Circulating water and water replenishing optimization control method for standardized heat exchange unit

Abstract

The invention relates to the technical field of heat exchange unit optimization control, and discloses a standardized heat exchange unit circulating water and water supplementing optimization control method which comprises the following steps: S1, in a heat exchange unit circulating water system, installing a plurality of sensors, collecting data in real time, and establishing a database to record parameter changes; s2, analyzing the multi-parameter data monitored in real time by using an intelligent prediction algorithm; s3, according to a temperature change prediction result, determining the adjustment requirements of the water supplementing temperature and the water supplementing amount, and adjusting the water supplementing temperature in advance based on the prediction model to realize accurate control; and S4, monitoring the temperature, the flow, the pressure and the environment temperature of the circulating water in real time. By means of multi-parameter real-time monitoring and intelligent control over the circulating water and water supplementing system of the heat exchange unit, accurate adjustment of the water supplementing temperature and the water supplementing amount is achieved, the problem that temperature lag control is not accurate in a traditional system is solved, the heat exchange efficiency is improved, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of heat exchange unit optimization control, and in particular to a method for optimizing control of circulating water and make-up water of a standardized heat exchange unit. Background Art

[0002] In the operation of the heat exchanger unit, the circulating water and make-up water system are important links to ensure heat exchange efficiency and equipment stability. In order to maintain the efficient operation of the unit, the circulating water temperature needs to be within a stable range, and the precise control of the make-up water temperature and flow rate is the key to achieving this goal. However, in the actual operation process, the heat exchanger unit will be affected by many factors such as external ambient temperature, load fluctuations, circulating water quality, etc., all of which will cause changes in the circulating water temperature.

[0003] In the existing circulating water and make-up water control methods of heat exchange units, timed make-up water or control methods based on fixed temperature thresholds are usually adopted. These traditional control methods mainly rely on simple temperature measurement and static parameter settings, lack the ability to dynamically respond to real-time working conditions, and have obvious shortcomings. First, traditional systems often use fixed make-up water temperature settings, which cannot be dynamically adjusted according to the actual circulating water temperature changes, resulting in a mismatch between the make-up water temperature and system requirements. For example, when the external ambient temperature or load changes, the fixed make-up water strategy cannot respond quickly, which can easily cause temperature fluctuations in the system, reduce heat exchange efficiency, and even cause thermal shock and damage to the equipment. In addition, the temperature hysteresis problem is also a major defect of the existing technology. Due to the lack of an intelligent prediction mechanism, the system can only make water adjustments after the temperature deviates from the target range, and the adjustment process often lags behind the temperature change, thus affecting the overall performance of the system.

[0004] In addition, the existing technology lacks real-time monitoring and adjustment methods for the water quality of circulating water and make-up water. Water quality parameters such as impurities, mineral content, pH value and conductivity in circulating water have a direct impact on the heat exchange effect and equipment life, but traditional control methods usually can only rely on manual regular inspections and cannot respond in time when water quality parameters are abnormal. Therefore, scaling, corrosion and other problems are prone to occur inside the heat exchanger, resulting in reduced heat exchange efficiency and increased equipment maintenance costs. What's more serious is that when water quality problems accumulate to a certain extent, they will also cause equipment failures and affect the normal operation of the system.

[0005] In addition, the traditional water replenishment control system lacks flexibility in dealing with abnormal conditions. When the temperature, flow or pressure of the circulating water system is abnormal, usually only passive manual intervention measures can be taken, and timely and effective emergency treatment cannot be carried out through the automation system. This passive treatment method not only increases the risk of system operation, but also relies heavily on the experience of operation and maintenance personnel, and it is difficult to meet the operation requirements of the heat exchanger unit under complex working conditions and changing environments.

[0006] To this end, those skilled in the art provide a standardized heat exchanger unit circulating water and make-up water optimization control method to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a standardized heat exchange unit circulating water and make-up water optimization control method, which solves the problems of the prior art in lacking flexibility in dealing with abnormal conditions and lacking real-time monitoring and adjustment means.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a standardized heat exchange unit circulating water and water replenishment optimization control method, comprising the following steps:

[0009] S1: Install multiple sensors in the circulating water system of the heat exchanger unit to collect the circulating water temperature, make-up water temperature, flow rate, pressure and external ambient temperature in real time, and establish a database to record parameter changes;

[0010] S2: Use intelligent prediction algorithms to analyze the multi-parameter data monitored in real time, predict the trend of circulating water temperature changes in the short term, and form a prediction model;

