Power plant circulating cooling water intelligent operation management method based on artificial intelligence

Abstract

The invention belongs to the field of cooling water management, and particularly relates to a power plant circulating cooling water intelligent operation management method based on artificial intelligence, and the method comprises the steps: cooling water flow screening, cooling water consumption, cooling demand prediction, current water price and electricity price obtaining, and cooling system operation strategy optimization. According to the scheme, the circulating cooling water system is analyzed through fluid dynamics, a transfer model of a flow meter is established, according to residual signal characteristics, primary screening of common faults is achieved, false detection and missing detection are reduced, and the faults of the water circulation system are monitored and managed in a centralized mode; two advantages of zero water consumption of a dry air cooler and high efficiency of a wet cooling tower are integrated, according to cooling requirements, through a simulated annealing algorithm, economic benefits of a power plant are maximized, an operation strategy of a circulating cooling water system is solved, operation time of the dry air cooler and the wet cooling tower is optimized, and frequent start and stop of cooling equipment are reduced.

Description

Technical Field

[0001] The present invention relates to the field of cooling water management, and specifically to an intelligent operation management method for circulating cooling water in a power plant based on artificial intelligence. Background Art

[0002] With the rapid development of the economy, energy consumption and environmental pollution are becoming increasingly serious. Power plants, as large energy consumers, have attracted widespread attention. The intelligent operation and management method of circulating cooling water in power plants based on artificial intelligence refers to a method that uses deep learning technology to support the energy-saving operation of circulating cooling water systems.

[0003] Among the existing approximate solutions, for example, CN117951631B is an intelligent constant temperature and constant pressure cooling water circulation system. This solution aims at the technical problem that the existing technology cannot determine abnormal data in a timely and accurate manner, thereby affecting the efficient operation of the cooling water circulation system. It uses LOF abnormality detection algorithm and other means to screen out a first abnormal point set according to the distribution of water circulation data sample points in the sample space, calculate the position deviation characteristics of each sample point, and combine the position deviation characteristics and the degree of aggregation to screen out a second abnormal point set, determine the final abnormal point set, and control and adjust the water circulation parameters of the abnormal monitoring nodes, thereby achieving the technical effect of accurate detection and timely control of abnormal data in the cooling water circulation system. However, the existing technology mainly relies on the LOF algorithm for abnormality detection, and the LOF algorithm is more sensitive to noise data, which may cause false detection and missed detection, and the existing technology mainly focuses on the abnormality of water circulation parameters and lacks the technical problem of identifying system faults;

[0004] In addition, for example, CN112836935B is a smart water management platform suitable for power plants. This solution targets the technical problem of unclear underground pipe networks in thermal power plants. By surveying the locations of the pipe networks, flow meters and online water quality detection instruments throughout the plant, a three-dimensional pipe network of the entire plant is drawn, and a thermal model is established for the water-using equipment in the entire plant. According to the ambient temperature, medium temperature and unit load conditions at different times, the reasonable water consumption is calculated, and automatic adjustment is achieved to achieve the technical effect of saving water. In the circulating cooling water system, the wet cooling tower uses the evaporation of water to absorb heat to reduce the temperature of the cooling water. The cooling water circulates in the tower and exchanges heat with the air. In exchange, water vapor will evaporate from the cooling water, taking away heat and lowering the water temperature. Wet cooling towers are highly efficient but consume a lot of water resources. Air coolers use air as a cooling medium to transfer heat from the cooling water to the air. The cooling water flows in the pipes, and the outside of the pipes is air. The air flow takes away heat and lowers the water temperature. Air coolers do not consume water resources, but their efficiency is relatively low. Therefore, the existing technology has the problem that in high temperature environments, the use of wet cooling towers can significantly reduce the condensation temperature and improve power generation efficiency, but when water prices are high, the cost of using wet cooling towers may exceed the benefits they bring, and the implementation and maintenance of the system may require high-cost technical issues. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an intelligent operation and management method for circulating cooling water in a power plant based on artificial intelligence. In view of the fact that the prior art mainly relies on the LOF algorithm for anomaly detection, and the LOF algorithm is more sensitive to noise data, which may cause false detection and missed detection, and the prior art mainly focuses on the abnormality of water circulation parameters and lacks the technical problem of identifying system faults, this scheme uses fluid dynamics to analyze the circulating cooling water system, establishes a transfer model of the flow meter, and realizes preliminary screening of common faults according to the characteristics of the residual signal, reduces false detection and missed detection, and centrally monitors and manages water circulation system faults; in view of the technical problem that the use of wet cooling towers can significantly reduce the condensation temperature and improve the power generation efficiency in a high temperature environment, but when the water price is high, the use cost of the wet cooling tower may exceed the benefits it brings, and the implementation and maintenance of the system may require high costs, this scheme combines the two advantages of zero water consumption of dry air coolers and high efficiency of wet cooling towers, and according to the cooling demand, through the simulated annealing algorithm, maximizes the economic benefits of the power plant, solves the operation strategy of the circulating cooling water system, optimizes the operation time of dry air coolers and wet cooling towers, and reduces the frequent start and stop of cooling equipment.

