Cooling tower operation analysis optimization system and method based on industrial mechanism model

Abstract

The invention belongs to the field of industrial cooling systems, and discloses a cooling tower operation analysis optimization system and method based on an industrial mechanism model, and the system comprises a sensor network, a data collection and processing module, the industrial mechanism model, an optimization algorithm module, a control execution unit, and a monitoring and feedback module. The core of the invention lies in that the operation of the water cooling tower is accurately simulated and analyzed by using an industrial mechanism model, and parameterization processing and optimization are carried out in combination with temperature, humidity, energy consumption and other data monitored in real time. Key operation parameters such as the starting and stopping number of the water cooling tower and the fan operation time are dynamically adjusted through an intelligent algorithm, operation optimization of the water cooling tower is achieved, the cooling efficiency is improved, energy consumption is reduced, the operation and maintenance cost is reduced, and important practical value and wide application prospects are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of industrial cooling systems, and in particular relates to a cooling tower operation analysis and optimization system and method based on an industrial mechanism model. Background Art

[0002] A cooling tower is a device, facility or structure used to cool water. It is commonly seen in power plants, chemical plants, cement plants and other factories that require a large amount of water temperature control. The scale is calculated based on the amount of heat exchange. It is a device for saving water and circulating water. The working principle of a cooling tower: the principle of heat exchange. It exchanges heat between cooling water and air, transferring heat from the cooling water to the air, thereby reducing the temperature of the cooling water and achieving a cooling effect. Specifically, the working process of a closed cooling tower can be divided into the following steps:

[0003] 1. Pumping: The water in the water tank at the bottom of the closed cooling tower is pumped out by a water pump and enters the top of the cooling tower.

[0004] 2. Water spraying: The water pump sprays water into fine droplets, which are sprayed out from the nozzle and enter the water distribution tank of the cooling tower. These water droplets will spread on the surface of the cooling tower filler to form a layer of water film.

[0005] 3. Heat absorption: Air enters from the bottom of the cooling tower and exchanges heat with the water film. Heat is transferred from the water film to the air, causing the air temperature to rise while the water temperature to drop.

[0006] 4. Exhaust: After absorbing heat, the air is discharged from the top of the cooling tower. At this time, the temperature of the air has increased, while the temperature of the water has further decreased.

[0007] 5. Return water: Water flows back to the water pump from the bottom of the cooling tower to complete the cycle. Through this series of circulation processes, heat is continuously discharged from the cooling water to achieve a cooling effect.

[0008] Some common shortcomings in the operation of existing cooling towers mainly include:

[0009] Low energy efficiency: Traditional cooling tower systems often lack sophisticated control mechanisms, resulting in ineffective adjustment under different ambient temperatures and cooling requirements, resulting in energy waste. High maintenance costs: Due to the lack of effective operation monitoring and preventive maintenance measures, cooling tower systems are prone to failure, which not only leads to increased downtime, but also increases the cost of repairs and replacement parts. Operation efficiency is greatly affected by the environment: The cooling effect of the cooling tower is greatly affected by the ambient temperature and humidity, which greatly reduces its cooling efficiency in high temperature or high humidity environments. Lack of intelligent control: Most existing cooling towers lack intelligent control systems and cannot achieve data-driven decision-making and optimization, which limits their operational flexibility and efficiency. Summary of the invention

[0010] In order to overcome the above technical problems, the present invention provides a cooling tower operation analysis and optimization system and method based on an industrial mechanism model, aiming to effectively solve the problems of low energy efficiency, high maintenance cost, large environmental impact on operating efficiency and lack of intelligent control of cooling towers through intelligent data analysis and control strategies. In addition, the present invention also pays special attention to the problems of fault monitoring and uneven equipment operation time, and improves the stability and reliability of the cooling tower system by establishing a remote management and guarantee system for cooling tower faults and energy consumption data.

