Condensation-free efficient heat exchange control method and system for full-premixing water heater

Abstract

The invention discloses a condensation-free efficient heat exchange control method and system for a full-premixing water heater, and the method comprises the steps: collecting the multi-source information, such as air inlet temperature, humidity, gas flow, flue gas oxygen concentration and wall temperature, in real time, calculating a predicted dew point based on a dynamic dew point prediction model, and setting a dynamic safety boundary; the condensation risk is judged by comparing the wall temperature with the safety boundary, and active intervention is carried out in a staged cooperation mode that fuel gas and air volume are continuously adjusted firstly and then a partial combustion zone sectioning valve is rapidly turned off; the system comprises a main controller, a mesh-shaped infrared radiation burner, a sectional valve assembly, a gas proportional valve, a frequency conversion fan and related sensors. According to the invention, the system runs close to the condensation boundary through intelligent control, and the advantage of high operation heat efficiency is realized on the premise of ensuring high reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas water heaters, in particular to a full premix water heater non-condensing high-efficiency heat exchange control method and system. BACKGROUND

[0002] The full premix gas water heater has become an important development direction for the industry to achieve high thermal efficiency due to its full combustion and low harmful emissions. Currently, achieving ultra-high heat exchange efficiency (calculated based on the low heat value of fuel, exceeding 100%) mainly relies on the condensing heat exchange principle in technology, that is, by reducing the flue gas temperature below the water dew point to recover the latent heat released by water vapor condensation. However, while obtaining high thermal efficiency, corrosive acidic condensate is inevitably produced, which not only requires additional collection, neutralization and discharge devices, increasing the complexity and cost of the system, but also poses a long-term corrosion threat to core components such as heat exchangers and flues, affecting the reliability and service life of the product.

[0003] To avoid the condensation corrosion problem, another type of technical solution adopts a non-condensing design to ensure that the heat exchange wall temperature is always higher than the dew point by increasing the exhaust gas temperature. However, this method directly gives up latent heat recovery and results in a large amount of high-temperature flue gas sensible heat loss, making it difficult to break through the traditional level of thermal efficiency. Although there are some improved technologies that try to compromise between the two, such as monitoring individual operating parameters (such as water temperature) to roughly control the fan speed to adjust the exhaust gas temperature, or using special structural design to isolate the low-temperature area, these methods generally have significant shortcomings: first, they rely on fixed or single judgment thresholds and cannot accurately perceive and respond to the dynamic fluctuations of flue gas dew point due to changes in gas composition, environmental humidity and water load; second, the control logic is simple and the response is lagging, lacking foresight, making it difficult to accurately protect the non-condensing safety boundary in real time under complex working conditions; third, the control means is single and slow, and cannot quickly and effectively suppress the sudden condensation risk in the efficiency optimal range.

[0004] Therefore, there is an urgent need for an innovative control method and system that can accurately predict the dynamically changing flue gas dew point in real time through multi-source information fusion and set a dynamic safety boundary based on it, and then use a hierarchical collaborative control strategy including rapid discrete intervention, so as to achieve efficient recovery of flue gas sensible heat while avoiding the production of condensate water, solving the core technical problem of the full premix water heater field that high reliability and high efficiency cannot be reconciled. SUMMARY

[0005] The purpose of the present application is to provide a full premix water heater non-condensing high-efficiency heat exchange control method and system, which can effectively solve the problems in the background art.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows: A kind of full premix water heater no condensation high-efficiency heat exchange control method, comprising the following steps:

[0007] Multi-source information perception: real-time acquisition multi-source operating information, the multi-source operating information at least includes inlet temperature , inlet relative humidity , gas flow , oxygen concentration in flue gas And the real-time wall temperature of heat exchanger key area ; Dynamic dew point prediction and safety boundary setting: based on the multi-source operating information, the predicted dew point temperature of current flue gas state is calculated by pre-set dynamic dew point prediction model , and wall temperature guardian threshold is dynamically generated according to formula , wherein , wherein It is the pre-set safety margin;The dynamic dew point prediction model is a prediction model considering system time lag characteristic, and the target time of prediction is Future time; State monitoring and risk determination: for comparing the real-time wall temperature With the wall temperature guardian threshold , when satisfying , it is judged that there is condensation risk, wherein It is the buffer value for risk warning, and ; Hierarchical collaborative intervention control: for when it is judged that there is condensation risk, first, the first intervention based on continuous adjustment gas supply parameter and combustion air supply parameter is executed;At the same time, based on wall temperature dynamic prediction model and the adjustment amount of the first intervention, the dynamic change trajectory of wall temperature in future period of time is predicted;If the predicted trajectory shows that the real-time wall temperature Cannot rise to the wall temperature guardian threshold Within the pre-set safety time window , then the second intervention based on operation segmented valve assembly is executed.

