SOEC system coupling waste heat recovery control method for full-premixing condensation heat exchanger of wall-hanging stove and heat exchanger

Abstract

The invention discloses an SOEC system coupling waste heat recovery control method for a full premixing condensation heat exchanger of a wall-hanging stove and the heat exchanger, and belongs to the technical field of energy management and self-adaptive control, and the method builds an intelligent system integrating prediction, optimization and control: firstly, predicting a user heat load through an LSTM neural network; a model predictive control MPC algorithm is utilized, maximization of the comprehensive efficiency of the system is taken as a target, multiple constraints such as economy, safety and comfort are considered at the same time, and an optimal control instruction set is generated; and finally, hydrogen-natural gas mixed combustion is accurately controlled through a dynamic air-fuel ratio algorithm. The matched heat exchanger is provided with a unique phase-change heat exchange part and an SOEC heat supply runner, and waste heat of smoke and condensate water can be efficiently recycled and distributed to an SOEC system or a heating system according to needs. According to the invention, deep coupling and intelligent collaborative optimization of the household wall-hanging stove and the SOEC system are realized for the first time, and the comprehensive utilization efficiency of energy is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy management and automatic adaptive control technology, specifically relating to an SOEC coupled waste heat recovery control method and heat exchanger for a wall-hung boiler with fully premixed condensing heat exchanger. Background Technology

[0002] With the development of hydrogen energy technology, coupling solid oxide electrolyzer (SOEC) systems with household energy systems to achieve cascaded energy utilization has become an important research direction. Although existing household gas wall-hung boilers have generally adopted condensation technology to recover waste heat from flue gas, their thermal efficiency improvement has reached a bottleneck. On the other hand, the SOEC system's hydrogen production process requires a large amount of heat energy, which is usually provided by external electric heating or independent heat sources, increasing system complexity and operating costs.

[0003] Currently, there are existing technical solutions that attempt to couple SOEC systems with heat sources, but they have obvious drawbacks: First, the coupling method is relatively simple and crude, often only achieving unidirectional heat transfer without forming deep energy integration; Secondly, there is a lack of intelligent control methods for this coupled system, making it impossible to optimize in real time based on dynamic factors such as user load and energy prices. Furthermore, the hydrogen produced by SOEC is usually stored or transported directly, failing to form a closed loop with the wall-hung boiler combustion system and failing to fully utilize the value of hydrogen energy. The existing waste heat recovery control strategies for wall-hung boilers are relatively fixed and cannot achieve prediction-based, personalized, on-demand recovery and distribution. Therefore, existing technologies lack a comprehensive solution that can deeply integrate SOEC systems with household wall-hung boilers and achieve coordinated optimization management and safe and efficient operation of multiple energy sources such as electricity, heat, and hydrogen through intelligent algorithms. Summary of the Invention

[0004] To overcome the problems of the prior art, this invention discloses a SOEC coupled waste heat recovery control method and heat exchanger for a wall-mounted boiler with fully premixed condensing heat exchanger.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A method for controlling waste heat recovery coupled in a fully premixed condensing heat exchanger of a wall-hung boiler, the fully premixed condensing heat exchanger of the wall-hung boiler includes an oxygen sensor installed on its flue gas passage, and a hydrogen recirculation flow regulating valve installed on the hydrogen supply pipeline of the fully premixed condensing heat exchanger of the wall-hung boiler. The method includes the following steps: S1, based on historical operating data and real-time environmental data, predicts user heat load demand through an LSTM neural network model; S2, based on the predicted user heat load demand, real-time electricity price signal, natural gas price signal and SOEC hydrogen production concentration, the optimal control instruction set for the current control cycle is generated through the system efficiency model predictive control MPC algorithm; The optimal control instruction set includes: combustion power setpoint, water pump flow rate setpoint, SOEC system electrolysis power setpoint, and hydrogen flow rate setpoint returned to the fully premixed condensing heat exchanger of the wall-hung boiler; S3, send the hydrogen flow rate setpoint to the hydrogen recirculation flow regulating valve, send the combustion power setpoint and water pump flow rate setpoint to the MPC controller executing the MPC algorithm, and send the SOEC system electrolysis power setpoint to the SOEC system; S4. Based on the hydrogen flow rate setpoint and the residual oxygen content in the flue gas fed back by the oxygen sensor, the combustion air flow rate on the fully premixed burner of the fully premixed condensing heat exchanger of the wall-hung boiler is calculated and adjusted in real time through a dynamic air-fuel ratio control algorithm to achieve stable and efficient combustion of hydrogen-natural gas mixed fuel.

