher power, resulting in increased energy consumption. In terms of cooling tower selection, wet bulb approach is a key parameter. The wet bulb temperature is different in different regions. Under the same heat load and water flow, the area with lower wet bulb temperature requires a relatively smaller cooling tower capacity, and the selection will also be different.

[0061] Based on this, from the perspective of the entire central air-conditioning system, the wet-bulb approach and the operating parameters of the chiller, water pump, etc. are mutually constrained. Optimizing the wet-bulb approach can enable the cooling tower to achieve energy-saving operation of the entire system while meeting the cooling demand. For example, by accurately calculating the wet-bulb approach and reasonably controlling the wet-bulb approach, the energy efficiency ratio of the chiller can be improved, thereby reducing the comprehensive energy consumption of the central air-conditioning system.

[0062] In this embodiment, as can be seen from the above formula, by calculating the wet-bulb approximation by considering the synergistic effect of air relative humidity, wind speed and the impact of dynamic changes in cooling tower load rate on wet-bulb temperature, it can more accurately reflect the actual operating environment and provide a reliable basis for determining the control strategy.

[0063] The comprehensive energy efficiency of the central air-conditioning system is affected by many factors, such as the performance of the compressor, the efficiency of the heat exchanger, the type of refrigerant, the control strategy of the system, the performance of the cooling tower, the operating efficiency of the water pump, and the matching degree of the entire system. Determining the control strategy to be optimized with the lowest power consumption value of the cooling tower as the goal can effectively reduce the operating cost. By adjusting the modulation coefficient of the cooling tower model and considering the interactive information between the cooling tower and the adjacent cooling towers to optimize the control strategy and execute it, the coordinated operation of multiple cooling towers can be achieved, the comprehensive energy efficiency of the central air-conditioning system can be improved, the comprehensive energy efficiency of the current moment can be maintained greater than or equal to the comprehensive energy efficiency of the previous moment, and the efficient and stable operation of the central air-conditioning system can be ensured.

[0064] The calculation formula of the refrigeration station comprehensive energy efficiency (SCOP) of the central air-conditioning system is usually: SCOP = total cooling capacity of the refrigeration station / total energy consumption of the refrigeration station. Among them, the total cooling capacity of the refrigeration station refers to the sum of all cooling capacities provided by the refrigeration station within a certain period of time, which can be determined by the sum of the cooling capacity of the main unit and the cooling capacity provided by other auxiliary equipment (if there are auxiliary equipment providing cooling capacity). The total energy consumption of the refrigeration station includes the power of the main unit, the power of the chilled water pump, the power of the cooling water pump, the power of the water tower, and the total energy consumption of other related equipment.

[0065] In order to improve the comprehensive energy efficiency SCOP, it is usually possible to increase the sum of the cooling capacity of the main units, such as selecting the unit with the best COP, including adopting multiple main unit energy efficiency optimization strategies, the lower the cooling water temperature, the better (water tower energy efficiency), the higher the chilled water outlet temperature, the better (temperature and humidity separation), the lower the approach temperature, the better (thorough scale prevention); and reduce the power of the chilled water pump, such as hydraulic balance of the refrigeration system, the greater the chilled water temperature difference, the better, intelligent adjustment of the head and flow, and the minimum water pump power; or reduce the cooling water pump power and cooling tower power, such as reducing resistance, reducing the water pump head to reduce the water pump power, giving priority to wet bulb approach, using the surplus area of ​​the filler, and reasonably reducing the fan power.

[0066] In addition, the EER of the refrigeration station = host COP × host energy consumption ratio = host COP × (1-pump tower energy consumption ratio). EER (Energy Efficiency Ratio): Refrigeration station energy efficiency ratio, which reflects the overall energy utilization efficiency of the refrigeration station. The higher the value, the less energy is consumed when the same cooling capacity is produced. Host COP (Coefficient of Performance): Host performance coefficient, that is, the ratio of cooling capacity to host input power. It measures the efficiency of the refrigeration host in converting input energy into cooling capacity. Host energy consumption ratio: refers to the proportion of the refrigeration host in the energy consumption of the entire refrigeration station. Generally, in addition to the host energy consumption, the energy consumption of the refrigeration station also includes the energy consumption of auxiliary equipment such as pumps and cooling towers. Pump tower energy consumption ratio: the proportion of the energy consumption of pumps and cooling towers in the energy consumption of the entire refrigeration station. Since the total energy consumption of the refrigeration station is 100%, the host energy consumption ratio is equal to 1 minus the pump tower energy consumption ratio.

