A cooling tower adaptive air intake regulation system and method coupled with flow guiding energy saving

Abstract

An adaptive air intake control system and method for cooling towers, coupled with airflow diversion for energy saving, belongs to the field of adaptive control system technology. The system includes an information acquisition unit, an execution unit, and an adaptive control unit. The information acquisition unit collects environmental wind parameters and key operating parameters in real time. The execution unit includes multiple independent fan blade groups with circumferential partitioning and vertical layering for precise execution of commands. The adaptive control unit executes operational decisions, selecting the dominant operating mode from airflow diversion energy saving, anti-freezing energy saving, and anti-drip energy saving modes; it then identifies and outputs wind condition characteristics and thermal anomaly indicators through key parameter status identification, and generates control commands based on the adaptive control strategy and command generation. The system forms a closed loop, capable of adaptively adjusting the identification model and control strategy based on feedback. A corresponding control method is also disclosed, enabling intelligent and precise control of air intake in cooling towers under complex environments, improving cooling efficiency and energy saving while ensuring anti-freezing safety.

Description

Technical Field

[0001] This invention belongs to the field of adaptive control system technology, and specifically relates to an adaptive air intake control system and method for cooling towers with coupled flow guidance and energy saving. Background Technology

[0002] Cooling towers are crucial heat dissipation devices in circulating water systems of industrial production and thermoelectric fields, and their performance directly affects the energy efficiency and safety of the main process system. Traditional cooling tower ventilation control relies heavily on natural ventilation or fixed logic-based adjustment methods, and their control behavior typically only responds to a single parameter (such as ambient temperature), essentially constituting simple open-loop or closed-loop control. However, the actual operating environment of cooling towers is complex and variable. There is a strong, nonlinear, and time-varying dynamic relationship between ambient wind parameters (such as wind direction and speed) and internal water parameters (water temperature distribution) of the cooling tower. This makes it difficult for traditional control methods to achieve an adaptive balance between ensuring safety (such as antifreeze) and improving efficiency (such as optimizing heat dissipation and reducing drift).

[0003] Specifically, existing technologies have the following main limitations: First, the control basis is singular, relying heavily on temperature thresholds to trigger global antifreeze actions, failing to combine real-time wind field information to identify local risks, which can easily lead to overprotection or blind spots in protection; Second, they lack dynamic adaptability, with fixed control strategies that cannot be autonomously adjusted and optimized based on real-time operating conditions such as wind direction changes and water temperature distribution; Third, the control is coarse, typically applying uniform operation to the entire air intake surface, failing to achieve fine-grained adjustments for different orientations and heights; Fourth, the systems are mostly in open-loop or weak feedback states, making it difficult to evaluate the control effect in real time and optimize it in a closed-loop manner.

[0004] Chinese invention patent CN102538504A discloses an optimized energy-saving and cold-proof system and method for the air inlet side of a cooling tower. This system reduces the impact of cold air intrusion on the water temperature inside the tower by installing an adjustable wind deflector on the air inlet side and adjusting the deflector angle according to ambient temperature and wind speed. While this solution enhances the anti-freezing capability during winter operation to some extent, its control logic still relies primarily on limited parameters such as ambient temperature and wind speed. It fails to achieve real-time monitoring and dynamic identification of the water temperature distribution inside the tower, nor does it consider the varying wind field effects around the tower under different wind directions. Therefore, this system remains a rule-based, single-variable response control, making it difficult to adaptively adjust based on the complex coupling relationship between the actual thermal state inside the tower and the ambient wind field. Its control accuracy and adaptability remain insufficient when facing actual operating conditions such as variable wind directions and non-uniform temperature fields.

[0005] Therefore, existing cooling tower ventilation control systems exhibit significant deficiencies in intelligence and adaptability when facing complex, coupled, and time-varying environments and operating conditions. There is an urgent need for an advanced control system capable of real-time fusion of multi-dimensional parameters, intelligent identification of dynamic operating conditions (wind field and temperature field), autonomous generation and execution of zoned refined control strategies, and closed-loop feedback and online adjustment based on execution results. Such a system can proactively adapt to external environmental disturbances and internal state changes, achieving a dynamic optimal balance among multiple objectives such as anti-freezing safety and operational efficiency, thereby significantly improving the reliability, economy, and environmental friendliness of cooling towers. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a coupled flow-guiding energy-saving adaptive air intake control system and method for cooling towers. By integrating multi-dimensional information collection of ambient wind and tower water temperature, the system achieves intelligent identification and decision-making regarding wind field dynamics and operating modes. Based on independently controllable fan blade groups in zones and layers, the system can generate refined control commands according to real-time operating conditions. After execution, it dynamically optimizes the control model through closed-loop feedback, thereby proactively adapting to complex and changing environments. This achieves an adaptive balance among multiple objectives such as antifreeze safety, heat dissipation efficiency, and anti-drip, improving the intelligent operation level of the cooling tower.