[0006] Preferably, the method further includes step S0: The operation data of the fully premixed condensing heat exchanger and SOEC coupling system of the wall-hung boiler are acquired through the data acquisition and sensing module. The runtime data includes: Outlet water temperature, return water temperature, flue gas temperature, condensate temperature, gas flow rate, water and electricity metering data, and hydrogen flow rate and concentration data of the SOEC system; Provide data input for steps S1 and S2.

[0007] Preferably, in step S1, the historical operating data includes historical heat load curves and historical weather data; Real-time environmental data includes outdoor temperature, indoor set temperature, and solar irradiance. The LSTM neural network model is trained on data that has been denoised and normalized.

[0008] Preferably, in step S2, the optimization process of the predictive control MPC algorithm depends on the system efficiency model, which is established in the following way: Establish a sub-model of the thermal efficiency of the wall-hung boiler, whose inputs include combustion power, air-fuel ratio, and return water temperature, and whose output is the real-time thermal efficiency. A sub-model for SOEC electrolysis efficiency is established, with inputs including electrolysis power and waste heat temperature from the wall-hung boiler, and outputs hydrogen production efficiency and hydrogen production concentration. Establish a sub-model for the economic operation of the system, whose inputs include real-time electricity price, natural gas price, and hydrogen value, and whose output is the operating cost; The MPC controller performs rolling optimization with the objective function of maximizing the overall system efficiency. The objective function is as follows:

[0009] in, This is the overall system efficiency index, where a, b, and c are weighting coefficients. For the thermal efficiency of wall-hung boilers, The value represents SOEC electrolysis efficiency, and Cost represents operating costs.

[0010] Preferably, in step S2, the MPC algorithm generates the optimal control instruction set by processing the following constraints based on the prediction of the system efficiency model: The user thermal comfort constraints, the minimum and maximum combustion power constraints of the wall-hung boiler, the safe operating temperature and power constraints of the SOEC system, and the hydrogen regeneration ratio safety constraints are used as the boundary conditions of the optimization problem. The MPC algorithm employs interior-point optimization to search within the feasible region that satisfies all the aforementioned constraints, aiming to find the system's overall efficiency index. The largest optimal solution is the optimal control instruction set.

[0011] Preferably, the parameters of the system efficiency model are collected and updated in real time through an edge computing smart terminal deployed locally on the wall-hung boiler; The rolling optimization computation of the MPC controller is deployed on a cloud platform; Edge computing smart terminals and cloud platforms interact with each other and issue commands through wireless communication modules.

[0012] Preferably, in step S4, the specific process of the dynamic air-fuel ratio control algorithm is as follows: Real-time monitoring of the flow rate and concentration of reflux hydrogen, as well as the flow rate of natural gas; Calculate the theoretical air volume required for the current fuel mixture based on the volumetric flow rate, calorific value, and stoichiometric ratio of hydrogen and natural gas. Based on the residual oxygen content in the flue gas fed back by the oxygen sensor, a PID control algorithm is used to correct the theoretical air volume and obtain the target combustion air flow rate value. Adjust the fan speed or damper opening of the fully premixed burner to match the target combustion air flow rate.

[0013] Preferably, the flue gas and condensation waste heat generated by the wall-hung boiler are recovered through a heat exchange pipeline connected between the wall-hung boiler and the SOEC system, and connected to the SOEC system to provide the heat required for electrolysis of the wall-hung boiler. Based on this, the method further includes the following steps: According to the optimal control command set, the valve opening in the heat exchange pipeline is dynamically adjusted to distribute the recovered waste heat and adjust the proportion of waste heat flowing to the SOEC system and the user-end heating system.

[0014] Preferably, sending the SOEC system electrolysis power setpoint to the SOEC system in step S3 specifically includes: The SOEC system receives the electrolysis power setpoint and performs the following cooperative operations accordingly: In terms of power response, the DC power rectifier of the SOEC system adjusts the output power to match the set value; Thermal management: The SOEC system's thermal management module distributes waste heat recovered by the fully premixed condensing heat exchanger of the wall-hung boiler to maintain the optimal operating temperature of the electrolysis stack. In the gas generation process, the gaseous mixture generated by the electrolysis stack is separated and purified by the hydrogen separation and purification module of the SOEC system.