[0011] The present invention adopts the following technical solutions:

[0012] Cooling tower operation analysis and optimization system based on industrial mechanism model, including:

[0013] A sensor network is deployed at key locations of the cooling tower to collect real-time operating data of the cooling tower;

[0014] A data acquisition and processing module, which receives data from the sensor network and performs preliminary data preprocessing to ensure the accuracy and reliability of the data;

[0015] Industrial mechanism model, which is a mathematical model established according to the working principle and physical characteristics of the cooling tower, and is used to simulate the operation process of the cooling tower and provide a theoretical basis and calculation method for the optimization of operating parameters;

[0016] An optimization algorithm module, wherein the optimization algorithm module optimizes the operating parameters of the cooling tower through an optimization algorithm according to the analysis results of the industrial mechanism model to achieve the lowest energy consumption and the highest efficiency;

[0017] A control execution unit, which actually adjusts the operating parameters of the cooling tower according to the output instructions of the optimization algorithm module to ensure that the cooling tower operates in an optimal state, while taking into account the requirement that each device has fault detection, remote switching, interlock switching, and start and stop automatic control according to the longest running time and the shortest running time comparison function;

[0018] The monitoring and feedback module monitors the operating status and environmental conditions of the cooling tower in real time, and feeds back the actual operating data to the data acquisition and processing module and the optimization algorithm module to form a closed-loop control to ensure dynamic optimization and adaptive adjustment of the system.

[0019] Preferably, the sensor network is composed of a variety of high-precision sensors distributed in key locations of the cooling tower including the water inlet, water outlet, cooling area, and pump area; real-time monitoring data include the temperature, humidity, water flow, and fan speed of the cooling tower.

[0020] Preferably, the industrial mechanism model includes a water consumption analysis calculation module, an energy balance calculation module, and a cooling medium heat load value calculation module.

[0021] The cooling tower operation analysis and optimization method based on the industrial mechanism model includes the following steps:

[0022] S1. Data collection: Use a high-precision sensor network placed at key locations of the cooling tower to collect real-time data including temperature, humidity, water flow, and fan speed. These data can fully reflect the operating status of the cooling tower and provide a basis for subsequent analysis and optimization;

[0023] S2. Data preprocessing: The collected data will be sent to the data acquisition and processing module for preprocessing, including data cleaning, denoising, and normalization, to improve the accuracy and consistency of the data. The preprocessed data is more suitable for further analysis and model calculation;

[0024] S3. Model analysis: The preprocessed data will be input into the established industrial mechanism model, which simulates and analyzes the operation of the cooling tower based on its working principle and physical characteristics, and identifies the key factors affecting the operating efficiency and energy consumption;

[0025] S4. Parameter optimization: Based on the analysis results of the industrial mechanism model, the optimization algorithm module optimizes the operating parameters of the cooling tower, including adjusting the fan speed, operating time, and working intensity mode to achieve the best operating efficiency and the lowest energy consumption;

[0026] S5. Execution and adjustment: The optimized operating parameters are implemented through the control execution unit to actually adjust the operation of the cooling tower. At the same time, the monitoring and feedback module will continuously monitor the operating status of the cooling tower and changes in the external environment, and dynamically adjust the operating parameters according to the actual situation to ensure that the cooling tower always operates in the optimal state;

[0027] S6. Fault warning and maintenance: The monitoring and feedback module also has a fault warning function, which can timely detect potential equipment problems based on data analysis so as to carry out maintenance and repair in advance, reduce fault downtime, and improve system reliability and stability.

[0028] Preferably, S3 includes water consumption analysis and calculation, and the water consumption analysis and calculation includes the following:

[0029] Data collection: Collect data related to water consumption, including water intake, discharge, and circulating water flow;

[0030] Water balance calculation: Perform water balance calculation to understand the water flow in the closed cooling tower system;

[0031] Water consumption index calculation: Calculate water consumption index based on the results of data collection and water balance calculation, including water consumption per unit output and water consumption per unit cooling capacity;

[0032] Anomaly detection and analysis: Detect and identify possible anomalies through analysis of water consumption data;

[0033] Optimization measures: formulate corresponding optimization measures according to the results of water consumption analysis;

[0034] Monitoring and continuous improvement: Continuously monitor and record water consumption data, compare it with the results after implementing optimization measures, evaluate the optimization effect based on the monitoring results, and make necessary adjustments and improvements.

[0035] Preferably, S3 includes energy balance calculation, and the energy balance calculation includes the following:

[0036] Determine energy input and output: First, determine the energy input and output items of the closed cooling tower system;

[0037] Calculate energy input items: For energy input items, calculate by measuring and recording relevant parameters;

[0038] Calculate energy output items: For energy output items, it is necessary to estimate or measure them according to system characteristics and actual conditions;

[0039] Energy balance calculation: compare energy input and output to perform energy balance calculation;

[0040] Systematic analysis of energy differences: If energy input and output are not balanced, analyze the reasons for the energy differences.