[0007] The specific technical solution of this invention to solve the above-mentioned technical problems is as follows: a coupled flow-guiding energy-saving adaptive air intake control system for cooling towers, characterized in that the system is a closed-loop control system, including an information acquisition unit, an execution unit, and an adaptive control unit; the information acquisition unit is used to collect environmental wind parameters and key operating parameters of the cooling tower in real time; wherein, the environmental wind parameters include at least environmental atmospheric pressure, environmental temperature, environmental humidity, environmental wind speed, and environmental wind direction; the key operating parameters include decisive parameters and influencing parameters; the decisive parameters include at least unit load and circulating water flow rate; the influencing parameters include at least internal water temperature parameters. The system includes the number and drift droplet state parameters; the execution unit comprises multiple independently driveable fan blade groups distributed circumferentially along the air inlet of the cooling tower, each fan blade group containing at least one independently adjustable control layer in the vertical direction for regulating the air inlet of the cooling tower; the adaptive control unit is communicatively connected to the information acquisition unit and the execution unit, respectively, for executing an adaptive control process based on data from the information acquisition unit, generating control commands, and sending them to the execution unit for zoning and layering control; the execution unit is communicatively connected to the adaptive control unit, for responding to the commands of the adaptive control unit to zon the air inlet of the cooling tower. The adaptive control process includes: a layered control system; and an operation decision module, which, based on ambient wind parameters, inlet wind parameters, and water temperature distribution parameters, and in accordance with preset decision rules, determines one or more operation modes to be activated from a variety of operation modes, including flow-guiding energy-saving mode, anti-freezing energy-saving mode, and anti-drip energy-saving mode, and designates one as the dominant operation mode; a key parameter status identification module, which, based on the ambient wind parameters and inlet wind parameters, performs dynamic wind field identification and outputs the wind condition characteristics of the circumferential zones of the cooling tower; simultaneously, based on the water temperature distribution parameters, it performs thermodynamic state identification and outputs the markers of thermally abnormal areas within the tower. The adaptive control unit includes an adaptive control strategy module, which, based on the selected dominant operating mode, the wind condition characteristics, and the thermal anomaly area identifier, calls the corresponding control strategy model to form a control opening degree for the partition and layer of the execution unit; an instruction generation module, which converts the control opening degree into a control instruction and sends it to the execution unit; and a model optimization module, which, based on the updated parameters fed back by the information acquisition unit after the execution unit's action, adaptively adjusts and optimizes at least one of the models, algorithms, and / or control strategy models used for key parameter state identification, according to the updated parameters fed back by the information acquisition unit after the execution unit's action.

[0008] The control layer includes at least three types: a first control layer, an intermediate control layer, and a second control layer. The blade width of the intermediate control layer is greater than or equal to the blade width of the intermediate control layer and less than or equal to the blade width of the second control layer. The first control layer is preferentially regulated in the anti-freeze energy-saving mode, and the second control layer is preferentially regulated in the flow-guiding energy-saving mode. Each type of control layer contains at least one sub-layer in the vertical direction.

[0009] The cooling tower is dynamically divided into a windward side zone, a leeward side zone, and a crosswind side zone based on the results of the dynamic wind field identification. The fan blades in the windward side zone are equipped with at least the first control layer, and the fan blades in the leeward side zone are equipped with at least the second control layer.

[0010] The decision rules of the operation decision module specifically include: if the ambient temperature is lower than a first threshold, the anti-freeze energy-saving mode is selected as the dominant operation mode; if the ambient temperature is not lower than the first threshold, the airflow-guiding energy-saving mode is selected as the dominant operation mode; if the ambient wind speed exceeds a second threshold, the anti-drip energy-saving mode is selected as the dominant operation mode; when multiple operation modes are activated simultaneously, the dominant operation mode is determined according to the preset priority order of the anti-freeze energy-saving mode, the anti-drip energy-saving mode, and the airflow-guiding energy-saving mode; wherein, the first threshold and the second threshold are configured and calibrated by the user according to the on-site geographical location and seasonal characteristics.

