A circulating water anti-freezing system for power plant in cold area

Abstract

The application relates to the technical field of circulating water antifreezing, and discloses a circulating water antifreezing system suitable for power plants in cold regions, which comprises a temperature sensing module, an antifreezing heating module, a water flow disturbance module, an ice layer processing module, an intelligent regulation and control core and a fault safety module; when the circulating water system of the power plant operates in a cold region, the internal water temperature of key nodes of a circulating water pipe network and external environmental parameters are monitored in real time, a low-temperature early warning signal is generated based on a temperature field reconstruction algorithm of machine learning, temperature abnormal fluctuations can be identified in time, early warning delay and false reports caused by wind speed and humidity interference can be avoided, the safety operation continuity of the system under extreme environments can be ensured, in the circulating water antifreezing process, the output power of a flexible electric heating body is dynamically adjusted through a hierarchical heating mechanism, a turbulent flow generator is linked to optimize water flow distribution, the change of pipeline thermal inertia is adapted, and the risk of local icing caused by sudden temperature drop is prevented.

Description

Technical Field

[0001] This invention relates to the field of circulating water antifreeze technology, specifically to an antifreeze system for circulating water in power plants suitable for cold regions. Background Technology

[0002] There are two main types of circulating water: industrial and domestic. The primary purpose of both is to conserve water. Industrial circulating water is mainly used in cooling water systems, hence the name circulating cooling water. The circulating cooling water system is an important part of a power plant, and its antifreeze performance directly affects the safety and efficiency of the power plant's operation. This is a key infrastructure for maintaining the stable operation of a power plant.

[0003] Currently, because power plant circulating water antifreeze systems in cold regions operate in extreme low-temperature environments, the distributed sensors in the temperature sensing module cannot eliminate the interference of environmental wind speed and humidity fluctuations on temperature data in real time when monitoring water temperature changes. When sensor data deviates due to strong wind heat dissipation and frost, it will cause low-temperature warning signals to be delayed and false alarms to occur, making it impossible to guarantee the accuracy and timeliness of the warning.

[0004] Therefore, a power plant circulating water antifreeze system suitable for cold regions is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a power plant circulating water antifreeze system suitable for cold regions. The power plant circulating water antifreeze system provided by this invention solves the problems of low temperature early warning signal delay and false alarms, as well as the inability to guarantee the accuracy and timeliness of early warnings, as mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a power plant circulating water antifreeze system suitable for cold regions, the system comprising:

[0007] The temperature sensing module collects the internal water temperature and external ambient temperature parameters of key nodes in the circulating water network in real time. When the internal water temperature is lower than the preset temperature threshold, a low temperature warning signal is generated and output to the antifreeze heating module.

[0008] The antifreeze heating module, upon receiving the low temperature warning signal, activates a graded heating mechanism, conducts heat to the circulating water through a flexible electric heating element, and simultaneously generates a disturbance activation command based on the internal temperature change rate and outputs it to the water flow disturbance module.

[0009] The water flow disturbance module responds to the disturbance activation command by starting the turbulence generator, which prevents local icing by periodically adjusting the flow velocity distribution, and feeds back the pipeline flow data to the ice treatment module.

[0010] The ice layer processing module receives the pipeline flow data and uses ice layer detection technology to detect the thickness of the ice layer on the pipe wall. When the ice layer thickness is greater than or equal to its safety limit, it triggers a coordinated ablation operation of mechanical scraping and high-frequency oscillation, and at the same time uploads the ice layer status parameters to the intelligent control center.

[0011] The intelligent control center integrates meteorological forecast data, ice layer state parameters and real-time operating parameters to construct a pipeline thermodynamic model and dynamically generate optimal heating strategy commands, disturbance frequency commands and de-icing timing commands, which are fed back to the antifreeze heating module, water flow disturbance module and ice layer treatment module, respectively.

[0012] The fail-safe module automatically isolates the faulty pipe section and switches to the backup circulation channel when it detects an abnormal and sudden drop in local pipe temperature, while simultaneously sending corresponding status alarm information to the intelligent control center.

[0013] Preferably, the temperature sensing module includes:

[0014] A distributed temperature sensor array is equidistantly arranged along the axial direction of the circulating water pipeline network, covering straight pipe sections, bends, and valve connections;

[0015] An environmental parameter acquisition unit is installed on the outer wall of the pipe to synchronously acquire wind speed and humidity data;

[0016] A temperature field reconstruction algorithm based on machine learning is used to infer the real-time temperature distribution of the entire pipeline network based on sparse monitoring point data.