[0011] S3: Determine the adjustment requirements of water replenishment temperature and water replenishment amount according to the temperature change prediction results, and adjust the water replenishment temperature in advance based on the prediction model to achieve precise control;

[0012] S4: Based on the real-time monitored circulating water temperature, flow, pressure and ambient temperature, the adaptive control algorithm is used to adjust the replenishment water temperature and replenishment water volume in a coordinated manner to ensure that the system is in the optimal state;

[0013] S5: Feedback the adjusted water replenishment temperature and water replenishment amount to the control center, determine the current adjustment effect and dynamically modify the control strategy based on the feedback information;

[0014] S6: Before the make-up water enters the circulating water system, the temperature regulating device in the buffer tank is used to preheat or precool the make-up water so that the make-up water temperature is close to the circulating water temperature to reduce temperature shock;

[0015] S7: Monitor the quality of circulating water and make-up water in real time, detect the impurities and mineral content in the water, and ensure that the water quality meets the heat exchange requirements;

[0016] S8: Real-time monitoring of circulating water temperature, make-up water temperature, flow rate and pressure. When an abnormal situation occurs, an alarm is triggered and preset emergency control measures are executed;

[0017] S9: Based on the system operation data and control effect, the machine learning algorithm is used to perform self-learning optimization on the water replenishment control strategy and adjust the prediction model and control algorithm parameters.

[0018] Preferably, in step S2, a neural network model is used to perform temperature prediction on the multi-parameter data monitored in real time, and the output of the prediction model is the trend of circulating water temperature change. The calculation formula of the prediction model is:

[0019] T pred =f(W·X+b)

[0020] Among them, T prcd is the predicted circulating water temperature, X is the input multi-parameter vector, including the current circulating water temperature, flow rate, pressure, external ambient temperature, etc., W and b are the weights and biases of the neural network model, and f is the activation function.

[0021] Preferably, the step S3 includes dynamically adjusting the water replenishment temperature based on the predicted circulating water temperature change trend, and the adjustment formula is:

[0022] ΔT 补水 =K p ×(T 设定 -T 当前 )

[0023] Where, ΔT 补水 is the adjustment amount of the water supply temperature, T 设定 is the set target temperature, T 当前 is the current circulating water temperature, K p is the adjustment factor.

[0024] Preferably, in step S4, the replenishment temperature and replenishment amount are dynamically adjusted through an adaptive control algorithm according to the circulating water temperature, flow rate, pressure and ambient temperature changes monitored in real time, so that the circulating water temperature is maintained within the set range, and the fuzzy control algorithm is used to optimize the regulation of the replenishment amount.

[0025] Preferably, the step S5 includes using a PID controller to adjust the water supply temperature and flow rate, and the PID control formula is:

[0026]

[0027] Where u(t) is the control output, e(t) = T 设定 -T 当前 is the temperature error, K p , K i , K d are the proportional, integral and differential coefficients respectively.

[0028] Preferably, the S6 step includes setting a temperature regulating module in the buffer tank to monitor the water temperature in the buffer tank in real time; when it is detected that the difference between the make-up water temperature and the circulating water temperature exceeds a preset threshold, starting the heating or cooling module in the buffer tank to pre-treat the make-up water to smooth the change in the make-up water temperature.

[0029] Preferably, the S7 step further includes: real-time monitoring of the pH value, conductivity and turbidity of the replenishment water and circulating water, and when the water quality parameters are monitored to be out of the set range, adjusting the replenishment strategy to dilute the impurity content in the circulating water, and triggering an alarm to prompt manual intervention.

[0030] Preferably, in the step S8, a multi-level alarm threshold is set, and the circulating water temperature and the make-up water temperature are detected in real time. When the temperature deviates from the set range, the temperature deviation value is calculated. When the deviation value exceeds a specific threshold, the system triggers alarm signals of different levels and executes corresponding emergency control measures.

[0031] Preferably, in step S9, a machine learning algorithm is used to analyze historical operation data and real-time control effects, and the weight parameters of the prediction model are adjusted by self-learning to make the prediction results more accurate, and the water replenishment strategy is optimized to reduce the energy consumption of the system.

[0032] Preferably, in the step of dynamically adjusting the water replenishment strategy, the adjustment rate r of the water replenishment amount is determined by the following formula:

[0033]

[0034] Among them, γ is the adjustment coefficient, ΔT 预测 is the predicted temperature change, and Δt is the time interval.

[0035] The invention provides a method for optimizing and controlling circulating water and replenishing water of a standardized heat exchange unit.