[0011] When the dominant operating mode is the anti-freeze energy-saving mode, the control strategy model called in the adaptive control strategy module is the anti-freeze graded control model; the anti-freeze graded control model specifically includes: locating the target fan blade group to be protected according to the thermal anomaly area identifier; and performing graded opening output according to the number of times the same target fan blade group is continuously identified: when identified for the first time, the opening of the first control layer is output as fully closed; when identified again, the opening of both the first control layer and the intermediate control layer is output as fully closed; when the number of consecutive identifications is ≥3, while outputting the opening of the aforementioned control layer as fully closed, a preset restrictive control opening is output for its second control layer, and a prompt for manual intervention is given.

[0012] When the dominant operating mode is the airflow-energy-saving mode, the control strategy model called in the adaptive control strategy module is the airflow-zone control model. The airflow-zone control model specifically includes: calculating the air inlet angle α at the position of each fan blade group based on the wind condition characteristics; for each fan blade group, if its air inlet angle α ≤ 10°, the output opening is fully open; otherwise, according to the wind condition characteristics of the fan blade group, the optimal opening that maximizes its theoretical airflow is determined through the pre-stored flow rate-opening function model; wherein, the air inlet angle refers to the angle between the ambient wind direction and the radial direction of the fan blade group; the flow rate-opening function model specifically refers to the relationship function between the fan blade opening, the inlet wind direction, the inlet wind speed and the airflow, and the maximum airflow is obtained by adjusting the fan blade opening, wherein the inlet wind direction and the inlet wind speed are obtained from the angular relationship between the ambient wind speed, the ambient wind direction and the fan blade opening.

[0013] When the anti-drip energy-saving mode is activated, the adaptive control strategy module further includes: applying an opening constraint of no more than 45° to at least one control layer located above the air inlet in the windward side zone based on the dripping state parameters, and ensuring that the opening of the lower fan blades in the same group is not less than the opening of the upper fan blades.

[0014] The adaptive control unit also includes an online prediction module, which integrates a data-driven water temperature prediction model to predict the lowest water temperature inside the tower in a future period based on historical and current data. The water temperature prediction model uses a neural network to couple historical operating data with current measured data to predict the lowest water temperature of the cooling tower in a future set period after the execution of the current control command. It can adaptively update and train according to the new data continuously fed back by the information acquisition unit at a set period. The adaptive control unit uses the prediction results to provide feedback correction to the opening degree formed in the anti-freeze energy-saving mode.

[0015] A cooling tower adaptive air intake control method with coupled flow guidance and energy saving is characterized in that the method is executed by a cooling tower adaptive air intake control system with coupled flow guidance and energy saving, and is invoked at least when the dominant operating mode is the flow guidance and energy saving mode. Its core steps include the operation performed by the adaptive control strategy module.

[0016] An adaptive air intake control method for cooling towers with coupled airflow guidance and energy saving is characterized in that the method is executed by any of the above-mentioned systems, including: selecting an operating mode based on an operating decision module; obtaining the wind field and thermal state through a key parameter state identification module; adaptively generating control instructions for circumferential partitions and vertical layered fan blade groups according to the selected mode; and a closed-loop control step of online optimization based on execution feedback.

[0017] The advantages of this invention compared to existing technologies are as follows: The system collects and integrates multi-dimensional dynamic parameters such as ambient wind, inlet air, and internal water temperature distribution in real time. Combined with dynamic wind field and thermodynamic state identification technology, it intelligently judges and adaptively switches between operating modes such as airflow diversion energy saving, anti-freezing energy saving, and anti-drip energy saving. This solves the core pain point of traditional methods, which rely on single variables and cannot respond to complex coupled operating conditions. Through independent execution units with circumferential partitioning and vertical layering, the system can implement differentiated and precise adjustments to the airflow at different locations and heights of the cooling tower based on real-time wind conditions and areas of thermal anomalies, completely changing the traditional extensive mode of global uniform operation. More importantly, the system constructs a complete "perception-decision-execution-feedback" closed-loop optimization circuit, which can adjust the control model online based on the execution effect. This allows for simultaneous consideration of anti-freezing safety and operational energy efficiency in harsh environments, maintaining dynamic optimality in variable operating conditions and significantly improving the adaptability, economy, and reliability of the cooling tower. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system structure of an adaptive air intake control system and method for coupled flow guidance and energy saving in a cooling tower.

[0019] Figure 2 A schematic diagram of the layout of an adaptive air intake control system comprising three control layers.

[0020] Figure 3 This is a top-view diagram illustrating the fan blade assembly operation in the anti-drip energy-saving mode, which is executed synchronously in the circumferential zones.

[0021] Figure 4 This is a schematic diagram of the layout of an adaptive air intake control system that includes two types of control layers.

[0022] Figure 5 This is a flowchart of a cooling tower flow diversion energy-saving control method.

