Automatic dosing control method and system based on fouling thermal resistance

Abstract

The invention discloses an automatic dosing control method and system based on fouling thermal resistance, and belongs to the technical field of circulating water treatment.The method comprises the following steps that historical data of circulating water and water quality parameters monitored in real time are obtained, training historical data and water quality parameters by utilizing regression, SVM (Support Vector Machine) or a decision tree computer learning model, and calculating a fouling thermal resistance value in real time; according to the relation between the fouling thermal resistance value and the preset thermal resistance critical value, chemical adding is automatically controlled and adjusted, chemical concentration changes and fouling thermal resistance value changes are monitored in real time, a chemical adding strategy is controlled and adjusted in real time, and a water quality and strategy report is generated. According to the automatic dosing control method and system based on the fouling thermal resistance, intelligent management of circulating water can be achieved, the water treatment efficiency and the operation safety of equipment are improved, and remarkable economic and social benefits are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of circulating water treatment, and in particular relates to an automatic dosing control method based on fouling thermal resistance and a system thereof. Background Art

[0002] In the circulating cooling water system of a thermal power plant, the quality of the circulating water directly affects the heat exchange efficiency of the equipment and the safety of the system. Since the circulating cooling water system of a thermal power plant is responsible for equipment cooling, changes in its water quality will directly affect the heat exchange performance of key equipment such as boilers, condensers, and heat exchangers. If pollutants such as mineral scale, bacteria, and algae accumulate in the circulating water, it will increase the water flow resistance, reduce the heat transfer efficiency, and may even cause equipment corrosion and scaling, increase operating costs, and even cause equipment failures, affecting the long-term stability of the system. The deterioration of water quality will not only affect the operating efficiency of the equipment, but may also cause the power plant to shut down or require early maintenance due to equipment failure. In serious cases, it may affect the operating safety of the entire power grid.

[0003] Traditional water quality monitoring methods mainly rely on manual operation and regular sampling and analysis, which cannot achieve real-time monitoring and precise control of water quality. Manual monitoring usually has problems such as delayed data updates and untimely information transmission, resulting in failure to detect water quality changes in a timely manner, increasing the risk of mineral scale and bacteria growth. For example, when water quality parameters such as pH, conductivity, and temperature fluctuate, manual intervention may miss the best time for treatment due to slow response. In addition, traditional water quality monitoring equipment lacks intelligent functions, making it difficult to conduct real-time analysis and prediction of the correlation between different water quality parameters, resulting in an imperfect early warning mechanism and an inability to effectively avoid scaling, corrosion and other problems.

[0004] Existing methods of adding reagents usually rely on manual experience and timed dosing. This method is difficult to achieve precise control and often results in overdosage or underdosage. For example, when water quality fluctuates greatly, it is difficult to accurately match the amount of added reagent to the actual water quality requirements, resulting in overdosage. This not only leads to a waste of reagents, but may also have a negative impact on the water body and cause secondary pollution. On the contrary, if the amount of reagent added is insufficient and the water treatment effect is poor, it may still lead to dirt accumulation and bacterial growth, further affecting the quality of the circulating water and the operating efficiency of the equipment. Existing methods of adding reagents also often have the problem of incomplete reagent reaction. Some reagents fail to fully react in the system, resulting in a waste of drug efficacy and failure to achieve the expected water treatment effect.

[0005] In order to solve these problems, many studies in recent years have begun to explore solutions based on intelligent water treatment systems, especially technologies that automatically adjust the dosage of chemicals, monitor water quality in real time, and provide feedback control. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an automatic dosing control method and system based on dirt thermal resistance. By real-time monitoring of water quality parameters, especially the dirt thermal resistance value, the dosage of the agent is automatically adjusted to achieve precise management of water quality. This can not only reduce manual intervention, but also optimize the efficiency of agent use, reduce maintenance costs, and improve safety and stability, thereby ensuring the efficient and safe operation of the circulating water system.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides an automatic dosing control method based on fouling thermal resistance, comprising the following steps: Obtain historical data of circulating water and real-time monitored water quality parameters, including pH value, conductivity, temperature, and turbidity; perform preprocessing and feature extraction operations on the acquired data, where data preprocessing includes cleaning, standardization, and denoising; feature extraction includes extracting key features that affect the structure; use regression, SVM, or decision tree computer learning models to train historical data and water quality parameters and calculate the fouling thermal resistance value in real time; According to the relationship between the thermal resistance value of the dirt and the preset thermal resistance critical value, the agent dosage is automatically controlled and adjusted, and the changes in agent concentration and the thermal resistance value of the dirt are monitored in real time to control and adjust the agent dosage strategy in real time and generate water quality and strategy reports.

[0008] Furthermore, a machine learning algorithm is used to optimize the linear relationship between the change in thermal resistance value of the dirt and the dosage of the agent, and based on the linear relationship, the thermal resistance critical values ​​are divided into a first critical value, a second critical value and a third critical value in order from small to large; When the thermal resistance value of the dirt reaches the first critical value, an early warning is triggered, automatic control is performed, and the agent addition is started according to the first dosage; When the thermal resistance value of the dirt reaches the second critical value, the dosage of the agent is increased according to the second dosage and the cleaning process is automatically started; When the dirt thermal resistance value reaches the third critical value, the strong cleaning mode is started, and the dosage of the agent is further increased according to the third dosage.

[0009] Furthermore, the first critical value, the second critical value and the third critical value of the thermal resistance critical value are 1.77×10 -4 m 2 ∙ K / W, 3.44×10 -4 m 2 ∙ K / W and 4.0×10 -4 m 2 ∙ K / W.

[0010] Furthermore, real-time control and adjustment of the agent dosing strategy include: Determine the operating parameters of the circulating water, including pipe material type, flow rate and operating time, and monitor the water quality parameters, fouling type and fouling thermal resistance of the circulating water in real time; Based on the pipe material type, flow rate, water quality parameters, dirt type, operating time and dirt thermal resistance of the circulating water, as well as the linear relationship between the change in dirt thermal resistance and the amount of reagent added, real-time control and adjustment of the reagent combination, reagent addition amount, reagent addition continuity and reagent concentration are carried out to stabilize the water quality and maximize the heat exchange efficiency.

[0011] Furthermore, the pre-stored drug addition strategy includes the following steps: Determine the type of pipe material to guide the selection of the type of agent or combination of agents: if it is a metal pipe, choose an acidic or compound agent to remove scale; if it is a plastic pipe, choose a mild agent to avoid corrosion of the pipe; Determine the flow rate of circulating water to guide the adjustment of the dosage, continuity and concentration of the reagent: if the flow rate is high, the dosage should be moderate and the concentration should be increased appropriately to ensure that the decontamination effect is not affected by the flow rate; if the flow rate is low and the dirt accumulation is more significant, increase the dosage and continuity of the reagent; Determine the pH value and hardness of water quality parameters. If the water quality is acidic, choose neutral or alkaline agents; if the water quality is alkaline, choose acidic agents; if the water quality is high in hardness, increase the dosage of the agent and choose compound acid agents or complexing agents; Determine the type of dirt. If it is scale, choose an acidic cleaning agent and adjust the concentration of the agent to adapt to different water hardness. If it is serious scale accumulation, use a high concentration of agent or extend the agent addition cycle. If it is oil and organic dirt, choose a surfactant or solvent suitable for removing oil. If it is a complex type of dirt, use a composite agent combination and adjust the agent concentration and addition frequency. Determine the running time of circulating water. If it is a short-term operation, appropriately reduce the dosage of the agent so that the dosage and concentration of the agent can be maintained at the normal level; if it is a long-term operation, dynamically adjust the dosage of the agent according to the change of the fouling thermal resistance value, and gradually increase the concentration of the agent. When the fouling thermal resistance value increases significantly, adopt the method of continuous dosage of the agent; Determine the change of thermal resistance value of dirt. If the thermal resistance value of dirt increases, the dosage of the agent should be increased, and the change of thermal resistance value should be monitored in real time to restore the heat exchange efficiency in a short time; when the thermal resistance value of dirt decreases, the dosage of the agent should be appropriately reduced to avoid excessive cleaning, and the dosage of the agent should be controlled in real time according to the change of thermal resistance value of dirt. When the thermal resistance value is high, the dosage of the agent should be increased, and the concentration of the agent should be gradually reduced accordingly after the thermal resistance value returns to normal.