[0006] The technical solution adopted by the present invention is as follows: The present invention provides an intelligent operation management method for circulating cooling water in a power plant based on artificial intelligence, and the method comprises the following steps:

[0007] Step S1: Cooling water flow screening to obtain cooling water flow;

[0008] Step S2: Calculation of cooling water consumption. Specifically, the circulating cooling water system consists of a dry air cooler and a wet cooling tower. The water evaporation amount during the operation of the wet cooling tower and the water replenishment amount of the wet cooling tower are obtained. The formula used is as follows: ;

[0009] In the formula, Indicates the amount of water evaporation in a wet cooling tower. represents the air mass flow rate, It represents the humidity ratio of the air at the outlet of the wet cooling tower. Indicates the humidity ratio of the wet cooling tower inlet air; ;

[0010] In the formula, Indicates the water replenishment amount of the wet cooling tower. Indicates the amount of water evaporation in a wet cooling tower. represents the circulating concentration ratio;

[0011] Step S3: Cooling demand prediction, specifically, collecting cooling historical data, the cooling historical data includes the operating status of the chiller, outdoor temperature, cooling water loop temperature, total power generation and refrigeration energy consumption, using linear interpolation to fill in the missing values ​​of the cooling historical data, and using deep learning technology to predict the cooling demand for a period of time in the future in advance. The cooling demand is expressed by the following formula: ;

[0012] In the formula, Indicates the cooling demand, represents the dry bulb temperature, represents the wet bulb temperature, Represents the model fitted by the deep learning technique;

[0013] Step S4: Calculation of net power output. Obtain the current water price and electricity price. Subtract the cooling energy consumption of the circulating cooling water system from the total power generation of the power plant as the net power output. Construct the total objective function based on the current water price and electricity price required for the operation of the circulating cooling water system. The formula used is as follows: ;

[0014] In the formula, represents the overall objective function, Indicates the electricity price, represents the net electrical energy production, Indicates the power consumption of wet cooling tower, Indicates the power consumption of dry air cooler. Indicates the number of wet cooling towers, Number of dry air coolers, represents the water replenishment amount of the wet cooling tower, Indicates the water price, represents the cooling water density, represents the cooling requirement, represents the weight coefficient;

[0015] Step S5: Optimize the cooling system operation strategy, which is used to maximize the total objective function through the simulated annealing algorithm, and solve the circulating cooling water system operation strategy. Specifically, by maximizing the total objective function, the economic benefits of the power plant are maximized, and the operation configuration parameters of the circulating cooling water system are adjusted. The operation configuration parameters include the number of dry air coolers, the number of wet cooling towers, the condensing temperature, the fan speed, the water pump speed, and the cooling water temperature. The circulating water-to-air ratio is calculated, the ratio range is preset, and the constraints in the maximization process are determined. The constraints include that the target cooling temperature must be lower than the steam temperature, the cooling water temperature must be higher than the wet bulb temperature of the wet cooling tower, and the circulating water-to-air ratio must be within the ratio range. The calculation formula of the circulating water-to-air ratio is as follows: ;

[0016] In the formula, Indicates the circulating water-air ratio, represents the cooling water density, represents the cooling water flow rate, Indicates the air mass flow rate.

