Cooling tower partition heat exchange efficiency intelligent regulation and control method based on multi-factor collaborative simulation

Abstract

The invention provides a cooling tower partition heat exchange efficiency intelligent regulation and control method based on multi-factor collaborative simulation. The method is applied to the technical field of cooling tower partition heat exchange efficiency regulation and control, and comprises the steps that S1, the cross section of the cooling tower is divided into a plurality of concentric annular partitions, air temperature, humidity, wind speed and spraying water film thickness data in all the partitions synchronously collected through a distributed sensor array are fused into working condition vectors Vn of the corresponding partitions, n is the number of the partitions and is larger than or equal to 1 and smaller than or equal to k, and k is larger than or equal to 1 and smaller than or equal to k; n is a positive integer, and k is the number of partitions; s2, inputting the Vn into a reduced-order CFD model to obtain V'n; according to the V'n, the corrected k-epsilon model and the corrected water distribution field dispersed phase model, the actual heat exchange temperature difference delta Tn and the pressure drop delta Pn of each partition are output; and S3, with maximization of the partition heat exchange efficiency ratio eta as the target, the spraying density Sn, the wind speed Wn and the filler inclination angle theta n of the corresponding partitions are adjusted through an improved NSGA-II algorithm. In this way, the partition heat exchange efficiency of the cooling tower can be improved.

Description

Technical Field

[0001] This invention relates to the field of cooling tower zone heat exchange efficiency control technology, and in particular to an intelligent control method for cooling tower zone heat exchange efficiency based on multi-factor collaborative simulation. Background Technology

[0002] As a key component in industrial heat dissipation systems, the heat exchange efficiency of cooling towers directly impacts production energy consumption and operating costs. Traditional cooling towers typically employ a holistic control strategy, optimizing heat exchange performance through adjustments to individual meteorological conditions and global spray water flow and fan speed. However, in actual operation, the non-uniformity of the airflow and water distribution fields within the tower results in significant differences in the heat exchange environment across different circumferential zones. This leads to heat exchange efficiency bottlenecks in some areas due to mismatched heat exchange parameters, while other areas experience energy waste due to excessive resource allocation. This contradiction between non-uniformity and the centralized adjustment mode of traditional control strategies is the core challenge that has long constrained the improvement of zoned heat exchange efficiency in cooling towers.

[0003] Existing technologies for improving heat exchange efficiency suffer from the following shortcomings: Conventional solutions only collect overall tower parameters through a few sensors, neglecting the differentiated dynamic characteristics of air temperature, humidity, wind speed, and water film thickness in different annular regions within the tower. This makes it difficult to construct accurate operating condition feature vectors for each zone in real time, leading to blind control strategies. Traditional full-order CFD simulations are computationally intensive, making real-time parameter prediction and dynamic adjustment difficult. Furthermore, conventional k-ε turbulence models and water distribution field models do not consider the multi-parameter coupling effects of actual operating conditions, resulting in significant deviations in the predicted heat exchange temperature difference ΔT and pressure drop ΔP, which cannot guide actual optimization. Traditional optimization algorithms in cooling tower control often focus on a single objective and fail to effectively address the coupled adjustment of multiple parameters such as spray density (S), wind speed (W), and packing inclination angle (θ). Existing control schemes largely rely on human experience or pre-defined rule bases, lacking intelligent analysis capabilities for dynamic environmental data and unable to autonomously adapt to the real-time changing heat exchange demands of different regions, resulting in a failure to continuously improve overall efficiency. Summary of the Invention

[0004] This invention provides a method for intelligent control of zoned heat exchange efficiency in cooling towers based on multi-factor collaborative simulation. The method includes:

[0005] S1, the cross-section of the cooling tower is divided into several concentric annular zones, and the air temperature, humidity, wind speed, and spray water film thickness data of each zone, which are synchronously collected by a distributed sensor array, are fused into the corresponding zone's operating condition vector V. n n is the partition number, 1≤n≤k, n is a positive integer, k is a positive integer greater than or equal to 7, and k is the number of partitions;

[0006] S2, Vn Input a reduced-order CFD model to obtain V' n According to V' n The modified k-ε model and the modified discrete phase model of the water distribution field output the actual heat transfer temperature difference ΔT in each zone. n and pressure drop ΔP n ;

[0007] S3, aiming to maximize the heat transfer efficiency ratio η of the zones, uses an improved NSGA-II algorithm to adjust the spray density S of the corresponding zones. n Wind speed W n , packing inclination angle θ n Adjustments will be made.