[0012] Secondly, the automatic dosing control system based on fouling thermal resistance includes the following modules: Water quality monitoring module, used for real-time monitoring of water quality parameters, including pH value, conductivity, temperature, turbidity, and operating parameters of circulating water, including pipe material type, flow rate and operating time; as well as real-time monitoring of changes in reagent concentration and fouling thermal resistance value; The fouling control module is used to train historical data and water quality parameters using regression, SVM or decision tree computer learning models and calculate the fouling thermal resistance value in real time; the reagent dosing module is automatically controlled to adjust the reagent dosing according to the relationship between the fouling thermal resistance value and the preset thermal resistance critical value; The reagent dosing module is used to adjust and implement the reagent dosing strategy in real time under the control of the fouling control module; The data platform is used to store historical data of circulating water, receive and store real-time monitoring water quality parameters, operating parameters, changes in reagent concentration and changes in fouling thermal resistance transmitted by the water quality monitoring module, and receive and store reagent dosing strategies transmitted by the fouling control module.

[0013] Furthermore, the drug dosing module includes a drug storage tank, a micro pump, a drug dosing pipeline, a drug dosing point, a fluorescence monitoring sensor and a feedback and control system; The chemical storage tank is connected in series with the micro pump, the chemical dosing pipeline, and the chemical dosing point in sequence. The chemical dosing point is connected to the circulating water pipeline system. The fluorescence monitoring sensor is arranged in the circulating water pipeline system to monitor the change of chemical concentration, and the communication transmission is connected to the dirt control module; the feedback and control system is used to connect with the micro pump to adjust the pump operation to control the chemical concentration.

[0014] Furthermore, the water quality monitoring module includes: High-precision water quality sensor for real-time monitoring of pH, conductivity, temperature, and turbidity parameters of circulating water; Data acquisition unit, used to aggregate and pre-process sensor monitoring data; The wireless transmission unit is used to wirelessly transmit the sensor monitoring data processed by the data acquisition unit to the data platform.

[0015] In a third aspect, the present invention provides an electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the automatic dosing control method based on fouling thermal resistance as described in any one of the first aspects.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the automatic dosing control method based on fouling thermal resistance as described in any one of the first aspects.

[0017] The present invention utilizes advanced sensor technology, real-time data acquisition and analysis technology, and automated control technology to monitor water quality parameters in real time and dynamically adjust the dosage of the agent according to changes in water quality. It can not only avoid the lag of traditional manual control, but also reduce agent waste and system operating costs by accurately controlling the use of agents. In addition, by combining machine learning algorithms, historical data and real-time monitoring data are analyzed to predict water quality change trends and the risk of scale formation, so that preventive measures can be taken. Through the intervention of the prediction system, the intelligent water treatment system can significantly reduce scaling, corrosion, bacterial growth and other problems, and improve the service life of the equipment and water treatment efficiency.

[0018] The real-time feedback control system based on water quality parameters and fouling thermal resistance (such as scaling degree) comprehensively considers water quality changes and heat exchange effects, and adjusts the dosage of reagents in real time to ensure that the water quality is in the best state, thereby improving heat exchange efficiency and reducing the maintenance frequency of equipment. At the same time, the use of intelligent methods can establish a linear relationship between reagent concentration and fouling thermal resistance, providing a scientific decision-making basis for the control system.

[0019] The present invention adopts and pre-stores the dosing strategy scheme based on multiple factors (including pipe material, flow rate, water quality parameters, dirt type, operation time and dirt thermal resistance) to optimize the dosage, type, continuity and concentration of the agent. Through real-time monitoring and dynamic regulation, it can ensure the maximum dirt removal effect during the cleaning process, restore heat exchange efficiency, and avoid agent waste.

[0020] 1) Pipe material type Metal pipes (such as stainless steel, copper, etc.) are usually prone to scale formation. Acidic agents with strong scale dissolving ability should be selected to remove scale; if it is a plastic pipe, a mild agent should be selected to avoid corrosion of the pipe. The specific dosing strategy is that when the system is a metal pipe, an acidic or compound agent can be selected, and the dosage can be appropriately increased, especially when the system has been running for a long time or the water quality is hard.

[0021] 2) Flow rate At high flow rates, dirt is easily carried away, but it may also increase the dirt deposited on the heat exchange surface. At this time, the dosage of the agent should be moderate, and the concentration of the agent should be increased appropriately to ensure that the decontamination effect is not affected by the flow rate. At low flow rates, the accumulation of dirt is more significant. At this time, the dosage and continuity of the agent should be increased, especially for those mineral dirt that are more difficult to clean.

[0022] 3) Water quality parameters (pH value, hardness) pH value is an important factor affecting the reaction of reagents. For acidic water (such as low pH value), neutral or alkaline reagents should be selected to avoid excessive corrosion of the system; while for alkaline water (such as high pH value), acidic reagents can be selected to help dissolve scale better. Water with higher hardness is more likely to form hard scale, so the dosage of reagents should be increased, and reagents specifically for scale (such as compound acid reagents or complexing agents) should be selected.

[0023] 4) Dirt type For scale, you can choose an acidic cleaning agent and adjust the concentration of the agent to suit different water hardness. For severe scale accumulation, you can use a high concentration of agent or extend the agent dosing cycle. For oil and organic dirt, you can choose a surfactant or solvent suitable for removing oil and adjust the dosage to ensure the effect. For complex dirt types, it is recommended to use a composite agent combination and adjust the agent concentration and dosing frequency to maximize the decontamination effect.

[0024] 5) Running time In the case of short-term operation, due to the short operation time, the dirt accumulation is less, and the dosage of the agent can be appropriately reduced. At this time, the dosage and concentration of the agent can be maintained at the normal level. In the case of long-term operation, as time goes by, more dirt accumulates, and the dosage of the agent needs to be dynamically adjusted according to the change of the dirt thermal resistance value. The concentration of the agent should be gradually increased, especially when the dirt thermal resistance value increases significantly, and the method of continuous agent addition should be adopted to ensure efficient cleaning of the system.

[0025] 6) Linear relationship between the change of thermal resistance value of dirt and the dosage of reagent When the dirt thermal resistance value increases, it means that the dirt gradually accumulates and affects the heat transfer efficiency. At this time, the dosage of the agent should be increased, and the changes in the thermal resistance value should be monitored in real time to restore the heat exchange efficiency in a short time; when the dirt thermal resistance value decreases, it indicates that the dirt has been removed and the heat transfer efficiency of the system has been restored. At this time, the dosage of the agent can be appropriately reduced to avoid excessive cleaning. According to the changes in the dirt thermal resistance value, the system will control the dosage of the agent in real time, increase the dosage of the agent when the thermal resistance value is high, and gradually reduce the concentration of the agent after the thermal resistance value returns to normal to maintain stable water quality.

[0026] 7) Adjustment of reagent combination, dosage, dosage continuity and concentration Chemical combination: Choose the appropriate chemical combination for different types of dirt. For example, acidic chemicals are used for scale and surfactants are used for oil.

