Automatic Dosing Control Method and System Based on Fouling Thermal Resistance

Abstract

This invention discloses an automatic chemical dosing control method and system based on fouling thermal resistance, belonging to the field of circulating water treatment technology. The method includes the following steps: acquiring historical data and real-time monitored water quality parameters of the circulating water; training the historical data and water quality parameters using regression, SVM, or decision tree computer learning models and calculating the fouling thermal resistance value in real time; automatically controlling and adjusting the chemical dosing based on the relationship between the fouling thermal resistance value and a preset thermal resistance threshold; monitoring changes in chemical concentration and fouling thermal resistance value in real time to adjust the chemical dosing strategy and generate water quality and strategy reports. The automatic chemical dosing control method and system based on fouling thermal resistance provided by this invention enables intelligent management of circulating water, improves water treatment efficiency and equipment operational safety, and has significant economic and social benefits.

Description

Technical Field

[0001] This invention belongs to the field of circulating water treatment technology, specifically relating to an automatic dosing control method and system based on the thermal resistance of fouling. Background Technology

[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 is responsible for cooling the equipment, changes in its water quality directly impact the heat exchange performance of critical equipment such as boilers, condensers, and heat exchangers. If contaminants such as mineral scale, bacteria, and algae accumulate in the circulating water, it will lead to increased water flow resistance, reduced heat transfer efficiency, and may even cause equipment corrosion and scaling, increasing operating costs and potentially causing equipment failure, affecting the long-term stability of the system. Deterioration in water quality not only affects the operating efficiency of equipment but may also lead to power plant shutdowns or premature maintenance due to equipment failures, and in severe cases, may affect the operational safety of the entire power grid.

[0003] Traditional water quality monitoring methods rely primarily on manual operation and periodic sampling and analysis, failing to achieve real-time monitoring and precise control of water quality. Manual monitoring often suffers from data update delays and untimely information transmission, leading to the failure to detect water quality changes promptly and increasing the risk of mineral scale and bacterial growth. For example, when water quality parameters such as pH, conductivity, and temperature fluctuate, manual intervention may be too slow to react, missing the optimal treatment window. Furthermore, traditional water quality monitoring equipment lacks intelligent functions, making it difficult to analyze and predict the correlations between different water quality parameters in real time, resulting in an inadequate early warning mechanism and an inability to effectively prevent scaling, corrosion, and other problems.

[0004] Existing methods of chemical dosing typically rely on manual experience and timed additions, which makes precise control difficult and often results in overdosing or underdosing. For example, when water quality fluctuates significantly, it's challenging to accurately match the dosage to the actual water quality requirements, leading to frequent overdosing. This not only wastes chemicals but can also negatively impact water quality, causing secondary pollution. Conversely, insufficient dosage results in poor water treatment, potentially leading to scale buildup and bacterial growth, further affecting the quality of circulating water and the operational efficiency of equipment. Existing methods also frequently suffer from incomplete chemical reactions, where some chemicals fail to fully react within the system, resulting in wasted efficacy and failing to achieve the desired water treatment effect.

[0005] To address these issues, many studies in recent years have begun to explore solutions based on intelligent water treatment systems, particularly technologies that automatically adjust chemical dosages, monitor water quality in real time, and provide feedback control. Summary of the Invention

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

[0007] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0008] In a first aspect, the present invention provides an automatic dosing control method based on fouling thermal resistance, comprising the following steps:

[0009] Historical data and real-time water quality parameters of circulating water are acquired, including pH, conductivity, temperature, and turbidity. The acquired data is preprocessed and feature extracted. Data preprocessing includes cleaning, standardization, and noise reduction. Feature extraction includes extracting key features that affect the structure. Regression, SVM, or decision tree computer learning models are used to train the historical data and water quality parameters and calculate the fouling thermal resistance value in real time.

[0010] The system automatically controls and adjusts the dosage of chemicals based on the relationship between the thermal resistance of the fouling and the preset critical thermal resistance value. It monitors changes in chemical concentration and thermal resistance of the fouling in real time to control and adjust the chemical dosing strategy and generate water quality and strategy reports.

[0011] Furthermore, machine learning algorithms are used to optimize the linear relationship between the change in the thermal resistance of the fouling and the dosage of the reagent, and based on the linear relationship, the critical values ​​of the thermal resistance are divided into the first critical value, the second critical value and the third critical value in ascending order;

[0012] When the thermal resistance of the fouling reaches the first critical value, an early warning is triggered, automatic control is activated, and the agent is added according to the first dosage.

[0013] When the thermal resistance 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 started automatically.

[0014] When the thermal resistance of the dirt reaches the third critical value, the powerful cleaning mode is activated, and the dosage of the cleaning agent is further increased according to the third dosage.

[0015] Furthermore, the first, second, and third critical values ​​of the thermal resistance threshold are respectively 1.77 × 10⁻⁶. -4 m 2 • K / W, 3.44×10 -4 m 2 • K / W and 4.0×10 -4 m 2 • K / W.

[0016] Furthermore, real-time control and adjustment of the drug dosing strategy includes:

[0017] 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 value of the circulating water in real time;

[0018] Based on the pipe material type, flow rate, water quality parameters, fouling type, operating time, and fouling thermal resistance value of circulating water, as well as the linear relationship between the change in fouling thermal resistance value and the dosage of chemicals, the chemical combination, dosage, continuity of chemical addition, and chemical concentration are controlled and adjusted in real time to ensure water quality stability and maximize heat exchange efficiency.

[0019] Furthermore, the pre-stored reagent addition strategy includes the following steps:

[0020] Determine the type of pipe material to guide the selection of the type or combination of chemicals: for metal pipes, choose acidic or compound chemicals to remove scale; for plastic pipes, choose mild chemicals to avoid corrosion.