[0017] Furthermore, in step S1, the cooling water flow screening specifically includes the following steps:

[0018] Step S11: Establishing a model, specifically, using fluid dynamics to analyze the flow meter in the circulating cooling water system, and establishing a transfer model of the flow meter. The transfer model is a mathematical model used to describe the input-output relationship of the flow meter. The transfer model construction formula is as follows: ;

[0019] In the formula, represents the transfer model, represents a complex variable, represents the gain, and All represent time constants;

[0020] Step S12: converting the transfer model into a linear time-varying system;

[0021] Step S13: Calculate the estimated output of the linear time-varying system, using the following formula: ;

[0022] In the formula, represents the estimated output, represents the state estimate, represents the output matrix, represents the direct transfer matrix, represents the input vector;

[0023] Step S14: Fault screening, specifically, comparing the estimated output of the linear time-varying system with the actual measured output of the flow meter, generating a residual signal, and detecting the fault of the flow meter. In the absence of faults, the residual signal is close to zero. When the flow meter in the circulating cooling water system fails, the residual increases.

[0024] Furthermore, in step S14, the fault screening specifically includes the following steps:

[0025] Step S141: Measure the flow rate, preset the residual threshold, and calculate the cooling water flow rate corresponding to the actual measured output of the flow meter. The formula used is as follows: ;

[0026] In the formula, Indicates the cooling water flow rate, represents the cross-sectional area of ​​the pipe, Indicates the ratio between the actual cooling water flow rate and the theoretical cooling water flow rate. Indicates the ratio of flow meter diameter to pipe diameter. and represent the upstream and downstream pressures respectively, Indicates cooling water density;

[0027] Step S142: residual operation, recording the difference between the actual measured output and the estimated output as the residual signal, and calculating the root mean square value of the residual signal. The formula used is as follows: ;

[0028] In the formula, represents the RMS value of the residual signal, represents the RMS operation, represents the residual signal, represents the actual measured output, represents the estimated output;

[0029] Step S143: Fault determination: if the RMS value exceeds the residual threshold, it is determined that there is a fault in the circulating cooling water system, and step S144 is executed; otherwise, step S144 is skipped;

[0030] Step S144: Fault classification, using the amplitude and slope of the residual signal as input features, using a neural network to classify the fault into drift fault, open circuit fault and short circuit fault, and informing the specialist to perform maintenance.

[0031] The present invention provides an intelligent operation management method for circulating cooling water in a power plant based on artificial intelligence. The beneficial effects achieved by the present invention using the above scheme are as follows:

[0032] (1) In view of the fact that the existing technology mainly relies on the LOF algorithm for anomaly detection, and the LOF algorithm is sensitive to noise data, which may cause false detection and missed detection, and the existing technology mainly focuses on the abnormalities of water circulation parameters and lacks the technical problem of identifying system faults, this solution uses fluid dynamics to analyze the circulating cooling water system, establishes the transfer model of the flow meter, and realizes the preliminary screening of common faults based on the characteristics of the residual signal, reduces false detection and missed detection, and centrally monitors and manages water circulation system faults;

[0033] (2) In high temperature environments, the use of wet cooling towers can significantly reduce the condensation temperature and improve power generation efficiency. However, when water prices are high, the cost of using wet cooling towers may exceed the benefits they bring, and the implementation and maintenance of the system may require high costs. This solution combines the two advantages of zero water consumption of dry air coolers and high efficiency of wet cooling towers. According to the cooling needs, the simulated annealing algorithm is used to maximize the economic benefits of the power plant, solve the operating strategy of the circulating cooling water system, optimize the operating time of dry air coolers and wet cooling towers, and reduce the frequent start and stop of cooling equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of the process of the intelligent operation management method of circulating cooling water in a power plant based on artificial intelligence provided by the present invention;

[0035] Figure 2 is a schematic diagram of step S1;

[0036] Figure 3 is a schematic diagram of step S14.