[0017] Preferably, the graded heating mechanism of the antifreeze heating module includes:

[0018] Level 1 heating mode: When the water temperature is between 0℃ and -5℃, the basic heat preservation power is activated to maintain the water temperature;

[0019] Secondary heating mode: When the water temperature is below -5℃ and the temperature drop rate exceeds 0.5℃ / min, a step-by-step power increase strategy is activated until the temperature rises back to the safe range. The power regulation follows the following linear control law:

[0020] ;

[0021] in For real-time heating power, This is the power-temperature proportionality coefficient. The difference between the current water temperature and the freezing point. This is the base power offset;

[0022] Three-level heating mode: When the ambient temperature remains below -15℃ for more than 2 hours, it switches to full power operation and activates the water flow disturbance module to enhance heat exchange efficiency.

[0023] Preferably, the flexible electric heating element adopts a multi-layer composite structure, comprising:

[0024] A thermally conductive silicone layer that adheres to the outer wall of the pipe;

[0025] The carbon nanotube heating wire network embedded in the thermally conductive silicone layer has its topological distribution density adaptively adjusted according to the curvature of the pipe.

[0026] The heat-insulating and reflective film covering the surface reduces heat loss to the environment.

[0027] Preferably, the turbulence generator of the water flow disturbance module includes:

[0028] A miniature vortex blade array installed on the inner wall of the pipe generates swirling flow through a servo motor.

[0029] An adjustable orifice throttling valve assembly periodically changes the local flow cross-sectional area to create pulsed water flow impact;

[0030] By optimizing the perturbation frequency and amplitude using a CFD computational fluid dynamics model, full-area anti-freezing coverage can be achieved with minimal energy consumption.

[0031] Preferably, the activation logic of the turbulence generator is as follows:

[0032] When the heating power is increased to level two or above, the blade rotation frequency is automatically matched as a function of the heating power;

[0033] When the standard deviation of water temperature distribution is detected to exceed 0.8℃, the throttle valve group is activated to perform regional flow velocity correction.

[0034] Preferably, the coordinated ablation operation of the ice treatment module includes:

[0035] Mechanical scraping unit: A retractable blade driven by shape memory alloy spirals axially along the inner wall of the pipe to physically peel off the attached ice layer;

[0036] High-frequency oscillator: emits 20-40kHz ultrasonic waves during the scraping operation to induce resonance and fragmentation of the ice crystal structure;

[0037] Negative pressure recovery device: Real-time suction of detached ice debris to prevent secondary condensation.

[0038] Preferably, the ice detection technology includes:

[0039] Three sets of probes are arranged circumferentially at 120° at key nodes of the pipeline for ice detection;

[0040] Ice thickness was measured using time-domain reflectometry with a resolution of 0.1 mm.

[0041] Establish an ice thickness growth prediction model and combine it with historical icing data to predict when to start de-icing operations.

[0042] Preferably, the operation optimization method of the intelligent control center includes:

[0043] Access to 72-hour cold wave warning data issued by the meteorological observatory;

[0044] Construct a thermal inertia matrix for the pipeline network to quantify the heat capacity and heat dissipation characteristics of different pipe sections;

[0045] A heating-perturbation-de-icing coordinated strategy is dynamically generated using a reinforcement learning algorithm. The objective function is to achieve Pareto optimality by maximizing antifreeze reliability and minimizing energy consumption. The optimization objective is defined by the following function:

[0046] ;

[0047] in, To comprehensively optimize the target value, As a weight for antifreeze reliability, As a weight for energy efficiency, For antifreeze reliability indicators, This represents the total energy consumption of the system.

[0048] Preferably, the fail-safe module specifically includes:

[0049] Fail-safe mechanism: When the system detects an abnormal temperature drop in a local pipe section that exceeds the preset safety tolerance threshold, it automatically isolates the pipe section and starts the backup circulation channel;

[0050] Energy recovery unit: Utilizes low-temperature water generated during the ablation process to perform cascade heat exchange with waste heat from the power plant;

[0051] Digital twin monitoring interface: Visually displays real-time temperature field, ice layer distribution and energy consumption data of the pipeline network, and supports manual intervention.

[0052] Compared with the prior art, the present invention provides a power plant circulating water antifreeze system suitable for cold regions, which has the following beneficial effects:

[0053] 1. In this invention, when the power plant circulating water system is operating in a cold region, the water temperature and ambient temperature of key nodes in the circulating water network are monitored in real time, and a low temperature early warning signal is generated based on a temperature field reconstruction algorithm using machine learning. This can promptly identify abnormal temperature fluctuations, avoid early warning delays and false alarms caused by wind speed and humidity interference, improve the accuracy and timeliness of low temperature early warnings, and ensure the continuous safe operation of the system in extreme environments.