[0067] S200. Determine, according to the wet-bulb approximation, a control strategy for the cooling tower to be optimized corresponding to when the power consumption value of the cooling tower is the lowest.

[0068] Specifically, an initial wet-bulb approximation is selected according to the wet-bulb approximation, and a positive optimal approximation value and a negative optimal approximation value are respectively set in the positive and negative directions of the initial approximation value based on a preset optimal resolution;

[0069] The power consumption values ​​of the benchmark control strategy, the control strategy obtained by positive optimization and the control strategy obtained by negative optimization corresponding to the initial wet-bulb approximation are calculated, the wet-bulb approximation of the control strategy with the lowest power consumption value is defined as the new initial approximation, and the control strategy with the lowest power consumption value is determined as the control strategy to be optimized.

[0070] In one possible implementation, the range of temperature and humidity values ​​can be accurately determined based on historical meteorological data, so as to accurately calculate the range of wet-bulb temperature. The wet-bulb temperature range is carefully divided into multiple intervals. Based on the cooling tower equipment parameters and the midpoint value of the wet-bulb temperature corresponding to each wet-bulb temperature interval, the initial value of the wet-bulb approximation is set for each interval, and then the approximation model is constructed. This step provides a key basis for the subsequent calculation of the cooling water outlet temperature, etc.

[0071] It should be noted that historical meteorological data is not limited to simply analyzing the temperature and humidity ranges in historical meteorological data, but can further analyze the seasonal and periodic changes in meteorological data. For example, it can distinguish the typical characteristics of temperature and humidity changes in different seasons and the extreme values ​​under special weather conditions. When dividing the wet-bulb temperature interval, it can be more flexible and reasonable to divide the intervals according to multiple factors such as seasons and weather types. For example, in the summer period of high temperature, high humidity and frequent thunderstorms, a separate wet-bulb temperature interval group for the meteorological characteristics of this period is divided out, and the range of each interval is smaller and more in line with the actual operating environment requirements. When calculating the midpoint value of the wet-bulb temperature interval, a weighted average algorithm is introduced. For those wet-bulb temperature values ​​corresponding to meteorological conditions that have a greater impact on the operation of the cooling tower, a higher weight is given. For example, in the transition season, although some temperature and humidity combinations do not appear most frequently, once they appear, they will have a greater fluctuation in the efficiency of the cooling tower. When calculating the midpoint value, increase its weight proportion so that the obtained midpoint value can better reflect the key meteorological factors that actually affect the operation of the cooling tower.

[0072] A dynamic adjustment strategy can be used to set the initial value of the approximation. In addition to the midpoint value of the wet-bulb temperature range and the parameters of the cooling tower equipment, dynamic adjustments are made based on the previous operating feedback data of the cooling tower under similar meteorological conditions and load demands. For example, when it is detected that the cooling tower is within a certain wet-bulb temperature range and the load demand is within a certain range, its actual operating efficiency deviates from the expected one, and the system automatically fine-tunes the initial value of the approximation based on this deviation. At the same time, a self-checking mechanism for the initial value of the approximation is established. At regular intervals or under specific operating condition triggering conditions, the set initial value of the approximation is checked for rationality, and the difference between the actual operating data and the model prediction data is compared to correct unreasonable initial values ​​in a timely manner.

[0073] In one possible implementation, various data are collected during the operation of the cooling tower, including the ambient air wet bulb temperature, cooling tower outlet water temperature, fan speed, pump flow rate, and power consumption. By analyzing these data, the relationship between wet bulb approximation and power consumption is found. For example, a graph can be drawn to observe how power consumption changes accordingly with changes in wet bulb approximation. Then, adjust the control variables. Adjust the control variables of the cooling tower, such as fan speed and pump flow rate. Gradually change these variables and observe the changes in wet bulb approximation and power consumption. In this way, the degree of influence of each variable on wet bulb approximation and power consumption can be determined.

[0074] On this basis, a cooling tower model is established based on the results of data collection and control variable adjustment. This model can describe the relationship between wet bulb approximation, power consumption and control variables. Then, optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, are used to find the control strategy corresponding to the lowest power consumption value based on the model. These optimization algorithms can automatically search for the best combination of control variables to achieve efficient operation of the cooling tower.