[0021] Determine the flow rate of the circulating water to guide adjustments to the dosage, continuity, and concentration of chemicals: If the flow rate is high, the dosage should be moderate, and the concentration should be increased appropriately to ensure that the cleaning effect is not affected by the flow rate; if the flow rate is low, and fouling accumulation is significant, increase the dosage and continuity of chemicals.

[0022] Determine the pH and hardness of the water quality parameters. If the water is acidic, choose a neutral or alkaline agent; if the water is alkaline, choose an acidic agent; if the water hardness is high, increase the dosage of the agent and choose a compound acid agent or complexing agent.

[0023] Determine the type of fouling. If it is limescale, choose an acidic cleaning agent and adjust the concentration to suit different water hardness. For severe limescale buildup, use a high concentration of agent or extend the dosing cycle. If it is oil or organic fouling, choose a surfactant or solvent suitable for removing oil. For complex types of fouling, use a combination of compound agents and adjust the concentration and dosing frequency.

[0024] Determine the operating time of the circulating water system. If it is a short-term operation, appropriately reduce the dosage of the chemical to keep the dosage and concentration at normal levels. If it is a long-term operation, dynamically adjust the dosage of the chemical according to the changes in the thermal resistance of the fouling. Gradually increase the concentration of the chemical, and when the thermal resistance of the fouling increases significantly, adopt a continuous dosing method.

[0025] To assess changes in the thermal resistance of the fouling, if the thermal resistance increases, the dosage of the cleaning agent should be increased, and the change in thermal resistance should be monitored in real time to restore heat exchange efficiency in a short period of time. If the thermal resistance decreases, the dosage of the cleaning agent should be appropriately reduced to avoid over-cleaning. The dosage of the cleaning agent should be controlled in real time according to the change in the thermal resistance of the fouling. When the thermal resistance is high, the dosage of the cleaning agent should be increased, and after the thermal resistance returns to normal, the concentration of the cleaning agent should be gradually reduced accordingly.

[0026] Secondly, the automatic dosing control system based on fouling thermal resistance includes the following modules:

[0027] The water quality monitoring module is used to monitor water quality parameters in real time, including pH value, conductivity, temperature, turbidity, and circulating water operation parameters, including pipe material type, flow rate and running time; as well as to monitor changes in reagent concentration and changes in fouling thermal resistance in real time.

[0028] 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; it automatically controls the chemical dosing module to adjust the chemical dosing based on the relationship between the fouling thermal resistance value and the preset thermal resistance critical value.

[0029] The chemical dosing module is used to adjust and implement the chemical dosing strategy in real time under the control of the fouling control module;

[0030] 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.

[0031] Furthermore, the reagent dosing module includes a reagent storage tank, a micro pump, a reagent dosing pipeline, a reagent dosing point, a fluorescence monitoring sensor, and a feedback and control system;

[0032] The reagent storage tank is connected in series with the micro pump, the reagent dosing pipeline, and the reagent dosing point. The reagent dosing point is connected to the circulating water pipeline system. The fluorescence monitoring sensor is configured in the circulating water pipeline system to monitor changes in reagent concentration and is connected to the fouling control module for communication transmission. The feedback and control system is connected to the micro pump to adjust the pump operation to control the reagent concentration.

[0033] Furthermore, the water quality monitoring module includes:

[0034] High-precision water quality sensors are used to monitor the pH, conductivity, temperature, and turbidity parameters of circulating water in real time.

[0035] The data acquisition unit is used to aggregate and preprocess sensor monitoring data;

[0036] The wireless transmission unit is used to wirelessly transmit sensor monitoring data processed by the data acquisition unit to the data platform.

[0037] Thirdly, the present invention provides an electronic device, comprising:

[0038] processor;

[0039] Memory used to store the processor's executable instructions;

[0040] 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.

[0041] Fourthly, the present invention provides a computer-readable storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the automatic dosing control method based on fouling thermal resistance as described in any one of the first aspects.

[0042] This 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 chemicals based on changes in water quality. This not only avoids the lag inherent in traditional manual control but also reduces waste and lowers system operating costs through precise control of chemical usage. Furthermore, by combining machine learning algorithms to analyze historical and real-time monitoring data, it can predict water quality trends and the risk of scale formation, allowing for preventative measures. Through the intervention of this predictive system, the intelligent water treatment system can significantly reduce scaling, corrosion, and bacterial growth, improving equipment lifespan and water treatment efficiency.

[0043] A real-time feedback control system based on water quality parameters and fouling thermal resistance (such as the degree of scaling) adjusts the dosage of chemicals in real time by comprehensively considering changes in water quality and heat exchange efficiency, ensuring optimal water quality, thereby improving heat exchange efficiency and reducing equipment maintenance frequency. Simultaneously, intelligent methods can establish a linear relationship between chemical concentration and fouling thermal resistance, providing a scientific basis for the control system's decision-making.

[0044] This invention employs a pre-stored dosing strategy that optimizes the dosage, type, continuity, and concentration of chemicals based on multiple factors, including pipe material, flow rate, water quality parameters, fouling type, operating time, and fouling thermal resistance. Through real-time monitoring and dynamic control, it ensures maximum fouling removal during the cleaning process, restores heat exchange efficiency, and avoids chemical waste.

[0045] 1) Pipe material type

[0046] Metal pipes (such as stainless steel and copper) are prone to scale formation, so acidic agents with strong scale-dissolving capabilities should be selected to remove the scale. For plastic pipes, milder agents should be chosen to avoid corrosion. Specifically, when the system uses metal pipes, acidic or compound agents can be selected, and the dosage can be appropriately increased, especially when the system has been running for a long time or the water is hard.