[0041] Preferably, S3 includes calculation of the heat load value of the cooling medium, and the calculation of the heat load value of the cooling medium includes the following:

[0042] Flow and temperature difference method: measure the inlet and outlet temperatures and flow rate of the cooling medium, and calculate the heat load of the cooling medium;

[0043] Measuring the mass flow rate of cooling medium: Use a flow meter to measure the mass flow rate of cooling medium;

[0044] Temperature measurement: Use temperature sensors to measure the inlet and outlet temperatures of the cooling medium;

[0045] Heat load calculation: Use the mass flow and temperature difference data measured above to calculate according to the heat load calculation formula.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The above technical solution can not only significantly improve the operating efficiency and stability of the cooling tower system, but also reduce maintenance and operating costs, with good economic and social benefits:

[0048] Improve energy efficiency and reduce costs: By optimizing the operating parameters of the cooling tower, such as the number of starts and stops and the fan operating time, higher energy efficiency and lower operating costs can be achieved;

[0049] Intelligent fault monitoring and prevention: Using advanced sensors and data analysis technology to achieve real-time monitoring and early warning of potential faults in cooling towers, reducing the risk and impact of faults;

[0050] Balanced management of equipment operation time: through intelligent analysis of equipment operation data, the operation time of each device can be balanced to avoid excessive wear and inefficient operation, thus extending the life of the equipment;

[0051] Improved environmental adaptability and stability: Ensure that the cooling tower system can operate efficiently and stably under different environmental conditions and has stronger adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the control screen of the system of the present invention;

[0053] Figure 2 This is the system time accumulation and monitoring screen of the present invention. DETAILED DESCRIPTION

[0054] The embodiments of the present invention are described in detail below. Unless otherwise specified, the raw materials and equipment used can be purchased from the market or are commonly used in the art. The methods in the embodiments, unless otherwise specified, are conventional methods in the art. The following embodiments are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0055] The cooling tower operation analysis and optimization system based on the industrial mechanism model includes:

[0056] 1. Sensor network: This network consists of multiple high-precision sensors distributed in key parts of the cooling tower, such as the water inlet, outlet, cooling area, pump operation status, etc. These sensors are responsible for real-time monitoring of key operating parameters of the cooling tower, such as temperature, humidity, water flow, fan speed, etc., to ensure the real-time and accuracy of the data, and provide a reliable data basis for subsequent data analysis and operation optimization.

[0057] 2. Data acquisition and processing module: This module pre-processes the data collected from the sensor network, including data cleaning, denoising, normalization and other steps. For example, for the accumulation of pump running time, the purpose of pre-processing is to improve data quality, eliminate outliers and interference, and ensure the accuracy of subsequent analysis and optimization. This example uses the Siemens PLC1200 series card 1215C master control.

[0058] 3. Industrial mechanism model: A mathematical model based on the physical characteristics and operating principles of the cooling tower. This model can simulate the heat exchange process and energy balance of the cooling tower under different operating conditions, and provide theoretical support and calculation methods for the optimization of the cooling tower operating parameters.

[0059] 4. Optimization algorithm module: Use advanced optimization algorithms (such as fuzzy algorithms, genetic algorithms, particle swarm optimization algorithms, etc.) to dynamically optimize the operating parameters of the cooling tower. Based on the analysis results of the industrial mechanism model, this module intelligently adjusts the number of cooling tower starts and stops, fan speed and other parameters to maximize energy efficiency and minimize energy consumption. The use of adaptive control algorithms can adjust the control parameters of the system according to real-time data to achieve optimal performance. These algorithms can be based on feedback control theory and automatically adapt to different working conditions and environmental changes by continuously monitoring and adjusting the system status. Adaptive control of the closed cooling tower system can adjust the fan operating time and the flow rate of the cooling medium circulation pump in real time according to parameters such as the inlet and outlet water temperature difference, cooling load and ambient temperature to maximize energy efficiency.