[0023] Figure 6 This is a schematic diagram of the layout of an adaptive air intake control system that only includes a second control layer and has only a flow diversion energy-saving control mode.

[0024] Figure 7 A schematic diagram showing the layout of an adaptive air intake control system with one layer for each type of control layer.

[0025] In the diagram: 1-Information acquisition unit, 2-Adaptive control unit, 3-Execution unit, 4-Operation decision module, 5-Key parameter status identification module, 6-Adaptive control strategy module, 7-Instruction generation module, 8-Model optimization module, 9-Online prediction module, 10-Cooling tower, 11-Fan blade assembly, 12-First control layer, 13-Intermediate control layer, 14-Second control layer, 15-Windward side zone, 16-Leafward side zone, 17-Crosswind side zone. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the scope of protection of the present invention.

[0027] The structures, proportions, sizes, etc., shown in the accompanying drawings are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0028] The flow rate-opening function model is established by combining computational fluid dynamics simulation with historical operating data calibration. Its core is to establish a database or fitting formula for the mapping relationship between opening, inlet wind speed, inlet angle α and local flow rate under a specific fan blade structure, and to solve for the opening that maximizes the flow rate by querying or interpolation.

[0029] Furthermore, the flux-aperture function model can be an empirical formula, lookup table, or machine learning model based on data fitting, and its specific parameters are obtained through experimental or simulation calibration for a specific cooling tower structure.

[0030] Example 1: The main operating mode is the anti-freeze and energy-saving mode.

[0031] like Figure 1 , Figure 2 As shown, this embodiment details the system's graded antifreeze and forward-looking optimization capabilities under severe cold conditions, particularly highlighting the specific structure and collaborative working mechanism of the device. The system is installed in a 90-meter-diameter natural draft wet cooling tower 10 at a thermal power plant in northern China. The tower's air inlet height is 8 meters. The execution unit 3 is specifically arranged as follows: 32 independently controlled fan blade groups 11 are evenly arranged circumferentially as the core execution mechanism. Each fan blade group 11 adopts three differentiated designs in the vertical direction: the upper part is the first control layer 12, which includes two layers, each using narrow fiberglass blades with a width of 500 mm, totaling 256 blades; the middle part is the intermediate control layer 13, which includes two layers, each using medium-width aluminum alloy blades with a width of 750 mm, also totaling 256 blades; the lower part is the second control layer 14, which includes one layer, using wide reinforced plastic blades with a width of 900 mm, totaling 96 blades. Each layer is driven by an independent waterproof and explosion-proof servo motor through a precision gearbox, which can achieve stepless adjustment within the range of 0° to 90°, with a position feedback accuracy of ±0.5°.

[0032] Information acquisition unit 1 includes an anemometer mounted on the meteorological mast and 64 PT100 platinum resistance temperature sensors embedded in the support structure below the packing layer inside the tower. These sensors are distributed in an 8×8 grid, with a measurement range of -20℃ to 80℃ and an accuracy of ±0.1℃. Adaptive control unit 2 uses an industrial computer based on an industrial Ethernet architecture and runs dedicated control algorithm software.

[0033] During the specific control process, when the ambient temperature sensor detects that the temperature has dropped to -10℃, the operation decision module 4 decides to enter the anti-freeze energy-saving mode. Real-time data from the water temperature sensor array is uploaded via the Modbus TCP protocol, showing that the water temperature in the northwest quadrant of the tower (corresponding to fan blades 1-8) dropped from 8℃ to 4.2℃ within 30 minutes. The water temperature distribution model of the adaptive control unit 2 identifies high-risk areas and first generates instructions to drive the first control layer 12 servo motor of fan blades 1-8, synchronously closing all 128 narrow blades to the 0° position within 15 seconds. Twenty minutes later, monitoring data shows that the water temperature in the core area corresponding to fan blades 4-6 continues to drop to 3.5℃. The system then activates secondary protection, instructing the closure of the middle control layer 13 blades of these three fan blade groups.

[0034] After the action is completed, the data re-collected by the information acquisition unit 1 is processed by the model optimization module 8 to correct and optimize the parameters in the adaptive control strategy module, thereby improving the accuracy of conforming to reality.

[0035] Meanwhile, the LSTM neural network model in online prediction module 9 (trained based on three years of historical operating data) integrates current operating conditions and weather forecast data to predict that the water temperature in the area will drop below 1°C within the next 40 minutes. The system immediately sends a level-three warning and displays detailed operating suggestions on the control interface: adjust the opening of the second control layer 14 (36 wide blades in total) of the 4th to 6th groups of fan blades from the current 85° to the recommended 25°. After confirmation by the operator, the command is issued, and the corresponding servo motor smoothly adjusts the blade angle at a rate of 5° per second. The entire process, coordinated by the control logic, achieves a three-in-one anti-freezing protection system that integrates tiered, zoned, and predictive measures.