[0008] Furthermore:

[0009] The area ratio of each partition is partition 1: partition 2: ...: partition k = 1:2: ...:k; where partition 1 is the central region of the concentric ring partitions and is numbered sequentially from the inside out.

[0010] If any two adjacent zones have a humidity gradient ≤15%RH / m, the cross-section of the cooling tower is re-divided, and the area ratio of the re-divided zones is 1:3:...:k×1.5.

[0011] Furthermore:

[0012] If it continues for 10 minutes, ΔT k If -ΔT1 > 3℃, the cross-section of the cooling tower is re-divided, and the area of ​​each re-divided section is in the ratio of section 1: section 2: ...: section k = 1:4: ...:k×2; where ΔT k Let ΔT1 be the heat exchange temperature difference of the kth partition, and ΔT1 be the heat exchange temperature difference of the 1st partition.

[0013] Furthermore, the revised k-ε model includes an added humidity modification term.

[0014] Furthermore, the revised discrete phase model of the water distribution field includes a modification for the amount of secondary splashing of the filler material.

[0015] Furthermore, the heat transfer efficiency of the nth partition is higher than that of η. n The calculation formula is:

[0016]

[0017] Where, ΔT n,max β is the ideal value obtained through a reduced-order CFD model. n H is the environmental compensation coefficient for the nth partition.n Let be the humidity of the nth partition.

[0018] Furthermore:

[0019] If ┃ΔT n -ΔT n-1 If the temperature exceeds 2℃, then the spray density S corresponding to the nth and (n-1)th zones will be adjusted synchronously. n Wind speed W n , packing inclination angle θ n Spray density S n-1 Wind speed W n-1 , packing inclination angle θ n-1 For n≤k, during the adjustment process, the first objective function is to maximize the sum of the heat exchange efficiency ratios of all cooling tower zones, and the second objective function is to minimize the pressure drop variance of all cooling tower zones.

[0020] Furthermore, regarding the spray density S n During the adjustment process:

[0021] like The spray density is 12-15 kg / m³ 2 The spray angle is dynamically adjusted and fixed at 30° to penetrate the high-temperature core airflow. Indicates rounding down;

[0022] like The spray density is 5-8 kg / m³ 2 The spray angle is dynamically adjusted between 45° and 60° to suppress water droplet escaping.

[0023] like The spray density is 2-3 kg / m³ 2 The spray angle is dynamically adjusted between 60° and 75°.

[0024] Furthermore, at wind speed W n During the adjustment process:

[0025] like η n >η n-1 Then the wind speed in the nth zone after adjustment is W. b ×80%, allowing heat to dissipate outwards, W b Standard wind speed;

[0026] like Then obtain the wind speed W outside the tower in real time. env If W env If the wind speed is greater than 3 m / s, then the adjusted wind speed in the nth zone is W. env ×1.2.

[0027] Furthermore: at the packing inclination angle θ n During the adjustment process, the packing material in each zone is composed of deformable corrugated plates with an inclination angle θ. n The adjustment range is 0–30°, and the step accuracy is 0.5°.

[0028] like And η n If the value is less than 80% of the set value for 3 consecutive hours, the corrugated plate pitch of the nth section will be compressed to 20mm, and the packing inclination angle θ will be adjusted. n Set to 0 to enhance turbulence;

[0029] like And η n If the flow rate is less than 80% of the set value for 3 consecutive hours, the corrugated plate pitch of the nth zone will be expanded to 50mm to guide the airflow, and the packing inclination angle θ will be adjusted. n Set to 30°.

[0030] This invention divides the cross-section of a cooling tower into multiple concentric annular zones. By combining real-time data from distributed sensors, the operating characteristics of each zone can be obtained. Compared with traditional overall control, this zoning strategy can make targeted adjustments based on the actual operating conditions of different zones, significantly improving the overall heat exchange efficiency. By aiming to "maximize the zone heat exchange efficiency ratio," the system uses a multi-objective optimization algorithm to collaboratively optimize parameters, maximizing the heat exchange temperature difference while ensuring reasonable system pressure loss, thereby significantly improving the overall heat exchange performance and energy efficiency of the cooling tower. Through real-time synchronized sensor data and a rapidly reduced-order CFD model, the system can dynamically adjust the spray density, wind speed, and packing inclination angle of each zone, avoiding energy waste caused by excessive spraying or air supply.