[0036] It has the following beneficial effects:

[0037] 1. The present invention realizes precise regulation of replenishment temperature and replenishment amount through real-time monitoring and intelligent control of multiple parameters of circulating water and replenishment system of heat exchange unit, solves the problem of inaccurate temperature lag control in traditional system, improves heat exchange efficiency and reduces energy consumption.

[0038] 2. The present invention introduces a temperature prediction model based on a neural network and a self-learning optimization mechanism of a machine learning algorithm. The system can adjust the water replenishment strategy in real time to keep the water replenishment temperature consistent with the circulating water temperature, effectively avoiding the decrease in heat exchange efficiency and thermal shock to the equipment caused by excessive temperature difference, thereby extending the service life of the equipment.

[0039] 3. The present invention monitors water quality parameters in real time, and automatically adjusts the water replenishment strategy to dilute the impurity content when it exceeds the set range, while triggering an alarm to prompt manual intervention, thereby ensuring the stable operation of the system and that the water quality meets the heat exchange requirements, and preventing the occurrence of problems such as scaling and corrosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a process step diagram of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Please see attached Figure 1 The embodiment of the present invention provides a method for optimizing and controlling circulating water and replenishing water of a standardized heat exchange unit, comprising the following steps:

[0043] S1: Install multiple sensors in the circulating water system of the heat exchanger unit to collect the circulating water temperature, make-up water temperature, flow rate, pressure and external ambient temperature in real time, and establish a database to record parameter changes;

[0044] Specifically, in the circulating water system of the heat exchanger unit, in order to achieve optimal control of the circulating water and make-up water, a plurality of high-precision sensors are installed at key positions of the system, and implemented in combination with the control method of the present invention. First, a platinum resistance temperature sensor with an accuracy of ±0.1°C is installed on the supply pipe and return pipe of the circulating water to collect the supply and return water temperature data of the circulating water in real time. These data are important parameters for evaluating the heat exchange efficiency of the system and are also the direct basis for adjusting the make-up water temperature. In addition, a temperature sensor with the same accuracy is installed on the make-up water pipeline to monitor the real-time changes of the make-up water temperature, ensure that the make-up water temperature matches the overall heat exchange requirements of the system, and avoid the adverse effects of temperature shock on the heat exchange effect.

[0045] At the same time, ultrasonic flow meters are installed on the circulating water and water supply pipelines to monitor the changes in water flow in real time with an accuracy of 0.5% to ensure the accuracy of water volume regulation. The flow data is not only used to calculate the thermal load of the system, but also provides a reference for the dynamic adjustment of the water supply volume. In order to ensure the stability of the system operation, highly sensitive pressure sensors are installed at the key nodes of the circulating water pipeline, especially at the water pump outlet and the heat exchanger inlet, to collect circulating water pressure data in real time. By monitoring the pressure changes, the operating status of the system can be judged, and a reference can be provided for the system's water supply volume and pump speed adjustment to prevent equipment failure caused by abnormal pressure.

[0046] In addition, an environmental sensor with temperature and humidity acquisition function is installed in the external environment of the heat exchanger to collect the external environment temperature and humidity in real time. The external environment temperature data is an important input parameter for water supply temperature prediction, which can be used to adjust the water supply temperature when the weather changes suddenly, ensuring that the system can maintain efficient heat exchange performance under different environmental conditions. The data of all sensors are transmitted to the data acquisition module of the central control unit in real time via industrial Ethernet or wireless transmission. The module records all collected data every 5 seconds and stores it in the local database to form a historical data archive.

[0047] S2: Use intelligent prediction algorithms to analyze the multi-parameter data monitored in real time, predict the trend of circulating water temperature changes in the short term, and form a prediction model;

[0048] A neural network model is used to predict the temperature of multi-parameter data monitored in real time. The output of the prediction model is the trend of circulating water temperature change. The calculation formula of the prediction model is:

[0049] T pred =f(W·X+b)

[0050] Among them, T prcd is the predicted circulating water temperature, X is the input multi-parameter vector, including the current circulating water temperature, flow rate, pressure, external ambient temperature, etc., W and b are the weights and biases of the neural network model, and f is the activation function.

[0051] Specifically, a neural network model is used to predict the circulating water temperature in real time. The input data of the model is composed of multiple parameters collected in real time in the system, including the current circulating water temperature, make-up water temperature, flow rate, pressure, and external ambient temperature. These parameters fully reflect the operating status of the system and the impact of the external environment on temperature changes, providing a data basis for achieving high-precision temperature prediction.