[0060] 5. Control execution unit: According to the output instructions of the optimization algorithm module, the operating parameters of the cooling tower are actually adjusted, such as adjusting the fan speed, changing the working mode of the water pump, etc., to ensure that the cooling tower operates in the optimal state. According to the output instructions of the optimization algorithm module, each device is required to have the functions of fault detection, remote switching, interlocking switching, start and stop according to the longest running time, shortest running time comparison and other functions for automatic control. This unit ensures that the cooling tower operates in the optimized state, while improving the response speed and execution efficiency of the system.

[0061] 6. Monitoring and feedback module: monitors the operating status and environmental conditions of the cooling tower in real time, and feeds back the actual operating data to the data acquisition and processing module and the optimization algorithm module to form a closed-loop control. It monitors the layout of the system in real time, including fault warning, equipment health status assessment, and dynamic adjustment mechanism based on feedback to ensure dynamic optimization and adaptive adjustment of the system.

[0062] The cooling tower operation analysis and optimization methods based on industrial mechanism models include:

[0063] S1. Data collection: Use a high-precision sensor network placed at key locations of the cooling tower to collect real-time data including temperature, humidity, water flow, and fan speed. These data can fully reflect the operating status of the cooling tower and provide a basis for subsequent analysis and optimization;

[0064] S2. Data preprocessing: The collected data will be sent to the data acquisition and processing module for preprocessing, including data cleaning, denoising, and normalization, to improve the accuracy and consistency of the data. The preprocessed data is more suitable for further analysis and model calculation;

[0065] S3. Model analysis: The preprocessed data will be input into the established industrial mechanism model, which simulates and analyzes the operation of the cooling tower based on its working principle and physical characteristics, and identifies the key factors affecting the operating efficiency and energy consumption;

[0066] Calculation of water consumption analysis:

[0067] 1. Data collection: Collect data related to water consumption, including water intake, water discharge, circulating water flow, etc. These data can be monitored in real time through sensors such as flow meters and water meters, or reviewed and sorted through historical records.

[0068] 2. Water balance calculation: Perform water balance calculation to understand the flow of water in the closed cooling tower system. Water balance calculation includes calculating the relationship between water intake, water discharge and circulating water flow. Through the analysis of water balance, possible water leakage or water loss problems can be found.

[0069] 3. Calculation of water consumption index: Based on the results of data collection and water balance calculation, calculate water consumption indexes, such as water consumption per unit output, water consumption per unit cooling capacity, etc. These indicators can help evaluate the water use efficiency of the closed cooling tower system and compare it with industry standards or similar systems.

[0070] 4. Anomaly detection and analysis: Detect and identify possible anomalies through analysis of water consumption data. For example, sudden increase in water consumption, continuous water loss, etc. Conduct in-depth analysis of anomalies to find possible causes and solutions.

[0071] 5. Optimization measures: According to the results of water consumption analysis, formulate corresponding optimization measures. These measures may include repairing water leaks, improving water treatment systems, optimizing water circulation methods, etc. By implementing optimization measures, water consumption can be reduced and water use efficiency can be improved.

[0072] 6. Monitoring and continuous improvement: Continuously monitor and record water consumption data and compare it with the results after implementing optimization measures. Based on the monitoring results, evaluate the optimization effect and make necessary adjustments and improvements.

[0073] By conducting a water consumption analysis, we can gain a deeper understanding of the water usage in the closed cooling tower system, identify problems and propose solutions, thereby improving the water usage efficiency of the system and reducing water consumption. This will help achieve the goals of sustainable development and resource conservation.

[0074] Calculation of energy balance:

[0075] 1. Determine energy input and output: First, determine the energy input and output items of the closed cooling tower system. Energy input usually includes the heat load of the cooling medium, the power of the circulating water pump, and the power of the fan. Energy output usually includes the temperature drop of the cooling medium, the heat of the discharged water, and the heat dissipation of the fan.

[0076] 2. Calculate energy input items: For energy input items, calculate by measuring and recording relevant parameters. For example, the heat load of the cooling medium can be calculated by measuring the inlet and outlet water temperatures and flow rates. The power of the circulating water pump and fan can be calculated by measuring the current and voltage, as well as the relevant power factor.