[0031] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0032] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of the invention. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0033] Figure 1 A flowchart of a method for intelligent control of cooling tower zone heat exchange efficiency based on multi-factor collaborative simulation according to an embodiment of the present invention is shown. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0036] Figure 1 A flowchart of an intelligent control method for zoned heat exchange efficiency of a cooling tower based on multi-factor collaborative simulation according to an embodiment of the present invention is shown. The method includes:

[0037] S1, the cross-section of the cooling tower is divided into several concentric annular zones, and the air temperature, humidity, wind speed, and spray water film thickness data of each zone, which are synchronously collected by a distributed sensor array, are fused into the corresponding zone's operating condition vector V. n n is the partition number, 1≤n≤k, n is a positive integer, k is a positive integer greater than or equal to 7, and k is the number of partitions;

[0038] S2, V n Input a reduced-order CFD model to obtain V' n According to V' n The modified k-ε model and the modified discrete phase model of the water distribution field output the actual heat transfer temperature difference ΔT in each zone. n and pressure drop ΔP n ;

[0039] S3, aiming to maximize the heat transfer efficiency ratio η of the zones, uses an improved NSGA-II algorithm to adjust the spray density S of the corresponding zones. n Wind speed W n , packing inclination angle θ n Adjustments will be made.

[0040] According to embodiments of the present invention, by dividing the cross-section of the cooling tower into multiple concentric annular zones and combining real-time data from distributed sensors, the operating characteristics of each zone can be obtained. Compared with traditional overall control, this zoning strategy can make targeted adjustments based on the actual operating conditions of different zones, significantly improving the overall heat exchange efficiency. By aiming to "maximize the zone heat exchange efficiency ratio," the system uses a multi-objective optimization algorithm to collaboratively optimize parameters, maximizing the heat exchange temperature difference while ensuring reasonable system pressure loss, thereby significantly improving the overall heat exchange performance and energy efficiency of the cooling tower. Through real-time synchronized sensor data and a rapidly reduced-order CFD model, the system can dynamically adjust the spray density, wind speed, and packing inclination angle of each zone, avoiding energy waste caused by excessive spraying or air supply.

[0041] In some embodiments, the area ratio of each partition is 1:2:...:k = partition 1:2:...:k; wherein partition 1 is the central region of the concentric ring partitions and is numbered sequentially from the inside out.

[0042] In some embodiments, if the humidity gradient between any two adjacent partitions is ≤15%RH / m, the cross-section of the cooling tower is re-divided, and the area ratio of each re-divided partition is 1:3:...:k×1.5.

[0043] In some embodiments, if ΔT lasts for 10 minutes, k If -ΔT1 > 3℃, the cross-section of the cooling tower is re-divided, and the area of ​​each re-divided section is in the ratio of section 1: section 2: ...: section k = 1:4: ...:k×2; where ΔT k Let ΔT1 be the heat exchange temperature difference of the kth partition, and ΔT1 be the heat exchange temperature difference of the 1st partition.

[0044] In some embodiments, a differential pressure sensor is provided at each partition boundary to monitor airflow crosstalk.

[0045] In some embodiments, S2 includes: adding a humidity modification term to the initial k-ε model. The corrected k-ε model is obtained; where ρ n Let μ be the water vapor density of the nth partition. i Let x be the component of the airflow velocity in the i-th direction. i Let be the coordinates of the spatial location in the direction i, where n ≤ k.

[0046] According to embodiments of the present invention, by adding a humidity modification term to the k-ε model, the influence of water vapor diffusion on turbulent kinetic energy and dissipation rate is explicitly quantified. The traditional k-ε model does not consider the humidity gradient, while changes in water vapor concentration within the cooling tower significantly affect the distribution of air kinetic energy and heat and mass transfer efficiency. The humidity gradient alters local air density and buoyancy effects, directly affecting turbulence intensity and mixing efficiency. This modification term enables the model to capture the additional turbulence generation / dissipation caused by humidity changes, thereby more accurately predicting the heat exchange rate at the gas-liquid interface. The core heat transfer process of the cooling tower relies on the latent heat of vaporization at the water film-air interface. Adding a humidity term couples mass transfer and energy transfer into the model, more realistically reflecting the physical mechanism of heat and moisture exchange.