[0054] 2. In this invention, during the circulating water antifreeze process, the output power of the flexible electric heating element is dynamically adjusted through a graded heating mechanism, and the water flow distribution is optimized by linking a turbulence generator to adapt to changes in the thermal inertia of the pipeline, preventing the risk of local icing caused by a sudden drop in temperature. At the same time, the heat conduction loss is compensated through a real-time feedback mechanism to ensure the synergistic efficiency of heating and disturbance operation, reduce the rate of unplanned downtime, and maintain stable equipment operation.

[0055] 3. In this invention, in the overall system control process, the intelligent control center integrates meteorological forecast data and real-time operating parameters to dynamically generate a coordinated strategy for heating, disturbance and de-icing. Combined with the automatic isolation function of the fault safety mechanism, it achieves accurate matching between ice layer growth prediction and melting operation, avoids ice residue and secondary icing problems, optimizes antifreeze reliability, improves energy utilization efficiency, and enhances the system's adaptability under complex working conditions. Attached Figure Description

[0056] Figure 1 This is a structural diagram of a power plant circulating water antifreeze system suitable for cold regions according to the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] For specific implementation examples, please refer to: Figure 1 A system for preventing freezing of circulating water in power plants in cold regions. The system architecture adopts a modular and collaborative design, and achieves freezing prevention through closed-loop control logic. Specifically, it includes:

[0059] Temperature sensing module: As the system sensing layer, it collects the internal water temperature and external environmental parameters of key nodes in the circulating water network in real time, including easily frozen elbows, valves, and straight pipe sections. When the internal water temperature is lower than the preset temperature threshold, it immediately generates a low temperature warning signal with high timeliness and transmits it to the antifreeze heating module.

[0060] Anti-freeze heating module: As one of the execution units, it activates a preset graded heating mechanism upon receiving a low-temperature warning signal. It employs a flexible electric heating element for efficient contact heat transfer to the circulating water. Simultaneously, it possesses intelligent judgment capabilities, dynamically generating a disturbance activation command based on the monitored temperature drop rate and transmitting this command to the water flow disturbance module to trigger water flow intervention.

[0061] Water flow disturbance module: In response to the disturbance activation command from the antifreeze heating module, it starts the turbulence generator to work. By periodically adjusting the velocity distribution of the water flow in the pipe, including creating swirling and pulsed flows, it breaks the laminar flow state, ensures that the water is fully mixed, and prevents the risk of local freezing caused by water flow stagnation. At the same time, it feeds back the pipe flow data to the ice layer treatment module.

[0062] Ice layer treatment module: Receives pipeline flow data from the water flow disturbance module and actively detects the thickness of ice layer formed on the pipe wall using ice layer detection technology. When the ice layer thickness is greater than or equal to its safety limit, it will affect flow and insulation performance. At this time, a coordinated ablation operation combining physical mechanical scraping and high-frequency energy oscillation is immediately triggered to remove the ice layer. Simultaneously, key ice layer status parameters, including thickness, location, and growth trend, are uploaded to the intelligent control center. Specific ice layer status parameters include:

[0063] Ice thickness parameter: Time-domain reflectometry is used to measure the ice thickness on the pipe wall. This parameter represents the actual physical thickness of the ice layer and is the core indicator for determining whether the ice layer exceeds the limit. When the thickness exceeds the safety threshold, ablation is triggered. The measurement is based on three sets of probes distributed circumferentially at 120° around key nodes of the pipeline, ensuring full circumferential coverage without blind spots.

[0064] Ice layer location parameters: These parameters indicate the specific location of the ice layer within the pipeline, including axial coordinates and circumferential distribution. This parameter is used to locate high-risk areas, guide the directional operation of the mechanical scraping unit and high-frequency oscillator, and avoid wasting resources on ice removal.

[0065] Ice growth trend parameters: generated by the ice thickness growth prediction model, including the ice growth rate and predicted future ice state. This model combines real-time water temperature, flow velocity, ambient temperature, and historical icing data to predict the timing of de-icing operations, enabling preventative intervention.

[0066] Intelligent Control Center: As the system's decision-making brain, it integrates multi-dimensional data sources, including external meteorological forecast data, internal ice layer state parameters, and real-time operating parameters, to construct a pipeline thermodynamic model reflecting the actual thermodynamic characteristics of the pipeline network. Based on this model, the intelligent control center can calculate and dynamically generate optimal control commands in real time, including heating strategy commands, disturbance frequency commands, and de-icing timing commands, which are fed back to the antifreeze heating module, water flow disturbance module, and ice layer treatment module, respectively, directing the coordinated operation of each execution module.

[0067] Fail-safe module: As a safety layer, it monitors the system's operating status in real time. When it detects an abnormal drop in local pipe temperature that exceeds the preset tolerance range, indicating a rupture or severe blockage, it immediately executes emergency procedures: automatically isolating the faulty pipe section, cutting off the water flow in the faulty pipe section, and switching to the preset backup circulation channel to ensure uninterrupted operation of the main system, while simultaneously sending corresponding detailed status alarm information to the intelligent control center.

