Central air conditioning cooling tower fan control method based on energy efficiency optimization

Abstract

The present application relates to the technical field of central air conditioner intelligent control, in particular to a central air conditioner cooling tower fan control method based on energy efficiency optimization, comprising: obtaining cooling water system operation state data set, the data set containing cooling water inlet and outlet water temperature, flow and environmental wet bulb temperature, calculating cooling tower real-time heat dissipation and chiller unit real-time cooling load, based on environmental wet bulb temperature and approximation degree threshold value dynamically determining cooling water outlet water temperature target set value, inputting the above parameters into a multivariable optimization control model, cooperatively analyzing the coupling relationship of fan and refrigeration host energy consumption, and generating an optimized speed instruction set containing each fan target speed and speed adjustment time sequence through model solving. The method dynamically adapts the outlet water temperature set value, coordinates the energy consumption coupling relationship, realizes fine regulation and control of the cooling tower fan, and optimizes the cooling water system operation state.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for central air conditioning, and in particular to a control method for cooling tower fans of central air conditioning based on energy efficiency optimization. Background Technology

[0002] Existing central air conditioning cooling tower fan control technology mainly relies on the cooling water inlet temperature, outlet temperature, and cooling water flow rate to regulate fan speed. The cooling water outlet temperature is generally set using a fixed value. The control process does not include the ambient wet-bulb temperature as a key reference indicator for outlet temperature setting. Multiple cooling tower fans only execute a unified speed regulation command, and no corresponding speed control scheme and adjustment sequence are developed for each individual fan.

[0003] Existing control schemes fail to conduct a coordinated analysis of the coupling relationship between cooling tower fan energy consumption and chiller unit energy consumption. They rely solely on a single operating parameter to passively adjust fan speed. The fixed outlet water temperature setting mode cannot adapt to changes in environmental conditions in real time, making it difficult to establish a stable match between the actual heat dissipation of the cooling tower and the cooling load of the chiller unit. The unplanned adjustment of multiple fans leads to an imbalance in the heat dissipation distribution of the cooling water system, preventing coordinated optimization of fan and chiller energy consumption and hindering the improvement of overall system energy efficiency.

[0004] To address the issues of rigid outlet water temperature settings, lack of consideration for energy consumption coupling characteristics, and coarse fan control methods in existing control methods, it is necessary to dynamically determine the target setpoint for cooling water outlet temperature by combining ambient wet-bulb temperature and approximation threshold. At the same time, a multivariate optimization control model should be used to conduct collaborative analysis of fan energy consumption and main unit energy consumption. The model solution should generate optimized instructions that include the target speed of each fan and the speed adjustment sequence, thereby achieving precise control of the cooling tower fans. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a central air conditioning cooling tower fan control method based on energy efficiency optimization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a central air conditioning cooling tower fan control method based on energy efficiency optimization, comprising: Obtain the operating status dataset of the cooling water system, which includes the inlet temperature of the cooling water entering the cooling tower, the outlet temperature of the cooling water flowing out of the cooling tower, the cooling water flow rate, and the current ambient wet-bulb temperature. Calculate the real-time heat dissipation of the cooling tower and the real-time cooling load of the refrigeration unit based on the cooling water inlet temperature, the cooling water outlet temperature and the cooling water flow rate. Based on the current ambient wet-bulb temperature and the pre-stored approximation threshold, the target set value of the cooling water outlet temperature is dynamically determined; The real-time heat dissipation, the real-time cooling load, and the target setpoint are input into a pre-established multivariate optimization control model. The multivariate optimization control model is used to collaboratively analyze the coupling relationship between the cooling tower fan energy consumption and the refrigeration unit energy consumption. By solving the multivariate optimization control model, an optimized speed command set for the cooling tower fan is generated. The optimized speed command set includes the target speed and speed adjustment sequence for each cooling tower fan.

[0007] As a further aspect of the present invention, the real-time heat dissipation of the cooling tower and the real-time cooling load of the chiller unit are calculated based on the cooling water inlet temperature, the cooling water outlet temperature, and the cooling water flow rate, including: The difference between the cooling water inlet temperature and the cooling water outlet temperature is multiplied by the cooling water flow rate and the specific heat capacity constant of water to obtain a first calculation result, which is the real-time heat dissipation of the cooling tower. Obtain the chilled water inlet temperature, chilled water outlet temperature, and chilled water flow rate on the evaporator side of the chiller unit; The difference between the inlet and outlet temperatures of the chilled water on the evaporator side is multiplied by the chilled water flow rate and the specific heat capacity constant of water to obtain a second calculation result, which is the real-time cooling load of the refrigeration unit.

[0008] As a further aspect of the present invention, based on the current ambient wet-bulb temperature and a pre-stored approximation threshold, a target setpoint for the cooling water outlet temperature is dynamically determined, including: The preset cooling water approximation degree is read from the system configuration parameters. The cooling water approximation degree is the theoretical minimum difference between the cooling water outlet temperature and the wet-bulb temperature. The current ambient wet-bulb temperature is summed with the preset cooling water approximation value to obtain a preliminary cooling water temperature setting reference value. Monitor the condensing pressure and compressor operating current of the refrigeration unit to determine whether the unit is under high load or high condensing pressure. If the host is under high load or high condensing pressure, the initial cooling water temperature setting reference value is lowered by a dynamic adjustment amount to obtain the target setting value of the cooling water outlet temperature. If the host is not under high load or high condensing pressure, the preliminary cooling water temperature setting reference value is directly used as the target setting value of the cooling water outlet temperature.

[0009] As a further aspect of the present invention, the pre-established multivariate optimization control model is constructed through the following steps: A cooling tower fan energy consumption calculation sub-model is established. The cooling tower fan energy consumption calculation sub-model takes the fan speed as the input variable and calculates the total electrical power consumption of all cooling tower fans at the corresponding speed. Its internal relationship is determined by the power-speed characteristic curve of the fan. A sub-model for calculating the energy consumption of a refrigeration unit is established. The sub-model uses the cooling water inlet temperature as the key input variable to calculate the performance coefficient and power consumption of the refrigeration unit under different cooling water inlet temperatures. Its internal relationship is established based on the performance spectrum of the unit's load rate and condensing temperature. Establish a coupled optimization objective function, which is defined as the sum of the total power consumption output by the cooling tower fan energy consumption calculation sub-model and the host power consumption output by the refrigeration host energy consumption calculation sub-model; Set constraints for the coupled optimization objective function. The constraints include at least the cooling water outlet temperature not exceeding the target set value, the fan speed being within its safe operating speed range, and the cooling tower heat dissipation being greater than or equal to the real-time heat dissipation. The solution process of the multivariable optimization control model is to find a set of wind turbine speed combinations that minimizes the value of the coupled optimization objective function under the constraints.