[0077] 3. Calculate energy output items: For energy output items, it is necessary to estimate or measure them according to system characteristics and actual conditions. For example, the temperature drop of the cooling medium can be calculated by measuring the temperature difference between the inlet and outlet water. The heat of the discharged water can be calculated by measuring the outlet water temperature and flow rate. The heat dissipation of the fan can be calculated by measuring the power and efficiency of the fan.

[0078] 4. Energy balance calculation: Compare energy input and output to perform energy balance calculation. Normally, energy input should equal energy output. If there is an energy difference, it may indicate energy loss or unaccounted energy input items.

[0079] 5. Systematic analysis of energy differences: If the energy input and output are not balanced, analyze the reasons for the energy differences. Possible reasons include unconsidered energy input items in the system, energy loss, measurement errors, etc. By analyzing the energy differences, possible problems can be found and corresponding measures can be taken to adjust and improve.

[0080] Energy balance calculation is an important method to evaluate the energy flow of closed cooling tower system. Through this method, we can understand the efficiency of energy utilization, find out the causes of energy loss, and take measures to optimize the energy utilization of the system and improve the energy efficiency performance of the system.

[0081] Calculation of heat load value of cooling medium:

[0082] 1. Flow and temperature difference method: measure the inlet and outlet temperatures and flow of the cooling medium to calculate the heat load of the cooling medium. This method is based on the principle of heat balance, that is, the heat load is equal to the mass flow of the cooling medium multiplied by the temperature difference. The formula is: heat load = mass flow * (outlet water temperature - inlet water temperature).

[0083] 2. Measure the mass flow rate of the cooling medium: Use a flow meter to measure the mass flow rate of the cooling medium. The flow meter can select a suitable type according to the characteristics of the medium, such as electromagnetic flow meter, turbine flow meter, ultrasonic flow meter, etc. Ensure the accuracy and stability of the flow meter to obtain reliable mass flow data.

[0084] 3. Temperature measurement: Use temperature sensors to measure the inlet and outlet temperatures of the cooling medium. Temperature sensors can be contact or non-contact, and the appropriate type should be selected according to the specific application and environment, such as thermocouples, thermistors, infrared temperature sensors, etc.

[0085] 4. Heat load calculation: Use the mass flow and temperature difference data measured above to calculate according to the heat load calculation formula. Get the heat load value of the cooling medium, which represents the amount of heat transferred by the cooling medium per unit time.

[0086] S4. Parameter optimization: Based on the analysis results of the industrial mechanism model, the optimization algorithm module optimizes the operating parameters of the cooling tower, including adjusting the fan speed, operating time, and working intensity mode to achieve the best operating efficiency and the lowest energy consumption;

[0087] Establish fuzzy rules and membership functions:

[0088] 1. Design a fuzzy controller to control the temperature of a closed cooling tower system. The input is the current return water temperature, and the output is the cooling intensity of the closed cooling tower. Three fuzzy sets can be defined to describe the indoor temperature: cold, comfortable, and hot, and three fuzzy sets can be defined to describe the cooling intensity of the air conditioner: low, medium, and high.

[0089] a. Membership function definition:

[0090] Cold: This can be described using a triangular membership function, such as the temperature range of cold is 18°C ​​to 22°C, and the membership degree gradually decreases from 1 to 0.

[0091] Comfort: It can be described using a trapezoidal membership function, such as a comfortable temperature range of 20°C to 25°C, with the membership degree maintained at 1.

[0092] Heat: can be described using a triangular membership function, such as the temperature range of heat is 23℃ to 27℃, and the membership degree gradually increases from 0 to 1.

[0093] Low, medium and high cooling intensity membership functions can be defined according to specific requirements.

[0094] b. Definition of fuzzy rules:

[0095] If the temperature is cold, the cooling intensity is high.

[0096] If the temperature is comfortable, the cooling intensity is medium.

[0097] If the temperature is hot, the cooling intensity is low.

[0098] More fuzzy rules can be set according to actual needs.

[0099] In practical applications, the shape and parameters of fuzzy rules and membership functions can be determined through data-based training. After the fuzzy rules and membership functions are established, the fuzzy inference engine can be used to perform fuzzy inference based on the current input values ​​to obtain the corresponding output control signal.

[0100] 2. Set according to cooling load:

[0101] 1. Input variables: cooling load (L) and ambient temperature (T).

[0102] 2. Output variable: fan running time (F).