[0068] The architecture and function of the temperature sensing module are to construct a high-precision, full-coverage temperature monitoring network, including a distributed temperature sensor array, an environmental parameter acquisition unit, an environmental interference compensator, and a supporting temperature field reconstruction algorithm, specifically:

[0069] Distributed temperature sensor array: Adopting the principle of axial equidistant distribution, it is densely deployed throughout the circulating water pipeline network, focusing on covering weak links with complex thermodynamic characteristics and easy freezing, including straight pipe sections that are prone to laminar flow low temperature zone, elbows that are prone to eddy current heat dissipation, and valve connections that are prone to local low temperature due to structure.

[0070] Environmental parameter acquisition unit: Installed at a specific location on the outer wall of the pipeline, it synchronously collects key environmental parameters that directly affect the heat dissipation of the pipeline, including ambient temperature, wind speed and humidity data. Among them, ambient temperature determines the heat exchange rate between the pipeline and the environment, wind speed affects the intensity of convective heat dissipation, and humidity affects the rate of frosting and condensation.

[0071] Environmental interference compensator: Receives raw data from distributed temperature sensors and environmental parameters, calculates air cooling deviation and frost deviation using a physical model, and outputs the corrected water temperature. The specific compensation logic is as follows:

[0072] ;

[0073] in, For actual water temperature measured by the sensor, For real-time wind speed, The coefficient of performance is the air-cooling factor. For ambient humidity, The critical humidity for frosting. This is the frosting coefficient;

[0074] The corrected data is input into the temperature field reconstruction algorithm to eliminate monitoring errors caused by environmental fluctuations;

[0075] A temperature field reconstruction algorithm based on machine learning is used to infer the real-time temperature distribution of the entire pipeline network based on sparse monitoring point data. Based on machine learning technology, using the limited number of sensor node data from sparse monitoring points, the algorithm intelligently infers and reconstructs the real-time and continuous temperature distribution map of the inner and outer surfaces of the entire pipeline network, overcoming the problem of limited physical sensor placement and realizing full-area temperature visualization monitoring.

[0076] The graded heating mechanism of the antifreeze heating module intelligently adjusts the heating power according to the severity of the risk of frost damage:

[0077] Level 1 heating mode, preventative heat preservation: When the water temperature is in the critical range near the freezing point, from 0℃ to -5℃, the risk of freezing begins to appear. The system starts at the lowest power level: basic heat preservation power, the main purpose of which is to maintain the water temperature stable and prevent it from dropping further, with relatively low energy consumption.

[0078] In the secondary heating mode, active temperature rise is employed: when the water temperature drops below the -5℃ safety line and a rapid temperature decrease is detected, exceeding 0.5℃ / min, indicating a strong cold wave or sudden malfunction, the system determines this as a high-risk state and immediately activates a stepped power increase strategy. Its power regulation follows the following linear control law: ;in, For real-time heating power, This is the power-temperature proportionality coefficient. The difference between the current water temperature and the freezing point. This is the base power offset; it can be seen that the real-time heating power increases linearly with the increase of the temperature difference until the water temperature is pulled back to the safe range.

[0079] Three-level heating mode for full protection: When encountering continuous extreme cold, with the ambient temperature below -15℃ for more than 2 hours, the system judges it to be at the highest risk level, switches to the maximum power operation state, and actively activates the water flow disturbance module to maximize heat exchange efficiency by enhancing water flow disturbance, ensuring that heat can be quickly and evenly transferred to all parts of the water body.

[0080] The flexible electric heating element adopts a multi-layer composite structure design, which balances efficient heat transfer and energy saving. It includes a thermally conductive silicone layer, a carbon nanotube heating wire network, and a heat-insulating reflective film. Specifically:

[0081] Thermally conductive silicone layer: As a base layer, it is directly and tightly attached to the outer wall of the pipe. Its high thermal conductivity and flexibility ensure low thermal resistance contact between the heating element and the pipe surface, maximizing heat transfer efficiency.

[0082] Carbon nanotube heating wire network: As the core heating layer, it is embedded inside the thermally conductive silicone layer. It adopts a topological structure design, and its topological distribution density is adaptively adjusted according to the curvature of the pipe: The distribution density of the heating wire can be adaptively adjusted according to the geometric curvature of the pipe surface, including straight sections and bends: the wiring is denser at bends with large curvature and fast heat dissipation to provide stronger thermal compensation; and relatively sparse in straight sections to save energy.

[0083] Thermal insulation and reflective film: As the outermost covering, its function is to form a thermal barrier and reduce heat loss to the environment. On the one hand, it reduces the ineffective loss of heat generated by the heating element to the external environment through low thermal conductivity, and on the other hand, it reflects radiant heat back to the direction of the pipe through high reflectivity. The dual effect improves the thermal energy utilization rate.

