Intelligent control method for cooling tower fan and regulator

Abstract

The invention provides an intelligent control method for a cooling tower fan and a regulator. The method comprises the following steps that the outlet water temperature of a cooling tower is monitored in real time through a temperature sensor, and the outdoor wet bulb temperature of the space where the cooling tower is located is monitored in real time through an outdoor air parameter sensor; calculating the real-time approximation degree of the cooling tower according to the obtained water outlet temperature of the cooling tower and the outdoor wet bulb temperature; the fan rotating speed of the cooling tower is monitored in real time through a frequency converter, and the approximation degree set value is dynamically optimized according to the fan rotating speed, the cooling tower outlet water temperature and the cooling tower real-time approximation degree; and according to the optimized approximation degree set value, the water outlet temperature set value of the cooling tower is adjusted, and the running speed of a draught fan of the cooling tower is controlled. By comprehensively considering the rotating speed of the draught fan, the water outlet temperature of the cooling tower and the real-time approximation degree and dynamically adjusting the approximation degree set value, on the premise that the cooling effect is guaranteed, balance between the energy consumption of the draught fan and the energy consumption of the cold source system is achieved, the energy consumption of the whole system is reduced, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cooling tower equipment, and in particular to an intelligent control method and regulator for a cooling tower fan. Background Art

[0002] As a key heat dissipation device for water-cooled central air-conditioning systems and industrial circulating water systems, cooling towers are widely used in many industries. In traditional designs, cooling towers are usually selected according to the maximum load demand to ensure that the system's heat dissipation requirements can still be met under the most extreme working conditions. However, in actual operation, the load demand of the cooling tower will fluctuate with changes in external environmental conditions, production process requirements and other factors. Due to the lack of a control strategy that can accurately match the heat exchange capacity of the cooling tower with the actual load demand, the heat exchange efficiency of the cooling tower cannot be fully utilized, resulting in a large amount of energy waste.

[0003] At present, the control method of the cooling tower system is relatively simple. The commonly used control method is to adjust the number of cooling towers in operation and the fan speed according to the set cooling water supply temperature. However, this control method has obvious defects. On the one hand, the existing cooling tower water supply temperature setting values ​​are mostly set manually, lacking a scientific and reasonable calculation method, and cannot be dynamically adjusted according to actual operating conditions. This makes the operating state of the cooling tower mismatched with the actual heat dissipation requirements of the system, making it difficult to minimize the energy consumption of the system. On the other hand, the traditional control method fails to fully consider the balance between the energy consumption of the cooling tower fan and the energy consumption of the cold source system, and cannot achieve energy-saving and optimized operation of the entire system while ensuring the cooling effect.

[0004] In view of this, the present application proposes a cooling tower fan intelligent control method and regulator, which can improve the operating efficiency of the cooling tower system and reduce energy consumption. Summary of the invention

[0005] In order to solve the problems of low intelligence level and unreasonable control strategy in the existing cooling tower system control method, the present application provides a cooling tower fan intelligent control method and regulator to solve the above technical defects.

[0006] On the one hand, the present invention provides a cooling tower fan intelligent control method, comprising the following steps:

[0007] S1. Monitor the outlet water temperature of the cooling tower in real time through the temperature sensor, and monitor the outdoor wet-bulb temperature of the space where the cooling tower is located in real time through the outdoor air parameter sensor;

[0008] S2, calculating the real-time approximation of the cooling tower according to the obtained cooling tower outlet water temperature and outdoor wet bulb temperature;

[0009] S3, monitor the fan speed of the cooling tower in real time through the frequency converter, and dynamically optimize the approach degree setting value according to the fan speed, cooling tower outlet water temperature and cooling tower real-time approach degree;

[0010] S4. According to the optimized approach degree setting value, adjust the cooling tower outlet water temperature setting value and control the fan operating speed of the cooling tower.