[0103] Definition of membership function:

[0104] Cooling load (L): can be described using a triangular membership function, such as a low load range of 0-20%, a medium load range of 15-45%, and a high load range of 40-100%.

[0105] Ambient temperature (T): can be described using a triangular membership function, such as low temperature range of 15-20°C, medium temperature range of 18-25°C, and high temperature range of 23-30°C.

[0106] Fan run time (F): can be described using a trapezoidal membership function, such as a low fan run time range of 0-30 minutes, a medium fan run time range of 20-60 minutes, and a high fan run time range of 50-120 minutes.

[0107] Definition of fuzzy rules:

[0108] If the cooling load is low and the ambient temperature is low, the fan run time is high.

[0109] If the cooling load is low and the ambient temperature is medium, the fan run time is medium.

[0110] If the cooling load is low and the ambient temperature is high, the fan run time is low.

[0111] If the cooling load is medium and the ambient temperature is low, the fan run time is medium.

[0112] If the cooling load is medium and the ambient temperature is medium, the fan run time is medium.

[0113] If the cooling load is medium and the ambient temperature is high, the fan run time is low.

[0114] If the cooling load is high and the ambient temperature is low, the fan run time is low.

[0115] If the cooling load is high and the ambient temperature is medium, the fan run time is medium.

[0116] If the cooling load is high and the ambient temperature is high, the fan run time is medium.

[0117] 3. Defined by water inlet temperature, cooling load and fan operating time:

[0118] 1) Input variables: inlet water temperature (T_i n) and cooling load (L).

[0119] 2) Output variable: fan running time (F).

[0120] Definition of membership function:

[0121] Inlet water temperature (T_i n): can be described using a triangular membership function, such as the low temperature range is 10-15°C, the medium temperature range is 12-18°C, and the high temperature range is 15-20°C.

[0122] Cooling load (L): can be described using a triangular membership function, such as a low load range of 0-30%, a medium load range of 20-70%, and a high load range of 60-100%.

[0123] Fan run time (F): can be described using a trapezoidal membership function, such as a low fan run time range of 0-20 minutes, a medium fan run time range of 15-45 minutes, and a high fan run time range of 40-90 minutes.

[0124] Definition of fuzzy rules:

[0125] If the inlet water temperature is low and the cooling load is low, the fan run time is high.

[0126] If the inlet water temperature is low and the cooling load is medium, the fan operating time is medium.

[0127] If the inlet water temperature is low and the cooling load is high, the fan run time is low.

[0128] If the inlet water temperature is medium and the cooling load is low, the fan run time is medium.

[0129] If the inlet water temperature is medium and the cooling load is medium, the fan run time is medium.

[0130] If the inlet water temperature is medium and the cooling load is high, the fan run time is low.

[0131] If the inlet water temperature is high and the cooling load is low, the fan run time is low.

[0132] If the inlet water temperature is high and the cooling load is medium, the fan operating time is medium.

[0133] If the inlet water temperature is high and the cooling load is high, the fan operating time is medium.

[0134] 4. Defined by cooling water and fan speed:

[0135] 1) Input variables: cooling water temperature (T_water) and cooling water flow (F_water).

[0136] 2) Output variable: cooling tower fan speed (R_fan).

[0137] Definition of membership function:

[0138] Cooling water temperature (T_water): can be described using a triangular membership function, such as low temperature range of 15-20°C, medium temperature range of 18-25°C, and high temperature range of 23-30°C.

[0139] Cooling water flow (F_water): can be described using a triangular membership function, such as a low flow range of 0-30m 3 / h, medium flow range is 25-50m 3 / h, high flow range is 45-80m 3 / h.

[0140] Cooling tower fan speed (R_fan): can be described using a trapezoidal membership function, such as a low speed range of 0-50RPM, a medium speed range of 40-80RPM, and a high speed range of 70-100RPM.

[0141] Definition of fuzzy rules:

[0142] If the cooling water temperature is low and the cooling water flow is low, the cooling tower fan speed is high.

[0143] If the cooling water temperature is low and the cooling water flow is medium, the cooling tower fan speed is medium.

[0144] If the cooling water temperature is low and the cooling water flow is high, the cooling tower fan speed is low.

[0145] If the cooling water temperature is medium and the cooling water flow is low, the cooling tower fan speed is medium.