[0010] As a further aspect of the present invention, the real-time heat dissipation, the real-time cooling load, and the target setpoint are input into a pre-established multivariate optimization control model, including: The target setpoint of the cooling water outlet temperature is used as the upper limit of the constraint on the cooling water outlet temperature in the coupled optimization objective function and input into the multivariate optimization control model. The real-time heat dissipation is used as the lower limit of the cooling tower heat dissipation in the constraint conditions and input into the multivariate optimization control model. The real-time cooling load of the refrigeration unit is input into the energy consumption calculation sub-model of the refrigeration host to determine the current load rate of the refrigeration host, and then the baseline performance coefficient and power consumption curve under the current load rate are determined by querying the performance spectrum. The optimization solver is invoked to solve the multivariable optimization control model. Under the premise of satisfying all constraints, the optimization solver iteratively calculates the coupled optimization objective function value corresponding to different wind turbine speed combinations, and finally outputs the optimal wind turbine speed combination that minimizes the total energy consumption.

[0011] As a further aspect of the present invention, by solving the multivariate optimization control model, an optimized speed command set for the cooling tower fan is generated, including: The optimal fan speed combination output by the optimization solver is analyzed to obtain the theoretical optimal speed for each cooling tower fan. The theoretical optimal speed of each wind turbine is compared with the current actual speed of the wind turbine, and the speed adjustment difference is calculated. Based on the magnitude of the speed adjustment difference for each wind turbine, the mechanical characteristics of the wind turbine, and the start-stop restrictions, a speed adjustment strategy is formulated. The speed adjustment strategy includes using uniform and gradual adjustment for small differences, using segmented acceleration adjustment for large differences, and avoiding the resonance speed range of the wind turbine. Based on the aforementioned speed adjustment strategy, control instructions are formulated for each cooling tower fan, including the target speed value, adjustment rate, and adjustment start time. The control commands of all cooling tower fans are summarized to form the optimized speed command set, and sorted according to the order in which the adjustment start time is determined.

[0012] As a further aspect of the present invention, the method further includes a feedback correction step based on the operating status of the cooling tower fan: After executing the optimized speed command set, the actual change value of the cooling water outlet temperature is collected in real time; The actual change in the cooling water outlet temperature is compared with the expected temperature change curve to calculate the temperature tracking error. If the temperature tracking error continues to exceed the allowable error threshold, the control parameter self-tuning process is triggered. During the self-tuning process of the control parameters, the model coefficients used to correlate the fan speed and heat dissipation efficiency in the multivariable optimization control model are adjusted. Using the adjusted model coefficients and the latest operating state dataset, the optimized speed command set is recalculated and generated.

[0013] As a further aspect of the present invention, the trigger control parameter self-tuning process includes: Record the running status dataset, the set of optimized speed commands executed, and the corresponding actual value of cooling water outlet temperature for a period of time before and after the triggering time; The analysis examines the systematic deviation between the actual rate of decrease of cooling water outlet temperature and the rate of decrease predicted by the model under the same or similar operating conditions. Based on the direction and magnitude of the systematic deviation, the heat transfer efficiency coefficient of the cooling tower heat dissipation sub-model in the multivariate optimization control model is proportionally corrected. The corrected heat transfer efficiency coefficient is updated in the multivariate optimization control model, replacing the original model coefficient.

[0014] As a further aspect of the present invention, the method also includes additional control logic to prevent cooling tower icing: While acquiring the aforementioned operational status dataset, the ambient dry bulb temperature and the air temperature at the cooling tower packing outlet are continuously monitored. When the ambient dry-bulb temperature is lower than the icing warning temperature threshold, the icing risk monitoring mode is activated. In the freezing risk monitoring mode, the upper limit of the constraint on the cooling water outlet temperature in the multivariate optimization control model is replaced with an antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature. The antifreeze temperature setpoint is higher than the conventional target setpoint. Based on the antifreeze temperature setpoint, the solution process of the multivariate optimization control model is re-executed to generate a cooling tower fan speed command with antifreeze as the priority objective. Meanwhile, an instruction to periodically switch the start-stop sequence of the fans is added to the optimized speed instruction set to balance the water spray density of each cooling tower.

[0015] As a further aspect of the present invention, the upper limit value of the constraint on the cooling water outlet temperature in the multivariate optimization control model is replaced with an antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature, including: Establish an antifreeze temperature setpoint lookup table, which defines the minimum safe outlet water temperature corresponding to different ambient dry bulb temperature ranges. Based on the real-time monitored ambient dry-bulb temperature, the corresponding minimum safe outlet water temperature is obtained by querying the antifreeze temperature setpoint lookup table. The minimum safe outlet water temperature obtained from the query is used as a new constraint condition to replace the original target setting value of the cooling water outlet water temperature, and is input into the multivariate optimization control model for constraint.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: By combining the ambient wet-bulb temperature with a pre-stored approximation threshold, the target setpoint for the cooling water outlet temperature is determined. This eliminates the need for a fixed outlet temperature setting in the control logic. The target cooling water outlet temperature can be dynamically adapted to real-time changes in the ambient wet-bulb temperature. The introduction of the approximation threshold ensures that the outlet temperature setting closely matches the actual heat dissipation conditions of the cooling tower. The dynamic adjustment of the target outlet temperature can synchronously match the external environmental conditions and the real-time operating status of the cooling tower, eliminating the operational parameter adaptation deviation caused by a fixed temperature setting. This ensures that the cooling water outlet temperature always closely matches the actual heat dissipation needs of the cooling tower, establishing a real-time correspondence between the temperature parameters of the cooling water system and the external environmental conditions. This avoids mismatches between heat dissipation capacity and operating conditions caused by a fixed temperature setting, enabling dynamic adaptation between the cooling tower's heat output and environmental conditions. This optimizes the adaptability of cooling water temperature control, ensuring that the outlet temperature setting always matches the real-time operating conditions of the system.