[0011] Preferably, in step S3, the approximation setting value is optimized according to the fan speed, the cooling tower outlet water temperature and the cooling tower real-time approximation, including:

[0012] S31, preset the optimal value of the fan speed of the cooling tower, the minimum value and the maximum value of the approximation degree, the minimum value and the maximum value of the outlet water temperature;

[0013] S32, determining whether the fan speed of the current cooling tower is less than the optimal fan speed value, if it is less than the optimal fan speed value, continuing to perform the operation of step S33, otherwise performing the operation of step S35;

[0014] S33, judging whether the real-time approximation of the cooling tower is greater than the minimum approximation value, if so, continuing to perform the operation of step S34, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0015] S34, judging whether the cooling tower outlet water temperature setting value is greater than the minimum outlet water temperature value, if it is greater than the minimum outlet water temperature value, then reducing the approximation degree setting value by N, where N is a natural number greater than 0;

[0016] S35, judging whether the real-time approximation of the cooling tower is less than the maximum approximation value, if so, continuing to perform the operation of step S36, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0017] S36. Determine whether the set value of the cooling tower outlet water temperature is less than the maximum value of the outlet water temperature. If it is less than the maximum value of the outlet water temperature, add N to the approximation set value, where N is a natural number greater than 0.

[0018] Further preferably, in step S21, the optimal value of the fan speed is calculated based on the balance relationship between the fan energy consumption and the cold source system energy consumption in the historical operation data of the cooling tower.

[0019] Further preferably, in step S21, a minimum value and a maximum value of the approximation are preset according to the performance parameters of the cooling tower and the environmental conditions.

[0020] Further preferably, in step S21, a minimum value and a maximum value of the outlet water temperature are preset according to a safe operating range of the cooling tower system.

[0021] Preferably, in step S2, the cooling tower real-time approximation is the difference between the cooling tower outlet water temperature and the outdoor wet bulb temperature.

[0022] Preferably, in step S4, the cooling tower outlet water temperature setting value is calculated by the following expression:

[0023] Cooling tower outlet water temperature setting value = cooling tower approach setting value + outdoor wet bulb temperature.

[0024] On the other hand, the present application proposes a cooling tower intelligent controller, which includes:

[0025] Input terminal, output terminal, data processing unit, communication module and data processing unit;

[0026] The input end is connected to a temperature sensor to monitor the inlet and outlet cooling water temperature of the cooling tower;

[0027] The output end is connected to the frequency converter, and outputs instructions to the frequency converter in the form of switch signals and analog signals, and then controls the start and stop and speed of the fan through the frequency converter;

[0028] The communication module is used to read data remotely and communicate with the inverter and outdoor air parameter sensor to obtain the real-time operating power, current data and outdoor wet-bulb temperature of the fan;

[0029] The data processing unit calculates the real-time approximation of the cooling tower according to the cooling tower outlet water temperature obtained by the input end and the outdoor wet-bulb temperature obtained by the communication module, and dynamically optimizes the approximation setting value, and controls the fan operating speed of the cooling tower through the output end.

[0030] Further preferably, the built-in optimization logic of the data processing unit includes:

[0031] S31, preset the optimal value of the fan speed of the cooling tower, the minimum value and the maximum value of the approximation degree, the minimum value and the maximum value of the outlet water temperature;

[0032] S32, determining whether the fan speed of the current cooling tower is less than the optimal fan speed value, if it is less than the optimal fan speed value, continuing to perform the operation of step S33, otherwise performing the operation of step S35;

[0033] S33, judging whether the real-time approximation of the cooling tower is greater than the minimum approximation value, if so, continuing to perform the operation of step S34, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0034] S34, determining whether the cooling tower outlet water temperature setting value is greater than the minimum outlet water temperature value, if so, reducing the approximation setting value by N, where N is a natural number greater than 0;

[0035] S35, judging whether the real-time approximation of the cooling tower is less than the maximum approximation value, if so, continuing to perform the operation of step S36, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0036] S36. Determine whether the cooling tower outlet water temperature setting value is less than the maximum outlet water temperature. If so, add N to the approximation setting value, where N is a natural number greater than 0.