[0037] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0040] Example 1, see Figures 1 to 3 The present invention provides an artificial intelligence-based intelligent operation management method for circulating cooling water in a power plant, the method comprising the following steps:

[0041] Step S1: Cooling water flow screening to obtain cooling water flow;

[0042] Step S2: Calculation of cooling water consumption. Specifically, the circulating cooling water system consists of a dry air cooler and a wet cooling tower. The water evaporation amount during the operation of the wet cooling tower and the water replenishment amount of the wet cooling tower are obtained. The formula used is as follows: ;

[0043] In the formula, Indicates the amount of water evaporation in a wet cooling tower. represents the air mass flow rate, It represents the humidity ratio of the air at the outlet of the wet cooling tower. Indicates the humidity ratio of the wet cooling tower inlet air; ;

[0044] In the formula, Indicates the water replenishment amount of the wet cooling tower. Indicates the amount of water evaporation in a wet cooling tower. represents the circulating concentration ratio;

[0045] Step S3: Cooling demand prediction, specifically, collecting cooling historical data, the cooling historical data includes the operating status of the chiller, outdoor temperature, cooling water loop temperature, total power generation and refrigeration energy consumption, using linear interpolation to fill in the missing values ​​of the cooling historical data, and using deep learning technology to predict the cooling demand for a period of time in the future in advance. The cooling demand is expressed by the following formula: ;

[0046] In the formula, Indicates the cooling demand, represents the dry bulb temperature, represents the wet bulb temperature, Represents the model fitted by the deep learning technique;

[0047] Step S4: Calculation of net power output. Obtain the current water price and electricity price. Subtract the cooling energy consumption of the circulating cooling water system from the total power generation of the power plant as the net power output. Construct the total objective function based on the current water price and electricity price required for the operation of the circulating cooling water system. The formula used is as follows: ;

[0048] In the formula, represents the overall objective function, Indicates the electricity price, represents the net electrical energy production, Indicates the power consumption of wet cooling tower, Indicates the power consumption of dry air cooler. Indicates the number of wet cooling towers, Number of dry air coolers, represents the water replenishment amount of the wet cooling tower, Indicates the water price, represents the cooling water density, represents the cooling requirement, represents the weight coefficient;

[0049] Step S5: Optimize the cooling system operation strategy, which is used to maximize the total objective function through the simulated annealing algorithm, and solve the circulating cooling water system operation strategy. Specifically, by maximizing the total objective function, the economic benefits of the power plant are maximized, and the operation configuration parameters of the circulating cooling water system are adjusted. The operation configuration parameters include the number of dry air coolers, the number of wet cooling towers, the condensing temperature, the fan speed, the water pump speed, and the cooling water temperature. The circulating water-to-air ratio is calculated, the ratio range is preset, and the constraints in the maximization process are determined. The constraints include that the target cooling temperature must be lower than the steam temperature, the cooling water temperature must be higher than the wet bulb temperature of the wet cooling tower, and the circulating water-to-air ratio must be within the ratio range. The calculation formula of the circulating water-to-air ratio is as follows: ;

[0050] In the formula, Indicates the circulating water-air ratio, represents the cooling water density, represents the cooling water flow rate, Indicates the air mass flow rate.