[0075] Finally, a test is conducted in the actual operating environment to compare the wet bulb approximation and power consumption changes before and after the new control strategy is adopted. If the verification results are in line with expectations, that is, the power consumption is indeed reduced and the wet bulb approximation is within a reasonable range, then the control strategy can be implemented in the cooling tower system of the entire refrigeration station.

[0076] Possible control strategies include fan control, water pump control, cooling tower structure optimization, and environmental factors. For example, the fan speed can be automatically adjusted according to the difference between the wet bulb temperature and the cooling tower outlet water temperature, and the frequency conversion control technology can be used to enable the fan to accurately adjust the speed to reduce energy waste; optimize the water pump flow rate, and automatically adjust the water pump operation frequency according to the refrigeration system load and cooling tower demand; check the cooling tower filler and nozzle components to ensure that they are in good working condition, optimize the cooling tower air inlet and outlet design to improve cooling efficiency; pay attention to changes in ambient air humidity and temperature, take shading measures or choose a suitable installation location to reduce the cooling tower inlet water temperature, etc.

[0077] In a possible implementation manner, the cooling tower model is determined according to the following formula:

[0078]

[0079] in, A set of cooling tower operating parameters. is the humidity coefficient of the cooling tower, is the cooling tower temperature coefficient, is the energy saving coefficient of the cooling tower itself, is the energy consumption control condition, B is the cooling tower connection instruction, and Set thresholds for average energy consumption and energy consumption of cooling towers respectively; , are the maximum and minimum values ​​of the cooling tower automatic monitoring protection mechanism coefficient, n is the cooling tower electrification authority, , are the adjustment factors of the original first-stage and second-stage cooling tower controllers, , They are the performance requirements of the original first-stage and second-stage cooling tower controllers. is the power factor for electrification of cooling towers, It is the change value of electrical engineering debugging quantity per unit time.

[0080] In this embodiment, by setting corresponding energy consumption constraints, optimizing the energy-saving coefficient of the cooling tower itself, controlling energy consumption conditions, and adjusting parameters such as the electrification authority, the energy consumption of the cooling tower is reduced to achieve the goal of energy saving and consumption reduction. Considering factors such as humidity and temperature, combined with the optimization of the automatic monitoring protection mechanism and the controller, the performance of the cooling tower can be improved, ensuring its stable operation under different environmental conditions and providing a reliable cooling effect. The setting of parameters such as the automatic monitoring protection mechanism coefficient and the electrification execution authority coefficient ensures the safe operation of the cooling tower and avoids equipment failure or safety accidents caused by abnormal conditions. The existence of multiple parameters enables the operation of the cooling tower to be flexibly adjusted and controlled according to actual needs. For example, the adjustment factor and performance requirement index of the controller can be adjusted according to different load requirements, environmental conditions and performance requirements to achieve personalized operation control. The cooling tower connection instruction and the synergy with other equipment can achieve the optimized operation of the entire cooling system and improve the overall efficiency and performance of the central air-conditioning system.

[0081] S300, optimizing the control strategy to be optimized by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and executing the optimized control strategy to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment.

[0082] Specifically, the modulation coefficient of the cooling tower model is adjusted so that the energy efficiency corresponding to the set of operating parameters of the cooling tower under the control strategy to be optimized is greater than or equal to the energy efficiency before adjustment; a distributed probability estimation algorithm is used to optimize the operating parameters of the parallel cooling towers through the control strategy to be optimized to determine the set of operating parameters corresponding to the highest energy efficiency, thereby completing the optimization of the control strategy to be optimized.

[0083] In this embodiment, the modulation coefficient of the cooling tower model can adjust the operating characteristics of the cooling tower. By adjusting this coefficient, the operating parameters of the cooling tower, such as the air volume and water volume, can be changed, thereby affecting the energy efficiency performance of the cooling tower. For example, increasing the modulation coefficient may increase the air volume of the cooling tower and improve the cooling effect, but it may also increase the energy consumption of the fan; reducing the modulation coefficient may reduce energy consumption, but it may affect the cooling capacity.

[0084] After determining the set of operating parameters of the cooling tower under the control strategy to be optimized, the energy efficiency changes of the cooling tower under different modulation coefficients are observed by adjusting the modulation coefficient. The energy efficiency value corresponding to each modulation coefficient is calculated to find the modulation coefficient that makes the energy efficiency greater than or equal to the energy efficiency before adjustment. For example, a step-by-step adjustment method can be used to increase or decrease a small modulation coefficient increment each time, and the corresponding energy efficiency value is calculated until a modulation coefficient that meets the conditions is found.