[0017] The real-time heat dissipation of the cooling tower, the real-time cooling load of the chiller unit, and the dynamically determined target setpoint of the cooling water outlet temperature are input into a multivariate optimization control model. The model can collaboratively analyze the coupling relationship between the energy consumption of the cooling tower fans and the energy consumption of the chiller unit. Through the optimized speed command set generated by the model solution, the target speed of each cooling tower fan can be clearly defined, and the corresponding speed adjustment sequence can be planned. Differentiated speed settings for individual fans replace the uniform speed control mode. The planning of the speed adjustment sequence allows the operating rhythm of multiple fans to match the real-time operating status of the cooling water system, thus optimizing the energy consumption of both the fans and the chiller unit. The linkage characteristics are fully adapted, the precision of fan operation control is improved, the heat dissipation output of multiple fans can be accurately matched with the real-time demand of cooling tower heat dissipation and chiller unit cooling load, the timing of fan speed adjustment can avoid the operation fluctuations caused by synchronous adjustment of multiple fans, the coupling characteristics of fan energy consumption and host energy consumption are fully utilized, the operation parameter adjustment of cooling water system is more in line with actual load demand, the pertinence and rationality of fan operation control are strengthened, the system energy consumption distribution is more in line with the overall operating conditions, and the control of fan speed forms a coordinated matching relationship with system load and energy consumption characteristics. Attached Figure Description

[0018] Figure 1 This is a flowchart of a central air conditioning cooling tower fan control method based on energy efficiency optimization, as described in this invention. Figure 2 A flowchart for calculating real-time heat dissipation and real-time cooling load; Figure 3 A flowchart for establishing a multivariable optimization control model. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] See Figure 1This invention provides a central air conditioning cooling tower fan control method based on energy efficiency optimization, the implementation process of which is as follows: A dataset of the cooling water system's operating status is acquired, including the inlet temperature of the cooling water entering the cooling tower, the outlet temperature of the cooling water flowing out of the cooling tower, the cooling water flow rate, and the current ambient wet-bulb temperature. Based on the inlet temperature, outlet temperature, and flow rate, the real-time heat dissipation of the cooling tower and the real-time cooling load of the chiller unit are calculated. Based on the current ambient wet-bulb temperature and a pre-stored approximation threshold, the target setpoint for the cooling water outlet temperature is dynamically determined. The real-time heat dissipation, real-time cooling load, and target setpoint are input into a pre-established multivariate optimization control model. This multivariate optimization control model is used to collaboratively analyze the coupling relationship between the cooling tower fan energy consumption and the chiller unit energy consumption. By solving the multivariate optimization control model, an optimized speed command set for the cooling tower fans is generated. This optimized speed command set includes the target speed and speed adjustment sequence for each cooling tower fan.

[0022] In one embodiment of the present invention, the real-time heat dissipation of the cooling tower and the real-time cooling load of the refrigeration unit are calculated, see reference. Figure 2 The difference between the cooling water inlet temperature and the cooling water outlet temperature is multiplied by the cooling water flow rate and the specific heat capacity constant of water to obtain the first calculation result, which is the real-time heat dissipation of the cooling tower. The evaporator side cold water inlet temperature, cold water outlet temperature and cold water flow rate of the chiller unit are obtained. The difference between the evaporator side cold water inlet temperature and the cold water outlet temperature is multiplied by the cold water flow rate and the specific heat capacity constant of water to obtain the second calculation result, which is the real-time cooling load of the chiller unit. The target setpoint for the cooling water outlet temperature is dynamically determined by reading a preset cooling water approximation value from the system configuration parameters. This approximation value is the theoretical minimum difference between the cooling water outlet temperature and the wet-bulb temperature. The current ambient wet-bulb temperature is summed with the preset cooling water approximation value to obtain a preliminary cooling water temperature setpoint. The condensing pressure and compressor operating current of the refrigeration unit are monitored to determine whether the unit is under high load or high condensing pressure. If the unit is under high load or high condensing pressure, the preliminary cooling water temperature setpoint is dynamically adjusted down to obtain the target setpoint for the cooling water outlet temperature. If the unit is not under high load or high condensing pressure, the preliminary cooling water temperature setpoint is directly used as the target setpoint for the cooling water outlet temperature.

[0023] In practical implementation, calculating the real-time heat dissipation of a cooling tower requires obtaining the inlet temperature of the cooling water entering the tower, the outlet temperature of the cooling water leaving the tower, and the flow rate of the cooling water passing through the tower. The difference between the inlet and outlet temperatures of the cooling water is multiplied by the flow rate of the cooling water and the specific heat capacity constant of water to obtain the first calculation result, which is the real-time heat dissipation of the cooling tower. Calculating the real-time cooling load of a chiller unit requires obtaining the inlet and outlet temperatures of the chilled water on the evaporator side of the chiller unit, as well as the flow rate of the chilled water. The difference between the inlet and outlet temperatures of the chilled water on the evaporator side is multiplied by the flow rate of the chilled water and the specific heat capacity constant of water to obtain the second calculation result, which is the real-time cooling load of the chiller unit. The specific heat capacity constant of water is a fixed physical constant. In some embodiments, the cooling water flow rate and cold water flow rate are measured by an electromagnetic flow meter or an ultrasonic flow meter installed on the pipeline, and the cooling water inlet temperature, cooling water outlet temperature, cold water inlet temperature, and cold water outlet temperature are measured by a platinum resistance temperature sensor. All measurement data are uploaded to the control system in real time through a data acquisition module.