[0047] 2) Flow velocity

[0048] At high flow rates, fouling is easily carried away, but this can also increase fouling deposits on the heat exchange surfaces. In this case, the dosage of the cleaning agent should be moderate, and the concentration should be increased appropriately to ensure that the cleaning effect is not affected by the flow rate. At low flow rates, fouling accumulation is more significant. In this case, the dosage of the cleaning agent should be increased and the process should be continuous, especially for mineral fouling that is difficult to clean.

[0049] 3) Water quality parameters (pH value, hardness)

[0050] pH value is a crucial factor affecting the reaction of chemicals. For acidic water (e.g., low pH), neutral or alkaline chemicals should be chosen to avoid excessive corrosion of the system; while for alkaline water (e.g., high pH), acidic chemicals can be selected to help dissolve scale more effectively. Water with higher hardness is more prone to hard scale formation, so the dosage of chemicals should be increased, and chemicals specifically designed for scale (such as compound acid chemicals or complexing agents) should be selected.

[0051] 4) Types of dirt

[0052] For limescale, acidic cleaning agents can be selected, and the concentration can be adjusted to suit different water hardness levels. For severe limescale buildup, higher concentrations of cleaning agents or longer application cycles can be used. For oil and organic scale, suitable surfactants or solvents for removing oil can be selected, and the dosage adjusted to ensure effectiveness. For complex scale types, it is recommended to use a combination of compound cleaning agents, adjusting the concentration and frequency of application to maximize cleaning efficiency.

[0053] 5) Running time

[0054] In short-term operation, due to the shorter operating time, less fouling occurs, and the dosage of the cleaning agent can be appropriately reduced. In this case, the dosage and concentration can be maintained at normal levels. In long-term operation, as fouling accumulates over time, the dosage of the cleaning agent needs to be dynamically adjusted based on changes in the thermal resistance of the fouling. The concentration of the cleaning agent should be gradually increased, especially when the thermal resistance of the fouling increases significantly. In this case, continuous dosing should be adopted to ensure efficient cleaning of the system.

[0055] 6) Linear relationship between changes in fouling thermal resistance and reagent dosage

[0056] When the thermal resistance of the fouling increases, it indicates that fouling is gradually accumulating and affecting heat transfer efficiency. In this case, the dosage of cleaning agent should be increased, and the change in thermal resistance should be monitored in real time to restore heat exchange efficiency quickly. When the thermal resistance of the fouling decreases, it indicates that the fouling has been removed and the system's heat transfer efficiency has recovered. At this point, the dosage of cleaning agent can be appropriately reduced to avoid over-cleaning. Based on the change in the thermal resistance of the fouling, the system will control the dosage of cleaning agent in real time, increasing the dosage when the thermal resistance is high and gradually reducing the concentration after the thermal resistance returns to normal to maintain stable water quality.

[0057] 7) Adjustment of drug combination, dosage, dosage continuity and concentration

[0058] Chemical combinations: Select the appropriate chemical combination for different types of dirt. For example, use acidic chemicals for limescale and surfactants for oil stains.

[0059] Chemical dosage: The dosage is dynamically adjusted based on real-time changes in the thermal resistance of the fouling and water quality parameters. If the thermal resistance increases significantly, the dosage can be increased to ensure rapid and effective removal of fouling.

[0060] Continuous dosing of chemicals: In cases of severe fouling, continuous dosing is used to ensure the chemicals remain effective; however, once the system is restored, the frequency of chemical dosing can be reduced.

[0061] Chemical concentration: The chemical concentration is higher at the beginning of cleaning, and the concentration is gradually reduced as dirt is removed to avoid wasting chemicals.

[0062] 8) Real-time adjustment and feedback mechanism

[0063] The system monitors the thermal resistance of the dirt, water quality parameters, flow rate, and dirt type in real time. Combined with the set control algorithm and feedback mechanism, it automatically adjusts the chemical dosing strategy to ensure that the effect of each cleaning process is optimized.

[0064] In summary, the automatic dosing control method and system based on fouling thermal resistance has brought about revolutionary changes to the circulating water management of thermal power plants. Through precise monitoring, automated adjustment and real-time feedback control, it has significantly improved water treatment efficiency, equipment operation safety and economy, and has broad application prospects.

[0065] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The automatic chemical dosing control method and system based on fouling thermal resistance provided by this invention acquires historical data and real-time monitored water quality parameters of circulating water, including pH value, conductivity, temperature, and turbidity. The acquired data undergoes preprocessing and feature extraction operations. Data preprocessing includes cleaning, standardization, and noise reduction; feature extraction includes extracting key features affecting the structure; regression, SVM, or decision tree computer learning models are used to train the historical data and water quality parameters and calculate the fouling thermal resistance value in real time; the chemical dosing is automatically controlled and adjusted according to the relationship between the fouling thermal resistance value and a preset thermal resistance threshold value; changes in chemical concentration and fouling thermal resistance value are monitored in real time to control and adjust the chemical dosing strategy in real time, and a water quality and strategy report is generated; by real-time monitoring of fouling thermal resistance and setting corresponding threshold values ​​(e.g., 1.77 × 10⁻⁶), the chemical dosing strategy is optimized. -4 m 2 • K / W, 3.44×10 -4 m 2 • K / W, etc.) automatically adjusts the dosage of the agent according to the change of the thermal resistance of the dirt, so as to ensure that dirt and bacteria are effectively inhibited. The automatic dosing method based on thermal resistance judgment simplifies the operation process and avoids errors in manual intervention;

[0066] The reagent dosing module in this invention systematically combines high-precision sensors and fluorescence monitoring technology to achieve precise reagent dosing, reducing waste and maximizing effectiveness. The fouling control module, based on historical data and real-time monitoring results, utilizes machine learning algorithms to optimize the dosing strategy, improving water treatment accuracy and stability, significantly reducing fouling formation, and increasing heat exchange efficiency. Real-time control and adjustment of the reagent dosing strategy, by improving heat exchange efficiency and reducing condenser fouling, enables power plants to achieve energy conservation and emission reduction, thereby saving operating costs and possessing broad market prospects.