[0051] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S1, the cooling water flow screening specifically includes the following steps:

[0052] Step S11: Establishing a model, specifically, using fluid dynamics to analyze the flow meter in the circulating cooling water system, and establishing a transfer model of the flow meter. The transfer model is a mathematical model used to describe the input-output relationship of the flow meter. The transfer model construction formula is as follows: ;

[0053] In the formula, represents the transfer model, represents a complex variable, represents the gain, and All represent time constants;

[0054] Step S12: converting the transfer model into a linear time-varying system;

[0055] Step S13: Calculate the estimated output of the linear time-varying system, using the following formula: ;

[0056] In the formula, represents the estimated output, represents the state estimate, represents the output matrix, represents the direct transfer matrix, represents the input vector;

[0057] Step S14: Fault screening, specifically, comparing the estimated output of the linear time-varying system with the actual measured output of the flow meter, generating a residual signal, and detecting the fault of the flow meter. In the absence of faults, the residual signal is close to zero. When the flow meter in the circulating cooling water system fails, the residual increases.

[0058] Example 3, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S14, the fault screening specifically includes the following steps:

[0059] Step S141: Measure the flow rate, preset the residual threshold, and calculate the cooling water flow rate corresponding to the actual measured output of the flow meter. The formula used is as follows: ;

[0060] In the formula, Indicates the cooling water flow rate, represents the cross-sectional area of ​​the pipe, Indicates the ratio between the actual cooling water flow rate and the theoretical cooling water flow rate. Indicates the ratio of flow meter diameter to pipe diameter. and represent the upstream and downstream pressures respectively, Indicates cooling water density;

[0061] Step S142: residual operation, recording the difference between the actual measured output and the estimated output as the residual signal, and calculating the root mean square value of the residual signal. The formula used is as follows: ;

[0062] In the formula, represents the RMS value of the residual signal, represents the RMS operation, represents the residual signal, represents the actual measured output, represents the estimated output;

[0063] Step S143: Fault determination: if the RMS value exceeds the residual threshold, it is determined that there is a fault in the circulating cooling water system, and step S144 is executed; otherwise, step S144 is skipped;

[0064] Step S144: Fault classification, using the amplitude and slope of the residual signal as input features, using a neural network to classify the fault into drift fault, open circuit fault and short circuit fault, and informing the specialist to perform maintenance.

[0065] Example 4, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S14, the actual measured output refers to the current signal 16mA output by the differential pressure transmitter of the flow meter.

[0066] Example 5, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S141, the residual threshold is fixed at 1.2 mA.

[0067] Example 6, see Figures 1 to 3 This embodiment is based on the above embodiment. This embodiment differs from the above embodiment only in the selection of the residual threshold, which is 15% of the estimated output.

[0068] Embodiment 7, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S144, the neural network is used to classify the fault into drift fault, open circuit fault and short circuit fault. Specifically,

[0069] Step S1441: If the residual gradually increases over time and the slope changes, it is determined to be a drift fault;

[0070] Step S1442: If the residual suddenly becomes zero, it is determined to be an open circuit fault;

[0071] Step S1443: If the residual suddenly changes and stabilizes at a fixed value, it is determined to be a short circuit fault.

[0072] Embodiment 8, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S2, the circulation concentration ratio is set to 3.

[0073] Embodiment 9, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S3, the period of time is specifically one day.

[0074] Embodiment 10, see Figures 1 to 3 This embodiment is based on the above embodiment. In step S5, the ratio range is set to 0.5 to 2.5.