[0024] To dynamically determine the target setpoint for the cooling water outlet temperature, a preset cooling water approximation value needs to be read from the system configuration parameters. This approximation value is the theoretical minimum difference between the cooling water outlet temperature and the wet-bulb temperature, a fixed parameter determined based on the cooling tower's design performance. The current ambient wet-bulb temperature, measured in real-time, is summed with the preset cooling water approximation value to obtain the initial cooling water temperature setpoint. The current ambient wet-bulb temperature is obtained from a wet-bulb temperature sensor installed near the louvers of the cooling tower. In practice, the condensing pressure and compressor operating current of the refrigeration unit are monitored to determine if the unit is under high load or high condensing pressure. The condensing pressure is obtained through a pressure transmitter installed at the condenser outlet, and the compressor operating current is obtained through a current transformer. If the condensing pressure exceeds a preset high-pressure threshold, or the compressor operating current consistently exceeds a specific percentage of the rated current, the unit is determined to be under high load or high condensing pressure. If the unit is under high load or high condensing pressure, the initial cooling water temperature setpoint is dynamically adjusted downwards to obtain the final target setpoint for the cooling water outlet temperature. Optionally, the dynamic adjustment amount is a preset constant based on the host's operating characteristics, such as 1 degree Celsius. If the host is not under high load or high condensing pressure, the initial cooling water temperature setting reference value is directly used as the target setting value for the cooling water outlet temperature. In some embodiments, the calculation process for the target setting value of the cooling water outlet temperature can be periodically executed by the control system's setting value calculation module, with the calculation cycle set to 5 minutes. Optionally, the mathematical expression for calculating the real-time heat dissipation is: in: This indicates the real-time heat dissipation of the cooling tower. This represents the specific heat capacity constant of water. Indicates the mass flow rate of cooling water. Indicates the outlet temperature of the cooling water. This indicates the inlet temperature of the cooling water. It can be understood as the mass flow rate of the cooling water. It is obtained by multiplying the volumetric flow rate by the density of water. The mathematical expression for calculating the real-time cooling load is: in: This indicates the real-time cooling load of the refrigeration unit. This represents the specific heat capacity constant of water. Indicates the mass flow rate of cold water. This indicates the cold water inlet temperature on the evaporator side. This indicates the evaporator-side chilled water outlet temperature. In practice, the control system acquires the measured values ​​from the temperature and flow sensors via the data bus, and updates the real-time heat dissipation and real-time cooling load values ​​in each control cycle based on the aforementioned calculation relationship. Simultaneously, it updates the target setpoint for the cooling water outlet temperature based on the host status judgment result.

[0025] In one embodiment of the present invention, the pre-established multivariable optimization control model is constructed through the following steps: (See attached) Figure 3A cooling tower fan energy consumption calculation sub-model is established, which uses fan speed as the input variable to calculate the total electrical power consumption of all cooling tower fans at the corresponding speed. Its internal relationship is determined by the power-speed characteristic curve of the fan. A refrigeration unit energy consumption calculation sub-model is also established, which uses cooling water inlet temperature as the key input variable to calculate the performance coefficient and power consumption of the refrigeration unit under different cooling water inlet temperatures. Its internal relationship is established based on the performance spectrum of the refrigeration unit's load rate and condensing temperature. A coupled optimization objective function is established, which is defined as the sum of the total electrical power consumption output by the cooling tower fan energy consumption calculation sub-model and the power consumption of the refrigeration unit output by the refrigeration unit energy consumption calculation sub-model. Constraints are set for this coupled optimization objective function, including at least the cooling water outlet temperature not exceeding the target set value, the fan speed being within its safe operating speed range, and the cooling tower heat dissipation being greater than or equal to the real-time heat dissipation. The solution process of the multivariate optimization control model is to find the set of fan speed combinations that minimizes the value of the coupled optimization objective function under the constraints. The real-time heat dissipation, real-time cooling load, and target setpoint are input into the pre-established multivariate optimization control model. The target setpoint of the cooling water outlet temperature is used as the upper limit of the constraint on the cooling water outlet temperature in the coupled optimization objective function and input into the multivariate optimization control model. The real-time heat dissipation is used as the lower limit of the cooling tower heat dissipation in the constraint condition and input into the multivariate optimization control model. The real-time cooling load of the chiller unit is input into the chiller host energy consumption calculation sub-model to determine the current load rate of the chiller host. Then, the baseline performance coefficient and power consumption curve under the current load rate are determined by querying the performance spectrum. The optimization solver is called to solve the multivariate optimization control model. Under the premise of satisfying all constraints, the optimization solver iteratively calculates the coupled optimization objective function value corresponding to different fan speed combinations and finally outputs the optimal fan speed combination that minimizes the total energy consumption.

[0026] In practical implementation, the construction of the multivariate optimization control model is completed through the following steps: First, a cooling tower fan energy consumption calculation sub-model is established. This sub-model uses fan speed as the input variable to calculate the total electrical power consumption of all cooling tower fans at the corresponding speed. Its internal relationship is determined by the fan's power-speed characteristic curve, which is usually provided by the fan manufacturer and reflects the functional relationship between the fan's input electrical power and speed. Second, a chiller unit energy consumption calculation sub-model is established. This sub-model uses cooling water inlet temperature as the key input variable to calculate the chiller unit's performance coefficient and power consumption under different cooling water inlet temperatures. Its internal relationship is established based on the performance spectrum of the chiller unit's load rate and condensing temperature. The performance spectrum is a two-dimensional data table or fitting formula recording the chiller unit's performance coefficient and power consumption at different load rates and condensing temperatures. Third, a coupled optimization objective function is established. This coupled optimization objective function is defined as the sum of the total electrical power consumption output by the cooling tower fan energy consumption calculation sub-model and the chiller unit power consumption output by the chiller unit energy consumption calculation sub-model. Constraints are set for the coupled optimization objective function. These constraints include, at a minimum, that the cooling water outlet temperature must not exceed the target setpoint, the fan speed must be within its safe operating speed range, and the cooling tower's heat dissipation must be greater than or equal to the real-time heat dissipation. The solution process for the multivariate optimization control model involves finding the set of fan speed combinations that minimizes the value of the coupled optimization objective function under these constraints.

[0027] In practical implementation, real-time heat dissipation, real-time cooling load, and target setpoints are input into a pre-established multivariate optimization control model. The target setpoint for the cooling water outlet temperature is used as the upper limit constraint value for cooling water outlet temperature in the coupled optimization objective function, and is input into the multivariate optimization control model. This means that when the target setpoint for the cooling water outlet temperature is 32℃, the predicted cooling water outlet temperature calculated for any combination of speeds during the model's solution process cannot exceed 32℃. Real-time heat dissipation is used as the lower limit of the cooling tower's heat dissipation in the constraints, and is input into the multivariate optimization control model. This implies that the fan speed combinations solved by the model must ensure that the predicted heat dissipation of the cooling tower is greater than or equal to the calculated real-time heat dissipation. The real-time cooling load of the chiller unit is input into the chiller's energy consumption calculation sub-model to determine the chiller's current load rate, and then the baseline performance coefficient and power consumption curve under the current load rate are determined by querying the performance spectrum. For example, if the real-time cooling load is 70% of the refrigeration unit's rated capacity, the refrigeration unit energy consumption calculation sub-model calls the data row corresponding to the 70% load rate in the performance graph. This data row describes the relationship between the unit's power consumption and the condensing temperature (which is strongly correlated with the cooling water inlet temperature). The optimization solver is then invoked to solve the multivariate optimization control model. Under the premise of satisfying all constraints, the optimization solver iteratively calculates the coupled optimization objective function values ​​corresponding to different fan speed combinations, and finally outputs the optimal fan speed combination that minimizes total energy consumption.