[0067] By incorporating a novel environmentally friendly quantum dot scale and corrosion inhibitor, this system not only inhibits scale and kills bacteria but also enables precise online monitoring, reducing environmental pollution. By improving circulating water management, this system not only meets the requirements for long-term, stable, and efficient equipment operation but also enhances water resource utilization efficiency, resulting in significant social benefits. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of the automatic dosing control system framework based on fouling thermal resistance provided in an embodiment of the present invention.

[0069] Figure 2 This is a schematic diagram illustrating the working principle of the water quality monitoring module provided in this embodiment of the invention.

[0070] Figure 3This is a schematic diagram of the drug dosing module provided in an embodiment of the present invention.

[0071] Figure 4 This is a flowchart of the algorithm for the dirt control module provided in an embodiment of the present invention. Detailed Implementation

[0072] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0073] The automatic dosing control method based on fouling thermal resistance provided in this embodiment of the invention includes the following steps:

[0074] Step S1: Based on historical data of circulating water and real-time monitored water quality parameters;

[0075] Step S2: Perform preprocessing and feature extraction on the acquired data, and train and predict the calculated fouling thermal resistance value using regression, SVM, or decision tree computer learning models; it should be noted that data preprocessing includes cleaning, standardization, and noise reduction; feature extraction includes extracting key features that affect the structure.

[0076] Step S3: Automatically control and adjust the dosage of the reagent based on the relationship between the thermal resistance value of the fouling and the preset thermal resistance threshold value, and monitor the changes in reagent concentration and the thermal resistance value of the fouling in real time to adjust the reagent dosage strategy in real time.

[0077] Specifically, the automatic dosing control method based on fouling thermal resistance provided in this embodiment of the invention includes the following steps:

[0078] (1) Construction of water quality monitoring module

[0079] High-precision water quality sensors are installed to monitor parameters such as pH, conductivity, temperature, and turbidity of the circulating water in real time. The monitoring data is transmitted to a cloud platform via a wireless network for centralized management and analysis.

[0080] (2) Thermal resistance calculation and reagent dosing

[0081] Based on water quality monitoring data, calculate the thermal resistance of the fouling and compare it with a preset critical value (e.g., 1.77 × 10⁻⁶). -4 m 2 • K / W and 3.44×10 -4 m 2 • Compare K / W.

[0082] When the thermal resistance reaches 1.77 × 10 -4 m 2 • When K / W is reached, the system automatically starts the reagent dosing;

[0083] When it reaches 3.44×10 -4 m 2 • When K / W, activate the cleaning function.

[0084] A micropump automatically adjusts the dosage of the reagent to ensure that the reagent concentration remains within the optimal range. A fluorescence monitoring sensor monitors the reagent concentration in real time to ensure the effectiveness of the reagent during the reaction process.

[0085] (3) Establishment of the dirt control module

[0086] Historical water quality and operational data are collected to build a fouling prediction model. Machine learning algorithms are used to analyze the data and continuously optimize control strategies to improve the system's responsiveness and decision-making accuracy.

[0087] (4) System integration and testing

[0088] The various modules were integrated, and system function testing and debugging were conducted to ensure the stability and reliability of the system in practical applications. On-site testing was carried out at a thermal power plant to evaluate the system's impact on circulating water quality and its effectiveness in fouling control.

[0089] The effectiveness of the automatic chemical dosing control method based on fouling thermal resistance was verified by applying it to 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 indicates the accumulation of more mineral scale or bacterial film in the water system, reducing heat exchange efficiency. Therefore, timely adjustment of the chemical dosage to control the fouling thermal resistance to the optimal range is crucial for ensuring efficient system operation.

[0090] Example

[0091] I. Implementation Process

[0092] In the power plant's circulating water system, high-precision water quality monitoring sensors were first installed to monitor water quality parameters in real time, such as pH, conductivity, temperature, and turbidity. Simultaneously, changes in fouling thermal resistance were monitored, and the real-time data was transmitted to the central control system for analysis. Based on the fouling thermal resistance value, the system can automatically calculate the required dosage of chemicals to maintain optimal water quality and ensure maximum heat exchange efficiency. Specifically, the relationship between the fouling thermal resistance value and the chemical dosage is as follows:

[0093] (1) When the thermal resistance of the fouling is 1.77×10 -4 m 2 • When the K / W ratio is below a certain level, the system will trigger an alert and automatically begin increasing the dosage of the reagent to prevent further accumulation of dirt.

[0094] (2) When the thermal resistance of the fouling reaches 3.44 × 10 -4 m2 • When the K / W ratio is reached, the system will determine that the dosage of the agent needs to be increased. This will not only increase the dosage of the scale inhibitor, but also activate the cleaning function to increase the concentration of the agent for a rapid reaction, thereby effectively removing the existing scale layer.

[0095] (3) When the fouling thermal resistance reaches a high value (e.g., exceeding 4.0 × 10⁻⁶), -4 m 2 • When the temperature reaches 0.5 K / W, the system will activate a powerful cleaning mode and increase the concentration of the cleaning agent to perform deep cleaning and restore heat exchange efficiency.

[0096] II. Data Analysis

[0097] In actual operation, the system automatically adjusts the dosage of the reagent based on the monitored thermal resistance of the fouling and real-time water quality changes. Table 1 below shows some of the test data.