[0008] Further, the step of calculating predicted dew point temperature By dynamic dew point prediction model includes: Based on the inlet temperature And inlet relative humidity , calculate inlet water vapor partial pressure ; Based on the gas flow And excess air coefficient α deduced from the oxygen concentration in flue gas , the water vapor partial pressure generated by combustion is calculated by combustion chemical reaction model ; According to the partial pressure of the intake water vapor partial pressure of water vapor generated by combustion Determine the total water vapor partial pressure in the flue gas. Thus, the predicted dew point temperature is obtained. .

[0009] Preferably, the dynamic dew point prediction model has an online self-calibration function; the online self-calibration function includes: based on the real-time wall temperature The deviation between the theoretical value of flue gas saturation temperature calculated by the dynamic dew point prediction model and the actual value is adjusted online using one or more key parameters in the model, including combustion efficiency coefficient, heat transfer coefficient, or flue gas flow time constant, by means of recursive least squares method or Kalman filter.

[0010] Furthermore, the safety margin ΔT ranges from 3°C to 10°C, and the buffer value δ used for risk warning is 2°C.

[0011] Furthermore, in the first-level intervention, the specific method for continuously adjusting the output power of the combustion system is to coordinate the opening of the gas proportional valve and the speed of the variable frequency fan; in the second-level intervention, the specific method for shutting off part of the combustion zone of the burner is to close the segment valve that controls the gas supply to the part of the combustion zone.

[0012] Preferably, the method further includes an adaptive optimization step: recording and storing different intake air temperatures. and intake relative humidity Under the combined condition, the optimal control parameter combination is the one corresponding to the system's stable operation without condensation. When the current intake conditions are detected to match the historical records, the corresponding control parameter combination is called as the initial control value. The control parameters are the fan speed and the opening degree of the basic gas proportional valve.

[0013] A fully premixed water heater with non-condensing, high-efficiency heat exchange system, comprising: The main controller, the mesh infrared radiation burner, the heat exchanger, the segmented valve assembly, the gas proportional valve, the variable frequency DC fan, and the inlet air temperature and humidity sensor, the wide-range oxygen sensor and the wall temperature sensor respectively connected to the main controller; The main controller is configured to execute the steps of multi-source information sensing, dynamic dew point prediction and safety boundary setting, state monitoring and risk assessment and hierarchical collaborative intervention control. The segmented valve assembly is disposed on the gas passage and is correspondingly connected to different combustion zones of the mesh infrared radiation burner; the wall temperature sensor is disposed in the downstream low-temperature zone of the heat exchanger of the system.

[0014] Furthermore, the main controller calculates the predicted dew point temperature through the following steps: calculating the partial pressure of water vapor in the intake air based on the signal from the intake air temperature and humidity sensor; calculating the partial pressure of water vapor generated by combustion through a combustion model based on the gas flow rate and the excess air coefficient inferred from the signal from the wide-range oxygen sensor; and determining the total partial pressure of water vapor by combining the two to obtain the predicted dew point temperature.

[0015] Preferably, the non-volatile memory of the main controller stores an online self-calibration algorithm program, which is configured to correct key parameters of the combustion model or system heat transfer model in real time based on the feedback signal of the wall temperature sensor.

[0016] Furthermore, each segment valve in the segment valve assembly has a preset correspondence with a specific combustion zone of the mesh infrared radiation burner; when the main controller performs secondary intervention, it cuts off the gas supply to the corresponding combustion zone by closing the specific segment valve.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Through a dual protection mechanism consisting of dynamic dew point prediction and real-time wall temperature feedback, combined with graded collaborative intervention, the system can operate infinitely close to the condensation boundary while absolutely avoiding condensation, thereby achieving ultra-high operating thermal efficiency close to that of a condensing water heater in the fully premixed non-condensing path.

[0018] 2. Based on the fusion of multi-source information such as intake humidity, gas flow rate and flue gas oxygen concentration, high-precision prediction of dynamic dew point is achieved, which transforms the anti-condensation control from a lagging response that relies on a fixed threshold to an active and proactive prevention based on dynamic safety boundaries.

[0019] 3. The graded intervention mechanism of "continuous adjustment + discrete rapid cut-off" combines the stability of adjustment with the speed of response, ensuring that the risk of condensation can be reliably and quickly eliminated when there are drastic changes in load, water temperature and ambient humidity, thus ensuring long-term stable operation.

[0020] 4. The mesh-shaped infrared radiation burner provides a uniform and efficient heat source while its high-temperature flameless combustion characteristics eliminate condensation in the high-temperature zone; it also works in deep collaboration with intelligent control strategies to achieve comprehensive optimization of high reliability and high efficiency at the system level. Attached Figure Description

[0021] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0022] Figure 1 This is a schematic flowchart of a non-condensing, high-efficiency heat exchange control method for a fully premixed water heater provided in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the composition of a fully premixed water heater non-condensing high-efficiency heat exchange system provided in an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of the structural composition of a fully premixed water heater applicable to the system of this invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0026] Example 1

[0027] like Figure 1 As shown, Embodiment 1 of the present invention discloses a non-condensing, high-efficiency heat exchange control method for a fully premixed water heater, comprising the following steps: Step S1: Multi-source information sensing: Real-time acquisition of multi-source operating information, including at least intake air temperature. Intake relative humidity Gas flow rate Oxygen concentration in flue gas and real-time wall temperature in key areas of the heat exchanger .