[0028] In some embodiments, the mathematical expression for the coupled optimization objective function is: in: This represents the value of the coupling optimization objective function, i.e., the total power consumption of the system. This indicates the total number of cooling tower fans. Indicates the first The power consumption of a typhoon generator is determined by its rotational speed. The function, This indicates the power consumption of the chiller unit, which is based on the predicted cooling water inlet temperature. and current real-time cooling load The function. It can be understood that the predicted cooling water inlet temperature... The calculations are based on the cooling tower heat dissipation model, taking into account the ambient wet-bulb temperature, fan speed combination, and cooling water flow rate. The optimization solver adjusts the speed of each fan. In order to satisfy , as well as Under equal constraints, find the way to The solution with the minimum value. Optionally, the optimization solver can employ a sequential quadratic programming algorithm or a genetic algorithm. In some embodiments, the safe operating range of the wind turbine speed is defined as follows. The parameters of the cooling tower heat dissipation model were initialized and configured during the system debugging phase. The model solving process is executed automatically once in each control cycle, and each solution is based on the latest operating status dataset.

[0029] In one embodiment of the present invention, an optimized speed command set for the cooling tower fans is generated by solving a multivariate optimization control model. This set is derived from the optimal fan speed combination output by the analytical optimization solver, yielding the theoretical optimal speed for each cooling tower fan. The theoretical optimal speed of each fan is compared with its current actual speed to calculate the speed adjustment difference. Based on the magnitude of the speed adjustment difference, the mechanical characteristics of the fans, and start / stop restrictions, a speed adjustment strategy is formulated. This strategy includes using uniform and gradual adjustment for small differences and segmented acceleration adjustment for large differences, while avoiding the resonant speed range of the fans. According to the speed adjustment strategy, control commands containing a target speed value, adjustment rate, and adjustment start time are formulated for each cooling tower fan. All control commands for the cooling tower fans are summarized to form an optimized speed command set, which is then sorted according to the order of adjustment start time.

[0030] In practical implementation, the optimal fan speed combination output by the analytical optimization solver is used to obtain the theoretical optimal speed for each cooling tower fan. The output of the optimization solver is typically a list of speed values ​​for multiple fans. In practice, the theoretical optimal speed of each fan is compared with its current actual speed to calculate the speed adjustment difference. This difference is the theoretical optimal speed minus the current actual speed, and can be positive, negative, or zero. The current actual speed is obtained from a speed sensor installed on the fan motor or converted from the frequency output of a frequency converter. Based on the magnitude of the speed adjustment difference for each fan, the fan's mechanical characteristics, and start / stop limitations, a speed adjustment strategy is formulated. This strategy includes using uniform, gradual adjustment for small differences and segmented, accelerated adjustment for large differences, while avoiding the fan's resonance speed range. In some embodiments, a threshold value for the speed adjustment difference is set, such as 30 rpm. When the absolute value of the speed adjustment difference is less than this threshold, a uniform and gradual adjustment is adopted, that is, adjusting to the target value with a fixed and low speed change rate. When the absolute value of the speed adjustment difference is greater than or equal to this threshold, a segmented and accelerated adjustment is adopted, that is, approaching the target value quickly with a higher change rate in the initial stage of adjustment, and reducing the change rate in the final stage to smoothly reach the target value. Optionally, the resonant speed range of the wind turbine is determined by testing during the wind turbine commissioning stage and stored in the system parameters. When formulating the speed adjustment strategy, it must be ensured that the adjustment path does not cross this range.

[0031] Based on the speed adjustment strategy, control commands are generated for each cooling tower fan, including a target speed value, adjustment rate, and adjustment start time. The target speed value is the theoretical optimal speed. The adjustment rate is determined according to the selected adjustment strategy. The adjustment start time can be staggered according to the urgency of system load changes and the coordination requirements between multiple fans to prevent the simultaneous large-scale start of all fans from impacting the power grid. All control commands from the cooling tower fans are aggregated to form an optimized speed command set, which is then sorted according to the order of adjustment start times. The sorted optimized speed command set is sent to the lower-level fan variable frequency speed controller for execution. In some embodiments, the speed adjustment difference... The calculation can be expressed as: in: This represents the theoretically optimal speed of the wind turbine given by the optimization solver. This indicates the current actual speed of the fan. This indicates the speed difference that needs to be adjusted. Optionally, an optimized speed command set including the three cooling tower fans is provided in Table 1. Table 1: Set of Commands for Optimizing Cooling Tower Fan Speed It is understandable that the adjustment rate and start time of the fan in the table are generated based on the preset adjustment strategy and system instruction sequencing logic. In specific implementation, the optimized speed instruction set is transmitted to the execution unit in the form of structured data messages. The execution unit gradually adjusts the inverter output according to the time and rate specified in the instructions, driving the fan motor to reach the target speed.

[0032] In one embodiment of the present invention, the feedback correction step based on the cooling tower fan operating status involves: after executing the optimized speed command set, collecting the actual change value of the cooling water outlet temperature in real time, comparing the actual change value of the cooling water outlet temperature with the expected temperature change curve, calculating the temperature tracking error, and if the temperature tracking error continues to exceed the allowable error threshold, triggering the control parameter self-tuning process. During the control parameter self-tuning process, adjusting the model coefficients in the multivariate optimization control model used to correlate fan speed and heat dissipation efficiency, using the adjusted model coefficients, and combining them with the latest operating status dataset, recalculating and generating an updated optimized speed command set. Triggering the control parameter self-tuning process involves recording the operating status dataset, the executed optimized speed command set, and the corresponding actual value of the cooling water outlet temperature for a period of time before and after the triggering time. Analyzing the systematic deviation between the actual rate of decrease of the cooling water outlet temperature and the model-predicted rate of decrease under the same or similar operating conditions, and proportionally correcting the heat transfer efficiency coefficient of the cooling tower heat dissipation sub-model in the multivariate optimization control model according to the direction and magnitude of the systematic deviation. The corrected heat transfer efficiency coefficient is then updated in the multivariate optimization control model, replacing the original model coefficients.