[0047] In some embodiments, S2 includes: adding a secondary splashing quantity modification term Q of the packing material to the discrete phase model of the initial water distribution field. splash =α×S n ×sinθ n The corrected discrete phase model of the water distribution field is obtained; where Q splash The unit is kg / s, α is the filler surface energy correction coefficient (0.8 for metal fillers, 1.2 for plastic fillers), S n The spray density for the nth zone is given in kg / m³. 2 s, θ n The packing angle for the nth partition is expressed in degrees.

[0048] In some embodiments, the construction process of the reduced-order CFD model is as follows: The cooling tower is simulated under multiple operating conditions using full-order CFD software (such as ANSYS Fluent) to generate a high-dimensional dataset; the main characteristic modes are extracted using POD, simplified equations are established, and V is input. n Output feature vector V' n (Dimensionality reduced to 10% of the original model), V' n Includes key features of the partitioned velocity field, temperature field, and pressure field, V' n As input, it is used for subsequent model correction calculations.

[0049] In some embodiments, the improved NSGA-II algorithm is an improvement on the standard algorithm, specifically: adaptive crossover probability: Pc = 0.9 - 0.5 × (t / T), where t is the current generation and T is the total number of generations; real number encoding: the optimization variables (Sn, Wn, θn) are represented by real numbers; constraint handling: a penalty function is added to handle Sn∈[2,15]kg / m 2 s, θn∈[0,30]°, etc.; First objective function: maximize∑{n=1} k η n, where ηn is the heat exchange efficiency ratio of the nth partition; the second objective function is: minimize Var(ΔP1,ΔP2,...,ΔPk), where Var is the variance calculation; the termination condition is: 1000 iterations or the change in the objective function is <0.1%.

[0050] In some embodiments, the heat transfer efficiency of the nth partition is greater than that of η. n The calculation formula is:

[0051]

[0052] Where, ΔT n,max β is the ideal value obtained through a reduced-order CFD model. n H is the environmental compensation coefficient for the nth partition. n Let be the humidity of the nth partition.

[0053] In some embodiments, if ┃ΔT n -ΔT n-1 If the temperature exceeds 2℃, then the spray density S corresponding to the nth and (n-1)th zones will be adjusted synchronously. n Wind speed W n , packing inclination angle θ n Spray density S n-1 Wind speed W n-1 , packing inclination angle θ n-1 For n≤k, during the adjustment process, the first objective function is to maximize the sum of the heat exchange efficiency ratios of all cooling tower zones, and the second objective function is to minimize the pressure drop variance of all cooling tower zones.

[0054] In some embodiments, when the spray density S n During the adjustment process,

[0055] like The spray density is 12-15 kg / m³ 2 The spray angle is dynamically adjusted and fixed at 30° to penetrate the high-temperature core airflow. Indicates rounding down;

[0056] like The spray density is 5-8 kg / m³ 2 The spray angle is dynamically adjusted between 45° and 60° to suppress water droplet escaping.

[0057] like The spray density is 2-3 kg / m³ 2 The spray angle is dynamically adjusted between 60° and 75°.

[0058] In some embodiments, at wind speed W n During the adjustment process,

[0059] like η n >η n-1 Then the wind speed in the nth zone after adjustment is W. b ×80%, allowing heat to dissipate outwards, W b Standard wind speed;

[0060] like Then obtain the wind speed W outside the tower in real time. env If W env If the wind speed is greater than 3 m / s, then the adjusted wind speed in the nth zone is W. env ×1.2.

[0061] In some embodiments, at the packing inclination angle θ n During the adjustment process, the packing material in each zone is composed of deformable corrugated plates with an inclination angle θ. n The adjustment range is 0–30°, and the step accuracy is 0.5°.

[0062] like And η n If the value is less than 80% of the set value for 3 consecutive hours, the corrugated plate pitch of the nth section will be compressed to 20mm, and the packing inclination angle θ will be adjusted. n Set to 0 to enhance turbulence;

[0063] like And η n If the flow rate is less than 80% of the set value for 3 consecutive hours, the corrugated plate pitch of the nth zone will be expanded to 50mm to guide the airflow, and the packing inclination angle θ will be adjusted. n Set to 30°.