[0084] The turbulence generator of the water flow disturbance module actively intervenes in the flow field using diversified physical methods, including a micro vortex blade array, an adjustable orifice throttling valve assembly, and corresponding CFD model optimization:

[0085] Miniature vortex blade array: Fixed at a specific position on the inner wall of the pipe, the blades are driven to rotate by a servo motor, creating a controllable vortex motion in the flow channel, breaking the original laminar flow state, forcing the upper and lower layers of water to mix, and eliminating temperature stratification and stagnant zones.

[0086] Adjustable orifice throttling valve assembly: By periodically and regularly changing the flow cross-sectional area of ​​a specific pipe section, i.e., adjusting the orifice size, pulse-like water flow impacts are created. This periodic contraction-expansion effect generates strong turbulence, scouring the pipe wall and preventing the boundary layer water from cooling down too quickly.

[0087] Based on the CFD computational fluid dynamics model, the disturbance frequency and amplitude are optimized to ensure full-area anti-freezing coverage with the lowest energy consumption: Under the premise of ensuring full-area anti-freezing coverage without dead water zones, the most energy-efficient combination of disturbance operation parameters is calculated, including the optimal rotation frequency range of the blades, the optimal opening and closing cycle and opening change range of the throttle valve, and the disturbance amplitude, to ensure that the optimal anti-freezing turbulence effect is achieved with the minimum energy consumption.

[0088] The activation logic of the turbulence generator lies in the intelligent linkage between its response strategy and the system operating state, including heating linkage logic and temperature uniformity correction logic, specifically:

[0089] Heating linkage logic: When the heating power of the antifreeze heating module increases to level two or higher, indicating a need for stronger heat transfer, the turbulence generator automatically starts and automatically matches the blade rotation frequency as a function of the heating power. Its key feature is the blade rotation frequency... Set to real-time heating power The function, ,in This is a proportionality coefficient. The higher the heating power, the higher the required heat exchange efficiency, and the higher the corresponding blade rotation frequency, ensuring that heat can be rapidly diffused with the strongly disturbed water flow.

[0090] Temperature uniformity correction logic: When the system detects excessive differences in water temperature distribution at different locations in the pipeline, with a standard deviation exceeding 0.8℃, it indicates temperature unevenness, which can easily lead to localized overcooling and icing. In this case, the throttling valve assembly for a specific area is activated. By adjusting the local flow resistance, the water flow velocity and flow distribution in that area are altered, achieving active correction of the regional flow velocity, promoting the mixing of hot and cold water, and making the overall water temperature distribution more uniform.

[0091] The synergistic ablation operation of the ice removal module involves using a multi-mechanism composite approach to remove ice from the pipe wall, including:

[0092] Mechanical scraping unit: The actuating component is a retractable cutter head driven by shape memory alloy. Its working principle is that the cutter head moves in an axial spiral trajectory along the inner wall of the pipe under control, using physical cutting force to directly peel off the ice layer attached to the pipe wall. It is suitable for handling thicker and more adhesive ice layers.

[0093] High-frequency oscillator: While the mechanical scraper is operating, it emits ultrasonic waves in a specific frequency range of 20-40kHz into the ice-water interface and the interior of the ice layer. This high-frequency mechanical wave can penetrate the ice layer, causing resonance in the ice crystal structure, leading to the breakage and fragmentation of its internal bonds, reducing the overall strength of the ice layer and its adhesion to the pipe wall, thus assisting and enhancing the mechanical scraping effect.

[0094] Negative pressure recovery device: This device generates and maintains local negative pressure near the scraping and ultrasonic crushing operation points, sucking away the ice debris and ice-water mixture generated during peeling and crushing in real time, and transporting it to the collection and treatment unit through dedicated pipelines. This prevents the removed ice debris from re-accumulating and settling in nearby pipe sections, causing secondary condensation and blockage.

[0095] When detecting the thickness of ice layer on the pipe wall, ice layer detection technology is used for high-precision measurement and prediction:

[0096] Probe deployment strategy: Key pipeline nodes for ice detection were identified, including bends prone to icing, upstream of valves, and low-flow-velocity sections. Three sets of ultrasonic probes were evenly distributed circumferentially at a 120-degree angle at these key nodes. This triangular layout covers the entire inner circumference of the pipeline, avoiding measurement blind spots and improving the comprehensiveness and reliability of ice thickness measurement.

[0097] Time-Domain Reflectometry (TDRS): This technology uses the time difference between the reflection of ultrasonic waves at the ice-water interface and the ice-pipe wall interface to calculate the ice thickness. The system measures the time interval between the transmission and reception of the reflected ultrasonic signals, and combines this with the speed of sound propagation in the ice to calculate the ice thickness with a resolution as high as 0.1 mm, meeting the requirements for high-precision monitoring.