[0037] Compared with the prior art, the beneficial results of the present invention are:

[0038] (1) Improve the level of intelligence: By real-time monitoring of key parameters such as cooling tower outlet water temperature and outdoor wet-bulb temperature, and dynamically optimizing the approach setting value based on these data, intelligent control of the cooling tower fan operation is achieved, effectively improving the automation and intelligence level of the system.

[0039] (2) Optimize energy consumption balance: By comprehensively considering the fan speed, cooling tower outlet water temperature and real-time approach, the approach setting value is dynamically adjusted. Thus, while ensuring the cooling effect, the fan energy consumption and the energy consumption of the cooling source system are balanced, reducing the energy consumption of the entire system and improving energy utilization efficiency.

[0040] (3) Enhanced operational stability: Relevant thresholds are preset based on the historical operating data and performance parameters of the cooling tower to ensure stable operation of the system under different working conditions, avoid frequent start-stop or abnormal operation of the equipment due to unreasonable parameter settings, and improve the reliability and service life of the cooling tower system.

[0041] (4) Improve environmental adaptability: The control strategy can be adjusted in time according to changes in outdoor environmental conditions, so that the cooling tower system can maintain a good operating state under various environmental conditions, enhancing the environmental adaptability of the system.

[0042] (5) Easy to integrate and expand: The proposed cooling tower intelligent controller has a clear structure and clear functions of each module, which makes it easy to integrate with other equipment or systems. It also provides convenience for subsequent functional expansion and upgrading.

[0043] (6) Precise control and efficient operation: The frequency converter is used to accurately control the start and stop and speed of the fan. Combined with real-time data monitoring and optimization algorithms, precise adjustment of the cooling tower fan operation is achieved, ensuring that the cooling tower system always operates in the high-efficiency range, further improving the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0045] Figure 1 is a flow chart of a cooling tower fan intelligent control method according to the present application;

[0046] Figure 2 is a schematic diagram of the optimization principle of the approach setting value of the cooling tower system according to the present application;

[0047] Figure 3 is a schematic diagram of a cooling tower system according to the present application;

[0048] Figure 4 is a schematic diagram of an intelligent controller for a cooling tower system according to the present application.

[0049] Figure numerals: 1 is a cooling tower inlet water temperature sensor, 2 is a cooling tower outlet water temperature sensor, 3 is a cooling tower fan inverter, and 4 is an ATPS outdoor air parameter sensor. DETAILED DESCRIPTION

[0050] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0051] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] Figure 1 The flowchart of the cooling tower fan intelligent control method of the present application is shown in FIG. Figure 1 , the method comprises the following steps:

[0053] S1. The outlet water temperature of the cooling tower is monitored in real time by a temperature sensor, and the outdoor wet-bulb temperature of the space where the cooling tower is located is monitored in real time by an outdoor air parameter sensor.

[0054] S2. Calculate the real-time approximation of the cooling tower according to the obtained cooling tower outlet water temperature and outdoor wet bulb temperature.

[0055] Specifically, the optimal fan speed can be calculated based on the balance relationship between the fan energy consumption and the cold source system energy consumption in the historical operation data of the cooling tower. The minimum and maximum approximation values ​​can be preset according to the performance parameters and environmental conditions of the cooling tower. The minimum and maximum outlet water temperature can be preset according to the safe operating range of the cooling tower system.

[0056] S3. Monitor the fan speed of the cooling tower in real time through the frequency converter, and dynamically optimize the approach setting value according to the fan speed, cooling tower outlet water temperature and cooling tower real-time approach.