[0027] Dosage of reagent: According to the real-time changes of the thermal resistance value of the dirt and water quality parameters, the dosage of the reagent is dynamically adjusted. If the thermal resistance value increases significantly, the dosage of the reagent can be increased to ensure rapid and effective removal of dirt.

[0028] Continuity of agent dosing: In the case of severe fouling, continuous dosing is adopted to keep the agent working continuously; after the system is restored, the frequency of agent dosing can be reduced.

[0029] Agent concentration: In the initial stage of cleaning, the agent concentration is high. As the dirt is removed, the concentration is gradually reduced to avoid wasting the agent.

[0030] 8) Real-time adjustment and feedback mechanism The system monitors the thermal resistance value of dirt, water quality parameters, flow rate and dirt type in real time, and automatically adjusts the agent dosing strategy in combination with the set control algorithm and feedback mechanism to ensure the optimization of the effect of each cleaning process.

[0031] In general, the automatic dosing control method and system based on fouling thermal resistance have brought revolutionary changes to the circulating water management of thermal power plants. Through precise monitoring, automatic adjustment and real-time feedback control, it has significantly improved the water quality treatment efficiency, equipment operation safety and economy, and has broad application prospects.

[0032] Compared with the prior art, the present invention has the following beneficial effects: the automatic dosing control method and system based on fouling thermal resistance provided by the present invention obtains historical data of circulating water and real-time monitored water quality parameters, including pH value, conductivity, temperature, and turbidity, and performs preprocessing and feature extraction operations on the acquired data, wherein data preprocessing includes cleaning, standardization, and denoising; feature extraction includes extracting key features that affect the structure; historical data and water quality parameters are trained using regression, SVM, or decision tree computer learning models and fouling thermal resistance values ​​are calculated in real time; the dosing of the agent is automatically controlled and adjusted according to the relationship between the fouling thermal resistance value and the preset thermal resistance critical value, and the change of the agent concentration and the fouling thermal resistance value are monitored in real time to control and adjust the agent dosing strategy in real time, and generate a water quality and strategy report; by real-time monitoring of the fouling thermal resistance and setting a corresponding critical value (such as 1.77×10 -4 m 2 ∙ K / W, 3.44×10 -4 m 2 ∙ K / W, etc.), automatically adjust the dosage of the agent according to the change of the thermal resistance of the dirt to ensure that the dirt and bacteria are effectively suppressed. The automatic dosing method based on thermal resistance judgment simplifies the operation process and avoids errors in manual intervention; The reagent dosing module in the present invention can systematically combine high-precision sensors and fluorescence monitoring technology to achieve accurate dosing of reagents, reduce waste of reagents, and ensure the maximization of the effect of the reagents; the fouling control module is based on historical data and real-time monitoring results. Through machine learning algorithms, the system can optimize the dosing strategy according to historical water quality data and real-time monitoring results, improve the accuracy and stability of water treatment, significantly reduce the fouling formation rate, and improve the heat exchange efficiency of the equipment; real-time control and adjustment of the reagent dosing strategy can improve the heat exchange efficiency and reduce the condenser scaling formation rate. The power plant can achieve energy conservation and emission reduction, thereby saving operating costs and has broad market prospects.

[0033] Combined with the new environmentally friendly quantum dot scale and corrosion inhibitor, it not only has the functions of scale prevention and sterilization, but also can realize online accurate monitoring and reduce pollution to the environment. By improving the level of circulating water management, the system not only meets the needs of long-term stable and efficient operation of equipment, but also improves the efficiency of water resource use, which has good social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the framework of an automatic dosing control system based on fouling thermal resistance provided in an embodiment of the present invention.

[0035] Figure 2 This is a working principle diagram of the water quality monitoring module provided in an embodiment of the present invention.

[0036] Figure 3 It is a schematic diagram of the structure of the drug dosing module provided in an embodiment of the present invention.

[0037] Figure 4 This is an algorithm flow chart of the dirt control module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0039] The automatic dosing control method based on fouling thermal resistance provided by the embodiment of the present invention comprises the following steps: Step S1: based on historical data of circulating water and water quality parameters monitored in real time; Step S2: preprocessing and feature extraction operations are performed on the acquired data, and regression, SVM or decision tree computer learning models are used to train and predict the fouling thermal resistance value; it should be noted that data preprocessing includes cleaning, standardization, and denoising; feature extraction includes extracting key features that affect the structure; Step S3: Automatically control and adjust the agent dosage according to the relationship between the dirt thermal resistance value and the preset thermal resistance critical value, and monitor the changes in agent concentration and dirt thermal resistance value in real time to adjust the agent dosage strategy in real time.

[0040] Specifically, the automatic dosing control method based on fouling thermal resistance provided by the embodiment of the present invention includes the following steps: (1) Construction of water quality monitoring module Install high-precision water quality sensors to monitor the pH value, conductivity, temperature, turbidity and other parameters of circulating water in real time. Transmit monitoring data to the cloud platform via wireless network for centralized management and analysis.

[0041] (2) Thermal resistance calculation and reagent addition According to the water quality monitoring data, the fouling thermal resistance value is calculated and compared with the preset critical value (such as 1.77×10 -4 m 2 ∙ K / W and 3.44×10 -4 m 2 ∙ K / W) for comparison.

[0042] When the thermal resistance reaches 1.77×10 -4 m 2 ∙ When K / W, the system automatically starts adding reagents; When it reaches 3.44×10 -4 m 2 ∙ When K / W, the cleaning function is activated.

[0043] The dosage of the reagent is automatically adjusted by a micro pump to ensure that the concentration of the reagent remains within the optimal range. The fluorescence monitoring sensor monitors the concentration of the reagent in real time to ensure the effectiveness of the reagent during the reaction.

[0044] (3) Establishment of the fouling control module Collect historical water quality and operation data to build a fouling prediction model. Use machine learning algorithms to analyze data and continuously optimize control strategies to improve system responsiveness and decision-making accuracy.

[0045] (4) System integration and testing Integrate each module, conduct system function testing and debugging to ensure the stability and reliability of the system in actual application. Conduct field tests in a thermal power plant to evaluate the impact of the system on the quality of circulating water and its effect in fouling control.

[0046] The effectiveness of the system was verified by applying the automatic dosing control method based on fouling thermal resistance in the circulating cooling water system of a thermal power plant. Fouling thermal resistance is a key factor affecting heat exchange efficiency. Higher thermal resistance usually means that more mineral scale or bacterial film has accumulated in the water system, reducing the heat exchange efficiency. Therefore, timely adjusting the dosage of the agent to control the fouling thermal resistance to the optimal range is the core to ensure the efficient operation of the system. Example

[0047] 1. Implementation process In the circulating water system of the power plant, high-precision water quality monitoring sensors were first installed to monitor water quality parameters such as pH, conductivity, temperature and turbidity in real time. At the same time, the changes in fouling thermal resistance are monitored and real-time data is transmitted to the central control system for analysis. According to the fouling thermal resistance value, the system can automatically calculate the required dosage of reagents to maintain the optimal water quality and ensure maximum heat exchange efficiency. Specifically, the value of fouling thermal resistance and the dosage of reagents show the following relationship: (1) When the thermal resistance of dirt is 1.77×10 -4 m 2 ∙ When K / W is below, the system will trigger an early warning and automatically start to increase the dosage of the agent to prevent further accumulation of dirt.

[0048] (2) When the thermal resistance of dirt reaches 3.44×10 -4 m 2 ∙ When K / W is reached, the system will determine that it is necessary to increase the dosage of the agent. It will not only increase the dosage of the antiscalant, but also enable the cleaning function to react quickly by increasing the concentration of the agent, thereby effectively removing the existing scale layer.