[0052] The neural network model learns the complex nonlinear relationship between the circulating water temperature and various parameters through training on a large amount of historical data, so that it can accurately predict the temperature change trend in the future. The prediction model is based on a multi-layer neural network. The input layer receives real-time data from various sensors, and the data is extracted and nonlinearly mapped through the hidden layer in the middle, and finally the prediction result of the circulating water temperature is obtained in the output layer. In order to ensure the accuracy of the prediction, the model continuously updates the internal parameters during the training process to adapt to the dynamic changes of the system status and environmental conditions.

[0053] In actual operation, the multi-parameter data monitored in real time will be continuously input into the neural network model. Based on these data, the model predicts the trend of circulating water temperature changes in the short term (for example, 5-10 minutes) in the future. The prediction results can provide guidance for the dynamic adjustment of the subsequent water replenishment strategy, making the adjustment of the water replenishment temperature more forward-looking, avoiding the problem of system temperature lag, and improving the heat exchange efficiency. At the same time, the neural network model has self-learning ability and can optimize the input data during continuous operation, thereby continuously improving the accuracy of temperature prediction. Through this real-time temperature prediction, it can be ensured that the system can maintain an efficient operating state under different working conditions and changes in the external environment, providing strong data support and decision-making basis for realizing the optimization control method of the present invention.

[0054] S3: According to the temperature change prediction results, determine the adjustment requirements of the water supply temperature and water supply amount, and adjust the water supply temperature in advance based on the prediction model to achieve precise control; based on the predicted circulating water temperature change trend, dynamically adjust the water supply temperature, and the adjustment formula is:

[0055] ΔT 补水 =K p ×(T 设定 -T 当前 )

[0056] Where, ΔT 补水 is the adjustment amount of the water supply temperature, T 设定 is the set target temperature, T 当前 is the current circulating water temperature, K p is the adjustment factor.

[0057] Specifically, when the prediction model shows that the circulating water temperature will rise or fall, the system will dynamically adjust the water supply temperature. At this time, the central control unit will calculate the required water supply temperature adjustment in real time based on the predicted temperature change and the set target temperature. The adjustment will be based on the difference between the current circulating water temperature and the target temperature, combined with the actual operating conditions, and the water supply temperature will be adjusted to compensate for the upcoming temperature fluctuations, ensuring heat exchange efficiency and system stability.

[0058] To achieve more accurate adjustment, the system also considers dynamic factors in the adjustment process, such as the current circulating water flow, external ambient temperature, pressure, etc. Based on these parameters, the adjustment rate is dynamically determined to avoid the impact of too fast or too slow water supply temperature changes on the system. In order to further improve the accuracy of the adjustment, the system has a built-in adjustment coefficient, which is adaptively adjusted by the central control unit according to the current system status to ensure that the adjustment of the water supply temperature matches the system requirements.

[0059] By adjusting the replenishment temperature and replenishment amount in advance, the system can effectively offset the fluctuation of circulating water temperature, reduce the impact of temperature lag, and improve heat exchange efficiency. At the same time, adjusting the replenishment temperature in advance can also avoid energy waste and extend the service life of the equipment. This dynamic adjustment strategy based on the temperature change trend ensures the adaptability of the method of the present invention under different working conditions and environmental conditions, and realizes the precise control of circulating water temperature.

[0060] S4: Based on the real-time monitored circulating water temperature, flow, pressure and ambient temperature, the adaptive control algorithm is used to adjust the make-up water temperature and make-up water volume in a linked manner to ensure that the system is in the optimal state; based on the real-time monitored circulating water temperature, flow, pressure and ambient temperature changes, the adaptive control algorithm is used to dynamically adjust the make-up water temperature and make-up water volume so that the circulating water temperature remains within the set range, and the fuzzy control algorithm is used to optimize the adjustment of the make-up water volume.

[0061] Specifically, when the real-time monitored circulating water temperature, flow, pressure or ambient temperature changes, the adaptive control algorithm will immediately analyze the changing trend of these parameters and the impact on the system, thereby dynamically adjusting the replenishment temperature and replenishment volume. The adaptive control algorithm has the ability to adjust its own parameters in real time, so it can calculate the most appropriate replenishment temperature and replenishment volume based on the current system status, ensuring that the system can quickly respond to changes in the external environment or internal working conditions, preventing the circulating water temperature from deviating from the set range, and ensuring the stability of the heat exchange effect.