[0028] Specifically, step S1 constitutes the perception layer and data input foundation of the entire intelligent control system. This step is achieved through the collaborative work of sensor arrays deployed at key physical nodes of the water heater. The main controller reads and analyzes the output signals of each sensor cyclically at a fixed sampling period (e.g., 100 milliseconds) through its integrated analog-to-digital converter (ADC) channel, digital input port, or specific communication bus interface (such as I2C, SPI).

[0029] The intake air temperature (Unit: degrees Celsius, °C) and relative humidity of intake air (Dimensionless) Measured by an integrated digital temperature and humidity sensor. This sensor is preferably installed in the air inlet of the water heater's bottom casing or in the air inlet duct of the inverter DC fan to ensure that the measured parameters are the actual ambient air parameters that will participate in combustion and are not affected by the internal heat source.

[0030] The gas flow rate There are two preferred methods for obtaining the flow rate (unit: cubic meters per hour, m³ / h): First, install a high-precision turbine flow meter or ultrasonic flow sensor in the inlet pipe of the gas proportional valve to directly output a pulse signal or analog voltage signal proportional to the volumetric flow rate; Second, use an indirect calculation method, whereby the main controller calculates the real-time flow rate by looking up a table and interpolating based on the current output gas proportional valve control signal (such as PWM duty cycle or stepper motor steps) and the gas pressure measured by the pressure sensor before the valve (if installed), combined with the flow characteristic curve of the proportional valve at a specific pressure pre-stored in the controller's memory. .

[0031] Oxygen concentration in the flue gas (Unit: volume percentage, %vol) is provided by a wide-range oxygen sensor installed between the flue gas outlet of the heat exchanger and the smoke hood. This sensor can output a voltage signal that is linearly related to the oxygen concentration over a wide range of air-fuel ratios (e.g., excess air coefficient α from 0.8 to infinity), and is a key device for real-time monitoring of combustion conditions and diagnosing whether the air-fuel ratio deviates from the ideal value.

[0032] The real-time wall temperature (Unit: degrees Celsius, ℃) is the core physical quantity used for direct feedback against condensation in this method. It is measured by a highly sensitive and stable temperature sensor (e.g., a thin-film platinum resistance thermometer PT1000 or a sheathed type K thermocouple) which is tightly fixed in the low-temperature zone downstream of the heat exchanger by mechanical clamps or high-temperature thermally conductive adhesive. This zone, determined by thermodynamic simulation and experiments, is typically located on the heat exchanger tube wall closest to the outlet or at the fin root of the last heat exchange process, and is the lowest temperature part of the entire heat exchanger, most likely to reach the dew point temperature first. The sensor's resistance or thermocouple signal is processed by a signal conditioning circuit (e.g., constant current source drive, amplification, and filtering) and then converted into a digital temperature value by the ADC port of the main controller.

[0033] Through step S1, the system constructs a framework covering the environmental state ( , ), fuel supply ( The data set of combustion chemical reaction state and heat exchange interface physical state is a full-dimensional, real-time dynamic dataset, providing accurate and reliable input for subsequent model prediction and intelligent decision-making.

[0034] Step S2: Dynamic Dew Point Prediction and Safety Boundary Setting: Based on the multi-source operating information, the predicted dew point temperature of the current flue gas state is calculated using a preset dynamic dew point prediction model. And according to the formula Dynamically generate wall temperature protection threshold ,in The preset safety margin; the dynamic dew point prediction model is a prediction model that considers the system's time delay characteristics, and its target time is... The future moments that follow.

[0035] Specifically, step S2 is the core algorithmic step in this method to achieve proactive and accurate perception of condensation risk. The main controller calls the enhanced dynamic dew point prediction model pre-installed in its internal non-volatile memory. This model is an intelligent predictor that integrates process dynamic time delay characteristics with online self-correction capabilities.

[0036] Specifically, step S2 includes: Step S21: Based on the intake air temperature and intake relative humidity Calculate the partial pressure of water vapor in the intake air. .

[0037] Specifically, the controller first determines the intake air temperature. The value is obtained by consulting the saturated water vapor pressure lookup table at the current intake air temperature. Saturated vapor pressure of pure water The unit is usually kilopascal (kPa). According to Dalton's law of partial pressures, the partial pressure of water vapor in intake air... From the formula The calculation shows that, among which It needs to be converted to decimal form (e.g., 60% corresponds to 0.6). The value quantifies the amount of water vapor carried by the ambient air into the combustion system.

[0038] Step S22: Based on the gas flow rate and the oxygen concentration in the flue gas The excess air coefficient calculated backwards The partial pressure of water vapor generated during combustion was calculated using a combustion chemical reaction model. .