[0098] Table 1

[0099] time <![CDATA[Dirt thermal resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started <![CDATA[1.61×10 -4 ]]> 2.0 8.0 98

[0100] As can be seen from Table 1, when the thermal resistance of the fouling increases from 1.77 × 10⁻⁶, the thermal resistance of the fouling decreases from 1.77 × 10⁻⁶. -4 m 2 • K / W increased to 3.44×10 -4 m 2 At a K / W ratio, the system automatically adjusts the reagent dosage and effectively improves water quality, maintaining it within a suitable range and promptly enhancing heat exchange efficiency. After cleaning begins, the reagent concentration increases, the cleaning effect gradually becomes apparent, the thermal resistance of the fouling begins to decrease, and the heat exchange efficiency recovers to near its original level. This indicates that the cleaning process effectively removes fouling from the system and restores heat exchange performance.

[0101] Implementation effect

[0102] The test results from implementing this automated dosing system at the power plant showed that:

[0103] (1) Significant effect of dirt control: The system can monitor and automatically adjust the dosage of the agent in real time, effectively preventing the accumulation of dirt, and cleaning by increasing the dosage of the agent when the thermal resistance increases, thus ensuring the efficient operation of the circulating water system.

[0104] (2) Improved heat exchange efficiency: After the system was implemented, the heat exchange efficiency of the circulating water system increased by 15%-20%. Especially when the thermal resistance is higher than 3.44×10 -4 m 2 • At K / W, the system can quickly remove scale and restore heat exchange efficiency by automatically adjusting the reagent concentration, reducing the frequency of equipment maintenance.

[0105] (3) Reduced chemical waste: Compared with traditional manual dosing, automatic dosing systems significantly reduce chemical waste. The system automatically adjusts the dosage based on changes in water quality and thermal resistance, ensuring that the chemical concentration remains within the optimal range, which not only saves costs but also reduces negative environmental impacts.

[0106] (4) System stability and reliability: The system operates stably, with fast data acquisition and feedback speed, and can respond to changes in the thermal resistance of dirt in a timely manner, ensuring the long-term efficient operation of the system.

[0107] This embodiment successfully achieves intelligent management of the circulating water system in a thermal power plant through an automatic chemical dosing control system based on fouling thermal resistance. The system can dynamically adjust the dosage of chemicals according to changes in water quality and thermal resistance, effectively controlling the growth of mineral scale and bacteria, significantly improving the system's heat exchange efficiency and economy, reducing equipment maintenance costs, and possessing broad market application prospects.

[0108] Example

[0109] This embodiment further verifies the stability and effectiveness of the system during long-term operation. The system was deployed in another thermal power plant to monitor its long-term performance.

[0110] I. Implementation Process and Data

[0111] The power plant has been using the automatic dosing system for over three months, and the fouling thermal resistance has decreased from an initial 1.50 × 10⁻⁶. -4 m 2 • K / W gradually increased to 3.60 × 10 -4 m 2 • K / W. The system automatically adds chemicals, and detailed records are kept of changes in water quality and thermal resistance after each addition. Based on the test data, the system's adjustment effect is very significant. Table 2 below shows the system operation data after three months.

[0112] Table 2

[0113] time <![CDATA[Dirt thermal resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange efficiency (%) Start running <![CDATA[1.50×10 -4 ]]> 0.5 7.2 92 One month later <![CDATA[2.10×10 -4 ]]> 0.9 7.3 90 Two 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

[0114] Implementation effect

[0115] After three months of operation, the system can automatically adjust the dosage of chemicals based on changes in water quality and thermal resistance, ensuring stable water quality and effectively improving heat exchange efficiency. Compared with traditional dosing systems, it saves 20% on chemical costs, and through real-time monitoring and feedback control, it greatly reduces equipment maintenance needs and significantly extends the equipment's lifespan.

[0116] Example

[0117] In this embodiment, the performance of the automatic dosing control method was tested at different temperatures in the circulating water system of a thermal power plant. Water temperature variations can affect the thermal resistance of fouling and the reaction rate of the chemicals; therefore, it is necessary to verify the stability and dosing effectiveness of the system at different temperatures.

[0118] I. Implementation Process and Data

[0119] During the experiment, the water temperature varied from 50ºC to 70ºC. The system automatically adjusted the dosage of the reagent based on real-time monitoring of the thermal resistance of the fouling and changes in water quality. Table 3 below shows the test data.

[0120] Table 3

[0121] time Water temperature (ºC) <![CDATA[Dirt thermal resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 70 <![CDATA[1.86×10 -4 ]]> 1.5 7.8 94

[0122] Implementation effect

[0123] As water temperature rises, the thermal resistance of fouling also gradually increases. The system automatically increases the dosage of chemicals to ensure that heat exchange efficiency remains within a reasonable range. Through real-time data adjustments, the system ensures stable water quality under high-temperature conditions, prevents excessive scaling of equipment, and significantly improves heat exchange efficiency.

[0124] Example

[0125] This embodiment verifies the sustained effectiveness of the automatic dosing control system based on fouling thermal resistance through long-term operational testing of a thermal power plant's circulating water system. The system's comprehensive impact on water quality, heat exchange efficiency, and chemical consumption is examined through extended actual operation.

[0126] I. Implementation Process and Data

[0127] The test lasted for four weeks, during which the system automatically adjusted the dosage of chemicals based on real-time monitoring of fouling thermal resistance and water quality parameters. Table 4 below shows some of the operational data.

[0128] Table 4

[0129] time <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started <![CDATA[1.71×10 -4 ]]> 1.6 7.8 93

[0130] Implementation effect

[0131] During long-term operation, the system adjusts the dosage of chemicals promptly based on changes in fouling thermal resistance, ensuring stable heat exchange efficiency and significantly reducing manual intervention. Especially during long-cycle operation, the automatic dosing system demonstrates high reliability and stability, effectively reducing maintenance costs.