[0015] Preferably, a fully premixed condensing heat exchanger for a wall-hung boiler, used to implement a SOEC system coupled waste heat recovery control method for the fully premixed condensing heat exchanger of the wall-hung boiler, includes: The primary heat exchange section is used to recover the sensible heat of high-temperature flue gas; The secondary condenser heat exchange section is used to recover the latent heat of the low-temperature flue gas and the sensible heat of the condensate. The phase change heat exchanger is filled with a phase change working fluid. The heat absorption end of the phase change heat exchanger is located in the condensate collection channel of the secondary condensation heat exchanger to absorb the latent heat of the condensate. The SOEC heating channel is connected to the heat release end of the phase change heat exchange section, and is used to transport the recovered high-quality latent heat to the SOEC system. The heating return flow channel is connected to the secondary condensing heat exchange section; The heating return water interface is connected to the heating return flow channel and is used to connect to the return water pipe of the user's heating system. Oxygen sensor, installed in the smoke exhaust duct; A hydrogen recirculation flow regulating valve is installed on the hydrogen supply line of the fully premixed burner; The SOEC heating flow channel and the heating return flow channel are respectively equipped with regulating valves. The regulating valves are configured to be controlled by the SOEC system coupled waste heat recovery control method of the wall-mounted boiler fully premixed condensing heat exchanger, so as to dynamically allocate the proportion of waste heat flowing to the SOEC system and the user-end heating system according to the optimal control instruction set.

[0016] The beneficial effects of this invention are as follows: By deeply coupling the flue gas and condensate waste heat of the wall-hung boiler to the SOEC system, the required reaction heat is provided for its electrolysis process, realizing the cascade utilization of energy, breaking the bottleneck of improving the thermal efficiency of traditional wall-hung boilers, and reducing the external heating energy consumption of the SOEC system, the overall energy utilization rate of the system is improved. Introducing an intelligent control core based on LSTM neural network model load forecasting and MPC multi-objective optimization, it can dynamically respond to real-time electricity prices, user heat demand and hydrogen value, and automatically find the best allocation strategy for the three energy sources of electricity, heat and hydrogen while ensuring comfort and safety, thereby reducing system operating costs. The dynamic air-fuel ratio control algorithm precisely regulates the hydrogen-natural gas mixture combustion process, and combined with multiple safety constraints, such as real-time monitoring and processing of hydrogen backfire ratio, equipment power and temperature limits, ensures the stability of hydrogen-blended combustion and the safe and reliable operation of the entire coupled system. The specially designed fully premixed condensing heat exchanger, especially the phase change heat exchange section and adjustable flow channel, provides physical support for intelligent control strategies, enabling precise on-demand control of waste heat recovery quantity and quality, and allowing advanced control algorithms to be effectively implemented. It pioneers a new path for the local consumption of household hydrogen energy, feeding back a portion of the hydrogen produced by SOEC to the wall-hung boiler for combustion, realizing the efficient local utilization of hydrogen energy on the user side, constructing a micro-circulation model of "production-storage-use", and providing a feasible technical paradigm for integrating hydrogen energy into the household energy system; In summary, this invention achieves a core breakthrough in dynamic energy regulation through intelligent prediction and multi-objective optimization control methods; ensures safe and stable system operation under multiple constraints through constraint processing and safety control algorithms; realizes cascaded utilization and precise management of energy flow through waste heat distribution and SOEC collaborative mechanisms; and finally provides hardware support for the above control methods through dedicated heat exchanger structural design. These interconnected and progressively advanced technologies form a complete technical system of software and hardware collaborative innovation, ultimately achieving the comprehensive benefits of improving energy efficiency, ensuring operational safety, and reducing system costs. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the steps of a SOEC-coupled waste heat recovery control method for a fully premixed condensing heat exchanger of a wall-mounted boiler, as provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of a fully premixed condensing heat exchanger provided in Embodiment 1 of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0019] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0020] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0021] Example 1 Please see Figure 1 , 2 A method for controlling waste heat recovery coupled in an SOEC system of a wall-hung boiler with a fully premixed condensing heat exchanger, the wall-hung boiler with a fully premixed condensing heat exchanger includes an oxygen sensor installed on its flue gas passage and a hydrogen recirculation flow regulating valve installed on the hydrogen supply pipeline of the wall-hung boiler with a fully premixed condensing heat exchanger. Includes the following steps: S1, based on historical operating data and real-time environmental data, predicts user heat load demand through an LSTM neural network model; S2, based on the predicted user heat load demand, real-time electricity price signal, natural gas price signal and SOEC hydrogen production concentration, the optimal control instruction set for the current control cycle is generated through the system efficiency model predictive control MPC algorithm; The optimal control command set includes: combustion power setpoint, water pump flow rate setpoint, SOEC system electrolysis power setpoint, and hydrogen flow rate setpoint returned to the fully premixed condensing heat exchanger of the wall-hung boiler. S3, the hydrogen flow rate setting value is sent to the hydrogen recirculation flow regulating valve, the combustion power setting value and the water pump flow rate setting value are sent to the MPC controller executing the MPC algorithm, and the SOEC system electrolysis power setting value is sent to the SOEC system; S4. Based on the hydrogen flow rate setpoint and the residual oxygen content in the flue gas fed back by the oxygen sensor, the combustion air flow rate on the fully premixed burner of the fully premixed condensing heat exchanger of the wall-hung boiler is calculated and adjusted in real time through a dynamic air-fuel ratio control algorithm to achieve stable and efficient combustion of hydrogen-natural gas mixed fuel.