[0062] In order to further optimize the adjustment of the water replenishment amount, a fuzzy control algorithm is also introduced in the present invention. The fuzzy control algorithm can determine the adjustment strategy of the water replenishment amount in a fuzzy rule manner according to the currently monitored temperature deviation, flow change, pressure fluctuation and change of the external ambient temperature. For example, if it is detected that the circulating water temperature is higher than the set value and the flow rate is large, the fuzzy control algorithm can increase the water replenishment amount and reduce the water replenishment temperature according to the preset rules to quickly reduce the circulating water temperature; if the temperature is low and the flow rate is small, the system state is adjusted by reducing the water replenishment amount and appropriately increasing the water replenishment temperature. Through this fuzzy control, the system can achieve refined management of water replenishment regulation.

[0063] In addition, the process of linkage adjustment also comprehensively considers the pressure changes of the system to avoid the instability of the system caused by adjusting the water replenishment volume too quickly or too slowly. The entire adjustment process is real-time and continuous. After receiving new real-time monitoring data, the adaptive control algorithm will quickly complete new calculations and adjust the water replenishment strategy to ensure that the system can maintain the best state under different working conditions and environmental conditions. This adjustment method not only effectively controls the circulating water temperature, but also optimizes the utilization of water resources and energy, improves the operating efficiency of the heat exchanger unit, and extends the service life of the equipment.

[0064] S5: Feedback the adjusted water supply temperature and water supply volume to the control center, judge the current adjustment effect and dynamically modify the control strategy according to the feedback information; use the PID controller to adjust the water supply temperature and flow rate, and the PID control formula is:

[0065]

[0066] Where u(t) is the control output, e(t) = T 设定 -T 当前 is the temperature error, K p , K i , K d are the proportional, integral and differential coefficients respectively.

[0067] Specifically, the adjusted water replenishment temperature and water replenishment volume will be fed back to the central control center in real time to determine the effect of the current adjustment, and dynamically modify the control strategy based on the feedback information to ensure that the system is continuously in the optimal state. After receiving the feedback information of the various parameters of the circulating water system, the central control center first determines whether the current circulating water temperature, flow rate, etc. have reached the set target range. If the system parameters do not meet expectations, the control center will immediately adjust the control strategy and optimize the water replenishment temperature and water replenishment volume.

[0068] In order to achieve high-precision temperature and flow regulation, the present invention uses a PID controller to finely adjust the water supply temperature and water supply flow. The PID controller dynamically adjusts the water supply strategy by calculating the deviation of the circulating water temperature in real time to ensure that the circulating water temperature is stable within the target range. The core of the PID controller is to use the three control items of proportion, integration and differentiation to accurately adjust the output, among which:

[0069] Proportional (P) control directly adjusts the water supply temperature and flow rate according to the current temperature deviation to quickly respond to temperature changes;

[0070] Integral (I) control eliminates long-term temperature deviations by accumulating the sum of temperature deviations, improving the steady-state accuracy of the system;

[0071] The differential (D) control is adjusted according to the rate of change of the temperature deviation, predicting the temperature change trend in advance and preventing system oscillation caused by over-adjustment.

[0072] In actual operation, the control center makes adaptive adjustments to various parameters of the PID controller based on real-time feedback information to ensure that the control output can accurately compensate for the temperature error. At the same time, the integral term can eliminate the steady-state error of the system, while the differential term effectively prevents the oscillation caused by over-adjustment, further improving the stability of the system. Through real-time feedback and dynamic correction strategies, the system achieves precise control of the feed water temperature and flow rate, ensuring that the circulating water temperature is maintained within the set range, optimizing the operating efficiency of the heat exchanger unit, and achieving the effect of energy saving and extending the service life of the equipment.

[0073] S6: Before the make-up water enters the circulating water system, the temperature regulating device in the buffer tank is used to preheat or precool the make-up water so that the make-up water temperature is close to the circulating water temperature to reduce temperature shock; a temperature regulating module is set in the buffer tank to monitor the water temperature in the buffer tank in real time; when it is detected that the difference between the make-up water temperature and the circulating water temperature exceeds the preset threshold, the heating or cooling module in the buffer tank is started to pre-treat the make-up water to smooth the change of the make-up water temperature.

[0074] Specifically, a high-precision temperature sensor and a temperature adjustment module are installed in the buffer tank to monitor the water temperature in the buffer tank in real time. The temperature sensor continuously monitors the make-up water temperature and transmits the measured data to the central control center in real time. The central control center will compare the make-up water temperature with the current circulating water temperature. If the temperature difference between the two exceeds the preset threshold (for example, 5°C), the heating or cooling module in the buffer tank will be started to pre-treat the make-up water. In this way, before the make-up water enters the circulating water system, its temperature can be adjusted to a temperature close to that of the circulating water, ensuring that the make-up water will not have a significant impact on the overall temperature after entering the system.