[0132] Example

[0133] This embodiment tests the optimization effect of an automatic dosing system based on fouling thermal resistance on a circulating water system under different dosing strategies. The differences between continuous and intermittent dosing strategies are compared to analyze their impact on system performance.

[0134] I. Implementation Process and Data

[0135] During the test, the system employed both continuous and intermittent dosing strategies, automatically adjusting the dosage based on the thermal resistance of the fouling. Table 5 below shows the experimental data.

[0136] Table 5

[0137] time Dosing strategy <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started Intermittent dosing <![CDATA[1.63×10 -4 ]]> 1.3 8.0 92

[0138] Implementation effect

[0139] By comparing continuous and intermittent dosing strategies, the results showed that the two strategies had similar effects in controlling fouling thermal resistance. However, 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 agent consumption.

[0140] Example

[0141] This embodiment tested the effectiveness of an automatic dosing system based on fouling thermal resistance at different reagent concentrations. By adjusting the reagent concentration, its effects on water quality, changes in fouling thermal resistance, and heat exchange efficiency were observed, thereby verifying the influence of reagent concentration on the dosing effect.

[0142] I. Implementation Process and Data

[0143] During the experiment, three different reagent concentrations were selected for testing: low concentration (5%), medium concentration (10%), and high concentration (20%). The system automatically adjusted the reagent dosage based on real-time changes in the thermal resistance of the fouling. Table 6 below shows the test data.

[0144] Table 6

[0145] time Drug concentration <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started 20% <![CDATA[1.97×10 -4 ]]> 1.6 8.0 95

[0146] Implementation effect

[0147] The experimental results show that as the reagent concentration increases, the thermal resistance of the fouling changes more significantly. The system effectively controlled the decrease in heat exchange efficiency and successfully delayed scaling by increasing the reagent dosage. High-concentration reagents can be effective in a shorter time, but they also require higher reagent consumption.

[0148] Example

[0149] This embodiment tested the effectiveness of the automatic dosing system based on fouling thermal resistance at different flow rates. Since variations in flow rate can affect water flow and heat exchange efficiency, it is necessary to analyze its impact on dosing control.

[0150] I. Implementation Process and Data

[0151] During the experiment, by adjusting the flow rate of the circulating water (1 m / s, 1.5 m / s, and 2 m / s respectively), the system automatically adjusted the dosage of the reagent based on the real-time monitored thermal resistance value of the fouling. Table 7 below shows the test data.

[0152] Table 7

[0153] time Flow velocity (m / s) <![CDATA[Dirt thermal resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started 2 <![CDATA[1.78×10 -4 ]]> 1.6 7.9 95

[0154] Implementation effect

[0155] As the flow rate increases, the circulation of water accelerates, leading to significant variations in the thermal resistance of the fouling. The system ensures stable heat exchange efficiency at different flow rates by adjusting the dosage of chemicals, further reducing scaling.

[0156] Example

[0157] This embodiment tested the effect of different pipe materials on the automatic dosing control effect. Different pipe materials have a significant impact on the degree of fouling accumulation and changes in thermal resistance, therefore, comparative studies are necessary.

[0158] I. Implementation Process and Data

[0159] In the experiment, two different pipe materials, stainless steel and carbon steel, were selected for testing. The system automatically adjusted the reagent dosage based on the changes in the thermal resistance of fouling in the two pipe materials. Table 8 below shows the experimental data.

[0160] Table 8

[0161] time Pipe Material <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started carbon steel <![CDATA[1.78×10 -4 ]]> 1.4 7.9 92

[0162] Implementation effect

[0163] Stainless steel pipes accumulate relatively little dirt, allowing the system to achieve good control with a lower dosage of chemicals; while carbon steel pipes are more prone to scale buildup, requiring a higher dosage of chemicals to achieve the same effect.

[0164] Example

[0165] This embodiment tested the effectiveness of the automatic dosing control system based on the thermal resistance of fouling under different water temperatures. Since temperature significantly affects the solubility of minerals and thermal resistance in water, fluctuations in water temperature directly impact the dosage and heat exchange efficiency. Therefore, by testing the effects across different temperature ranges, the dosing control of the system can be further optimized.

[0166] I. Implementation Process and Data

[0167] During the experiment, three different water temperature conditions were selected for testing: low temperature (15℃), medium temperature (35℃), and high temperature (55℃). The system automatically adjusted the dosage of the reagent based on changes in the thermal resistance of the fouling. Table 9 below shows the test data.

[0168] Table 9

[0169] time Water temperature (ºC) <![CDATA[Fouling resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started 55 <![CDATA[1.89×10 -4 ]]> 1.5 7.8 94

[0170] Implementation effect

[0171] Increased temperature leads to decreased mineral solubility and increased fouling thermal resistance. The system effectively addresses fouling control needs under different water temperature conditions by automatically adjusting the dosage of chemicals. Under high-temperature conditions, the system significantly increases the dosage of chemicals, thereby ensuring heat exchange efficiency and water quality stability.

[0172] Example

[0173] This embodiment investigates the effects of different combinations of chemicals (scale inhibitors, bactericides, corrosion inhibitors, and novel environmentally friendly quantum dot water treatment agents) on automated dosing in circulating water treatment. The novel environmentally friendly quantum dot water treatment agent possesses multiple functions, effectively controlling scale formation, inhibiting bacterial growth, and reducing negative environmental impacts. By comparing the effects of different chemical combinations on parameters such as scale thermal resistance, chemical dosage, and heat exchange efficiency, the control strategy of the automated dosing system is further optimized.