[0049] (3) When the fouling thermal resistance reaches a high value (such as more than 4.0×10 -4 m 2 ∙ K / W), the system will start the strong cleaning mode and increase the concentration of the agent to perform deep cleaning and restore the heat exchange efficiency.

[0050] 2. Data Analysis In actual operation, the system automatically adjusts the dosage of the reagent according to the monitored fouling thermal resistance value and real-time water quality changes. The following Table 1 is part of the test data.

[0051] Table 1 time <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage <![CDATA[1.77×10 -4 ]]> 0.5 7.2 92 2 hours later <![CDATA[2.10×10 -4 ]]> 0.8 7.3 91 4 hours later <![CDATA[2.60×10 -4 ]]> 1.0 7.5 89 6 hours later <![CDATA[3.00×10 -4 ]]> 1.2 7.7 87 8 hours later <![CDATA[3.44×10 -4 ]]> 1.5 7.8 85 After cleaning starts <![CDATA[1.61×10 -4 ]]> 2.0 8.0 98 It can be seen from Table 1 that when the fouling thermal resistance value is increased from 1.77×10 -4 m 2 ∙ K / W increased to 3.44×10 -4 m 2∙K / W, the system automatically adjusts the dosage of the agent and effectively improves the water quality, so that the water quality remains in the appropriate range and the heat exchange efficiency is improved in time. After the cleaning is started, the concentration of the agent increases, the cleaning effect gradually appears, the thermal resistance value of the dirt begins to decrease, and the heat exchange efficiency returns to a level close to the original level. This shows that the cleaning process effectively removes the dirt in the system and restores the heat exchange performance. Implementation Effect

[0052] By implementing the automatic dosing system in this power plant, the test results show that: (1) Significant fouling control effect: The system can monitor in real time and automatically adjust the dosage of the reagent, effectively preventing the accumulation of dirt, and cleaning by increasing the dosage of the reagent when the thermal resistance increases, ensuring the efficient operation of the circulating water system.

[0053] (2) Improve heat exchange efficiency: After the system is implemented, the heat exchange efficiency of the circulating water system is increased by 15%-20%. Especially when the thermal resistance value is higher than 3.44×10 -4 m 2 ∙ When the temperature is lower than 2000 K / W, the system can quickly remove the scale layer and restore the heat exchange efficiency by automatically adjusting the concentration of the reagent, thus reducing the maintenance frequency of the equipment.

[0054] (3) Reduced waste of chemicals: Compared with traditional manual dosing, the automatic dosing system significantly reduces the waste of chemicals. The system automatically adjusts the amount of chemicals according to changes in water quality and thermal resistance to ensure that the concentration of chemicals remains in the optimal range, which not only saves costs but also reduces the negative impact on the environment.

[0055] (4) System stability and reliability: The system operates stably, with fast data collection and feedback speeds, and can respond to changes in fouling thermal resistance in a timely manner, ensuring the long-term and efficient operation of the system.

[0056] This embodiment successfully realizes the intelligent management of the circulating water system of a thermal power plant through an automatic dosing control system based on fouling thermal resistance. The system can dynamically adjust the dosage of the agent according to the changes in water quality and thermal resistance, which not only effectively controls the growth of mineral scale and bacteria, but also greatly improves the heat exchange efficiency and economy of the system, reduces the maintenance cost of the equipment, and has broad market application prospects. Example

[0057] This example further verifies the stability and effect of the system in long-term operation. The system is deployed in another thermal power plant to monitor its long-term use effect.

[0058] 1. Implementation Process and Data The thermal power plant has been using this automatic dosing system for more than three months, and the fouling thermal resistance has been reduced from the initial 1.50×10 -4 m 2∙K / W gradually increased to 3.60×10 -4 m 2 ∙ K / W. The system automatically adds chemicals, and the water quality and thermal resistance changes after each addition are recorded in detail. According to the test data, the adjustment effect of the system is very obvious. The following Table 2 is the system operation data after three months.

[0059] Table 2 time <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Run Start <![CDATA[1.50×10 -4 ]]> 0.5 7.2 92 1 month later <![CDATA[2.10×10 -4 ]]> 0.9 7.3 90 2 months later <![CDATA[3.00×10 -4 ]]> 1.2 7.5 88 3 months later <![CDATA[3.60×10 -4 ]]> 1.5 7.7 86 After cleaning <![CDATA[1.80×10 -4 ]]> 2.0 8.0 98 Implementation Effect

[0060] After three months of operation, the system can automatically adjust the dosage of the agent according to the changes in water quality and thermal resistance, ensuring stable water quality and effectively improving heat exchange efficiency. Compared with the traditional dosing system, it saves 20% of the agent cost. At the same time, through real-time monitoring and feedback control, it greatly reduces the maintenance requirements of the equipment and significantly extends the service life of the equipment. Example

[0061] In this embodiment, the performance of the automatic dosing control method at different temperatures is tested by changing the water temperature in the circulating water system of a thermal power plant. The change in water temperature may affect the scale thermal resistance and the reaction speed of the agent, so it is necessary to verify the stability of the system and the dosing effect at different temperatures.

[0062] 1. Implementation Process and Data During the experiment, the water temperature varied from 50ºC to 70ºC, and the system automatically adjusted the dosage of the agent according to the real-time monitoring of the fouling thermal resistance and water quality changes. The following Table 3 is the test data.

[0063] Table 3 time Water temperature (°C) <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage 50 <![CDATA[1.55×10 -4 ]]> 0.4 7.1 91 2 hours later 55 <![CDATA[1.85×10 -4 ]]> 0.5 7.2 90 4 hours later 60 <![CDATA[2.30×10 -4 ]]> 0.7 7.4 88 6 hours later 65 <![CDATA[2.75×10 -4 ]]> 0.9 7.5 86 8 hours later 70 <![CDATA[3.46×10 -4 ]]> 1.2 7.6 84 After cleaning starts 70 <![CDATA[1.86×10 -4 ]]> 1.5 7.8 94 Implementation Effect

[0064] As the water temperature rises, the thermal resistance of fouling also gradually increases. The system automatically increases the dosage of reagents to ensure that the heat exchange efficiency remains within a reasonable range. Through real-time data adjustment, the system ensures stable water quality in high temperature environments, avoids excessive scaling of equipment, and significantly improves heat exchange efficiency. Example

[0065] This example verifies the sustained effect of the automatic dosing control system based on fouling thermal resistance by long-term operation test of the circulating water system of a thermal power plant. Through long-term actual operation, the comprehensive impact of the system on water quality, heat exchange efficiency and agent consumption is examined.

[0066] 1. Implementation Process and Data The test lasted for 4 weeks, during which the system automatically adjusted the dosage of the reagent according to the real-time monitoring of the fouling thermal resistance and water quality parameters. Table 4 below shows some of the operating data.

[0067] Table 4 time <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage <![CDATA[1.65×10 -4 ]]> 0.5 7.2 92 1 week later <![CDATA[2.05×10 -4 ]]> 0.7 7.3 90 2 weeks later <![CDATA[2.60×10 -4 ]]> 0.9 7.4 87 3 weeks later <![CDATA[3.10×10 -4 ]]> 1.2 7.6 85 4 weeks later <![CDATA[3.50×10 -4 ]]> 1.4 7.7 83 After cleaning starts <![CDATA[1.71×10 -4 ]]> 1.6 7.8 93 Implementation Effect

[0068] During long-term operation, the system timely adjusts the dosage of reagents according to the change of fouling thermal resistance, ensuring the stability of heat exchange efficiency and significantly reducing the intervention of manual operation. Especially in long-term operation, the automatic dosing system shows high reliability and stability, effectively reducing maintenance costs. Example

[0069] This example tests the optimization effect of the automatic dosing system based on fouling thermal resistance on the circulating water system under different dosing strategies. By testing and comparing the differences between the continuous dosing strategy and the intermittent dosing strategy, their impact on system performance is analyzed.