[0075] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0076] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0077] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An intelligent operation and management method for circulating cooling water in a power plant based on artificial intelligence, characterized in that: The method comprises the following steps: Step S1: Cooling water flow screening to obtain cooling water flow; Step S2: Calculate the cooling water consumption, specifically, the circulating cooling water system consists of a dry air cooler and a wet cooling tower, and obtain the water evaporation amount during the operation of the wet cooling tower and the water replenishment amount of the wet cooling tower; Step S3: Cooling demand prediction, specifically, collecting cooling historical data, the cooling historical data including total power generation and cooling energy consumption, using linear interpolation to fill in missing values ​​of cooling historical data, and using deep learning technology to predict cooling demand in the future in advance; Step S4: Calculate the net power output, obtain the current water price and electricity price, deduct the cooling energy consumption of the circulating cooling water system from the total power generation of the power plant as the net power output, and construct the total objective function according to the current water price and electricity price required for the operation of the circulating cooling water system; Step S5: Optimizing the cooling system operation strategy, which is used to maximize the total objective function through a simulated annealing algorithm to solve the circulating cooling water system operation strategy.

2. The method for intelligent operation and management of circulating cooling water in a power plant based on artificial intelligence according to claim 1 is characterized in that: In step S1, the cooling water flow screening specifically includes the following steps: Step S11: establishing a model, specifically, using fluid dynamics to analyze the flow meter in the circulating cooling water system and establishing a transfer model of the flow meter; Step S12: converting the transfer model into a linear time-varying system; Step S13: Calculate the estimated output of the linear time-varying system; Step S14: Fault screening, specifically, comparing the estimated output of the linear time-varying system with the actual measured output of the flow meter to generate a residual signal and detect faults of the flow meter.

3. The method for intelligent operation and management of circulating cooling water in a power plant based on artificial intelligence according to claim 2 is characterized in that: In step S14, the fault screening specifically includes the following steps: Step S141: Measure the flow rate, preset the residual threshold, and calculate the cooling water flow rate corresponding to the actual measurement output of the flow meter; Step S142: residual operation, recording the difference between the actual measured output and the estimated output as a residual signal, and calculating the root mean square value of the residual signal; Step S143: Fault determination: if the RMS value exceeds the residual threshold, it is determined that there is a fault in the circulating cooling water system, and step S144 is executed; otherwise, step S144 is skipped; Step S144: Fault classification, using the amplitude and slope of the residual signal as input features, using a neural network to classify the fault into drift fault, open circuit fault and short circuit fault, and informing the specialist to perform maintenance.

4. The method for intelligent operation and management of circulating cooling water in a power plant based on artificial intelligence according to claim 1 is characterized in that: In step S4, the overall objective function is constructed, specifically including the following operations: The total objective function is constructed based on the current water and electricity prices required to operate the circulating cooling water system. The formula used is as follows: ； In the formula, represents the overall objective function, Indicates the electricity price, represents the net electrical energy production, Indicates the power consumption of wet cooling tower, Indicates the power consumption of dry air cooler. Indicates the number of wet cooling towers, Number of dry air coolers, represents the water replenishment amount of the wet cooling tower, Indicates the water price, represents the cooling water density, represents the cooling requirement, Represents the weight coefficient.

5. The method for intelligent operation and management of circulating cooling water in a power plant based on artificial intelligence according to claim 1 is characterized in that: In step S5, the optimization of the cooling system operation strategy specifically includes the following operations: By maximizing the total objective function, maximizing the economic benefits of the power plant, adjusting the operating configuration parameters of the circulating cooling water system, calculating the circulating water-to-air ratio, presetting the ratio range, and determining the constraints in the maximization process, the calculation formula of the circulating water-to-air ratio is as follows: ； In the formula, Indicates the circulating water-air ratio, represents the cooling water flow rate, represents the air mass flow rate, Indicates the cooling water density.

6. The method for intelligent operation and management of circulating cooling water in a power plant based on artificial intelligence according to claim 3 is characterized in that: In step S141, the measured flow rate specifically includes the following operations: Calculate the cooling water flow corresponding to the actual measured output of the flow meter using the following formula: ； In the formula, Indicates the cooling water flow rate, represents the cross-sectional area of ​​the pipe, Indicates the ratio between the actual cooling water flow rate and the theoretical cooling water flow rate. Indicates the ratio of flow meter diameter to pipe diameter. and represent the upstream and downstream pressures respectively, Indicates the cooling water density.

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