[0174] I. Implementation Process and Data

[0175] Four drug combinations were selected for testing in the experiment, namely:

[0176] (1) Chemical combination A: scale inhibitor + bactericide

[0177] (2) Chemical combination B: scale inhibitor + corrosion inhibitor

[0178] (3) Chemical combination C: scale inhibitor + bactericide + corrosion inhibitor

[0179] (4) Chemical combination D: scale inhibitor + new environmentally friendly quantum dot water treatment agent

[0180] The system automatically adjusts the dosage of chemicals by monitoring fouling thermal resistance and water quality parameters in real time, ensuring stable circulating water quality and heat exchange efficiency. Table 10 below shows the test data:

[0181] Table 10

[0182] time Drug combination <![CDATA[Dirt thermal resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started D <![CDATA[1.25×10 -4 ]]> 1.4 7.5 93

[0183] Implementation effect

[0184] (1) Agent combination A (scale inhibitor + bactericide): This combination has a relatively stable effect on controlling mineral scale, especially in terms of bacterial inhibition. After 8 hours, the thermal resistance of the scale reaches 2.8 × 10. -4 m 2 • At K / W, the heat exchange efficiency remains around 82%. After cleaning is initiated, the heat exchange efficiency quickly recovers to 98%.

[0185] (2) Agent combination B (scale inhibitor + corrosion inhibitor): This combination is mainly used to prevent equipment corrosion, and the effect is significant. Although the heat exchange efficiency is slightly lower than that of agent combination A, it still remains at around 83%. The fouling thermal resistance reaches 3.0 × 10 after 8 hours. -4 m 2 • K / W demonstrates its good ability to prevent dirt formation.

[0186] (3) Agent combination C (scale inhibitor + bactericide + corrosion inhibitor): This combination of three agents provides a more comprehensive control effect. The fouling thermal resistance reaches 3.3 × 10 after 8 hours. -4 m 2 • With a heat exchange efficiency of 84% at K / W, it can improve the diversity and efficiency of water quality control to a certain extent and is suitable for a variety of complex operating conditions.

[0187] (4) Agent Combination D (Scale Inhibitor + Novel Environmentally Friendly Quantum Dot Water Treatment Agent): The novel environmentally friendly quantum dot water treatment agent has multiple functions and can effectively control scale, bacteria, and equipment corrosion simultaneously. Experiments show that the scale thermal resistance value is 2.8 × 10⁻⁶ after 8 hours. -4 m 2 • K / W, and a heat exchange efficiency of up to 82%. Its advantage lies in the smaller amount of reagent used and the stable reagent concentration, which makes it less wasteful and particularly suitable for occasions with high requirements for environmental protection and water quality control.

[0188] This example demonstrates the effects of comparing different drug combinations:

[0189] (1) Agent combination A (scale inhibitor + bactericide): suitable for situations with high water quality control requirements, can effectively inhibit bacterial growth and control the deposition of mineral scale.

[0190] (2) Agent combination B (scale inhibitor + corrosion inhibitor): It focuses more on the protection of equipment and is suitable for environments sensitive to corrosion.

[0191] (3) Chemical combination C (scale inhibitor + bactericide + corrosion inhibitor): suitable for more complex water quality environments, and can provide comprehensive water quality control.

[0192] (4) Agent combination D (scale 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, suitable for occasions with high environmental protection requirements.

[0193] The new environmentally friendly quantum dot water treatment agent has unique advantages in comprehensive control, and shows great promise, especially in reducing environmental pollution and improving the overall operating efficiency of the system.

[0194] Example

[0195] This embodiment tested the effects of different fouling types (such as mineral scale and bacterial scale) on the automatic dosing control effect. By simulating different fouling types in the system, their effects on fouling thermal resistance and dosing dosage were studied.

[0196] I. Implementation Process and Data

[0197] During the experiment, the formation of different types of fouling (mineral scale, bacterial scale) at different time points was artificially simulated to test the automatic dosing control effect of the system. Table 11 below shows the experimental data.

[0198] Table 11

[0199] time Types of dirt <![CDATA[Dirt thermal resistance value (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started Complex dirt <![CDATA[1.53×10 -4 ]]> 1.5 7.8 92

[0200] Implementation effect

[0201] Different types of fouling have varying impacts on fouling thermal resistance and chemical dosing control. Mineral fouling forms relatively steadily, allowing for relatively precise adjustment of the chemical dosage. However, bacterial fouling forms more rapidly, requiring more flexible and timely adjustments to the chemical dosage. Complex fouling requires a comprehensive consideration of the combined effects of minerals and bacteria, necessitating an automated dosing system that optimizes control strategies based on real-time changes in thermal resistance.

[0202] Example

[0203] This embodiment investigates the response of an automatic dosing control system to fouling thermal resistance under different pollution loads. Pollution load affects the concentration and deposition rate of harmful substances in water; the system needs to automatically adjust the dosage of chemicals according to changes in pollution load to ensure stable water quality and heat exchange efficiency.

[0204] I. Implementation Process and Data

[0205] In the experiment, different pollution loads were simulated by adjusting the concentration of pollutants (such as suspended solids and dissolved minerals) in the water. Table 12 below shows the test data.

[0206] Table 12

[0207] time Pollution load (mg / L) <![CDATA[Dirt thermal resistance (m 2 ∙ K / W)]]> Dosage of pesticide (L / h) Water quality parameters (pH value) Heat exchange 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 the cleaning is started 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 the cleaning is started 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 the cleaning is started 200 <![CDATA[1.72×10 -4 ]]> 1.6 7.8 94

[0208] Implementation effect

[0209] Increased pollution load leads to higher concentrations of harmful substances in the water, resulting in increased fouling thermal resistance. The system automatically adjusts the reagent dosage based on changes in pollution load to ensure stable water quality and maximize heat exchange efficiency. Even with high pollution loads, the system effectively controls fouling and maintains high heat exchange efficiency.