[0033] In practical implementation, based on the feedback correction steps of the cooling tower fan's operating status, after executing the optimized speed command set, the actual change value of the cooling water outlet temperature is collected in real time. This actual change value is continuously measured by a temperature sensor installed on the cooling tower's main outlet pipe. The actual change value of the cooling water outlet temperature is compared with the expected temperature change curve to calculate the temperature tracking error. The temperature tracking error is the difference between the actual measured value of the cooling water outlet temperature at the same moment and the cooling water outlet temperature value predicted by the multivariate optimization control model under the same input and optimized speed command. In practical implementation, the expected temperature change curve is the curve of the cooling water outlet temperature changing over time predicted by the cooling tower heat dissipation sub-model in the multivariate optimization control model based on the current operating conditions and the optimized speed command set. If the temperature tracking error continuously exceeds the allowable error threshold, the control parameter self-tuning process is triggered. The allowable error threshold is a preset constant, such as 0.5 degrees Celsius; continuously exceeding it means that the error exceeds this threshold in multiple consecutive sampling periods. During the control parameter self-tuning process, the model coefficients used to correlate fan speed and heat dissipation efficiency in the multivariate optimization control model are adjusted. These model coefficients are a key parameter in the cooling tower heat dissipation sub-model. Using the adjusted model coefficients and the latest operating state dataset, an updated set of optimized speed commands is recalculated and generated.

[0034] The trigger control parameter self-tuning process records the operating status dataset, the set of optimized speed commands executed, and the corresponding actual cooling water outlet temperature for a period of time before and after the trigger moment. For example, it records all relevant data from 5 minutes before the self-tuning trigger to 5 minutes after the trigger. The operating status dataset can be understood as including the inlet temperature of the cooling water entering the cooling tower, the outlet temperature of the cooling water flowing out of the cooling tower, the cooling water flow rate, and the current ambient wet-bulb temperature. The analysis examines the systematic deviation between the actual rate of decrease of the cooling water outlet temperature and the model-predicted rate of decrease under the same or similar operating conditions. Systematic deviation refers to the tendency for the actual cooling effect to consistently outperform or underperform the model's prediction. Based on the direction and magnitude of the systematic deviation, the heat transfer efficiency coefficient of the cooling tower heat dissipation sub-model in the multivariate optimization control model is proportionally corrected. Optionally, the correction formula for the heat transfer efficiency coefficient can be expressed as: in: This represents the corrected heat transfer efficiency coefficient. This represents the heat transfer efficiency coefficient before correction. This represents a preset self-tuning gain coefficient (usually less than 1). This represents the actual change in cooling water outlet temperature over a period of time under specific operating conditions and fan speed. This represents the temperature change predicted by the model under the same conditions. It can be understood that when the actual temperature drop is greater than the prediction... The formula will improve the heat transfer efficiency coefficient. This makes the model predictions closer to reality. The corrected heat transfer efficiency coefficients are updated in the multivariable optimization control model, replacing the original model coefficients. In some embodiments, the control system maintains a version of the coefficients, and after each self-tuning, the new heat transfer efficiency coefficients are used for calculations in all subsequent control cycles until the next self-tuning is triggered.

[0035] In one example scenario, the system detected that the actual cooling water outlet temperature was more than 0.6 degrees Celsius higher than the model prediction for three consecutive control cycles, exceeding the allowable error threshold of 0.5 degrees Celsius. The control system then triggered a self-tuning process for the control parameters. Some relevant data were recorded, as shown in Table 2. Table 2: Data recorded when the control parameter self-tuning process is triggered In some embodiments, analyzing the data in the table above, under the condition that the ambient wet-bulb temperature, cooling water inlet temperature, flow rate, and fan speed commands are basically stable, the model predicts that the cooling water outlet temperature should be stable at around 33.0°C, but the actual value is stable at around 33.6°C, with a systematic positive deviation of about 0.65°C, indicating that the model overestimates the cooling tower's heat dissipation capacity under the current conditions. The control parameter self-tuning process will calculate a new heat transfer efficiency coefficient according to the above formula, for example, lowering the original coefficient of 1.0 to 0.95, and updating the model with the corrected heat transfer efficiency coefficient. Optionally, the updated model will immediately be re-optimized based on the latest operating state dataset, and a new set of optimized speed commands based on the corrected model will be generated and issued to the fan for execution, so that the actual value of the cooling water outlet temperature can better track the target set value.

[0036] In one embodiment of the present invention, the additional control logic for preventing cooling tower icing involves continuously monitoring the ambient dry-bulb temperature and the air temperature at the cooling tower packing outlet while acquiring the operating status dataset. When the ambient dry-bulb temperature is lower than the icing warning temperature threshold, an icing risk monitoring mode is activated. In this mode, the upper limit of the constraint on the cooling water outlet temperature in the multivariate optimization control model is replaced with an antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature. This antifreeze temperature setpoint is higher than the conventional target setpoint. Based on the antifreeze temperature setpoint, the solution process of the multivariate optimization control model is re-executed to generate cooling tower fan speed commands with antifreeze as the priority objective. Simultaneously, commands for periodically switching the fan start-stop sequence are added to the optimized speed command set to balance the water spray density of each cooling tower. The upper limit of the constraint on cooling water outlet temperature in the multivariate optimization control model is replaced with the antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature. An antifreeze temperature setpoint lookup table is established, which defines the minimum safe outlet temperature corresponding to different ambient dry-bulb temperature ranges. Based on the real-time monitored ambient dry-bulb temperature, the corresponding minimum safe outlet temperature is obtained by querying the antifreeze temperature setpoint lookup table. The obtained minimum safe outlet temperature is used as the new constraint condition, replacing the original target setpoint of cooling water outlet temperature, and input into the multivariate optimization control model for constraint.