[0039] Specifically, this step begins with real-time diagnostics of the combustion conditions. The controller uses real-time measurements from a wide-range oxygen sensor... Concentration, using a formula applicable to fully premixed combustion Simplified calculations can be performed, or a more precise iterative algorithm that considers fuel characteristics and the concentration of carbon dioxide in combustion products can be used to back-calculate the excess air coefficient of the current combustion process in real time. (Dimensionless). Coefficient It is a dynamic variable that directly reflects the real-time matching relationship between the air volume provided by the variable frequency fan and the gas volume provided by the gas proportional valve.

[0040] Secondly, calculations are performed to determine the water production from the combustion chemical reaction. The controller has pre-stored the molar composition or molecular formula of a standard gas source (such as natural gas, whose main component is methane CH4). This is combined with the real-time acquired gas flow rate. (The molar flow rate needs to be converted to standard conditions based on the gas temperature and pressure.) and the calculated excess air coefficient Theoretical calculations are performed based on the chemical reaction equation for the complete combustion of the gas source. For example, for methane: (Using air as the oxygen source). From this, the molar amount of water vapor produced per unit time by the combustion chemical reaction itself can be calculated. .

[0041] Finally, calculate the partial pressure of water vapor produced by combustion. Given the total molar amount of dry flue gas produced by combustion (…),… It can be based on the reaction formula, and (Calculation) and the molar amount of water vapor produced by combustion Based on this, we assume that the total pressure of the flue gas is approximately equal to the local atmospheric pressure. (Approximately 101.325 kPa), then the partial pressure of the water vapor produced by combustion is... It can be estimated using the ideal gas partial pressure law: More accurate calculations require corrections for the molar volume of water vapor.

[0042] Step S23: Based on the inlet water vapor partial pressure partial pressure of water vapor generated by combustion Determine the total water vapor partial pressure in the flue gas. Thus, the predicted dew point temperature is obtained. .

[0043] Specifically, the total water vapor in the flue gas is carried by the intake air (corresponding to...). ) and combustion products (corresponding to It consists of two parts. Accurate calculation of total water vapor partial pressure. A molar-weighted average of these two parts needs to be performed. First, based on the intake air temperature... Pressure (approximately) The total molar amount of intake air can be calculated from the fan speed characteristic curve or model and the airflow rate. and the molar amount of water vapor therein (Depend on (Calculation). Then, combine the molar amount of dry flue gas calculated in step S22. Molar amount of water produced by combustion Total molar amount of flue gas Total molar amount of water vapor Ultimately, the total water vapor partial pressure .

[0044] get Then, by referring back to the saturated water vapor partial pressure-temperature relationship table, the corresponding saturation temperature can be determined. This temperature is the basic theoretical dew point temperature at which water vapor in the flue gas begins to condense under the current operating conditions. (Unit: °C)

[0045] Step S24: Time Delay Prediction Processing.

[0046] Specifically, considering the significant physical process delay in the process from flue gas generation and flow to reaching a stable heat exchange state in the low-temperature region of the heat exchanger, the dynamic dew point prediction model introduces a first-order inertial element with pure time lag. The model uses the basic theoretical dew point temperature calculated in step S23. As input, it is processed through a transfer function that characterizes the thermal dynamics of the system. This transfer function... It can be simplified to:

[0047] Where s is the complex frequency variable, and e is the base of the natural logarithm. For the delay time of flue gas flow, Let be the thermal response time constant of the heat exchanger wall. The main controller discretizes this continuous model. Finally, the model outputs the predicted dew point temperature. It is the future Time (e.g., The predicted value is given by (where k is an empirical coefficient between 0.5 and 2). This time-delay prediction makes the safety boundary... The setup is truly forward-looking, covering the main dynamic processes from the issuance of the intervention to its effect on the wall temperature.

[0048] Step S25: Online self-calibration.

[0049] Specifically, to address the slow drift of model parameters caused by burner aging and scale buildup on the heat exchange area, the dynamic dew point prediction model has an online self-calibration function. The system defines a parameter based on the combustion efficiency coefficient. An adjustable parameter vector with the effective heat transfer coefficient U as its core. When the system is operating under relatively stable conditions (such as when the load change rate is below a threshold), the self-correction algorithm is activated.

[0050] Data acquisition: Record the input vector u (including the current and previous several periods) , , , (fan speed) and the measured value of the wall temperature sensor, which serves as the actual output of the system. .

[0051] Model predictions and bias calculations: using current parameters Using the complete model including steps S21-S24, a corresponding theoretical wall temperature prediction value is calculated. Calculate the deviation between the predicted and measured values. .

[0052] Parameter update: The parameters are updated using the recursive least squares method. The algorithm maintains a parameter estimation error covariance matrix P, and recursively calculates new parameter estimates based on new data. ,in, To predict the residuals, K is the Kalman gain vector, derived from the formula... Calculations show that This is the regression vector related to the current input and output data. The covariance matrix is ​​then updated. ,in , , are the parameter estimation error covariance matrices before and after the update, respectively; I is the identity matrix of the same dimension; Represents the regression vector The transpose operation is performed. This update process aims to reduce the uncertainty of parameter estimation.