[0070] 1. Implementation Process and Data During the test, the system used two strategies, continuous dosing and intermittent dosing, to automatically adjust the dosage of the agent according to the thermal resistance of the dirt. The following Table 5 is the experimental data.

[0071] Table 5 time Dosing strategy <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage Continuous dosing <![CDATA[1.45×10 -4 ]]> 0.4 7.2 91 2 hours later Continuous dosing <![CDATA[1.80×10 -4 ]]> 0.5 7.3 89 4 hours later Continuous dosing <![CDATA[2.20×10 -4 ]]> 0.7 7.4 87 6 hours later Continuous dosing <![CDATA[2.70×10 -4 ]]> 0.9 7.6 85 8 hours later Continuous dosing <![CDATA[3.47×10 -4 ]]> 1.1 7.88 82 After cleaning starts Continuous dosing <![CDATA[1.58×10 -4 ]]> 1.4 8.0 94 Initial stage Intermittent dosing <![CDATA[1.50×10 -4 ]]> 0.3 7.2 92 2 hours later Intermittent dosing <![CDATA[1.60×10 -4 ]]> 0.4 7.3 90 4 hours later Intermittent dosing <![CDATA[2.40×10 -4 ]]> 0.6 7.5 88 6 hours later Intermittent dosing <![CDATA[2.80×10 -4 ]]> 0.8 7.7 86 8 hours later Intermittent dosing <![CDATA[3.52×10 -4 ]]> 1.0 7.8 83 After cleaning starts Intermittent dosing <![CDATA[1.63×10 -4 ]]> 1.3 8.0 92 Implementation Effect

[0072] By comparing the continuous dosing and intermittent dosing strategies, the results show that the two strategies have similar effects in controlling fouling thermal resistance, but the intermittent dosing strategy is more suitable for unstable water quality environments, can reasonably adjust the dosage of the agent under different fouling thermal resistance levels, and can effectively reduce the agent consumption. Example

[0073] This example tests the effect of the automatic dosing system based on fouling thermal resistance under different reagent concentrations. By adjusting the reagent concentration, its effect on water quality, fouling thermal resistance change and heat exchange efficiency is observed, thereby verifying the effect of reagent concentration on dosing effect.

[0074] 1. Implementation Process and Data During the experiment, three different concentrations of reagents were selected for testing, namely low concentration (5%), medium concentration (10%) and high concentration (20%). The system automatically adjusts the reagent dosage according to the real-time changes in the fouling thermal resistance. The following Table 6 is the test data.

[0075] Table 6 time Drug concentration <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage 5% <![CDATA[1.50×10 -4 ]]> 0.3 7.0 92 2 hours later 5% <![CDATA[2.00×10 -4 ]]> 0.4 7.1 90 4 hours later 5% <![CDATA[2.50×10 -4 ]]> 0.6 7.3 87 6 hours later 5% <![CDATA[3.00×10 -4 ]]> 0.8 7.5 85 8 hours later 5% <![CDATA[3.45×10 -4 ]]> 1.0 7.7 82 After cleaning starts 5% <![CDATA[1.58×10 -4 ]]> 1.2 7.8 92 Initial stage 10% <![CDATA[1.60×10 -4 ]]> 0.4 7.2 93 2 hours later 10% <![CDATA[2.10×10 -4 ]]> 0.5 7.3 91 4 hours later 10% <![CDATA[2.60×10 -4 ]]> 0.7 7.4 88 6 hours later 10% <![CDATA[3.20×10 -4 ]]> 1.0 7.6 86 8 hours later 10% <![CDATA[3.60×10 -4 ]]> 1.2 7.8 83 After cleaning starts 10% <![CDATA[1.68×10 -4 ]]> 1.5 8.0 94 Initial stage 20% <![CDATA[1.80×10 -4 ]]> 0.5 7.3 94 2 hours later 20% <![CDATA[2.30×10 -4 ]]> 0.6 7.4 92 4 hours later 20% <![CDATA[2.80×10 -4 ]]> 0.9 7.5 89 6 hours later 20% <![CDATA[3.40×10 -4 ]]> 1.1 7.7 87 8 hours later 20% <![CDATA[3.80×10 -4 ]]> 1.3 7.9 84 After cleaning starts 20% <![CDATA[1.97×10 -4 ]]> 1.6 8.0 95 Implementation Effect

[0076] From the experimental results, as the concentration of the reagent increases, the fouling thermal resistance value shows a more obvious change. The system effectively controls the decrease in heat exchange efficiency by increasing the dosage of the reagent and successfully delays the scaling phenomenon. High-concentration reagents can be effective in a shorter time, but they also require higher reagent consumption. Example

[0077] This embodiment tests the effect of the automatic dosing system based on fouling thermal resistance at different flow rates. Since changes in flow rate may affect the flow of water and heat exchange efficiency, it is necessary to analyze its impact on dosing control.

[0078] 1. Implementation Process and Data During the experiment, by adjusting the flow rate of the circulating water (1m / s, 1.5m / s, 2m / s respectively), the system automatically adjusts the dosage of the agent according to the real-time monitored thermal resistance value of the dirt. The following Table 7 is the test data.

[0079] Table 7 time Flow rate (m / s) <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage 1 <![CDATA[1.40×10 -4 ]]> 0.3 7.1 91 2 hours later 1 <![CDATA[1.80×10 -4 ]]> 0.4 7.2 89 4 hours later 1 <![CDATA[2.20×10 -4 ]]> 0.6 7.3 86 6 hours later 1 <![CDATA[2.60×10 -4 ]]> 0.8 7.5 83 8 hours later 1 <![CDATA[3.45×10 -4 ]]> 1.0 7.7 80 After cleaning starts 1 <![CDATA[1.43×10 -4 ]]> 1.4 7.8 92 Initial stage 1.5 <![CDATA[1.50×10 -4 ]]> 0.4 7.2 92 2 hours later 1.5 <![CDATA[2.00×10 -4 ]]> 0.5 7.3 90 4 hours later 1.5 <![CDATA[2.40×10 -4 ]]> 0.7 7.4 87 6 hours later 1.5 <![CDATA[2.80×10 -4 ]]> 0.9 7.5 84 8 hours later 1.5 <![CDATA[3.51×10 -4 ]]> 1.1 7.7 81 After cleaning starts 1.5 <![CDATA[1.63×10 -4 ]]> 1.5 7.8 94 Initial stage 2 <![CDATA[1.60×10 -4 ]]> 0.5 7.3 93 2 hours later 2 <![CDATA[2.10×10 -4 ]]> 0.6 7.4 91 4 hours later 2 <![CDATA[2.60×10 -4 ]]> 0.8 7.5 88 6 hours later 2 <![CDATA[3.10×10 -4 ]]> 1.0 7.6 85 8 hours later 2 <![CDATA[3.50×10 -4 ]]> 1.2 7.7 82 After cleaning starts 2 <![CDATA[1.78×10 -4 ]]> 1.6 7.9 95 Implementation Effect

[0080] As the flow rate increases, the flow of circulating water accelerates, and the scale thermal resistance changes greatly. The system ensures the stability of heat exchange efficiency at different flow rates by adjusting the amount of dosing, further reducing scaling. Example

[0081] This example tests the influence of different pipe materials on the automatic dosing control effect. Pipes of different materials have a significant influence on the degree of dirt accumulation and thermal resistance change, so a comparative study is needed.

[0082] 1. Implementation Process and Data In the experiment, two different pipe materials, stainless steel pipe and carbon steel pipe, were selected for testing. The system automatically adjusted the dosage of the reagent according to the change of the fouling thermal resistance of the two pipe materials. The following Table 8 is the experimental data.