[0075] The design of the temperature regulation module can include an electric heating device and a cooling device. When it is detected that the feed water temperature is lower than the circulating water temperature and the difference exceeds the preset threshold, the heating device will be activated to preheat the feed water so that its temperature gradually rises to near the circulating water temperature; when the feed water temperature is higher than the circulating water temperature, the cooling device will be activated to lower the feed water temperature to approach the circulating water temperature. This process is continuous and automated, and can dynamically adjust the temperature regulation strategy of the buffer tank based on the real-time monitored temperature data, thereby effectively alleviating the temperature difference before the feed water enters the circulating water system and reducing the thermal shock to the system.

[0076] This pretreatment method not only improves the stability of the system and prevents the reduction of heat exchange efficiency due to excessively high or low feed water temperature, but also reduces the extra load on the pump and extends the service life of the equipment. At the same time, buffer tank pretreatment can also provide more detailed control over the adjustment of feed water temperature, so that the circulating water system can maintain the best heat exchange performance under different working conditions, achieving energy saving and consumption reduction.

[0077] S7: Monitor the water quality of circulating water and make-up water in real time, detect impurities and mineral content in the water, and ensure that the water quality meets the heat exchange requirements; monitor the pH value, conductivity and turbidity of make-up water and circulating water in real time. When the water quality parameters are monitored to be out of the set range, adjust the make-up water strategy to dilute the impurity content in the circulating water, and trigger an alarm to prompt manual intervention.

[0078] Specifically, high-precision water quality sensors are installed in the circulating water and water supply pipes to detect the concentration of impurities, mineral content, pH value, conductivity and turbidity in the water. The pH sensor is used to monitor the pH of the water to ensure that the water quality is in the neutral range and prevent acidic or alkaline water from corroding the equipment; the conductivity sensor is used to detect the content of dissolved salts and minerals in the water, reflecting the conductivity of the water, which is crucial for judging the hardness and purity of the water quality; the turbidity sensor is used to detect the concentration of suspended matter and impurities in the water to prevent them from depositing in the heat exchanger and affecting the heat exchange effect.

[0079] All monitoring data is transmitted to the central control center in real time through sensors. The central control center analyzes these data. Once any water quality parameter is detected to be beyond the set safety range (for example, pH value deviates from the neutral range, conductivity exceeds the preset threshold, turbidity exceeds the standard, etc.), the system will immediately adopt the corresponding adjustment strategy. Specifically, the system will dynamically adjust the amount of water replenishment according to the water quality parameters that exceed the range to dilute the impurities and mineral content in the circulating water and reduce the negative impact of water quality on heat exchange efficiency and equipment stability.

[0080] At the same time, if the water quality parameters continue to be abnormal or seriously exceed the set range, the central control center will automatically trigger the alarm mechanism and issue a prompt to the operation and maintenance personnel, reminding them to intervene manually and check the operation of the water treatment device. At this time, the system will execute the preset emergency water replenishment strategy, such as increasing the water replenishment volume to quickly reduce the impurity concentration, or suspending the operation of some heat exchangers to prevent equipment damage caused by water quality deterioration.

[0081] Through real-time monitoring and adjustment of water quality, the present invention effectively ensures that the water quality of circulating water and replenishing water meets the heat exchange requirements, prevents scaling, corrosion and other problems, improves the overall operating efficiency of the heat exchange system, and extends the service life of the equipment. The implementation of this strategy not only ensures the long-term stable operation of the system, but also reduces the cost of manual maintenance and realizes intelligent and refined water quality management.

[0082] S8: Monitor the circulating water temperature, make-up water temperature, flow rate and pressure in real time. When an abnormal situation occurs, trigger an alarm and execute preset emergency control measures. Set multi-level alarm thresholds, detect the circulating water temperature and make-up water temperature in real time, and calculate the temperature deviation value when the temperature deviates from the set range. When the deviation value exceeds a specific threshold, the system triggers alarm signals of different levels and executes corresponding emergency control measures.