[0022] Furthermore, the SOEC system coupled waste heat recovery control method for a fully premixed condensing heat exchanger of a wall-hung boiler also includes step S0: The data acquisition and sensing module configuration revolves around key parameters. In terms of temperature monitoring, high-precision platinum resistance temperature sensors are installed at the heating outlet and return water pipes of the wall-hung boiler to monitor the supply and return water temperatures in real time. Install a type K thermocouple in the flue gas outlet duct of the wall-hung boiler to measure the flue gas temperature; A waterproof digital temperature sensor is used at the condensate collection point; For flow monitoring, a turbine flow meter is used to measure gas consumption, a thermal mass flow meter is used to measure hydrogen production, and an ultrasonic flow meter is installed in the heating return water pipeline to measure the circulating water volume. In addition, the wall-hung boiler also integrates a thermal conductivity hydrogen purity analyzer and a zirconium oxide oxygen sensor, which are used to monitor hydrogen concentration and residual oxygen in flue gas, respectively. Electricity metering is accomplished by smart meters, which are responsible for collecting the power consumption of the SOEC system and auxiliary equipment; The data collected above is uniformly collected by the distributed sensing unit and processed according to the following methods: All sensor data is tagged with a unified high-precision timestamp to ensure the temporal consistency between different parameters; Digital filtering algorithms are used to smooth the original signals, such as temperature and flow fluctuations, and eliminate instantaneous interference. Raw signal values ​​such as current, voltage, and pulse count are converted into physical quantities with engineering significance (such as degrees Celsius, cubic meters per hour, and kilowatt-hours) in real time. The preprocessed data is uploaded to the cloud platform via a 4G wireless communication module and is also stored locally; it provides a well-formatted historical dataset for the upper-layer LSTM load prediction model, which contains all feature variables related to heat load. Provide the MPC controller with real-time data streams, including the current state of the system, such as temperature, pressure, and boundary conditions, such as real-time electricity prices; All data services are provided through standardized application programming interfaces (APIs) to ensure the efficiency and reliability of data exchange between different modules; After the above data collection and processing steps, the data is organized into a standard format and directly provided to steps S1 and S2: Provide structured historical and real-time datasets to step S1 for training and running the LSTM model; Provide step S2 with a real-time data stream reflecting the current state of the system as input parameters and boundary conditions for the optimization model; Specifically, by selecting and deploying appropriate sensors, employing precise synchronous sampling strategies, implementing real-time data preprocessing procedures, and establishing standardized data service interfaces, the operating status of the physical system is transformed into high-quality information that can be directly utilized by intelligent algorithms.