[0053] Parameter application: Updated This will be immediately used in the dynamic dew point prediction calculations for all subsequent time points. This closed-loop correction mechanism ensures that the model's prediction accuracy for the condensation boundary remains at a high level even after long-term operation.

[0054] In precise calculation Subsequently, to address model calculation bias, sensor measurement errors, and system response delays, a preset safety margin ΔT is introduced. According to a preferred embodiment of the invention, the safety margin ΔT ranges from 3°C to 10°C and can be determined through experimental calibration; a typical value is 5°C. Then, according to the formula... Dynamically generate wall temperature protection threshold The aforementioned As a real-time, dynamic safety boundary for the control system to prevent condensation on the heat exchanger wall, its value is not fixed, but is intelligently adjusted according to changes in operating conditions, so that the system can operate as close as possible to the theoretical high-efficiency range while ensuring absolute safety.

[0055] Step S3: Status monitoring and risk assessment, used to measure the real-time wall temperature With the aforementioned wall temperature protection threshold Compare, when satisfied When it is determined that there is a risk of condensation, among which This serves as a buffer value for risk warning purposes, and .

[0056] Specifically, step S3 constitutes a real-time closed-loop monitoring and decision-making process to prevent condensation risks. During each control cycle, the main controller synchronously reads the real-time wall temperature measured by the wall temperature sensor. and the current dynamic wall temperature protection threshold calculated in step S2 To provide early warning and initiate early intervention when the wall temperature approaches the safety boundary, and to prevent the wall temperature from dropping suddenly due to system thermal inertia. A warning buffer value δ is specifically set. According to a specific embodiment of the present invention, the buffer value δ is 2℃.

[0057] The logic for risk assessment is as follows: the main controller continuously performs... The system determines the condition for temperature detection. When this condition is met, the system judges that the wall temperature in the low-temperature zone of the heat exchanger has entered the warning range, posing a risk of reaching or exceeding the condensation safety boundary. The control system must immediately switch from the normal "efficiency optimization mode" to the "anti-condensation protection mode." If the condition is not met, it indicates that the system is operating within the safe range and can continue to execute the control strategy that maximizes thermal efficiency. This step creatively incorporates the feedforward prediction value based on the physical model (… ) and direct feedback measurements based on physical sensors ( The combination of these two methods forms a risk assessment mechanism with dual protection of "prediction and early warning" and "actual verification," which significantly improves the timeliness, accuracy, and reliability of the system's perception of condensation risks.

[0058] Step S4: Tiered Coordinated Intervention Control: When a condensation risk is determined, firstly, a primary intervention based on continuously adjusting the gas supply parameters and combustion air supply parameters is executed; simultaneously, based on the wall temperature dynamic prediction model and the adjustment amount of the primary intervention, the dynamic trajectory of the wall temperature over a future period is predicted; if the predicted trajectory indicates that the real-time wall temperature... Unable to be within the preset safe time window Internal temperature rises to the aforementioned wall temperature protection threshold. The above will then trigger a secondary intervention based on the operation of the segmented valve assembly.

[0059] Specifically, step S4 is the execution step that proactively eliminates the risk of condensation and ensures the safe operation of the system. This step adopts a hierarchical and deeply collaborative intervention strategy, and its core feature is intelligent decision-making based on dynamic prediction.

[0060] Specifically, step S4 includes: Step S41: Initiation and execution of Level 1 intervention.

[0061] Specifically, once step S3 determines that the system has entered the "anti-condensation protection mode," the system immediately initiates Level 1 intervention. The goal of Level 1 intervention is to gently and continuously fine-tune the overall combustion conditions to increase the exhaust gas temperature and wall temperature, bringing them back to a safe range. The main controller synchronously generates adjustment commands: sending a signal to the drive circuit of the gas proportional valve to slightly reduce its opening (e.g., by 2%-5%) from its current position, thereby reducing the gas flow rate; simultaneously, sending a signal to the motor controller (usually a PWM speed controller) of the variable frequency DC fan to reduce its target speed, thereby reducing the combustion air flow rate. This coordinated operation of "reducing gas flow rate and reducing air volume" aims to reduce the combustion heat load while simultaneously reducing the flue gas flow rate and increasing the residence time of the flue gas in the heat exchange channel, jointly causing the flue gas outlet temperature and heat exchange wall temperature to rise. The adjustment range of Level 1 intervention is gradual, and the response speed is relatively continuous, striving to mitigate risks while maximizing the stability of the hot water output temperature and the user's bathing comfort.

[0062] Step S42: Intelligent triggering and execution of secondary intervention based on prediction.

[0063] Specifically, the triggering and execution of secondary intervention is not a simple timeout judgment, but a predictive decision-making process based on real-time simulation.

[0064] Dynamic Prediction Model Activation: Simultaneously with the activation of the primary intervention, the main controller activates a simplified dynamic prediction model for wall temperature. This model establishes differential equations based on the current energy balance of the heat exchanger wall: Where C is the equivalent heat capacity of the wall. Heat flow is input into the flue gas (calculated in real time based on the gas flow rate and combustion efficiency after primary intervention). For the heat exchange and heat loss of water (compared to current (Related to water temperature difference).