[0083] Specifically, in order to achieve effective abnormal monitoring and processing, first set multi-level alarm thresholds in the central control system, covering parameters such as circulating water temperature, make-up water temperature, flow rate and pressure. For example, the upper and lower limits of the circulating water temperature and make-up water temperature can be set as the safe range of operation; if the temperature deviates from this range, it is divided into multiple alarm levels depending on the degree of deviation, such as level 1 warning, level 2 warning and level 3 emergency alarm, so that different emergency measures can be taken in different degrees of abnormal situations. The real-time monitoring system will continuously obtain the current value of each parameter and compare it with the set threshold.

[0084] When the system detects that the circulating water or make-up water temperature deviates from the set range, the central control unit will calculate the temperature deviation value and determine whether the deviation exceeds a certain alarm threshold. If the temperature deviation value exceeds the preset specific threshold, the system will immediately trigger an alarm signal of the corresponding level. Different levels of alarm signals correspond to different emergency control measures. For example, in the first-level warning, the system may only issue an alarm to alert the operation and maintenance personnel, and at the same time start the operation of automatically adjusting the make-up water volume and temperature, trying to restore the system to normal; in the second-level warning, the system will implement a more active make-up water adjustment strategy, and may appropriately reduce the running speed of the water pump to reduce the impact of temperature fluctuations; in the third-level emergency alarm, the system will take more stringent emergency measures, such as temporarily stopping the operation of some heat exchange equipment, and notifying the operation and maintenance personnel to conduct emergency inspections and processing.

[0085] In addition to temperature monitoring, the system also performs real-time detection of flow and pressure. If abnormal flow or pressure is found (such as a sudden drop or sharp rise), an alarm will be graded according to the set threshold. In response to different abnormal conditions, the system has built-in multiple emergency control measures, such as increasing or decreasing the amount of water replenishment, adjusting the frequency of water pump operation, and even switching to a backup water source, etc., to ensure that the circulating water system can still operate safely under abnormal conditions.

[0086] S9: Based on the system operation data and control effect, the machine learning algorithm is used to self-learn and optimize the water replenishment control strategy, and the prediction model and control algorithm parameters are adjusted. The machine learning algorithm is used to analyze the historical operation data and real-time control effect, and the weight parameters of the prediction model are adjusted by self-learning to make the prediction results more accurate, and the water replenishment strategy is optimized to reduce the energy consumption of the system.

[0087] The adjustment rate r of the water replenishment is determined by the following formula:

[0088]

[0089] Among them, γ is the adjustment coefficient, ΔT 预测 is the predicted temperature change, and Δt is the time interval.

[0090] Specifically, the central control system will collect and analyze various operating parameters of the circulating water system in real time, including circulating water temperature, water replenishment temperature, flow, pressure, and water replenishment volume. At the same time, the system will record the actual effect after each water replenishment operation, such as temperature changes, balance state after flow adjustment, and the overall energy consumption level of the system. By analyzing this data, the machine learning algorithm identifies the relationship between the system operating status and the water replenishment adjustment effect, and then optimizes the control strategy.

[0091] The machine learning algorithm is mainly used to adaptively adjust the internal weight parameters of the prediction model to better reflect changes in the current state of the system. Through continuous learning of historical data and real-time monitoring data, the model can capture the impact of environmental changes, load fluctuations and other factors on the circulating water temperature, making the water replenishment strategy more accurate and in line with actual working conditions. As the running time increases, the algorithm will continue to improve itself, improve the accuracy of the prediction, and provide a more favorable basis for adjusting the water replenishment volume and water replenishment temperature.

[0092] In addition, the machine learning algorithm also optimizes the adjustment strategy of the water replenishment amount. According to the real-time predicted temperature change trend, the system will dynamically adjust the water replenishment amount to ensure that the water replenishment operation is more forward-looking. In order to avoid system instability caused by too fast or too slow water replenishment, the machine learning algorithm will comprehensively consider factors such as temperature, flow and pressure monitored in real time to determine the best water replenishment adjustment strategy. Through this adaptive control method, the system can not only effectively respond to different working conditions, but also achieve energy saving and consumption reduction, and extend the service life of the equipment.

[0093] The introduction of this self-learning optimization mechanism enables the system to continuously adjust the water replenishment strategy based on real-time feedback, thereby keeping the circulating water temperature within the set range and ensuring efficient operation of the heat exchanger unit. The entire process does not require human intervention and relies entirely on the adaptive optimization of the machine learning algorithm, reflecting the intelligence and refinement of the invention in water replenishment control.