[0083] Table 8 time Pipe material <![CDATA[Fouling resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage Stainless steel <![CDATA[1.20×10 -4 ]]> 0.3 7.0 90 2 hours later Stainless steel <![CDATA[1.70×10 -4 ]]> 0.4 7.1 88 4 hours later Stainless steel <![CDATA[2.20×10 -4 ]]> 0.6 7.3 85 6 hours later Stainless steel <![CDATA[2.60×10 -4 ]]> 0.8 7.5 82 8 hours later Stainless steel <![CDATA[3.3.48×10 -4 ]]> 1.0 7.7 79 After cleaning starts Stainless steel <![CDATA[1.36×10 -4 ]]> 1.4 7.8 92 Initial stage Carbon Steel <![CDATA[1.50×10 -4 ]]> 0.4 7.2 91 2 hours later Carbon Steel <![CDATA[2.00×10 -4 ]]> 0.5 7.3 89 4 hours later Carbon Steel <![CDATA[2.50×10 -4 ]]> 0.7 7.4 86 6 hours later Carbon Steel <![CDATA[3.00×10 -4 ]]> 0.9 7.6 83 8 hours later Carbon Steel <![CDATA[3.51×10 -4 ]]> 1.1 7.8 80 After cleaning starts Carbon Steel <![CDATA[1.78×10 -4 ]]> 1.4 7.9 92 Implementation Effect

[0084] Stainless steel pipes have relatively less dirt accumulation, and the system can achieve good control effects with a lower dosage of chemicals; however, carbon steel pipes are more prone to scale accumulation, so a higher dosage of chemicals is required to achieve the same effect. Example

[0085] This example tests the effect of the automatic dosing control system based on fouling thermal resistance under different water temperature conditions. Since temperature has a great influence on the solubility and thermal resistance of minerals in water, the fluctuation of water temperature will directly affect the dosing amount and heat exchange efficiency. Therefore, by testing the effect of different temperature ranges, the dosing control of the system is further optimized.

[0086] 1. Implementation Process and Data During the experiment, three different water temperature conditions were selected for testing: low temperature (15°C), medium temperature (35°C) and high temperature (55°C). According to the change of dirt thermal resistance, the system automatically adjusts the dosage of the agent. The following Table 9 is the test data.

[0087] Table 9 time Water temperature (°C) <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage 15 <![CDATA[1.20×10 -4 ]]> 0.3 6.9 92 2 hours later 15 <![CDATA[1.60×10 -4 ]]> 0.4 7.0 90 4 hours later 15 <![CDATA[2.00×10 -4 ]]> 0.5 7.2 88 6 hours later 15 <![CDATA[3.40×10 -4 ]]> 0.6 7.3 85 8 hours later 15 <![CDATA[3.80×10 -4 ]]> 0.8 7.5 82 After cleaning starts 15 <![CDATA[1.31×10 -4 ]]> 1.2 7.6 93 Initial stage 35 <![CDATA[1.50×10 -4 ]]> 0.4 7.1 93 2 hours later 35 <![CDATA[1.90×10 -4 ]]> 0.5 7.2 91 4 hours later 35 <![CDATA[2.30×10 -4 ]]> 0.6 7.3 88 6 hours later 35 <![CDATA[2.70×10 -4 ]]> 0.8 7.4 85 8 hours later 35 <![CDATA[3.44×10 -4 ]]> 1.0 7.5 82 After cleaning starts 35 <![CDATA[1.67×10 -4 ]]> 1.4 7.7 93 Initial stage 55 <![CDATA[1.80×10 -4 ]]> 0.5 7.3 94 2 hours later 55 <![CDATA[2.20×10 -4 ]]> 0.6 7.4 92 4 hours later 55 <![CDATA[2.60×10 -4 ]]> 0.7 7.5 89 6 hours later 55 <![CDATA[3.00×10 -4 ]]> 0.9 7.6 86 8 hours later 55 <![CDATA[3.46×10 -4 ]]> 1.1 7.7 83 After cleaning starts 55 <![CDATA[1.89×10 -4 ]]> 1.5 7.8 94 Implementation Effect

[0088] The increase in temperature leads to a decrease in the solubility of minerals and an increase in the thermal resistance of dirt. The system automatically adjusts the dosage of reagents to effectively respond to the needs of dirt control under different water temperature conditions. Under high temperature conditions, the system significantly increases the dosage of reagents, thereby ensuring the stability of heat exchange efficiency and water quality. Example

[0089] This example studies the automatic dosing effect of different combinations of agents (scaling inhibitors, bactericides, corrosion inhibitors, and new environmentally friendly quantum dot water treatment agents) in circulating water treatment. The new environmentally friendly quantum dot water treatment agent has multiple functions, which can not only effectively control the formation of scale, but also inhibit bacterial growth and reduce the negative impact on the environment. By comparing the effects of different combinations of agents on parameters such as scale thermal resistance, agent dosage, and heat exchange efficiency, the control strategy of the automatic dosing system is further optimized.

[0090] 1. Implementation Process and Data Four drug combinations were selected for testing in the experiment, namely: (1) Chemical combination A: antiscalant + bactericide (2) Chemical combination B: scale inhibitor + corrosion inhibitor (3) Chemical combination C: scale inhibitor + bactericide + corrosion inhibitor (4) Chemical combination D: scale inhibitor + new environmentally friendly quantum dot water treatment agent The system automatically adjusts the dosage of the reagent by real-time monitoring of the fouling thermal resistance and water quality parameters to ensure the stability of the circulating water quality and heat exchange efficiency. The following Table 10 is the test data: Table 10 time Drug combination <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage A <![CDATA[1.20×10 -4 ]]> 0.3 6.9 92 2 hours later A <![CDATA[1.80×10 -4 ]]> 0.4 7.0 90 4 hours later A <![CDATA[2.20×10 -4 ]]> 0.5 7.2 88 6 hours later A <![CDATA[3.15×10 -4 ]]> 0.6 7.3 85 8 hours later A <![CDATA[3.46×10 -4 ]]> 1.0 7.5 82 After cleaning starts A <![CDATA[1.35×10 -4 ]]> 1.3 7.6 94 Initial stage B <![CDATA[1.40×10 -4 ]]> 0.4 7.1 93 2 hours later B <![CDATA[1.90×10 -4 ]]> 0.5 7.2 91 4 hours later B <![CDATA[2.30×10 -4 ]]> 0.6 7.3 88 6 hours later B <![CDATA[2.87×10 -4 ]]> 0.8 7.4 85 8 hours later B <![CDATA[3.47×10 -4 ]]> 1.0 7.5 82 After cleaning starts B <![CDATA[1.42×10 -4 ]]> 1.4 7.7 93 Initial stage C <![CDATA[1.50×10 -4 ]]> 0.5 7.3 94 2 hours later C <![CDATA[2.00×10 -4 ]]> 0.6 7.4 92 4 hours later C <![CDATA[2.38×10 -4 ]]> 0.7 7.5 89 6 hours later C <![CDATA[3.16×10 -4 ]]> 0.9 7.6 86 8 hours later C <![CDATA[4.51×10 -4 ]]> 1.1 7.7 83 After cleaning starts C <![CDATA[1.53×10 -4 ]]> 1.5 7.8 94 Initial stage D <![CDATA[1.10×10 -4 ]]> 0.3 7.0 91 2 hours later D <![CDATA[1.60×10 -4 ]]> 0.4 7.1 90 4 hours later D <![CDATA[2.36×10 -4 ]]> 0.5 7.2 88 6 hours later D <![CDATA[2.95×10 -4 ]]> 0.6 7.3 85 8 hours later D <![CDATA[3.44×10 -4 ]]> 0.7 7.4 82 After cleaning starts D <![CDATA[1.25×10 -4 ]]> 1.4 7.5 93 Implementation Effect

[0091] (1) Combination A (scaling inhibitor + bactericide): This combination has a relatively stable control effect on mineral scale, especially in terms of bacterial inhibition. After 8 hours, the scale thermal resistance value reaches 2.8×10 -4 m 2 ∙ K / W, the heat exchange efficiency was maintained at around 82%. After the cleaning was started, the heat exchange efficiency quickly recovered to 98%.