[0065] Trajectory prediction: based on current measured data Using the initial values, the above differential equation is applied in the future time window. Perform numerical integration (e.g., using the Euler method) within 15 seconds to obtain a line. Predicted trajectory changing over time.

[0066] Intelligent triggering judgment based on multi-condition fusion: The system evaluates the following three conditions in parallel to form a composite triggering logic: Condition A (Prediction Failure): Analyze the predicted trajectory; if the future time window... Any point within, predicted The value is below the current security boundary. If so, it is determined that Level 1 intervention is insufficient to prevent the risk of condensation, thus meeting the triggering conditions.

[0067] Condition B (Deteriorating Trend): Real-time Calculation Measured rate of change If its rate of decline exceeds the emergency threshold (For example, -1.0℃ / second) indicates that the operating conditions are deteriorating rapidly, meeting the triggering conditions.

[0068] Condition C (Timeout Backup): If the duration of Level 1 intervention exceeds the maximum permissible time limit. (For example, 30 seconds), then regardless of the prediction result, the triggering condition is met.

[0069] The triggering logic is as follows: it is triggered when any one of the conditions A, B, or C is met.

[0070] Adaptive Execution: When a secondary intervention is triggered, the main controller not only executes the action of closing the segmented valve, but also adaptively adjusts the intervention strategy based on the triggering condition. If triggered by condition A, the system selectively closes the valves of the one or two zones that contribute the most to the thermal contribution of each combustion zone to the low-temperature zone, based on the weight of each combustion zone's contribution to the low-temperature zone in the prediction model.

[0071] If triggered by condition B, it indicates an urgent risk. The system may simultaneously shut down more zones (such as 2 or 3) or immediately reduce the fan speed to the preset minimum safe speed to increase the exhaust gas temperature as quickly as possible.

[0072] If triggered by condition C, then the preset standard secondary intervention action will be executed (such as closing the corresponding bottom partition).

[0073] In a preferred embodiment, the method further includes step S5.

[0074] Step S5: This also includes an adaptive optimization step: recording and storing different intake air temperatures. and intake relative humidity Under the combined condition, the optimal control parameter combination is the one corresponding to the system's stable operation without condensation. When the current intake conditions are detected to match the historical records, the corresponding control parameter combination is called as the initial control value. The control parameters are the fan speed and the opening degree of the basic gas proportional valve.

[0075] Specifically, step S5 endows the control system with a certain degree of self-learning and optimization capabilities to improve its long-term operating performance and efficiency under different environments. An experience database area is allocated in the main controller's non-volatile memory. During long-term system operation, at certain specific intake air temperatures... and intake relative humidity Under combined operating conditions, when the fan can continuously and stably operate in a highly efficient and condensation-free state, the controller automatically records the fan's reference speed N at that moment. opt(Unit: revolutions per minute, rpm) and the basic opening degree K of the gas proportional valve opt (For example, PWM duty cycle percentage or stepper motor steps), forming a line with ( , indexed by (N) opt ,K opt () is a data record containing the content.

[0076] After that, when the system restarts or detects the current ( , Match the operating point with a historical record in the database (within the set tolerance range, for example, ±1℃, When the value is ±5%, the N stored in that record will be automatically retrieved. opt and K opt This serves as the initial setting for controlling the fan and proportional valve. In this way, the system can skip the trial-and-error adjustment phase starting from general default parameters, and more quickly reach a near-optimal stable operating point, thereby shortening the settling time, improving steady-state energy efficiency, and enhancing the temperature stability of hot water output.

[0077] Through steps S1 to S5 above, the control method provided in Embodiment 1 constructs a complete intelligent control closed loop of "multi-source sensing → dynamic prediction (including time delay and self-correction) → risk assessment → graded intervention (including intelligent prediction triggering) → adaptive optimization". The core advancement of this method lies in: by introducing an enhanced dynamic dew point prediction model with time delay prediction and online self-correction functions, it achieves advanced, adaptive, and accurate perception of the condensation boundary; by introducing direct wall temperature feedback and dynamic safety boundary comparison, it establishes a dual safety guarantee combining feedforward and feedback; by designing intelligent triggering logic based on real-time dynamic prediction and multi-condition fusion judgment, it achieves deep synergy and optimized coupling of the two-level intervention strategies of "continuous fine-tuning" and "discrete fast switching"; finally, under the premise of ensuring the high-reliability hard constraint of "absolute no condensation" on the heat exchanger wall, intelligent control enables the system to continuously operate in the extreme high-efficiency range that infinitely approaches the dynamic safety boundary, thus successfully solving the core technical contradiction in the field of fully premixed gas water heaters where "high efficiency" and "no condensation" are difficult to achieve simultaneously.

[0078] Example 2 like Figure 2 and Figure 3 As shown, Embodiment 2 of the present invention discloses a fully premixed water heater non-condensing high-efficiency heat exchange system, including: a main controller, a mesh infrared radiation burner, a heat exchanger, a segmented valve assembly, a gas proportional valve, a variable frequency DC fan, and an inlet temperature and humidity sensor, a wide-range oxygen sensor, and a wall temperature sensor respectively connected to the main controller.