[0036] Example 2: The main operating mode is the anti-drip energy-saving mode, while the flow diversion energy-saving mode is executed at the same time.

[0037] like Figure 3 , Figure 4As shown, this embodiment details the system's synergistic optimization capabilities for improving airflow efficiency and preventing drift droplet loss during windy summer weather, focusing on the dynamic response mechanism of the device under different operating conditions. The system is deployed in a wet cooling tower 10 with a cooling area of ​​8500 square meters. The execution unit 3 is specifically arranged as follows: 44 sets of fan blades 11 are evenly spaced along the circumference of the air inlet of the tower. Each set of fan blades 11 adopts an innovative modular design: the upper first control layer 12 consists of 72 quick-detachable polyurethane blades (750 mm wide), and the lower second control layer 14 is equipped with 54 reinforced PVC blades (900 mm wide), with an opening range of 0°~90°. The drive system adopts an independent electric actuator scheme for each layer, equipped with an encoder to achieve closed-loop position control. The information acquisition unit 1 has added environmental meteorological measurement points at the four corners around the tower, which can construct a three-dimensional wind field model around the tower in real time, and arranges a composite sensor array of temperature, humidity, and pressure at different heights inside the tower.

[0038] Under typical summer wind conditions, a sustained southeast wind speed of 6.8 m / s and gusts exceeding 9 m / s were monitored, along with an ambient temperature of 32°C and a circulating water inlet temperature of 42°C. The adaptive control unit 2's operation decision module 4 algorithm simultaneously activates both the flow-guiding energy-saving mode and the anti-drip energy-saving mode, with the anti-drip energy-saving mode as the dominant operating mode. The key parameter status identification module 5 first dynamically divides the 44 fan blade groups 11 into three control zones based on real-time three-dimensional wind field data: the southeast side (12 groups) is the windward side zone 15, the northwest side (12 groups) is the leeward side zone 16, and the remaining 20 groups are the crosswind side zone 17.

[0039] like Figure 3 As shown, the anti-drip control is activated first. The adaptive control strategy module 6, in conjunction with the instruction generation module 7, generates instructions to limit the opening of all fan blade groups 11 of the first control layer 12 near the upper edge of the air inlet in the windward side zone 15 to 35°, and the opening of all fan blade groups 11 of the lower second control layer 14 to full open, forming an effective airflow barrier on the windward side of the tower. At the same time, to maximize heat dissipation efficiency, the fan blade group 11 in the leeward side zone 16 receives the full open instruction, and all its blades are fully extended to 90°. For the crosswind side zone 17, the system calls the optimization model library based on computational fluid dynamics pre-calculation to calculate the optimal opening of each group in each layer for the local wind direction angle of each group (such as the angle of 62° for group 15), thereby optimizing the flow distribution.

[0040] When meteorological conditions changed, the system's dynamic adaptability was fully demonstrated. Two hours later, the wind direction shifted to southerly, the wind speed dropped to 3.2 m / s, and the ambient temperature rose to 35°C. The system immediately reassessed the operating conditions, first removing the anti-drip opening restriction, and then re-zoning based on the updated wind field model. The blade assembly 11 of the original windward side zone 15 (now the crosswind side zone 17) was adjusted from 45° to 68° based on the new optimization algorithm, forming the optimal guide surface adapted to the new operating conditions. Throughout the process, data from the temperature sensor array showed that the water temperature distribution in all areas of the tower remained uniform, with no localized overheating, and the drip loss was consistently controlled below 70% of the design standard, achieving a multi-objective optimized balance of safety, efficiency, and environmental protection.

[0041] Example 3: Standalone operation of the diversion energy-saving mode.

[0042] like Figure 5 , Figure 6 As shown, this embodiment details the flow-enhancing effect of the system in a wet cooling tower 10 without antifreeze measures. The system is deployed in a high-level water-collecting cooling tower 10 with a cooling area of ​​5500 square meters. The execution unit 3 is specifically arranged as follows: 40 sets of fan blades 11 are evenly spaced around the air inlet of the tower. Each set of fan blades 11 includes a control layer, namely the second control layer 14, which contains three sub-layers and is equipped with 30 modified PVC blades (1000 mm wide), with an opening range of 0°~90°. The drive system adopts a scheme of electric actuator control for the upper two layers and the lower layer respectively. The information acquisition unit 1 has added environmental meteorological measurement points at the four corners around the tower, which can construct a three-dimensional wind field model around the tower in real time. Air inlet measurement points are set about 1 meter outside the air inlet of the fan blades 11 to collect the wind speed, wind direction and wind temperature entering the tower to construct a local wind field model. Temperature, humidity and pressure composite sensor arrays are arranged at different heights inside the tower.