[0092] (2) Chemical combination B (scaling inhibitor + corrosion inhibitor): This combination is mainly used to prevent equipment corrosion and has significant effects. Although the heat exchange efficiency is slightly lower than that of chemical combination A, it still remains at around 83%. The fouling thermal resistance after 8 hours reaches 3.0×10 -4 m 2 ∙ K / W, proving its good ability to prevent scale formation.

[0093] (3) Agent combination C (scaling inhibitor + bactericide + corrosion inhibitor): This combination of three agents can provide a more comprehensive control effect. The fouling heat resistance reaches 3.3×10 after 8 hours. -4 m 2 ∙ K / W, the heat exchange efficiency dropped to 84%, indicating that it can improve the diversity and efficiency of water quality control to a certain extent and is suitable for a variety of complex working conditions.

[0094] (4) Agent combination D (scaling inhibitor + new environmentally friendly quantum dot water treatment agent): The new environmentally friendly quantum dot water treatment agent has multiple functions and can effectively control dirt, bacteria and equipment corrosion at the same time. Experiments show that the thermal resistance value of dirt after 8 hours is 2.8×10 -4 m 2 ∙ K / W, and the heat exchange efficiency is 82%. Its advantage is that the dosage of reagents is small, and the concentration of reagents remains stable, which is not easy to waste. It is especially suitable for occasions with high requirements for environmental protection and water quality control.

[0095] By comparing the effects of different combinations of drugs, this example shows: (1) Chemical combination A (scalant inhibitor + bactericide): Suitable for occasions with high water quality control requirements. It can effectively inhibit bacterial growth and control the deposition of mineral scale.

[0096] (2) Chemical combination B (scaling inhibitor + corrosion inhibitor): focuses more on the protection of equipment and is suitable for environments that are sensitive to corrosion.

[0097] (3) Chemical combination C (scaling inhibitor + bactericide + corrosion inhibitor): suitable for more complex water quality environments and can provide comprehensive water quality control.

[0098] (4) Chemical combination D (scaling inhibitor + new environmentally friendly quantum dot water treatment agent): can effectively reduce the impact on the environment and has a good water quality control effect. It is suitable for occasions with high environmental protection requirements.

[0099] The new environmentally friendly quantum dot water treatment agent has unique advantages in comprehensive control, especially showing good prospects in reducing environmental pollution and improving the overall operating efficiency of the system. Example

[0100] This example tests the effect of different fouling types (such as mineral fouling and bacterial fouling) on ​​the automatic dosing control effect. By simulating different fouling types in the system, their effects on fouling thermal resistance and dosing amount are studied.

[0101] 1. Implementation Process and Data During the experiment, the automatic dosing control effect of the system was tested by artificially simulating the formation of different types of scale (mineral scale, bacterial scale) at different time points. The following Table 11 is the experimental data.

[0102] Table 11 time Dirt Type <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage Mineral scale <![CDATA[1.40×10 -4 ]]> 0.3 7.0 91 2 hours later Mineral scale <![CDATA[2.38×10 -4 ]]> 0.4 7.1 89 4 hours later Mineral scale <![CDATA[2.75×10 -4 ]]> 0.5 7.2 86 6 hours later Mineral scale <![CDATA[3.28×10 -4 ]]> 0.7 7.4 83 8 hours later Mineral scale <![CDATA[3.49×10 -4 ]]> 0.9 7.6 80 After cleaning starts Mineral scale <![CDATA[1.51×10 -4 ]]> 1.3 7.7 92 Initial stage Bacterial dirt <![CDATA[1.60×10 -4 ]]> 0.4 7.1 92 2 hours later Bacterial dirt <![CDATA[1.94×10 -4 ]]> 0.5 7.2 90 4 hours later Bacterial dirt <![CDATA[2.35×10 -4 ]]> 0.6 7.3 87 6 hours later Bacterial dirt <![CDATA[2.78×10 -4 ]]> 0.8 7.5 84 8 hours later Bacterial dirt <![CDATA[3.45×10 -4 ]]> 1.0 7.7 81 After cleaning starts Bacterial dirt <![CDATA[1.61×10 -4 ]]> 1.4 7.8 92 Initial stage Complex dirt <![CDATA[1.50×10 -4 ]]> 0.4 7.2 91 2 hours later Complex dirt <![CDATA[2.14×10 -4 ]]> 0.5 7.3 89 4 hours later Complex dirt <![CDATA[2.63×10 -4 ]]> 0.6 7.4 86 6 hours later Complex dirt <![CDATA[3.10×10 -4 ]]> 0.8 7.5 83 8 hours later Complex dirt <![CDATA[3.49×10 -4 ]]> 1.0 7.7 80 After cleaning starts Complex dirt <![CDATA[1.53×10 -4 ]]> 1.5 7.8 92 Implementation Effect

[0103] Different types of scale have different effects on scale thermal resistance and dosing control. Mineral scale forms relatively steadily, and the system can adjust the dosage of the agent relatively accurately, while bacterial scale forms quickly, and the adjustment of the agent needs to be more flexible and timely. Composite scale needs to comprehensively consider the dual effects of minerals and bacteria, and the automatic dosing system can optimize the control strategy according to the real-time thermal resistance changes. Example

[0104] This example studies the response of the automatic dosing control system to the fouling thermal resistance under different pollution loads. The pollution load affects the concentration and deposition rate of harmful substances in the water. The system needs to automatically adjust the dosage of the agent according to the change of the pollution load to ensure the stability of water quality and heat exchange efficiency.

[0105] 1. Implementation Process and Data In the experiment, different pollution load conditions were simulated by adjusting the pollutant concentration in the water (such as suspended solids, dissolved minerals, etc.). The following Table 12 is the test data.

[0106] Table 12 time Pollution load (mg / L) <![CDATA[Fouling resistance (m 2 ∙ K / W)]]> Dosage of reagent (L / h) Water quality parameters (pH value) Heat transfer efficiency (%) Initial stage 50 <![CDATA[1.20×10 -4 ]]> 0.3 6.9 92 2 hours later 50 <![CDATA[1.89×10 -4 ]]> 0.4 7.0 90 4 hours later 50 <![CDATA[2.58×10 -4 ]]> 0.5 7.2 88 6 hours later 50 <![CDATA[3.09×10 -4 ]]> 0.6 7.3 85 8 hours later 50 <![CDATA[3.52×10 -4 ]]> 0.8 7.5 82 After cleaning starts 50 <![CDATA[1.26×10 -4 ]]> 1.3 7.6 92 Initial stage 100 <![CDATA[1.40×10 -4 ]]> 0.5 7.1 93 2 hours later 100 <![CDATA[1.83×10 -4 ]]> 0.6 7.2 91 4 hours later 100 <![CDATA[2.45×10 -4 ]]> 0.7 7.3 88 6 hours later 100 <![CDATA[32.97×10 -4 ]]> 0.9 7.4 85 8 hours later 100 <![CDATA[3.45×10 -4 ]]> 1.1 7.5 82 After cleaning starts 100 <![CDATA[1.42×10 -4 ]]> 1.5 7.6 93 Initial stage 200 <![CDATA[1.60×10 -4 ]]> 0.6 7.3 94 2 hours later 200 <![CDATA[2.00×10 -4 ]]> 0.7 7.4 92 4 hours later 200 <![CDATA[2.58×10 -4 ]]> 0.8 7.5 89 6 hours later 200 <![CDATA[2.83×10 -4 ]]> 1.0 7.6 86 8 hours later 200 <![CDATA[3.46×10 -4 ]]> 1.2 7.7 83 After cleaning starts 200 <![CDATA[1.72×10 -4 ]]> 1.6 7.8 94 Implementation Effect

[0107] When the pollution load increases, the concentration of harmful substances in the water increases, resulting in an increase in the heat resistance of the dirt. The system can automatically adjust the dosage of the agent according to the change of the pollution load to ensure stable water quality and maximize the heat exchange efficiency. Even if the pollution load is high, the system can still effectively control the dirt and maintain a high heat exchange efficiency.