[0085] For parallel cooling towers, the distributed probability estimation algorithm can fully consider the interaction information between each cooling tower and realize overall optimization control. The algorithm estimates and updates the probability distribution of the operating parameters, gradually searches for the optimal operating parameter set, and determines the parameter combination corresponding to the highest energy efficiency. Specifically, the control strategy to be optimized is applied to the parallel cooling towers, and the operating data of each cooling tower is collected, including energy consumption, cooling effect, etc. The distributed probability estimation algorithm is used to establish a probability model of the operating parameters based on the collected data. This model can describe the energy efficiency performance under different operating parameter combinations. By continuously sampling and updating the probability model, it gradually converges to the operating parameter set corresponding to the highest energy efficiency. In this process, the algorithm will consider the interaction between the cooling towers, such as the change in the air volume of one cooling tower may affect the inlet air temperature of other cooling towers. After determining the operating parameter set corresponding to the highest energy efficiency, the optimization of the control strategy to be optimized is completed. The optimized control strategy is applied to the parallel cooling tower system to achieve efficient operation.

[0086] By utilizing the distributed characteristics of the distributed probability estimation algorithm, considering the interactive information between parallel cooling towers, and establishing an accurate operating parameter probability model, it is ensured that the distributed probability estimation algorithm can quickly converge to the optimal solution, thereby effectively optimizing the control strategy of the cooling tower, improving the comprehensive energy efficiency of the central air-conditioning system, and achieving energy-saving and efficient operation.

[0087] In the above embodiment, by calculating the wet-bulb approximation by considering the synergistic effect of air relative humidity, wind speed and the dynamic change of cooling tower load rate on wet-bulb temperature, the actual operating environment can be reflected more accurately, and a reliable basis can be provided for determining the control strategy. The control strategy to be optimized is determined with the lowest cooling tower power consumption as the goal, which can effectively reduce the operating cost. By adjusting the modulation coefficient of the cooling tower model and optimizing and executing the control strategy by considering the interaction information between the cooling tower and the adjacent cooling towers, the coordinated operation of multiple cooling towers can be achieved, the comprehensive energy efficiency of the central air-conditioning system can be improved, the comprehensive energy efficiency at the current moment can be maintained to be greater than or equal to the comprehensive energy efficiency at the previous moment, and the efficient and stable operation of the central air-conditioning system can be ensured.

[0088] The method of the embodiment of the present application is described in detail above, and the device of the embodiment of the present application is provided below.

[0089] See also Figure 2 , Figure 2 It is a structural schematic diagram of a cooling tower optimization control device for a central air-conditioning system provided in one embodiment of the present application.

[0090] The first calculation module 100 is used to obtain the wet bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air conditioning system under the current environment; wherein the wet bulb approximation is calculated based on the synergistic effect of the relative humidity of the air and the wind speed, and the influence of the dynamic change of the cooling tower load rate on the wet bulb temperature under the current environment;

[0091] A second calculation module 200 is used to determine, according to the wet-bulb approximation, a control strategy to be optimized for the cooling tower corresponding to when the power consumption value of the cooling tower is the lowest;

[0092] The optimization control module 300 is used to optimize the control strategy to be optimized by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and execute the optimized control strategy to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment.

[0093] In a possible implementation manner, the wet bulb approximation of the cooling tower is determined according to the following formula:

[0094] ;

[0095] in, is the wet bulb approximation, is the outlet water temperature of the cooling tower, is the wet bulb temperature, is the relative humidity of air, is the actual wind speed, is the reference wind speed; and are the corresponding coefficients, which respectively reflect the influence of relative air humidity and actual wind speed on the wet-bulb approximation. When the relative air humidity increases, the influence of wet-bulb temperature on the approximation decreases, while when the actual wind speed increases, the approximation decreases accordingly due to the enhanced heat exchange; is the actual load of the cooling tower, is the maximum design load of the cooling tower; is the cooling tower load rate influence coefficient. When the cooling tower load rate decreases, Approaches 0, when the cooling tower load rate increases, The wet bulb approach will increase accordingly to reflect the change in cooling effect of the cooling tower under high load.