[0094] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A standardized heat exchange unit circulating water and make-up water optimization control method, characterized in that: The following steps are involved: S1: Install multiple sensors in the circulating water system of the heat exchanger unit to collect the circulating water temperature, make-up water temperature, flow rate, pressure and external ambient temperature in real time, and establish a database to record parameter changes; S2: Use intelligent prediction algorithms to analyze the multi-parameter data monitored in real time, predict the trend of circulating water temperature changes in the short term, and form a prediction model; S3: Determine the adjustment requirements of water replenishment temperature and water replenishment amount according to the temperature change prediction results, and adjust the water replenishment temperature in advance based on the prediction model to achieve precise control; S4: Based on the real-time monitored circulating water temperature, flow, pressure and ambient temperature, the adaptive control algorithm is used to adjust the replenishment water temperature and replenishment water volume in a coordinated manner to ensure that the system is in the optimal state; S5: Feedback the adjusted water replenishment temperature and water replenishment amount to the control center, determine the current adjustment effect and dynamically modify the control strategy based on the feedback information; S6: Before the make-up water enters the circulating water system, the temperature regulating device in the buffer tank is used to preheat or precool the make-up water so that the make-up water temperature is close to the circulating water temperature to reduce temperature shock; S7: Monitor the quality of circulating water and make-up water in real time, detect the impurities and mineral content in the water, and ensure that the water quality meets the heat exchange requirements; S8: Real-time monitoring of circulating water temperature, make-up water temperature, flow rate and pressure. When an abnormal situation occurs, an alarm is triggered and preset emergency control measures are executed; S9: Based on the system operation data and control effect, the machine learning algorithm is used to perform self-learning optimization on the water replenishment control strategy and adjust the prediction model and control algorithm parameters.

2. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: In step S2, a neural network model is used to predict the temperature of the multi-parameter data monitored in real time. The output of the prediction model is the trend of circulating water temperature change. The calculation formula of the prediction model is: T pred =f(W·X+b) Among them, T prcd is the predicted circulating water temperature, X is the input multi-parameter vector, including the current circulating water temperature, flow rate, pressure, external ambient temperature, etc., W and b are the weights and biases of the neural network model, and f is the activation function.

3. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: The step S3 includes dynamically adjusting the water supply temperature based on the predicted circulating water temperature change trend, and the adjustment formula is: ΔT 补水 =K p ×(T 设定 -T 当前 ) Where, ΔT 补水 is the adjustment amount of the water supply temperature, T 设定 is the set target temperature, T 当前 is the current circulating water temperature, K p is the adjustment factor.

4. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: In step S4, the replenishment temperature and replenishment amount are dynamically adjusted through an adaptive control algorithm according to the real-time monitored circulating water temperature, flow, pressure and ambient temperature changes, so that the circulating water temperature is maintained within the set range, and the fuzzy control algorithm is used to optimize the adjustment of the replenishment amount.

5. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: The step S5 includes using a PID controller to adjust the water supply temperature and flow rate. The PID control formula is: Where u(t) is the control output, e(t) = T 设定 -T 当前 is the temperature error, K p , K i , K d are the proportional, integral and differential coefficients respectively.

6. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: The step S6 includes setting a temperature regulating module in the buffer tank to monitor the water temperature in the buffer tank in real time; when it is detected that the difference between the make-up water temperature and the circulating water temperature exceeds a preset threshold, starting the heating or cooling module in the buffer tank to pre-treat the make-up water to smooth the change of the make-up water temperature.

7. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: The step S7 further includes: real-time monitoring of the pH value, conductivity and turbidity of the replenishment water and circulating water. When the water quality parameters are monitored to be out of the set range, the replenishment strategy is adjusted to dilute the impurity content in the circulating water, and an alarm is triggered to prompt manual intervention.

8. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: In the step S8, a multi-level alarm threshold is set to detect the circulating water temperature and the make-up water temperature in real time. When the temperature deviates from the set range, the temperature deviation value is calculated. When the deviation value exceeds a specific threshold, the system triggers alarm signals of different levels and executes corresponding emergency control measures.

9. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 1, characterized in that: In the step S9, a machine learning algorithm is used to analyze historical operation data and real-time control effects, and the weight parameters of the prediction model are adjusted by self-learning to make the prediction results more accurate, and the water replenishment strategy is optimized to reduce the energy consumption of the system.

10. A standardized heat exchange unit circulating water and make-up water optimization control method according to claim 9, characterized in that: In the step of dynamically adjusting the water replenishment strategy, the adjustment rate r of the water replenishment amount is determined by the following formula: Among them, γ is the adjustment coefficient, ΔT 预测 is the predicted temperature change, and Δt is the time interval.

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