[0043] Under strong wind conditions, a sustained southeast wind speed of 7.2 m / s and gusts exceeding 10 m / s were monitored, along with an ambient temperature of 22°C and a circulating water inlet temperature of 38°C. The adaptive control unit 2's operation decision module 4 algorithm determined to enter the flow-guiding energy-saving mode. The system first dynamically divided the 40 fan blade groups 11 into three control zones based on real-time three-dimensional wind field data: 10 groups on the east side (windward side zone 15), 10 groups on the west side (leeward side zone 16), and the remaining 20 groups (crosswind side zone 17). Then, based on wind characteristics, the inlet angle α of each fan blade group 11 in each zone was determined. The windward side zone 15 and leeward side zone 16 were both set to fully open. The crosswind side zone 17, based on the flow rate-opening function model, calculated the control angles of each fan blade group 11 using the maximum flow rate, resulting in angles of 38°, 45°, 55°, 60°, 65°, 72°, 78°, 80°, 83°, and 88°.

[0044] Example 4: Smooth switching between antifreeze energy-saving mode and flow-directing energy-saving mode.

[0045] like Figure 7 As shown, the system is installed in a 90-meter-diameter natural draft wet cooling tower 10 at a thermal power plant in northern China. The tower's air inlet is 8 meters high, and 32 independently controlled fan blade groups 11 are evenly arranged circumferentially as the core actuators. Each fan blade group 11 employs a three-layer differentiated design in the vertical direction: the upper layer is the first control layer 12, consisting of one layer with 128 narrow fiberglass blades (500 mm wide); the middle layer is the intermediate control layer 13, consisting of one layer with 128 medium-width aluminum alloy blades (750 mm wide); and the lower layer is the second control layer 14, consisting of one layer with 96 wide reinforced plastic blades (900 mm wide). Each layer is driven by an independent waterproof and explosion-proof servo motor via a precision gearbox, enabling stepless adjustment within the range of 0° to 90°, with a position feedback accuracy of ±0.5°.

[0046] Information acquisition unit 1 includes an anemometer mounted on the meteorological mast and 56 PT100 platinum resistance temperature sensors embedded in the support structure below the packing layer inside the tower. These sensors are distributed in a 7×8 grid, with a measurement range of -20℃ to 80℃ and an accuracy of ±0.1℃. Adaptive control unit 2 uses an industrial computer based on an industrial Ethernet architecture and runs dedicated control algorithm software.

[0047] During operation, the key parameter status identification module 5 intelligently and smoothly switches between the airflow-guided energy-saving mode and the anti-freeze energy-saving mode based on real-time changes in ambient temperature, wind speed, and water temperature distribution within the tower. During the daytime, when the ambient temperature exceeds a preset first threshold (e.g., 5℃) and the wind speed is moderate, the system automatically enters the airflow-guided energy-saving mode. At this time, the adaptive control unit, based on the real-time wind field identification results, dynamically divides the cooling tower 10 circumferentially into the windward side zone 15, the leeward side zone 16, and the crosswind side zone 17. It then calls the airflow zoning control model and, based on the air inlet angle and inlet wind speed at each fan blade group 11, calculates the optimal opening using a pre-stored flow rate-opening function model, generating control commands to achieve maximum airflow and heat dissipation efficiency. For example, under southeast wind conditions, the fan blade group 11 in the windward side zone 15 is fully open, while the fan blade group 11 in the leeward side zone 16 adjusts its opening according to the optimization model, thereby forming an efficient and uniform airflow organization throughout the tower.

[0048] When night falls or a cold snap occurs, and the ambient temperature drops below the first threshold, the system automatically switches to the anti-freeze and energy-saving mode as the primary operating mode. At this time, the key parameter status identification module 5 first identifies and locates the low-temperature risk area within the tower (such as the northwest quadrant) based on real-time monitoring data from the water temperature sensor array and through thermodynamic state identification. Subsequently, the anti-freeze graded control model is activated: when an abnormal water temperature is first detected in the area corresponding to a certain blade group 11, its first control layer 12 is closed; if the water temperature in this area continues to drop and is identified again, its intermediate control layer 13 is further closed; when the system determines through the water temperature prediction model in the online prediction module that there is a risk of continuous freezing in this area, it outputs a restrictive control command to the second control layer 14 (such as an opening degree not exceeding 30°) and prompts the operator to intervene and confirm on the control interface. The entire anti-freeze control process emphasizes graded and zoned response, which avoids efficiency loss caused by over-protection and also prevents local protection blind spots.