[0096] In a possible implementation manner, the cooling tower model is determined according to the following formula:

[0097]

[0098] in, For the cooling tower operating parameter set, is the humidity coefficient of the cooling tower, is the cooling tower temperature coefficient, is the energy saving coefficient of the cooling tower itself, is the energy consumption control condition, B is the cooling tower connection instruction, and Set thresholds for average energy consumption and energy consumption of cooling towers respectively; , are the maximum and minimum values ​​of the cooling tower automatic monitoring protection mechanism coefficient, n is the cooling tower electrification authority, , are the adjustment factors of the original first-stage and second-stage cooling tower controllers, , They are the performance requirements of the original first-stage and second-stage cooling tower controllers. is the power factor for electrification of cooling towers, It is the change value of electrical engineering debugging quantity per unit time.

[0099] It should be noted that, in some embodiments, the functions or modules included in the device provided in the embodiments of the present application can be used to execute the method described in the above method embodiments. The specific implementation thereof can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0100] See also Figure 3 , Figure 3 A schematic diagram of the hardware structure of a cooling tower optimization control device for a central air-conditioning system provided in one embodiment of the present application.

[0101] The device 2 includes a processor 21, a memory 24, an input device 22, and an output device 23. The processor 21, the memory 24, the input device 22, and the output device 23 are coupled via a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present application. It should be understood that in each embodiment of the present application, coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0102] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor may also be other types of processors, etc., which are not limited in the embodiments of the present application.

[0103] The memory 24 can be used to store computer program instructions and various computer program codes including the program code for executing the program code of the present application. Optionally, the memory includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), which is used for related instructions and data.

[0104] The input device 22 is used to input data and / or signals, and the output device 23 is used to output data and / or signals. The output device 23 and the input device 22 may be independent devices or an integrated device.

[0105] It is understandable that in the embodiment of the present application, the memory 24 can not only be used to store related instructions, and the embodiment of the present application does not limit the specific data stored in the memory.

[0106] Understandably, Figure 3 Only a simplified design of the cooling tower optimization control device of the central air conditioning system is shown. In practical applications, the device may also include other necessary components, including but not limited to any number of input / output devices, processors, memories, etc., and all devices that can implement the embodiments of the present application are within the scope of protection of the present application.

[0107] In a third aspect, the present application provides a terminal, comprising a touch screen, a memory, a transceiver, one or more processors, multiple applications, and one or more programs; wherein the one or more programs are stored in the memory, and the transceiver is used to transmit or receive wireless signals; when the one or more processors execute the one or more programs, the terminal implements the cooling tower optimization control method of the central air-conditioning system as mentioned above.

[0108] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the cooling tower optimization control method of the central air-conditioning system as described above.

[0109] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present application have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0111] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0112] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. A cooling tower optimization control method for a central air conditioning system, characterized in that: The method at least comprises: Obtain the wet bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air conditioning system under the current environment; wherein the wet bulb approximation is calculated based on the synergistic effect of the relative humidity of the air and the wind speed, and the influence of the dynamic change of the cooling tower load rate on the wet bulb temperature under the current environment; Determining, according to the wet-bulb approximation, a control strategy to be optimized for the cooling tower corresponding to when the power consumption value of the cooling tower is the lowest; The optimization of the control strategy to be optimized is completed by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and the optimized control strategy is executed to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment; The wet bulb approximation of the cooling tower is determined according to the following formula: ； in, is the wet bulb approximation, is the outlet water temperature of the cooling tower, is the wet bulb temperature, is the relative humidity of air, is the actual wind speed, is the reference wind speed; and are the corresponding coefficients, which respectively reflect the influence of relative air humidity and actual wind speed on the wet-bulb approximation. When the relative air humidity increases, the influence of wet-bulb temperature on the approximation decreases, while when the actual wind speed increases, the approximation decreases accordingly due to the enhanced heat exchange; is the actual load of the cooling tower, is the maximum design load of the cooling tower; is the cooling tower load rate influence coefficient. When the cooling tower load rate decreases, Approaches 0, when the cooling tower load rate increases, becomes smaller, the wet bulb approach will increase accordingly to reflect the change in cooling effect of the cooling tower under high load; The cooling tower model is determined according to the following formula: in, For the cooling tower operating parameter set, is the humidity coefficient of the cooling tower, is the cooling tower temperature coefficient, is the energy saving coefficient of the cooling tower itself, is the energy consumption control condition, B is the cooling tower connection instruction, and Set thresholds for average energy consumption and energy consumption of cooling towers respectively; , are the maximum and minimum values ​​of the cooling tower automatic monitoring protection mechanism coefficient, n is the cooling tower electrification authority, , are the adjustment factors of the original first-stage and second-stage cooling tower controllers, , They are the performance requirements of the original first-stage and second-stage cooling tower controllers. is the power factor for electrification of cooling towers, It is the change value of electrical engineering debugging quantity per unit time.