[0037] In specific implementation, Example 5 involves additional control logic to prevent cooling tower icing. While acquiring the operating status dataset, it continuously monitors the ambient dry-bulb temperature and the air temperature at the cooling tower packing outlet. The ambient dry-bulb temperature is measured by a temperature sensor installed outside the louvers near the cooling tower, and the air temperature at the cooling tower packing outlet is measured by a temperature probe extending into the airflow channel at the bottom of the cooling tower packing. When the ambient dry-bulb temperature is lower than the icing warning temperature threshold, the icing risk monitoring mode is activated. The icing warning temperature threshold is a preset fixed value, such as 3 degrees Celsius. In the icing risk monitoring mode, the upper limit constraint value regarding the cooling water outlet temperature in the multivariate optimization control model is replaced with a freeze protection temperature setpoint dynamically calculated based on the ambient dry-bulb temperature. This freeze protection temperature setpoint is higher than the conventional target setpoint. Based on the freeze protection temperature setpoint, the solution process of the multivariate optimization control model is re-executed to generate a cooling tower fan speed command with freeze protection as the priority objective. It can be understood that prioritizing freeze protection means that satisfying the freeze protection temperature setpoint constraint has a higher priority than the energy-saving optimization objective in the optimization solution. Meanwhile, in the optimized speed command set, a command to periodically switch the start-stop sequence of the fans was added to balance the water spray density of each cooling tower and prevent local water flow stagnation and premature freezing.

[0038] In some embodiments, the upper limit of the constraint on the cooling water outlet temperature in the multivariate optimization control model is replaced with an antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature. This involves establishing an antifreeze temperature setpoint lookup table, which defines the minimum safe outlet temperature corresponding to different ambient dry-bulb temperature ranges. Optionally, the antifreeze temperature setpoint lookup table can be pre-set based on experience or antifreeze test data and stored in the system configuration. Based on the real-time monitored ambient dry-bulb temperature, the antifreeze temperature setpoint lookup table is consulted to obtain the corresponding minimum safe outlet temperature. It can be understood that the query operation is implemented by comparing the current ambient dry-bulb temperature with the temperature range defined in the lookup table. The minimum safe outlet temperature obtained from the query is used as a new constraint condition, replacing the original target setpoint for the cooling water outlet temperature, and input into the multivariate optimization control model for constraint. This means that in the freezing risk monitoring mode, the model solution must satisfy the condition that the predicted cooling water outlet temperature is not higher than (i.e., not colder than) this minimum safe outlet temperature. In specific implementations, the antifreeze temperature setpoint... With ambient dry bulb temperature The relationship can be defined through a piecewise function or a lookup table, and its mathematical expression can be described as: in: This indicates the antifreeze temperature setting (i.e., the minimum safe outlet water temperature). This indicates a function that maps tables. This represents the real-time monitored ambient dry-bulb temperature. For example, a simplified mapping is: when... hour, ;when hour, ;when hour, In some embodiments, the air temperature at the outlet of the cooling tower packing is also used as an auxiliary judgment parameter. When this temperature is close to the freezing point, the system may activate the icing risk monitoring mode in advance even if the ambient dry-bulb temperature is slightly higher than the warning threshold.

[0039] In practical implementation, when the system activates the icing risk monitoring mode, the multivariate optimization control model is re-solved using a new, higher antifreeze temperature setpoint as the upper limit of the cooling water outlet temperature constraint. Because the constraints become more stringent, the resulting fan speed commands typically tend to reduce fan speed or even stop some fans to reduce the cooling tower's heat dissipation intensity, thereby maintaining the cooling water temperature near the higher antifreeze temperature setpoint to prevent icing. Optionally, commands to periodically switch the fan start-stop sequence are superimposed on the generated optimized speed command set. For example, the command might require stopping one of the two currently running fans and starting the other previously idle fan every two hours. In some embodiments, the control system increases the sampling and monitoring frequency of the cooling water outlet temperature and the packing outlet air temperature in the icing risk monitoring mode, for example, from once per minute to once every ten seconds.

[0040] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A central air conditioning cooling tower fan control method based on energy efficiency optimization, characterized in that, The method includes: Obtain the operating status dataset of the cooling water system, which includes the inlet temperature of the cooling water entering the cooling tower, the outlet temperature of the cooling water flowing out of the cooling tower, the cooling water flow rate, and the current ambient wet-bulb temperature. Calculate the real-time heat dissipation of the cooling tower and the real-time cooling load of the refrigeration unit based on the cooling water inlet temperature, the cooling water outlet temperature and the cooling water flow rate. Based on the current ambient wet-bulb temperature and the pre-stored approximation threshold, the target set value of the cooling water outlet temperature is dynamically determined; The real-time heat dissipation, the real-time cooling load, and the target setpoint are input into a pre-established multivariate optimization control model. The multivariate optimization control model is used to collaboratively analyze the coupling relationship between the cooling tower fan energy consumption and the refrigeration unit energy consumption. By solving the multivariate optimization control model, an optimized speed command set for the cooling tower fan is generated. The optimized speed command set includes the target speed and speed adjustment sequence for each cooling tower fan.

2. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 1, characterized in that, Based on the cooling water inlet temperature, the cooling water outlet temperature, and the cooling water flow rate, calculate the real-time heat dissipation of the cooling tower and the real-time cooling load of the chiller unit, including: The difference between the cooling water inlet temperature and the cooling water outlet temperature is multiplied by the cooling water flow rate and the specific heat capacity constant of water to obtain a first calculation result, which is the real-time heat dissipation of the cooling tower. Obtain the chilled water inlet temperature, chilled water outlet temperature, and chilled water flow rate on the evaporator side of the chiller unit; The difference between the inlet and outlet temperatures of the chilled water on the evaporator side is multiplied by the chilled water flow rate and the specific heat capacity constant of water to obtain a second calculation result, which is the real-time cooling load of the refrigeration unit.

3. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 1, characterized in that, Based on the current ambient wet-bulb temperature and a pre-stored approximation threshold, the target setpoint for the cooling water outlet temperature is dynamically determined, including: The preset cooling water approximation degree is read from the system configuration parameters. The cooling water approximation degree is the theoretical minimum difference between the cooling water outlet temperature and the wet-bulb temperature. The current ambient wet-bulb temperature is summed with the preset cooling water approximation value to obtain a preliminary cooling water temperature setting reference value. Monitor the condensing pressure and compressor operating current of the refrigeration unit to determine whether the unit is under high load or high condensing pressure. If the host is under high load or high condensing pressure, the initial cooling water temperature setting reference value is lowered by a dynamic adjustment amount to obtain the target setting value of the cooling water outlet temperature. If the host is not under high load or high condensing pressure, the preliminary cooling water temperature setting reference value is directly used as the target setting value of the cooling water outlet temperature.

4. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 1, characterized in that, The pre-established multivariate optimization control model is constructed through the following steps: A cooling tower fan energy consumption calculation sub-model is established. The cooling tower fan energy consumption calculation sub-model takes the fan speed as the input variable and calculates the total electrical power consumption of all cooling tower fans at the corresponding speed. Its internal relationship is determined by the power-speed characteristic curve of the fan. A sub-model for calculating the energy consumption of a refrigeration unit is established. The sub-model uses the cooling water inlet temperature as the key input variable to calculate the performance coefficient and power consumption of the refrigeration unit under different cooling water inlet temperatures. Its internal relationship is established based on the performance spectrum of the unit's load rate and condensing temperature. Establish a coupled optimization objective function, which is defined as the sum of the total power consumption output by the cooling tower fan energy consumption calculation sub-model and the host power consumption output by the refrigeration host energy consumption calculation sub-model; Set constraints for the coupled optimization objective function. The constraints include at least the cooling water outlet temperature not exceeding the target set value, the fan speed being within its safe operating speed range, and the cooling tower heat dissipation being greater than or equal to the real-time heat dissipation. The solution process of the multivariable optimization control model is to find a set of wind turbine speed combinations that minimizes the value of the coupled optimization objective function under the constraints.

5. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 4, characterized in that, The real-time heat dissipation, the real-time cooling load, and the target setpoint are input into a pre-established multivariate optimization control model, including: The target setpoint of the cooling water outlet temperature is used as the upper limit of the constraint on the cooling water outlet temperature in the coupled optimization objective function and input into the multivariate optimization control model. The real-time heat dissipation is used as the lower limit of the cooling tower heat dissipation in the constraint conditions and input into the multivariate optimization control model. The real-time cooling load of the refrigeration unit is input into the energy consumption calculation sub-model of the refrigeration host to determine the current load rate of the refrigeration host, and then the baseline performance coefficient and power consumption curve under the current load rate are determined by querying the performance spectrum. The optimization solver is invoked to solve the multivariable optimization control model. Under the premise of satisfying all constraints, the optimization solver iteratively calculates the coupled optimization objective function value corresponding to different wind turbine speed combinations, and finally outputs the optimal wind turbine speed combination that minimizes the total energy consumption.

6. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 5, characterized in that, Solving the multivariate optimization control model generates a set of optimized speed commands for the cooling tower fan, including: The optimal fan speed combination output by the optimization solver is analyzed to obtain the theoretical optimal speed for each cooling tower fan. The theoretical optimal speed of each wind turbine is compared with the current actual speed of the wind turbine, and the speed adjustment difference is calculated. Based on the magnitude of the speed adjustment difference for each wind turbine, the mechanical characteristics of the wind turbine, and the start-stop restrictions, a speed adjustment strategy is formulated. The speed adjustment strategy includes using uniform and gradual adjustment for small differences, using segmented acceleration adjustment for large differences, and avoiding the resonance speed range of the wind turbine. Based on the aforementioned speed adjustment strategy, control instructions are formulated for each cooling tower fan, including the target speed value, adjustment rate, and adjustment start time. The control commands of all cooling tower fans are summarized to form the optimized speed command set, and sorted according to the order in which the adjustment start time is determined.

7. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 1, characterized in that, The method also includes a feedback correction step based on the operating status of the cooling tower fan: After executing the optimized speed command set, the actual change value of the cooling water outlet temperature is collected in real time; The actual change in the cooling water outlet temperature is compared with the expected temperature change curve to calculate the temperature tracking error. If the temperature tracking error continues to exceed the allowable error threshold, the control parameter self-tuning process is triggered. During the self-tuning process of the control parameters, the model coefficients used to correlate the fan speed and heat dissipation efficiency in the multivariable optimization control model are adjusted. Using the adjusted model coefficients and the latest operating state dataset, the optimized speed command set is recalculated and generated.

8. A central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 7, characterized in that, The trigger control parameter self-tuning process includes: Record the running status dataset, the set of optimized speed commands executed, and the corresponding actual value of cooling water outlet temperature for a period of time before and after the triggering time; The analysis examines the systematic deviation between the actual rate of decrease of cooling water outlet temperature and the rate of decrease predicted by the model under the same or similar operating conditions. Based on the direction and magnitude of the systematic deviation, the heat transfer efficiency coefficient of the cooling tower heat dissipation sub-model in the multivariate optimization control model is proportionally corrected. The corrected heat transfer efficiency coefficient is updated in the multivariate optimization control model, replacing the original model coefficient.

9. The central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 1, characterized in that, The method also includes additional control logic to prevent cooling tower icing: While acquiring the aforementioned operational status dataset, the ambient dry bulb temperature and the air temperature at the cooling tower packing outlet are continuously monitored. When the ambient dry-bulb temperature is lower than the icing warning temperature threshold, the icing risk monitoring mode is activated. In the freezing risk monitoring mode, the upper limit of the constraint on the cooling water outlet temperature in the multivariate optimization control model is replaced with an antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature. The antifreeze temperature setpoint is higher than the conventional target setpoint. Based on the antifreeze temperature setpoint, the solution process of the multivariate optimization control model is re-executed to generate a cooling tower fan speed command with antifreeze as the priority objective. Meanwhile, an instruction to periodically switch the start-stop sequence of the fans is added to the optimized speed instruction set to balance the water spray density of each cooling tower.

10. A central air conditioning cooling tower fan control method based on energy efficiency optimization according to claim 9, characterized in that, The upper limit of the constraint on the cooling water outlet temperature in the multivariate optimization control model is replaced with the antifreeze temperature setpoint dynamically calculated based on the ambient dry-bulb temperature, including: Establish an antifreeze temperature setpoint lookup table, which defines the minimum safe outlet water temperature corresponding to different ambient dry bulb temperature ranges. Based on the real-time monitored ambient dry-bulb temperature, the corresponding minimum safe outlet water temperature is obtained by querying the antifreeze temperature setpoint lookup table. The minimum safe outlet water temperature obtained from the query is used as a new constraint condition to replace the original target setting value of the cooling water outlet water temperature, and is input into the multivariate optimization control model for constraint.

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