[0098] Ice Thickness Growth Prediction Model: Based on real-time and historical monitoring data, including current ice thickness, water temperature, flow rate, ambient temperature, and historical icing patterns, and combining thermodynamics and mass transfer principles, a data-driven ice thickness growth prediction model is established. This model predicts the growth trend and rate of ice layer within a specific future time period, intelligently calculates and determines the optimal timing for initiating de-icing operations, and proactively intervenes before the ice layer reaches a critical dangerous thickness, achieving preventative de-icing.

[0099] Optimization methods for the operation of intelligent control centers:

[0100] External meteorological integration: Real-time access and analysis of authoritative cold wave warning data issued by meteorological departments, covering the next 72 hours, and using key forecast information on extreme low temperature, wind speed, and precipitation as important long-term forecast inputs to enable the system to be predictive.

[0101] Constructing a thermodynamic model of the pipeline network: Constructing a "thermal inertia matrix" to characterize the thermal properties of the pipeline network. This matrix quantifies the heat capacity (heat storage capacity) of different pipe sections in the pipeline network: heat storage capacity and heat dissipation characteristics: heat exchange rate with the environment, reflecting the differences in thermal response speed and insulation difficulty of each pipe section during the cooling process.

[0102] Reinforcement learning optimization: A reinforcement learning algorithm is employed, which continuously learns from real-time system data, including the effects and energy consumption of heating, perturbation, and de-icing operations. The objective function is to achieve Pareto optimality by maximizing antifreeze reliability and minimizing energy consumption. Specifically, the objective is defined as the comprehensive optimization target value. :

[0103] ;

[0104] in, To comprehensively optimize the target value, For antifreeze reliability indicators, This represents the total energy consumption of the system (heating + disturbance + de-icing). This represents the pursuit of minimizing energy consumption; α represents the reliability of antifreeze protection. The weight of β is 0.7. This indicator quantifies the system's ability to prevent icing and successfully de-ic, including fault-free operation time and the rate of ice exceeding limits; β represents the weight of energy efficiency, which is 0.3.

[0105] The algorithm dynamically generates a heating-disturbance-de-icing coordinated strategy, including optimal heating strategy instructions, disturbance frequency instructions, and de-icing timing instructions, striving to reduce the total system energy consumption β while ensuring high reliability and a high α weight.

[0106] The fail-safe module enables emergency response and performance enhancement, specifically including:

[0107] Fail-safe mechanism: The system monitors the temperature field in real time. When an abnormal drop in temperature is detected in a local pipe section, and the rate and absolute value of the drop exceed the preset safety tolerance threshold, it indicates that the pipe is prone to rupture and severe ice blockage. The system immediately and automatically triggers the protection program: quickly closes the isolation valves at both ends of the faulty pipe section to physically isolate it from the main circulation system. At the same time, it automatically opens the preset backup pipe valves to direct the water flow to the backup circulation channel to ensure that the power plant's cooling function is not interrupted. The alarm information, including fault location and severity level, is reported to the intelligent control center in real time.

[0108] Energy recovery unit: Utilizing the low-temperature water generated during ice melting as a cold source, this water undergoes cascade heat exchange with low-grade waste heat generated by the power plant, including low-parameter steam, flue gas waste heat, and equipment heat dissipation. This recovers some of the previously discarded cold energy and waste heat, improving the overall energy utilization efficiency of the system.

[0109] Digital Twin Monitoring Interface: This interface constructs and visualizes a digital twin of the pipeline network system. It highly integrates and graphically presents real-time operational data, including the pipeline's real-time temperature field (temperature field distribution cloud map), ice layer distribution (ice layer thickness and location markers), and energy consumption data from each module, providing operators with a global situational awareness. Simultaneously, the interface supports authorized personnel for manual intervention, including adjusting parameter weights, manually starting and stopping equipment, and setting special operating modes, enhancing human-machine collaboration capabilities.

[0110] Based on the above disclosures and practical applications, the operating steps of a power plant circulating water antifreeze system suitable for cold regions according to the present invention are as follows:

[0111] Step 1: Real-time perception of the overall status and initial risk assessment

[0112] After system startup, the temperature sensing module continuously collects the internal water temperature and external ambient temperature of key nodes in the circulating water network through a distributed sensor array. The environmental parameter acquisition unit simultaneously monitors the heat dissipation conditions of the pipe's outer wall. The temperature field reconstruction algorithm fuses multi-source data to generate a temperature distribution cloud map of the entire network. When the water temperature at any node approaches a preset temperature threshold, a low-temperature warning signal is generated and transmitted to the anti-freeze heating module in the execution layer.