[0079] Specifically, the main controller is the intelligent control core of the system, typically implemented using a microcontroller unit (MCU) based on the ARM Cortex-M series core, such as STMicroelectronics' STM32F4 series chip. This main controller integrates a central processing unit, flash memory, SRAM, analog-to-digital converter (ADC), timers, and various communication interfaces. Its flash memory stores the program code implementing the control method of this invention. The main controller is specifically configured to run an enhanced dynamic dew point prediction model with time-delay prediction and online self-correction functions, and execute intelligent hierarchical collaborative intervention control decision logic based on real-time effect prediction.

[0080] Specifically, the flash memory contains the following key algorithm modules.

[0081] Time-delay prediction algorithm module: includes the first-order inertial plus pure time-delay transfer function. Discretized implementation code for calculating the future Predicted dew point temperature at any time .

[0082] Online parameter identification module: Implements the recursive least squares (RLS) algorithm for real-time updating of the combustion efficiency coefficient. And key parameters such as heat transfer coefficient U.

[0083] The wall temperature dynamic prediction and decision module includes the simplified wall temperature differential equation model and numerical integrator, as well as the intelligent trigger judgment logic that integrates multiple conditions (prediction failure, trend deterioration, and timeout backup).

[0084] The main controller periodically collects data from each sensor through its input interface, runs the aforementioned enhancement algorithm in parallel, performs calculations and decisions, and sends control commands to each actuator through its output interface.

[0085] Specifically, the intake air temperature and humidity sensor is used to measure the intake air temperature of the ambient air. and intake relative humidity Typically, an integrated digital sensor is selected and installed inside the air inlet channel of the water heater, communicating with the main controller via an I2C bus. The wide-range oxygen sensor is installed near the flue gas outlet of the heat exchanger to measure the oxygen concentration in the flue gas in real time. Its signal output terminal is connected to the analog input or dedicated CAN interface of the main controller. The wall temperature sensor is used to measure the real-time wall temperature in critical areas of the heat exchanger. It is preferable to use a high-precision thin-film platinum resistance thermometer (PT1000), which is connected to the analog-to-digital conversion channel of the main controller through a signal conditioning circuit, and its probe is tightly fixed in the downstream low-temperature zone of the heat exchanger, such as the heat exchange tube wall closest to the outlet.

[0086] Specifically, the mesh-like infrared radiation burner is the core heat source component for achieving efficient and clean combustion in the system. According to a preferred embodiment of the invention, the mesh-like infrared radiation burner is made of metal fiber or metal felt material, with uniformly distributed micropores on its surface for flameless infrared radiation combustion of the premixed gas of fuel gas and air on and inside its surface. The burner is located directly below the heat exchanger, and its emitted infrared radiation energy is efficiently absorbed by the heat exchanger. The segmented valve assembly is disposed on the fuel gas passage, specifically downstream of the fuel gas proportional valve. Each segmented valve in the segmented valve assembly has a preset correspondence with a specific combustion zone of the mesh-like infrared radiation burner, and each segmented valve controls the fuel gas supply to an independent zone of the burner. This allows the main controller to quickly reduce the heat load of the corresponding local combustion zone by closing specific segmented valves when performing secondary intervention. The segmented valves are typically normally open solenoid valves, controlled by the digital output port of the main controller via a drive circuit.

[0087] Specifically, the gas proportional valve and the variable frequency DC fan are key actuators for performing primary intervention. The gas proportional valve is connected in series in the main gas passage, and its motor drive receives control signals (such as PWM or pulse signals) from the main controller to achieve continuous and precise adjustment of the opening degree, thereby controlling the gas flow. The variable frequency DC fan is installed at the air inlet of the burner assembly, and its motor controller receives speed control commands from the main controller to steplessly adjust its speed, thereby controlling the combustion air flow. The main controller achieves continuous fine-tuning of combustion power by coordinating the outputs of both.

[0088] The system described in Embodiment 2 provides a complete physical entity for the control method described in Embodiment 1 through the coordinated setup and connection of a main controller, a specific mesh-like infrared radiation burner, a segmented valve assembly, a gas proportional valve, a variable frequency DC fan, and inlet temperature and humidity sensors, a wide-range oxygen sensor, and a wall temperature sensor. The system's hardware structure directly supports the multi-source information acquisition required for dynamic dew point prediction, achieves highly efficient combustion of fully premixed flameless infrared radiation, and possesses graded intervention capabilities of "continuous adjustment" and "rapid discrete cutoff." Therefore, the system can physically eliminate condensation while achieving extremely high heat exchange efficiency, effectively solving the technical challenge of simultaneously balancing reliability, energy efficiency, and environmental protection.