[0146] If the cooling water temperature is medium and the cooling water flow is medium, the cooling tower fan speed is medium.

[0147] If the cooling water temperature is medium and the cooling water flow is high, the cooling tower fan speed is low.

[0148] If the cooling water temperature is high and the cooling water flow is low, the cooling tower fan speed is low.

[0149] If the cooling water temperature is high and the cooling water flow is medium, the cooling tower fan speed is medium.

[0150] If the cooling water temperature is high and the cooling water flow is high, the cooling tower fan speed is medium.

[0151] 5. More detailed definition examples:

[0152] 1) Input variables: cooling load (L) and ambient temperature (T).

[0153] Cooling load (L): can be described using a pentagonal membership function, such as the very low load range is 0-10%, the low load range is 5-20%, the medium load range is 15-30%, the high load range is 25-40%, and the very high load range is 35-50%.

[0154] Ambient temperature (T): It can be described using a pentagonal membership function, such as the extremely low temperature range is 10-15°C, the low temperature range is 12-18°C, the medium temperature range is 15-25°C, the high temperature range is 22-28°C, and the extremely high temperature range is 25-30°C.

[0155] 2) Output variable: energy consumption level (E).

[0156] Energy consumption level (E): can be described using a triangular membership function, such as a low energy consumption level range of 0-30%, a medium energy consumption level range of 20-60%, and a high energy consumption level range of 40-100%.

[0157] Definition of fuzzy rules:

[0158] If the cooling load is very low and the ambient temperature is very low, the energy consumption level is low.

[0159] If the cooling load is low and the ambient temperature is low, the energy consumption level is medium.

[0160] If the cooling load is medium and the ambient temperature is medium, the energy consumption level is medium.

[0161] If the cooling load is high and the ambient temperature is high, the energy consumption level is high.

[0162] If the cooling load is very high and the ambient temperature is very high, the energy consumption level is high.

[0163] Through these defined fuzzy rules and membership functions, the control indicators can be controlled and optimized according to actual conditions.

[0164] S5. Execution and adjustment: The optimized operating parameters are implemented through the control execution unit to actually adjust the operation of the cooling tower. At the same time, the monitoring and feedback module will continuously monitor the operating status of the cooling tower and changes in the external environment, and dynamically adjust the operating parameters according to the actual situation to ensure that the cooling tower always operates in the optimal state;

[0165] S6. Fault warning and maintenance: The monitoring and feedback module also has a fault warning function, which can timely detect potential equipment problems based on data analysis so as to carry out maintenance and repair in advance, reduce fault downtime, and improve system reliability and stability.

[0166] Through the above-mentioned components and work processes, the present invention can realize accurate monitoring, intelligent analysis and dynamic optimization of the operating status of the cooling tower, effectively improve the operating efficiency and energy utilization rate of the cooling tower, reduce operating costs, and enhance the stability and reliability of the system.

[0167] Although the 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 above embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. The cooling tower operation analysis and optimization system based on the industrial mechanism model is characterized by: include: A sensor network is deployed at key locations of the cooling tower to collect real-time operating data of the cooling tower; A data acquisition and processing module, which receives data from the sensor network and performs preliminary data preprocessing to ensure the accuracy and reliability of the data; Industrial mechanism model, which is a mathematical model established according to the working principle and physical characteristics of the cooling tower, and is used to simulate the operation process of the cooling tower and provide a theoretical basis and calculation method for the optimization of operating parameters; An optimization algorithm module, wherein the optimization algorithm module optimizes the operating parameters of the cooling tower through an optimization algorithm according to the analysis results of the industrial mechanism model to achieve the lowest energy consumption and the highest efficiency; A control execution unit, which actually adjusts the operating parameters of the cooling tower according to the output instructions of the optimization algorithm module to ensure that the cooling tower operates in an optimal state, while taking into account the requirement that each device has fault detection, remote switching, interlock switching, and start and stop automatic control according to the longest running time and the shortest running time comparison function; The monitoring and feedback module monitors the operating status and environmental conditions of the cooling tower in real time, and feeds back the actual operating data to the data acquisition and processing module and the optimization algorithm module to form a closed-loop control to ensure dynamic optimization and adaptive adjustment of the system.