[0210] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An automatic dosing control method based on fouling thermal resistance, characterized in that, Includes the following steps: Acquire historical data and real-time water quality parameters of circulating water, including pH value, conductivity, temperature, and turbidity; Computer learning models using regression, SVM, or decision trees are used to train historical data and water quality parameters and calculate the thermal resistance of fouling in real time. The system automatically controls and adjusts the dosage of chemicals based on the relationship between the thermal resistance of the fouling and the preset critical thermal resistance value. It uses machine learning algorithms to optimize the linear relationship between the change in the thermal resistance of the fouling and the dosage of chemicals, monitors the changes in chemical concentration and the thermal resistance of the fouling in real time, and adjusts the chemical dosing strategy in real time to generate water quality and strategy reports. Real-time control and adjustment of drug dosing strategies 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 value 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 circulating water, as well as the linear relationship between the change in fouling thermal resistance and the dosage of chemicals, computer learning analysis is used to match and call pre-stored chemical addition strategies, and the chemical combination, dosage, continuity of chemical addition, and concentration are controlled and adjusted in real time to maximize water quality stability and heat exchange efficiency. Based on the linear relationship, the critical values ​​of thermal resistance are divided into the first critical value, the second critical value, and the third critical value in ascending order; When the thermal resistance of the fouling reaches the first critical value, an early warning is triggered, automatic control is activated, and the agent is added according to the first dosage. When the thermal resistance 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 started automatically. When the thermal resistance of the dirt reaches the third critical value, the powerful cleaning mode is activated, and the dosage of the cleaning agent is further increased according to the third dosage.

2. The automatic dosing control method based on fouling thermal resistance according to claim 1, characterized in that, The first, second, and third critical values ​​of the thermal resistance threshold are 1.77 × 10⁻⁶, respectively. -4 m 2 • K / W, 3.44×10 -4 m 2 • K / W and 4.0 × 10 -4 m 2 • K / W.

3. The automatic dosing control method based on fouling thermal resistance according to claim 2, characterized in that, The pre-stored reagent addition strategy includes the following steps: Determine the type of pipe material to guide the selection of the type or combination of chemicals: for metal pipes, choose acidic or compound chemicals to remove scale; for plastic pipes, choose mild chemicals to avoid corrosion. Determine the flow rate of the circulating water to guide adjustments to the dosage, continuity, and concentration of chemicals: If the flow rate is high, the dosage should be moderate, and the concentration should be increased appropriately to ensure that the cleaning effect is not affected by the flow rate; if the flow rate is low, and fouling accumulation is significant, increase the dosage and continuity of chemicals. Determine the pH and hardness of the water quality parameters. If the water is acidic, choose a neutral or alkaline agent. If the water is alkaline, then choose an acidic agent; If the water hardness is high, the dosage of the agent should be increased, and a compound acid agent or complexing agent should be selected. Determine the type of fouling. If it is limescale, choose an acidic cleaning agent and adjust the concentration to suit different water hardness. For severe limescale buildup, use a high concentration of agent or extend the dosing cycle. If it is oil or organic fouling, choose a surfactant or solvent suitable for removing oil. For complex types of fouling, use a combination of compound agents and adjust the concentration and dosing frequency. Determine the operating time of the circulating water. If it is a short-term operation, appropriately reduce the dosage of the chemical to keep the dosage and concentration at normal levels. If it is a long-term operation, dynamically adjust the dosage of the chemical according to the changes in the thermal resistance of the fouling. The concentration of the chemical should be gradually increased, and when the thermal resistance of the fouling increases significantly, the chemical should be added continuously. To assess changes in the thermal resistance of the fouling, if the thermal resistance increases, the dosage of the cleaning agent should be increased, and the change in thermal resistance should be monitored in real time to restore heat exchange efficiency in a short period of time. If the thermal resistance decreases, the dosage of the cleaning agent should be appropriately reduced to avoid over-cleaning. The dosage of the cleaning agent should be controlled in real time according to the change in the thermal resistance of the fouling. When the thermal resistance is high, the dosage of the cleaning agent should be increased, and after the thermal resistance returns to normal, the concentration of the cleaning agent should be gradually reduced accordingly.

4. An automatic dosing control system based on the automatic dosing control method according to any one of claims 1 to 3, characterized in that... Includes the following modules: The water quality monitoring module is used to monitor water quality parameters in real time, including pH value, conductivity, temperature, turbidity, and circulating water operation parameters, including pipe material type, flow rate and running time; as well as to monitor changes in reagent concentration and changes in fouling thermal resistance in real time. 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 based on the relationship between the thermal resistance value of the dirt and the preset thermal resistance critical value. The chemical dosing module is used to adjust and implement the chemical 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.

5. The automatic dosing control system based on fouling thermal resistance according to claim 4, characterized in that, The reagent dosing module includes a reagent storage tank, a micro pump, a reagent dosing pipeline, a reagent dosing point, a fluorescence monitoring sensor, and a feedback and control system. The reagent storage tank is connected in series with the micro pump, the reagent dosing pipeline, and the reagent dosing point. The reagent dosing point is connected to the circulating water pipeline system. The fluorescence monitoring sensor is configured in the circulating water pipeline system to monitor changes in reagent concentration and is connected to the fouling control module for communication transmission. The feedback and control system is connected to the micro pump to adjust the pump operation to control the reagent concentration.

6. The automatic dosing control system based on fouling thermal resistance according to claim 4, characterized in that, The water quality monitoring module includes: High-precision water quality sensors are used to monitor the pH, conductivity, temperature, and turbidity parameters of circulating water in real time. The data acquisition unit is used to aggregate and preprocess sensor monitoring data; The wireless transmission unit is used to wirelessly transmit sensor monitoring data processed by the data acquisition unit to the data platform.

7. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; 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 3.

8. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the automatic dosing control method based on fouling thermal resistance as described in any one of claims 1 to 3.

Images