[0049] Crucially, the system achieves a smooth transition during mode switching. When switching from the flow guidance mode to the anti-freeze mode, the system does not immediately shut down all fan blade groups 11. Instead, based on the rate of water temperature decrease and changes in the wind field, it adjusts the tiered shutdown commands of fan blade groups 11 in high-risk areas in an orderly and gradual manner to avoid drastic fluctuations in the airflow and temperature field within the tower. Simultaneously, the online prediction module 9, based on an LSTM neural network model, integrates historical operating data and real-time monitoring information to make rolling predictions of the lowest water temperature within the tower in future periods. Based on this prediction, it proactively corrects the upcoming anti-freeze commands, thereby intervening before risks occur and enhancing the system's predictability and stability.

[0050] Through the aforementioned smooth switching mechanism, the system can ensure freeze protection during nighttime and low-temperature conditions, effectively preventing packing icing and structural damage, while also fully utilizing favorable weather conditions during the day to improve heat dissipation efficiency, especially during periods of significant diurnal temperature differences or seasonal transitions. Actual operating data shows that after adopting this system, the fluctuation range of water temperature inside the tower during mode switching was reduced by approximately 40%, the number of fan blade group 11 actions and corresponding energy consumption were reduced by approximately 25%, and the annual average energy efficiency ratio of the entire cooling tower 10 increased by more than 15%, fully demonstrating the significant advantages of this system in multi-mode adaptive coordination and closed-loop optimization control.

[0051] Furthermore, in the anti-freeze and energy-saving mode, depending on changes in the ambient weather, while ensuring the anti-freeze requirements of the cooling tower, a certain degree of opening can be adopted for the sub-layers in each control layer. Instead of closing them completely, a certain degree of ventilation opening is left, thereby improving the cooling performance under anti-freeze conditions.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects. The scope of the present invention is defined by the appended claims rather than the foregoing description. Therefore, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention, and no reference numerals in the claims should be regarded as limiting the scope of the claims.

[0053] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A coupled flow-guiding energy-saving adaptive air intake control system for cooling towers, characterized in that, This system is a closed-loop control system, comprising an information acquisition unit, an execution unit, and an adaptive control unit. The information acquisition unit is used to collect ambient wind parameters and key operating parameters of the cooling tower in real time. The ambient wind parameters include at least ambient temperature, ambient wind speed, and ambient wind direction. The execution unit includes multiple independently driveable fan blade groups distributed circumferentially along the air inlet of the cooling tower. Each fan blade group contains at least one type of independently adjustable control layer in the vertical direction. The control layers include at least a first control layer, an intermediate control layer, and a second control layer. The adaptive control unit is communicatively connected to the information acquisition unit and the execution unit, respectively, and is used to execute the adaptive control process. The adaptive control process includes: The operation decision module is used to activate one or more of the following modes based on the collected parameters and preset decision rules: flow diversion energy saving mode, anti-freeze energy saving mode, and anti-drip energy saving mode, and designate one of them as the dominant mode. The key parameter status identification module is used to identify dynamic wind field and thermodynamic state, and output the wind condition characteristics on the circumferential partition of the cooling tower and the identification of abnormal thermal areas inside the tower. The adaptive control strategy module is used to call the corresponding control strategy model based on the selected dominant mode, the wind condition characteristics, and the thermal anomaly area identifier, to form the control opening degree for the partition and layer of the execution unit; The instruction generation module is used to convert the control opening degree into a control instruction; The adaptive control process also includes a model optimization module, which is used to adaptively adjust and optimize the parameters or structure of at least one of the key parameter state identification model, algorithm and / or control strategy model based on the feedback parameters after the execution unit's action. The blade width of the intermediate control layer is greater than or equal to the blade width of the first control layer and less than or equal to the blade width of the second control layer, forming an effective airflow guiding surface to achieve active guidance and utilization of the ambient wind. According to the decision rules of the operation decision module, when the flow-guiding energy-saving mode is activated as the dominant mode, the control strategy model called in the adaptive control strategy module is the flow-guiding zoning control model. The flow-guiding zoning control model specifically includes: calculating the air inlet angle α at the position of each fan blade group based on the wind condition characteristics of the key parameter state identification module; wherein, the air inlet angle refers to the angle between the ambient wind direction and the radial direction of the fan blade group; according to the wind condition characteristics of the fan blade group output by the key parameter state identification module, the optimal opening degree that maximizes its theoretical airflow is determined through the pre-stored flow-opening function model; then, the instruction generation module converts the optimal opening degree into a control instruction; the flow-opening function model specifically refers to the relationship function between the fan blade opening degree, the inlet wind direction, the inlet wind speed and the inlet airflow, and the maximum inlet airflow is obtained by adjusting the fan blade opening degree; the inlet wind direction and the inlet wind speed are obtained by converting the angular relationship between the ambient wind speed, the ambient wind direction and the fan blade opening degree.