[0108] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Automatic dosing control method based on fouling thermal resistance, characterized in that: The steps include: Obtain historical data of circulating water and real-time monitoring of water quality parameters, including pH, conductivity, temperature, and turbidity; Use regression, SVM or decision tree computer learning models to train historical data and water quality parameters and calculate fouling thermal resistance in real time; According to the relationship between the thermal resistance value of the dirt and the preset thermal resistance critical value, the agent dosage is automatically controlled and adjusted, and the changes in agent concentration and the thermal resistance value of the dirt are monitored in real time to control and adjust the agent dosage strategy in real time and generate water quality and strategy reports.

2. The automatic dosing control method based on fouling thermal resistance according to claim 1 is characterized in that: A machine learning algorithm is used to optimize the linear relationship between the change in thermal resistance value of the dirt and the dosage of the reagent, and based on the linear relationship, the thermal resistance critical values ​​are divided into a first critical value, a second critical value and a third critical value in order from small to large; When the thermal resistance value of the dirt reaches the first critical value, an early warning is triggered, automatic control is performed, and the agent addition is started according to the first dosage; When the thermal resistance value of the dirt reaches the second critical value, the dosage of the agent is increased according to the second dosage and the cleaning process is automatically started; When the dirt thermal resistance value reaches the third critical value, the strong cleaning mode is started, and the dosage of the agent is further increased according to the third dosage.

3. The automatic dosing control method based on fouling thermal resistance according to claim 2 is characterized in that: The first critical value, the second critical value and the third critical value of the thermal resistance critical value are 1.77×10 -4 m 2 ∙ K / W, 3.44×10 -4 m 2 ∙K / W and 4.0×10 -4 m 2 ∙ K / W.

4. The automatic dosing control method based on fouling thermal resistance according to claim 1 or 3, characterized in that: Real-time control and adjustment of the agent dosing strategy include: Determine the operating parameters of the circulating water, including pipe material type, flow rate and operating time, and monitor the water quality parameters, fouling type and fouling thermal resistance of the circulating water in real time; Based on the pipe material type, flow rate, water quality parameters, fouling type, operating time and fouling thermal resistance of the circulating water, as well as the linear relationship between the change in fouling thermal resistance and the amount of reagent added, computer learning analysis is used to match and call the pre-stored reagent addition strategy plan, and real-time control is used to adjust the reagent combination, reagent addition amount, reagent addition continuity and reagent concentration to stabilize the water quality and maximize the heat exchange efficiency.

5. The automatic dosing control method based on fouling thermal resistance according to claim 4 is characterized in that: The strategy for adding pre-existing medicines includes the following steps: Determine the type of pipe material to guide the selection of the type of agent or combination of agents: if it is a metal pipe, choose an acidic or compound agent to remove scale; if it is a plastic pipe, choose a mild agent to avoid corrosion of the pipe; Determine the flow rate of circulating water to guide the adjustment of the dosage, continuity and concentration of the reagent: if the flow rate is high, the dosage should be moderate and the concentration should be increased appropriately to ensure that the decontamination effect is not affected by the flow rate; if the flow rate is low and the dirt accumulation is more significant, increase the dosage and continuity of the reagent; Determine the pH value and hardness of water quality parameters. If the water is acidic, choose a neutral or alkaline agent; If the water is alkaline, you can choose an acidic agent; If the water hardness is high, the dosage of the agent should be increased, and a composite acid agent or a complexing agent should be selected; Determine the type of dirt. If it is scale, choose an acidic cleaning agent and adjust the concentration of the agent to adapt to different water hardness. If it is serious scale accumulation, use a high concentration of agent or extend the agent addition cycle. If it is oil and organic dirt, choose a surfactant or solvent suitable for removing oil. If it is a complex type of dirt, use a composite agent combination and adjust the agent concentration and addition frequency. Determine the running time of circulating water. If it is a short-term operation, appropriately reduce the dosage of the agent so that the dosage and concentration of the agent can be maintained at the normal level; if it is a long-term operation, dynamically adjust the dosage of the agent according to the change of the fouling thermal resistance value, and gradually increase the concentration of the agent. When the fouling thermal resistance value increases significantly, adopt the method of continuous dosage of the agent; Determine the change of thermal resistance value of dirt. If the thermal resistance value of dirt increases, the dosage of the agent should be increased, and the change of thermal resistance value should be monitored in real time to restore the heat exchange efficiency in a short time; when the thermal resistance value of dirt decreases, the dosage of the agent should be appropriately reduced to avoid excessive cleaning, and the dosage of the agent should be controlled in real time according to the change of thermal resistance value of dirt. When the thermal resistance value is high, the dosage of the agent should be increased, and the concentration of the agent should be gradually reduced accordingly after the thermal resistance value returns to normal.

6. Automatic dosing control system based on fouling thermal resistance, characterized in that: Includes the following modules: Water quality monitoring module, used for real-time monitoring of water quality parameters, including pH value, conductivity, temperature, turbidity, and operating parameters of circulating water, including pipe material type, flow rate and operating time; as well as real-time monitoring of changes in reagent concentration and fouling thermal resistance value; A fouling control module that uses regression, SVM or decision tree computer learning models to train historical data and water quality parameters and calculates fouling thermal resistance in real time; Automatically control the agent dosing module to adjust the agent dosing according to the relationship between the thermal resistance value of the dirt and the preset thermal resistance critical value; The reagent dosing module is used to adjust and implement the reagent dosing strategy in real time under the control of the fouling control module; The data platform is used to store historical data of circulating water, receive and store real-time monitoring water quality parameters, operating parameters, changes in reagent concentration and changes in fouling thermal resistance transmitted by the water quality monitoring module, and receive and store reagent dosing strategies transmitted by the fouling control module.

7. The automatic dosing control system based on fouling thermal resistance according to claim 6 is characterized in that: The drug dosing module includes a drug storage tank, a micro pump, a drug dosing pipeline, a drug dosing point, a fluorescence monitoring sensor and a feedback and control system; The chemical storage tank is connected in series with the micro pump, the chemical dosing pipeline, and the chemical dosing point in sequence. The chemical dosing point is connected to the circulating water pipeline system. The fluorescence monitoring sensor is arranged in the circulating water pipeline system to monitor the change of chemical concentration, and the communication transmission is connected to the dirt control module; the feedback and control system is used to connect with the micro pump to adjust the pump operation to control the chemical concentration.

8. The automatic dosing control system based on fouling thermal resistance according to claim 6 is characterized in that: The water quality monitoring module comprises: High-precision water quality sensor for real-time monitoring of pH, conductivity, temperature, and turbidity parameters of circulating water; Data acquisition unit, used to aggregate and pre-process sensor monitoring data; The wireless transmission unit is used to wirelessly transmit the sensor monitoring data processed by the data acquisition unit to the data platform.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the automatic dosing control method based on fouling thermal resistance as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the automatic drug dosing control method based on dirt thermal resistance as claimed in any one of claims 1 to 5.

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