[0057] Figure 2 The schematic diagram of the optimization principle of the approximation setting value of the cooling tower system of the present application is shown as follows: Figure 2 As shown, in a specific embodiment, the approach degree setting value is optimized according to the fan speed, the cooling tower outlet water temperature and the cooling tower real-time approach degree, including the following steps:

[0058] S31, preset the optimal value of the fan speed of the cooling tower, the minimum value and the maximum value of the approximation degree, the minimum value and the maximum value of the outlet water temperature;

[0059] S32, determining whether the fan speed of the current cooling tower is less than the optimal fan speed value, if it is less than the optimal fan speed value, continuing to perform the operation of step S33, otherwise performing the operation of step S35;

[0060] S33, judging whether the real-time approximation of the cooling tower is greater than the minimum approximation value, if so, continuing to perform the operation of step S34, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0061] S34, judging whether the cooling tower outlet water temperature setting value is greater than the minimum outlet water temperature value, if it is greater than the minimum outlet water temperature value, then reducing the approximation degree setting value by N, where N is a natural number greater than 0;

[0062] S35, judging whether the real-time approximation of the cooling tower is less than the maximum approximation value, if so, continuing to perform the operation of step S36, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0063] S36. Determine whether the set value of the cooling tower outlet water temperature is less than the maximum value of the outlet water temperature. If it is less than the maximum value of the outlet water temperature, add N to the approximation set value, where N is a natural number greater than 0.

[0064] Specifically, when the cooling tower is operating in normal mode, the cooling tower fan speed is regulated based on the cooling tower outlet water temperature set value CTSTSP and the cooling tower outlet water temperature with the help of the PI control strategy; among them, the key point of optimization is to optimize the approach set value of the cooling tower, and the optimization logic is: during the normal mode operation of the cooling tower, the outdoor ambient wet-bulb temperature Tw, the cooling tower outlet water temperature CTST and the cooling tower fan speed CTFanSpd are monitored in real time.

[0065] If the cooling tower fan speed CTFanSpd is less than the optimal value OptCTFanSpd of the cooling tower fan speed, and the cooling tower real-time approach degree CTApproach is greater than CTApproachMin, the minimum value of the cooling tower approach degree, and the cooling tower outlet water temperature setting value CTSTSP is greater than (the minimum value of the cooling tower outlet water temperature setting value) CTSTSPMin+1, then the cooling tower approach degree setting value is reduced by 1;

[0066] If the cooling tower fan speed CTFanSpd>the cooling tower fan speed optimal value OptCTFanSpd, and the cooling tower real-time approach degree CTApproach<CTApproachMax the cooling tower approach degree maximum value, and the cooling tower outlet water temperature setting value CTSTSP<(cooling tower outlet water temperature setting value maximum value) CTSTSPMax-1, then the cooling tower approach degree setting value is increased by 1.

[0067] The real-time approach of the cooling tower CTApproach = the real-time outlet water temperature of the cooling tower CTST - the outdoor wet-bulb temperature Tw.

[0068] Cooling tower outlet water temperature setting value CTSTSP = cooling tower approach setting value CTApproachSP + outdoor wet bulb temperature Tw.

[0069] S4. According to the optimized approach degree setting value, adjust the cooling tower outlet water temperature setting value and control the fan operating speed of the cooling tower.

[0070] Figure 3 The schematic diagram of the cooling tower system of the present application is shown. Figure 4 The schematic diagram of the cooling tower system intelligent controller of the present application is shown, and the Figure 3 and Figure 4 The cooling tower intelligent controller proposed in this application includes the following modules:

[0071] Input terminal, output terminal, data processing unit, communication module and data processing unit;

[0072] The input end is connected to a temperature sensor to monitor the inlet and outlet cooling water temperature of the cooling tower;

[0073] The output end is connected to the frequency converter, and outputs instructions to the frequency converter in the form of switch signals and analog signals, and then controls the start and stop and speed of the fan through the frequency converter. The fan frequency converter is a necessary device to realize the intelligent control of the cooling tower system;

[0074] The communication module is used to read data remotely and communicate with the inverter and outdoor air parameter sensor (ATPS). The purpose of communicating with the inverter is to obtain data such as the real-time operating power and current of the fan, and the purpose of communicating with the ATPS is to obtain the wet-bulb temperature of the outdoor environment.