[0089] The foregoing has shown and described the basic principles, main features, and advantages of this invention. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the non-condensing, high-efficiency heat exchange of a fully premixed water heater, characterized in that: Includes the following steps: Multi-source information sensing: Real-time acquisition of multi-source operational information, including at least intake air temperature. Intake relative humidity Gas flow rate Oxygen concentration in flue gas and real-time wall temperature in key areas of the heat exchanger ; Dynamic dew point prediction and safety boundary setting: Based on the multi-source operating information, the predicted dew point temperature of the current flue gas state is calculated using a preset dynamic dew point prediction model. And according to the formula Dynamically generate wall temperature protection threshold ,in The preset safety margin; the dynamic dew point prediction model is a prediction model that considers the system's time delay characteristics, and its target time is... The future moment after; Status monitoring and risk assessment: used to measure the real-time wall temperature With the aforementioned wall temperature protection threshold Compare, when satisfied When it is determined that there is a risk of condensation, among which This serves as a buffer value for risk warning purposes, and ; Tiered coordinated intervention control: When a condensation risk is detected, a first-level intervention is first implemented based on continuously adjusting the gas supply parameters and combustion air supply parameters; simultaneously, based on the wall temperature dynamic prediction model and the adjustment amount of the first-level intervention, the dynamic trajectory of the wall temperature over a future period is predicted; if the predicted trajectory indicates that the real-time wall temperature... Unable to be within the preset safe time window Internal temperature rises to the aforementioned wall temperature protection threshold. The above will then trigger a secondary intervention based on the operation of the segmented valve assembly.

2. The method according to claim 1, characterized in that, The dew point temperature is predicted by calculating the dynamic dew point prediction model. The steps include: Based on the intake air temperature and intake relative humidity Calculate the partial pressure of water vapor in the intake air. ; Based on the gas flow rate and the oxygen concentration in the flue gas The excess air coefficient α, derived backwards, is used to calculate the partial pressure of water vapor produced during combustion using a combustion chemical reaction model. ; According to the partial pressure of the intake water vapor partial pressure of water vapor generated by combustion Determine the total water vapor partial pressure in the flue gas. Thus, the predicted dew point temperature is obtained. .

3. The method according to claim 2, characterized in that, The dynamic dew point prediction model has an online self-calibration function; the online self-calibration function includes: based on the real-time wall temperature... The deviation between the theoretical value of flue gas saturation temperature calculated by the dynamic dew point prediction model and the actual value is adjusted online using one or more key parameters in the model, including combustion efficiency coefficient, heat transfer coefficient, or flue gas flow time constant, by means of recursive least squares method or Kalman filter.

4. The method according to claim 1, characterized in that, The safety margin ΔT ranges from 3℃ to 10℃, and the buffer value δ used for risk warning is 2℃.

5. The method according to claim 1, characterized in that, In the first-level intervention, the specific method for continuously adjusting the output power of the combustion system is to coordinate the opening degree of the gas proportional valve and the speed of the variable frequency fan; in the second-level intervention, the specific method for shutting off part of the combustion zone of the burner is to close the segment valve that controls the gas supply to the part of the combustion zone.

6. The method according to any one of claims 1-5, characterized in that, It also includes an adaptive optimization step: recording and storing different intake air temperatures. and intake relative humidity Under the combined condition, the optimal control parameter combination is the one corresponding to the system's stable operation without condensation. When the current intake conditions are detected to match the historical records, the corresponding control parameter combination is called as the initial control value. The control parameters are the fan speed and the opening degree of the basic gas proportional valve.

7. A fully premixed water heater non-condensing high-efficiency heat exchange system, used to implement the control method according to any one of claims 1 to 6, characterized in that, include: The main controller, the mesh infrared radiation burner, the heat exchanger, the segmented valve assembly, the gas proportional valve, the variable frequency DC fan, and the inlet air temperature and humidity sensor, the wide-range oxygen sensor and the wall temperature sensor respectively connected to the main controller; The main controller is configured to execute the steps of multi-source information sensing, dynamic dew point prediction and safety boundary setting, state monitoring and risk assessment and hierarchical collaborative intervention control. The segmented valve assembly is disposed on the gas passage and is correspondingly connected to different combustion zones of the mesh infrared radiation burner; the wall temperature sensor is disposed in the downstream low-temperature zone of the heat exchanger of the system.

8. The system according to claim 7, characterized in that, The main controller calculates the predicted dew point temperature through the following steps: calculating the partial pressure of water vapor in the intake air based on the signal from the intake air temperature and humidity sensor; calculating the partial pressure of water vapor generated by combustion through a combustion model based on the gas flow rate and the excess air coefficient derived from the signal from the wide-range oxygen sensor; and determining the total partial pressure of water vapor by combining the two to obtain the predicted dew point temperature.

9. The system according to claim 8, characterized in that, The main controller's non-volatile memory stores an online self-calibration algorithm program, which is configured to correct key parameters of the combustion model or system heat transfer model in real time based on the feedback signal from the wall temperature sensor.

10. The system according to claims 7-9, characterized in that, Each segment valve in the segment valve assembly has a preset correspondence with a specific combustion zone of the mesh infrared radiation burner; when the main controller performs secondary intervention, it cuts off the gas supply to the corresponding combustion zone by closing the specific segment valve.

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