[0113] Step 2: Dynamically activate the graded antifreeze strategy

[0114] After receiving a low-temperature warning signal, the antifreeze heating module initiates a graded response based on the rate and absolute value of temperature drop:

[0115] Level 1 heat preservation mode: When the water temperature is in the critical range of 0℃ to -5℃, the lowest power flexible electric heating is activated, the carbon nanotube heating wire network operates at the basic power, and the heat insulation and reflective film reduces heat loss and maintains stable water temperature.

[0116] Two-stage heating mode: When the water temperature drops below -5℃ or the temperature drop rate exceeds 0.5℃ / min, the heating power is increased according to the linear control law, and the water flow disturbance module is activated to generate a turbulence generator. The micro vortex blades increase their rotation frequency proportional to the heating power to force water mixing.

[0117] Level 3 defense mode: When the ambient temperature is below -15℃ for more than 2 hours, it switches to maximum power heating and the throttling valve group starts pulse water flow impact simultaneously to eliminate the low temperature stagnation zone.

[0118] Step 3: Intelligent Ice Detection and Collaborative Ablation

[0119] The water flow disturbance module feeds back pipeline flow data to the ice treatment module in real time. A circumferentially distributed array of ultrasonic probes measures the ice thickness on the pipe wall using time-domain reflectometry. An ice thickness growth prediction model, combining water temperature, flow velocity, and historical data, predicts freezing trends. When the ice thickness exceeds a safe threshold:

[0120] A high-frequency oscillator emits 20-40kHz ultrasonic waves to induce resonance and fracturing of the ice layer.

[0121] A shape memory alloy-driven mechanical scraper spirals along the pipe wall to peel off residual ice.

[0122] The negative pressure recovery device continuously extracts ice chips to prevent secondary condensation and blockage.

[0123] Step 4: Multi-objective collaborative optimization decision-making

[0124] The intelligent control center integrates weather forecasts, ice layer conditions, and real-time operating parameters to construct a dynamic thermal inertia matrix model. A reinforcement learning algorithm continuously learns from historical operational data, including heating energy consumption, disturbance effects, and de-icing efficiency, to synthesize the objective function. To optimize guidance:

[0125] Prioritize ensuring the reliability weight α of antifreeze, which is achieved by extending the trouble-free operation time and reducing the occurrence rate of ice layer exceeding limits;

[0126] Cooperatively optimize the energy efficiency weight β to minimize the total energy consumption of the system;

[0127] The Pareto optimal instruction set for dynamically generating heating power levels, blade speeds, and de-icing sequences ensures a balance between antifreeze performance and energy consumption control.

[0128] Step 5: Emergency Response and System Protection

[0129] The fail-safe module scans for abnormal temperature drops in real time, indicating potential pipe ruptures or ice blockages. Once the tolerance threshold is triggered:

[0130] Automatically close the isolation valve of the faulty pipeline section and switch to the backup circulation channel;

[0131] The energy recovery unit is activated, utilizing the low-temperature water generated by melting to perform cascade heat exchange with the waste heat from the power plant;

[0132] The digital twin monitoring interface marks the location of the fault in real time, pushes alarms synchronously, and records operation logs.

[0133] Step Six: Continuous Learning and Strategy Evolution

[0134] After each cold wave cycle ends, the system automatically analyzes the operational data:

[0135] Statistics on energy consumption distribution and anti-freeze failure events for each module;

[0136] Evaluate the actual effect of α and β weights and optimize the objective function parameters;

[0137] Update the thermal inertia matrix and ice thickness prediction model parameters to improve the decision-making accuracy for the next cycle.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power plant circulating water anti-freezing system suitable for cold regions, characterized in that: The system comprises: A temperature sensing module that collects real-time internal water temperature and external environmental temperature parameters of key nodes in the circulating water pipe network. When the internal water temperature is lower than the preset temperature threshold, a low-temperature warning signal is generated and output to the anti-freezing heating module. An anti-freezing heating module that starts a hierarchical heating mechanism after receiving the low-temperature warning signal. It conducts contact heat conduction on the circulating water through a flexible electric heating body, and generates a disturbance activation instruction based on the internal temperature change rate and outputs it to the water flow disturbance module. A water flow disturbance module that starts a turbulent flow generator in response to the disturbance activation instruction. It adjusts the local freezing by periodically adjusting the flow rate distribution and feeds the pipe flow state data to the ice layer processing module. The turbulent flow generator of the water flow disturbance module comprises: A micro-vortex blade array installed on the inner wall of the pipe to generate rotational flow by servo motor driving; A throttle valve group with adjustable aperture to periodically change the local flow area to form pulse water flow impact; Based on the CFD computational fluid dynamics model, the disturbance frequency and amplitude are optimized to ensure full-area anti-freezing coverage at the lowest energy consumption. An ice layer processing module that receives the pipe flow state data and uses ice layer detection technology to detect the ice layer thickness on the pipe wall. When the ice layer thickness is greater than or equal to its safety limit, it triggers mechanical scraping and high-frequency oscillation to melt the ice layer, and uploads the ice layer state parameters to the intelligent control center. An intelligent control center that integrates weather forecast data, ice layer state parameters and real-time operating parameters, builds a pipe network thermodynamic model and dynamically generates optimal heating strategy instructions, disturbance frequency instructions and deicing timing instructions, which are fed back to the anti-freezing heating module, water flow disturbance module and ice layer processing module respectively. A fault safety module that automatically isolates the fault pipe section and switches to the standby circulating channel when detecting abnormal sudden drop of local pipe temperature, and sends corresponding state alarm information to the intelligent control center.