2. The cooling tower operation analysis and optimization system based on the industrial mechanism model according to claim 1 is characterized in that: The sensor network is composed of a variety of high-precision sensors, which are distributed in key locations of the cooling tower, including the water inlet, water outlet, cooling area, and machine pump area; real-time monitoring data include the temperature, humidity, water flow, and fan speed of the cooling tower.

3. The cooling tower operation analysis and optimization system based on the industrial mechanism model according to claim 1 is characterized in that: The industrial mechanism model includes a water consumption analysis calculation module, an energy balance calculation module, and a cooling medium heat load value calculation module.

4. The cooling tower operation analysis and optimization method based on the industrial mechanism model is characterized by: The cooling tower operation analysis and optimization system based on the industrial mechanism model described in claim 1 comprises the following steps: S1. Data collection: Use a high-precision sensor network placed at key locations of the cooling tower to collect real-time data including temperature, humidity, water flow, and fan speed. These data can fully reflect the operating status of the cooling tower and provide a basis for subsequent analysis and optimization; S2. Data preprocessing: The collected data will be sent to the data acquisition and processing module for preprocessing, including data cleaning, denoising, and normalization, to improve the accuracy and consistency of the data. The preprocessed data is more suitable for further analysis and model calculation; S3. Model analysis: The preprocessed data will be input into the established industrial mechanism model, which simulates and analyzes the operation of the cooling tower based on its working principle and physical characteristics, and identifies the key factors affecting the operating efficiency and energy consumption; S4. Parameter optimization: Based on the analysis results of the industrial mechanism model, the optimization algorithm module optimizes the operating parameters of the cooling tower, including adjusting the fan speed, operating time, and working intensity mode to achieve the best operating efficiency and the lowest energy consumption; S5. Execution and adjustment: The optimized operating parameters are implemented through the control execution unit to actually adjust the operation of the cooling tower. At the same time, the monitoring and feedback module will continuously monitor the operating status of the cooling tower and changes in the external environment, and dynamically adjust the operating parameters according to the actual situation to ensure that the cooling tower always operates in the optimal state; S6. Fault warning and maintenance: The monitoring and feedback module also has a fault warning function, which can timely detect potential equipment problems based on data analysis so as to carry out maintenance and repair in advance, reduce fault downtime, and improve system reliability and stability.

5. The cooling tower operation analysis and optimization method based on the industrial mechanism model according to claim 4 is characterized in that: S3 includes water consumption analysis and calculation, which includes the following: Data collection: Collect data related to water consumption, including water intake, discharge, and circulating water flow; Water balance calculation: Perform water balance calculation to understand the water flow in the closed cooling tower system; Water consumption index calculation: Calculate water consumption index based on the results of data collection and water balance calculation, including water consumption per unit output and water consumption per unit cooling capacity; Anomaly detection and analysis: Detect and identify possible anomalies through analysis of water consumption data; Optimization measures: formulate corresponding optimization measures according to the results of water consumption analysis; Monitoring and continuous improvement: Continuously monitor and record water consumption data, compare it with the results after implementing optimization measures, evaluate the optimization effect based on the monitoring results, and make necessary adjustments and improvements.

6. The cooling tower operation analysis and optimization method based on the industrial mechanism model according to claim 4 is characterized in that: S3 includes energy balance calculations, which include the following: Determine energy input and output: First, determine the energy input and output items of the closed cooling tower system; Calculate energy input items: For energy input items, calculate by measuring and recording relevant parameters; Calculate energy output items: For energy output items, it is necessary to estimate or measure them according to system characteristics and actual conditions; Energy balance calculation: compare energy input and output to perform energy balance calculation; Systematic analysis of energy differences: If energy input and output are not balanced, analyze the reasons for the energy differences.

7. The cooling tower operation analysis and optimization method based on industrial mechanism model according to claim 4 is characterized in that: S3 includes the calculation of the heat load value of the cooling medium, which includes the following: Flow and temperature difference method: measure the inlet and outlet temperatures and flow rate of the cooling medium, and calculate the heat load of the cooling medium; Measuring the mass flow rate of cooling medium: Use a flow meter to measure the mass flow rate of cooling medium; Temperature measurement: Use temperature sensors to measure the inlet and outlet temperatures of the cooling medium; Heat load calculation: Use the mass flow and temperature difference data measured above to calculate according to the heat load calculation formula.

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