2. The cooling tower optimization control method of the central air conditioning system according to claim 1, characterized in that: The step of determining the optimal control strategy of the cooling tower corresponding to the lowest power consumption value of the cooling tower according to the wet-bulb approximation comprises: Selecting an initial wet-bulb approximation according to the wet-bulb approximation, and setting a positive optimal approximation value and a negative optimal approximation value in the positive and negative directions of the initial approximation value based on a preset optimal resolution; The power consumption values ​​of the benchmark control strategy, the control strategy obtained by positive optimization and the control strategy obtained by negative optimization corresponding to the initial wet-bulb approximation are calculated, the wet-bulb approximation of the control strategy with the lowest power consumption value is defined as the new initial approximation, and the control strategy with the lowest power consumption value is determined as the control strategy to be optimized.

3. The cooling tower optimization control method of the central air conditioning system according to claim 1, characterized in that: The optimization of the control strategy to be optimized is completed by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, including: By adjusting the modulation coefficient of the cooling tower model, the energy efficiency corresponding to the set of operating parameters of the cooling tower under the control strategy to be optimized is greater than or equal to the energy efficiency before adjustment; A distributed probability estimation algorithm is used to optimize the operating parameters of the parallel cooling towers through the control strategy to be optimized, so as to determine the operating parameter set corresponding to the highest energy efficiency, thereby completing the optimization of the control strategy to be optimized.

4. A cooling tower optimization control device for a central air conditioning system, characterized in that: The device at least comprises: The first calculation module is used to obtain the wet bulb approximation of the cooling tower and the comprehensive energy efficiency of the central air conditioning system under the current environment; wherein the wet bulb approximation is calculated based on the synergistic effect of the relative humidity of the air and the wind speed, and the influence of the dynamic change of the cooling tower load rate on the wet bulb temperature under the current environment; A second calculation module is used to determine, according to the wet-bulb approximation, a control strategy to be optimized for the cooling tower corresponding to when the power consumption value of the cooling tower is the lowest; An optimization control module, used to optimize the control strategy to be optimized by adjusting the modulation coefficient of the cooling tower model and the interaction information between the cooling tower and the adjacent cooling towers, and execute the optimized control strategy to maintain the comprehensive energy efficiency of the central air-conditioning system at the current moment greater than or equal to the comprehensive energy efficiency at the previous moment; The wet bulb approximation of the cooling tower is determined according to the following formula: ； in, is the wet bulb approximation, is the outlet water temperature of the cooling tower, is the wet bulb temperature, is the relative humidity of air, is the actual wind speed, is the reference wind speed; and are the corresponding coefficients, which respectively reflect the influence of relative air humidity and actual wind speed on the wet-bulb approximation. When the relative air humidity increases, the influence of wet-bulb temperature on the approximation decreases, while when the actual wind speed increases, the approximation decreases accordingly due to the enhanced heat exchange; is the actual load of the cooling tower, is the maximum design load of the cooling tower; is the cooling tower load rate influence coefficient. When the cooling tower load rate decreases, Approaches 0, when the cooling tower load rate increases, becomes smaller, the wet bulb approach will increase accordingly to reflect the change in cooling effect of the cooling tower under high load; The cooling tower model is determined according to the following formula: in, For the cooling tower operating parameter set, is the humidity coefficient of the cooling tower, is the cooling tower temperature coefficient, is the energy saving coefficient of the cooling tower itself, is the energy consumption control condition, B is the cooling tower connection instruction, and Set thresholds for average energy consumption and energy consumption of cooling towers respectively; , are the maximum and minimum values ​​of the cooling tower automatic monitoring protection mechanism coefficient, n is the cooling tower electrification authority, , are the adjustment factors of the original first-stage and second-stage cooling tower controllers, , They are the performance requirements of the original first-stage and second-stage cooling tower controllers. is the power factor for electrification of cooling towers, It is the change value of electrical engineering debugging quantity per unit time.

5. A terminal, characterized in that: The terminal includes a touch screen, a memory, a transceiver, one or more processors, multiple applications, and one or more programs; wherein the one or more programs are stored in the memory, and the transceiver is used to transmit or receive wireless signals; when the one or more processors execute the one or more programs, the terminal implements the cooling tower optimization control method of the central air-conditioning system as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the cooling tower optimization control method for a central air-conditioning system as described in any one of claims 1-3.

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