2. The circulating water anti-freezing system for power plant in cold area according to claim 1, characterized in that: The temperature sensing module comprises: A distributed temperature sensor array arranged equidistantly along the axis of the circulating water pipe network, covering straight pipe sections, elbows and valve connections; An environmental parameter acquisition unit added to the outer wall of the pipe for synchronous acquisition of wind speed and humidity data; A temperature field reconstruction algorithm based on machine learning to deduce the real-time temperature distribution of the entire pipe network from sparse monitoring point data.

3. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The hierarchical heating mechanism of the anti-freezing heating module comprises: Primary heating mode: when the water temperature is in the range of 0℃ to -5℃, start the basic heat preservation power to maintain the water temperature; Secondary heating mode: when the water temperature is lower than -5℃ and the temperature drop rate exceeds 0.5℃ / min, enable the stepwise power-up strategy until the temperature rises to the safe range, and the power adjustment follows the following linear control law: ； wherein is the real-time heating power, is the power-temperature proportionality coefficient, is the difference between the current water temperature and the freezing point, is the base power offset; Tertiary heating mode: when the ambient temperature is continuously lower than -15℃ and lasts for more than 2 hours, switch to full power operation and coordinate the water flow disturbance module to enhance heat exchange efficiency.

4. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The flexible electric heating body adopts a multi-layer composite structure, including: A heat-conducting silicone layer that fits the outer wall of the pipe; A carbon nanotube heating wire network embedded in the heat-conducting silicone layer, whose topological distribution density is self-adaptively adjusted according to the pipe curvature; An insulating reflective film covering the surface layer to reduce heat loss to the environment.

5. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The activation logic of the turbulent flow generator is: When the heating power is increased to level two or above, the blade rotation frequency is automatically matched as a function of the heating power; When the standard deviation of water temperature distribution is detected to exceed 0.8℃, the throttle valve group is activated to perform regional flow velocity correction.

6. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The coordinated ablation operation of the ice treatment module includes: Mechanical scraping unit: A retractable blade driven by shape memory alloy spirals axially along the inner wall of the pipe to physically peel off the attached ice layer; High-frequency oscillator: emits 20-40kHz ultrasonic waves during the scraping operation to induce resonance and fragmentation of the ice crystal structure; Negative pressure recovery device: Real-time suction of detached ice debris to prevent secondary condensation.

7. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The ice layer detection technology includes: Three sets of probes are arranged circumferentially at 120° at key nodes of the pipeline for ice detection; Ice thickness was measured using time-domain reflectometry with a resolution of 0.1 mm. Establish an ice thickness growth prediction model and combine it with historical icing data to predict when to start de-icing operations.

8. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The operation optimization method of the intelligent control center includes: Access to 72-hour cold wave warning data issued by the meteorological observatory; Construct a thermal inertia matrix for the pipeline network to quantify the heat capacity and heat dissipation characteristics of different pipe sections; A heating-perturbation-de-icing coordinated strategy is dynamically generated using a reinforcement learning algorithm. The objective function is to achieve Pareto optimality by maximizing antifreeze reliability and minimizing energy consumption. The optimization objective is defined by the following function: ； wherein, is a comprehensive optimization target value, is a freeze protection reliability weight, is an energy efficiency weight, is a freeze protection reliability index, is a total system energy consumption.

9. The freeze protection system for circulating water of a power plant in cold regions according to claim 1, characterized in that: The fail-safe module specifically includes: Fail-safe mechanism: When the system detects an abnormal temperature drop in a local pipe section that exceeds the preset safety tolerance threshold, it automatically isolates the pipe section and starts the backup circulation channel; Energy recovery unit: Utilizes low-temperature water generated during the ablation process to perform cascade heat exchange with waste heat from the power plant; Digital twin monitoring interface: Visually displays real-time temperature field, ice layer distribution and energy consumption data of the pipeline network, and supports manual intervention.

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