[0064] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, as some steps can be performed in other orders or simultaneously according to the present invention. Secondly, those skilled in the art should also understand that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to the present invention. It should be understood that the various forms of processes described above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of the present invention can be achieved, and this is not limited herein. The above specific embodiments do not constitute a limitation on the scope of protection of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent control of zoned heat exchange efficiency in cooling towers based on multi-factor collaborative simulation, characterized in that, include: S1, the cross-section of the cooling tower is divided into several concentric annular zones, and the air temperature, humidity, wind speed, and spray water film thickness data of each zone, which are synchronously collected by a distributed sensor array, are fused into the corresponding zone's operating condition vector V. n n is the partition number, 1≤n≤k, n is a positive integer, k is a positive integer greater than or equal to 7, and k is the number of partitions; S2, V n Input a reduced-order CFD model to obtain V' n According to V' n The modified k-ε model and the modified discrete phase model of the water distribution field output the actual heat transfer temperature difference ΔT in each zone. n and pressure drop ΔP n ; S3, aiming to maximize the zone heat transfer efficiency ratio η, uses an improved NSGA-II algorithm to adjust the spray density S of the corresponding zone. n Wind speed W n , packing inclination angle θ n Adjustments will be made.

2. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation as described in claim 1, characterized in that: The area ratio of each partition is partition 1: partition 2: ...: partition k = 1:2: ...:k; where partition 1 is the central region of the concentric ring partitions and is numbered sequentially from the inside out. If any two adjacent zones have a humidity gradient ≤15%RH / m, the cross-section of the cooling tower is re-divided, and the area ratio of the re-divided zones is 1:3:...:k×1.

5.

3. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 2, characterized in that: If it continues for 10 minutes, ΔT k If -ΔT1 > 3℃, the cross-section of the cooling tower is re-divided, and the area of ​​each re-divided section is in the ratio of section 1: section 2: ...: section k = 1:4: ...:k×2; where ΔT k Let ΔT1 be the heat exchange temperature difference of the kth partition, and ΔT1 be the heat exchange temperature difference of the 1st partition.

4. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 3, characterized in that: The revised k-ε model includes an added humidity modification.

5. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 4, characterized in that: The revised discrete phase model of the water distribution field includes a modification for the amount of secondary splashing of the filler material.

6. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation as described in claim 5, characterized in that, The heat transfer efficiency of the nth partition is greater than that of η. n The calculation formula is: Where, ΔT n,max β is the ideal value obtained through a reduced-order CFD model. n H is the environmental compensation coefficient for the nth partition. n Let be the humidity of the nth partition.

7. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 6, characterized in that: If ┃ΔT n -ΔT n-1 If the temperature exceeds 2℃, then the spray density S corresponding to the nth and (n-1)th zones will be adjusted synchronously. n Wind speed W n , packing inclination angle θ n Spray density S n-1 Wind speed W n-1 , packing inclination angle θ n-1 For n≤k, during the adjustment process, the first objective function is to maximize the sum of the heat exchange efficiency ratios of all cooling tower zones, and the second objective function is to minimize the pressure drop variance of all cooling tower zones.

8. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 7, characterized in that, In terms of spray density S n During the adjustment process: like The spray density is 12-15 kg / m³ 2 The spray angle is dynamically adjusted and fixed at 30° to penetrate the high-temperature core airflow. Indicates rounding down; like The spray density is 5-8 kg / m³ 2 The spray angle is dynamically adjusted between 45° and 60° to suppress water droplet escaping. like The spray density is 2-3 kg / m³ 2 The spray angle is dynamically adjusted between 60° and 75°.

9. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 8, characterized in that, At wind speed W n During the adjustment process: like η n >η n-1 Then the wind speed in the nth zone after adjustment is W. b ×80%, allowing heat to dissipate outwards, W b Standard wind speed; like Then, the wind speed W outside the tower is obtained in real time. env If W env If the wind speed is greater than 3 m / s, then the adjusted wind speed in the nth zone is W. env ×1.

2.

10. The intelligent control method for zoned heat exchange efficiency of cooling towers based on multi-factor collaborative simulation according to claim 9, characterized in that: At the packing inclination angle θ n During the adjustment process, the packing material in each zone is composed of deformable corrugated plates with an inclination angle θ. n The adjustment range is 0–30°, and the step accuracy is 0.5°. like And η n If the value is less than 80% of the set value for 3 consecutive hours, the corrugated plate pitch of the nth section will be compressed to 20mm, and the packing inclination angle θ will be adjusted. n Set to 0 to enhance turbulence; like And η n If the flow rate is less than 80% of the set value for 3 consecutive hours, the corrugated plate pitch of the nth zone will be expanded to 50mm to guide the airflow, and the packing inclination angle θ will be adjusted. n Set to 30°.

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