2. The system according to claim 1, characterized in that, The first control layer is preferentially regulated in the anti-freeze energy-saving mode, and the second control layer is preferentially regulated in the flow-guiding energy-saving mode; the first control layer, the intermediate control layer and the second control layer, each type of control layer contains at least one sub-layer in the vertical direction.

3. The system according to claim 2, characterized in that, The cooling tower is dynamically divided into a windward side zone, a leeward side zone, and a crosswind side zone based on the results of the dynamic wind field identification. The fan blades in the windward side zone are equipped with at least the first control layer, and the fan blades in the leeward side zone are equipped with at least the second control layer.

4. The system according to claim 1, characterized in that, The key operating parameters of the cooling tower include decisive parameters and influencing parameters; the decisive parameters include at least unit load and circulating water flow rate; the influencing parameters include at least internal water temperature and droplet state parameters; the decision rules of the operation decision module specifically include: if the ambient temperature is lower than a first threshold, the anti-freeze energy-saving mode is selected as the dominant mode; if the ambient temperature is not lower than the first threshold, the flow-guiding energy-saving mode is selected as the dominant mode; if the ambient wind speed exceeds a second threshold, the anti-drip energy-saving mode is selected as the dominant mode; when multiple modes are activated simultaneously, the dominant mode is determined according to the preset priority order of anti-freeze energy-saving mode, anti-drip energy-saving mode, and flow-guiding energy-saving mode; wherein, the first threshold and the second threshold are configured and calibrated by the user according to the site's geographical location and seasonal characteristics.

5. The system according to claim 4, characterized in that, According to the decision rules of the operation decision module, when the anti-freezing energy-saving mode is activated as the dominant mode, the control strategy model called in the adaptive control strategy module is the anti-freezing graded control model. The anti-freezing graded control model specifically includes: locating the target fan blade group that needs protection based on the thermal anomaly area identification of the key parameter status identification module; and performing graded opening output based on the number of times the same target fan blade group is continuously identified: when identified for the first time, the opening of the first control layer is output as fully closed; when identified again, the opening of both the first control layer and the intermediate control layer is output as fully closed; when the number of continuous identifications is ≥3, while outputting the opening of the aforementioned control layer as fully closed, a preset restrictive control opening is output for its second control layer, and a prompt for manual intervention is given.

6. The system according to claim 1, characterized in that, The airflow zoning control model also includes: for each fan blade group, if its air inlet angle α≤10°, the output opening is fully open.

7. The system according to claim 4, characterized in that, When the anti-drip energy-saving mode is activated, the adaptive control strategy module further includes: applying an opening constraint of no more than 45° to at least one control layer located above the air inlet in the windward side zone based on the dripping state parameters, and ensuring that the opening of the lower fan blades in the same group is not less than the opening of the upper fan blades.

8. The system according to claim 1, characterized in that, The adaptive control unit also includes an online prediction module, which integrates a data-driven water temperature prediction model to predict the lowest water temperature inside the tower in a future period based on historical and current data. The water temperature prediction model uses a neural network to couple historical operating data with current measured data to predict the lowest water temperature of the cooling tower in a future set period after the execution of the current control command. It can also adaptively update and train according to the new data continuously fed back by the information acquisition unit at a set period. The adaptive control unit uses the prediction results to provide feedback correction to the opening degree formed in the anti-freeze energy-saving mode.

9. A coupled flow-guiding energy-saving adaptive air intake control method for cooling towers, characterized in that, The method is executed by the cooling tower adaptive air intake control system of any one of claims 1 to 8, and is invoked at least when the dominant mode is the flow-directing energy-saving mode. Its core steps include the operations performed by the adaptive control strategy module of claim 1.

10. A coupled flow-guiding energy-saving adaptive air intake control method for cooling towers, characterized in that, The method is executed by the system as described in any one of claims 1 to 8, and includes: selecting an operating mode based on the operating decision module; obtaining the wind field and thermal state through the key parameter state identification module; adaptively generating control instructions for the circumferential partition and vertical layered fan blade groups according to the selected mode; and performing closed-loop control steps for online optimization based on execution feedback.

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