[0075] The data processing unit is the core of the controller's data processing, with built-in control algorithms and optimization logic. It processes the data obtained from the input and communication terminals, and controls the fan speed through the output terminal. Specifically, the data processing unit calculates the real-time approximation of the cooling tower based on the cooling tower outlet water temperature obtained from the input terminal and the outdoor wet-bulb temperature obtained from the communication module, and dynamically optimizes the approximation setting value, and controls the fan speed of the cooling tower through the output terminal.

[0076] Among them, the built-in optimization logic of the data processing unit includes:

[0077] S31, preset the optimal value of the fan speed of the cooling tower, the minimum value and the maximum value of the approximation degree, the minimum value and the maximum value of the outlet water temperature;

[0078] S32, determining whether the fan speed of the current cooling tower is less than the optimal fan speed value, if it is less than the optimal fan speed value, continuing to perform the operation of step S33, otherwise performing the operation of step S35;

[0079] S33, judging whether the real-time approximation of the cooling tower is greater than the minimum approximation value, if so, continuing to perform the operation of step S34, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0080] S34, determining whether the cooling tower outlet water temperature setting value is greater than the minimum outlet water temperature value, if so, reducing the approximation setting value by N, where N is a natural number greater than 0;

[0081] S35, judging whether the real-time approximation of the cooling tower is less than the maximum approximation value, if so, continuing to perform the operation of step S36, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4;

[0082] S36. Determine whether the cooling tower outlet water temperature setting value is less than the maximum outlet water temperature. If so, add N to the approximation setting value, where N is a natural number greater than 0.

[0083] The present invention can balance the energy consumption of the fan and the energy consumption of the cold source system. The system optimizes the cooling tower approach degree setting value according to different cooling tower fan speeds, cooling tower outlet water temperatures and cooling tower real-time approach degrees, rather than using a fixed cooling tower outlet water temperature setting value. This method can give full play to the cooling capacity of the cooling tower and optimize the cooling tower outlet water temperature according to the energy consumption of the cold source system and the cooling tower energy consumption.

[0084] Compared with the traditional system, the structure of the control system (cooling tower intelligent controller) of the present application needs to meet the implementation of the proposed control method. Compared with the original system, it adds temperature sensors, fan inverters and outdoor environmental parameter sensors, and optimizes the fan speed of the cooling tower system based on the control of the outdoor environmental wet-bulb temperature and real-time approximation.

[0085] Through real-time monitoring, optimized control, fault prediction and diagnosis and other methods, the energy efficiency of the cooling tower system can be improved and the operating costs can be reduced. From the perspective of the equipment itself, it can greatly improve its own operating efficiency, reduce equipment loss and increase its service life; from the perspective of energy consumption, it can effectively reduce unnecessary energy loss, reduce production costs, respond to national policies, and achieve energy conservation and emission reduction.

[0086] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A cooling tower fan intelligent control method, characterized in that: The following steps are involved: S1. Monitor the outlet water temperature of the cooling tower in real time through a temperature sensor, and monitor the outdoor wet-bulb temperature of the space where the cooling tower is located in real time through an outdoor air parameter sensor; S2. Calculating the real-time approximation degree of the cooling tower according to the obtained cooling tower outlet water temperature and outdoor wet-bulb temperature; S3, monitoring the fan speed of the cooling tower in real time through a frequency converter, and dynamically optimizing the approach degree setting value according to the fan speed, the cooling tower outlet water temperature and the real-time approach degree of the cooling tower; S4. According to the optimized approach degree setting value, adjust the cooling tower outlet water temperature setting value and control the fan operating speed of the cooling tower.

2. The cooling tower fan intelligent control method according to claim 1 is characterized in that: In step S3, the approximation setting value is optimized according to the fan speed, the cooling tower outlet water temperature and the cooling tower real-time approximation, including: S31, presetting the optimal value of the fan speed, the minimum value and the maximum value of the approximation degree, the minimum value and the maximum value of the outlet water temperature of the cooling tower; S32, determining whether the current fan speed of the cooling tower is less than the optimal fan speed value, if it is less than the optimal fan speed value, continuing to perform the operation of step S33, otherwise performing the operation of step S35; S33, judging whether the real-time approximation of the cooling tower is greater than the minimum approximation value, if so, continuing to perform the operation of step S34, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4; S34, judging whether the cooling tower outlet water temperature setting value is greater than the outlet water temperature minimum value, if it is greater than the outlet water temperature minimum value, then reducing the approximation setting value by N, where N is a natural number greater than 0; S35, judging whether the real-time approximation of the cooling tower is less than the maximum approximation value, if so, continuing to perform the operation of step S36, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4; S36. Determine whether the cooling tower outlet water temperature setting value is less than the outlet water temperature maximum value. If it is less than the outlet water temperature maximum value, add N to the approximation setting value, where N is a natural number greater than 0.

3. The cooling tower fan intelligent control method according to claim 2 is characterized in that: In step S21, the optimal value of the fan speed is calculated based on the balance relationship between the fan energy consumption and the cold source system energy consumption in the historical operation data of the cooling tower.

4. The cooling tower fan intelligent control method according to claim 2 is characterized in that: In step S21, the minimum value and the maximum value of the approximation are preset according to the performance parameters of the cooling tower and the environmental conditions.

5. The cooling tower fan intelligent control method according to claim 2, characterized in that: In step S21, the minimum value and the maximum value of the outlet water temperature are preset according to the safe operation range of the cooling tower system.

6. The cooling tower fan intelligent control method according to claim 1, characterized in that: In step S2, the cooling tower real-time approximation is the difference between the cooling tower outlet water temperature and the outdoor wet bulb temperature.

7. The cooling tower fan intelligent control method according to claim 1, characterized in that: In step S4, the cooling tower outlet water temperature setting value is calculated by the following expression: Cooling tower outlet water temperature setting value = cooling tower approach setting value + outdoor wet bulb temperature.

8. A cooling tower intelligent controller, characterized in that: include: Input terminal, output terminal, data processing unit, communication module and data processing unit; The input end is connected to a temperature sensor for monitoring the inlet and outlet cooling water temperature of the cooling tower; The output end is connected to the frequency converter, outputs instructions to the frequency converter in the form of switch signals and analog signals, and then controls the start and stop and speed of the fan through the frequency converter; The communication module is used to read data remotely and communicate with the frequency converter and outdoor air parameter sensor, so as to obtain the real-time operating power, current data and outdoor wet-bulb temperature of the fan; The data processing unit calculates the real-time approximation of the cooling tower according to the cooling tower outlet water temperature obtained by the input end and the outdoor wet-bulb temperature obtained by the communication module, and dynamically optimizes the approximation setting value, and controls the fan operating speed of the cooling tower through the output end.

9. The cooling tower intelligent controller according to claim 8, characterized in that: The built-in optimization logic of the data processing unit includes: S31, presetting the optimal value of the fan speed, the minimum value and the maximum value of the approximation degree, the minimum value and the maximum value of the outlet water temperature of the cooling tower; S32, determining whether the current fan speed of the cooling tower is less than the optimal fan speed value, if it is less than the optimal fan speed value, continuing to perform the operation of step S33, otherwise performing the operation of step S35; S33, judging whether the real-time approximation of the cooling tower is greater than the minimum approximation value, if so, continuing to perform the operation of step S34, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4; S34, determining whether the cooling tower outlet water temperature setting value is greater than the outlet water temperature minimum value, if so, reducing the approximation setting value by N, where N is a natural number greater than 0; S35, judging whether the real-time approximation of the cooling tower is less than the maximum approximation value, if so, continuing to perform the operation of step S36, otherwise keeping the current approximation setting value unchanged, and continuing to perform the operation of step S4; S36. Determine whether the cooling tower outlet water temperature setting value is less than the outlet water temperature maximum value. If so, add N to the approximation setting value, where N is a natural number greater than 0.

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