[0023] Furthermore, in step S1, the historical operating data includes historical heat load curves and historical weather data; Real-time environmental data includes outdoor temperature, indoor set temperature, and solar irradiance. The LSTM neural network model is trained on data that has been denoised and normalized. Wavelet transform denoising is used to denoise historical operational data, specifically including: Three-level wavelet decomposition of historical heat load curve data was performed using wavelet basis functions. The high-frequency coefficients after decomposition are filtered using a soft thresholding method; The optimal threshold is determined using the Stein unbiased risk estimation principle; Wavelet reconstruction is performed on the filtered coefficients to obtain the denoised time series data; The denoised data is standardized using the Min-Max normalization method. Data of different dimensions, such as outdoor temperature, indoor set temperature, and solar irradiance, are uniformly mapped to the interval [0, 1]. Use the formula: Xnorm=(X-Xmin) / (Xmax-Xmin); Where Xnorm represents the normalized data, X represents the original data, and Xmin and Xmax represent the minimum and maximum values ​​of each feature dimension in the training set, respectively. Save the extreme value parameters of each feature dimension for subsequent normalization processing of real-time data; Train an LSTM neural network model using the preprocessed data: The network structure includes an input layer, two LSTM hidden layers containing 128 and 64 neurons respectively, and an output layer; Training samples were organized using a time-series sliding window method, with the window size set to 24 hours. Using the Adam optimizer, the learning rate was set to 0.001; Mean squared error is used as the loss function; Use an early stopping strategy to prevent overfitting; stop training when the validation set loss no longer decreases for 10 consecutive epochs. The final result is an LSTM neural network model that can predict user heat load demand; Real-time data collection of outdoor temperature, indoor set temperature, and solar irradiance; The same normalization process is applied to the real-time data using the extreme value parameters saved during training. The processed data is then input into the trained LSTM neural network model. Output the hourly forecast of user heat load demand for the next 24 hours; Specifically, wavelet transform denoising and Min-Max normalization are used to ensure the quality and consistency of the input data, providing a reliable data foundation for LSTM neural network model training. The design of the LSTM network structure and the setting of training parameters fully consider the time series characteristics of heat load prediction, which can effectively capture the changing patterns of user heat demand.

[0024] Furthermore, in step S2, the optimization process of the LSTM neural network model predictive control MPC algorithm depends on the system efficiency model, which is established in the following way: Establish a sub-model of the thermal efficiency of the wall-hung boiler, whose inputs include combustion power, air-fuel ratio, and return water temperature, and whose output is the real-time thermal efficiency. The thermal efficiency sub-model of the wall-hung boiler adopts a nonlinear regression model based on combustion dynamics, with input parameters collected in real time: Combustion power is calculated by measuring the volumetric flow rate of natural gas in real time using a high-precision gas flow meter, such as a vortex flow meter, and combining it with the calorific value data obtained from a natural gas composition analyzer. The instantaneous combustion power (unit: kW) is then calculated. The air-fuel ratio is calculated by real-time detection of oxygen volume concentration in flue gas using a zirconia oxygen content sensor, combined with gas flow data, according to the formula: Air-fuel ratio = (Actual air volume) / (Theoretical air requirement). The return water temperature is measured in real time using a PT100 platinum resistance temperature sensor to measure the temperature of the return water pipe of the wall-hung boiler. Establish a nonlinear mapping relationship between the thermal efficiency ηboiler of a wall-mounted fireplace and the input parameters:

[0025] Where P is the combustion power, λ is the air-fuel ratio, T is the return water temperature, and α0-α6 are the model coefficients obtained by fitting using the least squares method; The output of the wall-hung boiler thermal efficiency sub-model is updated every 5 seconds. A sub-model for SOEC electrolysis efficiency is established, with inputs including electrolysis power and waste heat temperature from the wall-hung boiler, and outputs hydrogen production efficiency and hydrogen production concentration. The SOEC electrolysis efficiency sub-model is based on an electrochemical-thermodynamic coupling model, with input parameters acquired in real time. Electrolysis power is measured in real time using a Hall effect DC power metering module to measure the DC input power of the SOEC stack. Waste heat temperature is measured using a type K thermocouple with a measurement range of 0-800℃, and the temperature of the waste heat medium connected to the SOEC system from the wall-hung boiler is measured in real time. Based on the Nernst equation and activation polarization theory, an electrolysis efficiency model is established:

[0026] in, Hydrogen has a high calorific value; The actual hydrogen production rate was measured using a hydrogen mass flow meter. Electrolytic power; The input thermal power is calculated using the waste heat medium flow rate and temperature difference; For heat exchange efficiency; Output parameter calculation: SOEC electrolysis efficiency Updated every 10 seconds; The hydrogen production concentration was monitored in real time using a thermal conductivity hydrogen purity analyzer. Establish a sub-model for the economic operation of the system, whose inputs include real-time electricity price, natural gas price, and hydrogen value, and whose output is the operating cost; The system's economic operation sub-model adopts a dynamic cost model based on market prices, with input parameters acquired in real time. Real-time electricity price data is obtained from the time-of-use electricity price data of the power grid company's cloud platform API interface via a 4G / 5G communication module, and is updated every 15 minutes. Natural gas prices are obtained from the gas company's data center, with tiered pricing information updated daily. The value of hydrogen is determined based on the local hydrogen market price (unit: yuan / Nm³). 3 Adjustments are made monthly based on market research. Establish a linear calculation model for operating cost (Cost):

[0027] in, The real-time electricity price is represented by i, which indicates each sampling period, and Δt is the sampling time interval. The volumetric consumption rate of natural gas. Let be the volumetric production rate of hydrogen. The value of hydrogen; Calculate the predicted total operating cost for the next 24 hours every 5 minutes, in yuan; The MPC controller performs rolling optimization with the objective function of maximizing the overall system efficiency. The objective function is as follows:

[0028] in, This is the overall system efficiency index, where a, b, and c are weighting coefficients. For the thermal efficiency of wall-hung boilers, The value represents SOEC electrolysis efficiency, and Cost represents operating costs. The MPC controller employs an interior-point-based optimization algorithm, running on an embedded industrial computer, and is configured... The control period is 5 minutes, the prediction time domain is 24 hours, and the control time domain is 4 hours. a = 0.3-0.5, dynamically adjusted according to heating priority; b = 0.3-0.5, dynamically adjusted according to hydrogen demand priority; c = 0.1-0.3, adjusted according to the economic operating model; Model predictive control algorithm is used for multi-objective rolling optimization to dynamically generate optimal control commands. At the same time, the accuracy of the model is ensured through parameter adaptive update mechanism, and the safety and physical constraints of system operation are strictly followed to achieve coordinated regulation and energy efficiency maximization of multiple energy sources of electricity, heat and hydrogen. Specifically, by establishing a sub-model of the thermal efficiency of the wall-hung boiler, a sub-model of the SOEC electrolysis efficiency, and a sub-model of the system's economic operation, the system performance is precisely quantified from the perspectives of thermodynamics, electrochemistry, and economics, respectively; then, with the goal of maximizing the overall system efficiency ηs, energy efficiency and economic indicators are integrated.

[0029] Further, in step S2, the MPC algorithm generates the optimal control instruction set by processing the following constraints based on the prediction of the system efficiency model: User thermal comfort constraints: Temperature sensors are installed at typical user locations with a sampling period of 30 seconds. The predicted average vote PMV index is used as the comfort evaluation standard, and the target range of the predicted average vote PMV is quantified as -0.5 to +0.5. Within the prediction time domain (20 steps, 5 minutes step size) of the MPC controller, the degree to which the PMV index deviates from the target range is transformed into a soft constraint penalty term and added to the optimization objective function; The power constraint model for the wall-hung boiler is an inequality constraint with a lower limit of 3kW and an upper limit of 35kW. The lower limit of 3kW is determined by the stable combustion limit of the fully premixed burner. Below this value, flameout or incomplete combustion may occur. Maximum power of 35kW: This is limited by the material heat resistance limit, structural strength and maximum heat exchange capacity of the premixed condensing heat exchanger of the wall-hung boiler. Exceeding this power will damage the equipment. The SOEC system safety constraints establish a temperature-power coupling model: 650℃≤Tstack≤850℃, where Tstack is the operating temperature of the SOEC electrolytic stack; The lower limit of 650℃ is determined by the activation temperature of the ionic conductivity of solid oxide electrolytes; below this temperature, the electrolysis efficiency drops sharply. Upper limit of 850℃: This is determined by the oxidation and creep resistance limits of the battery stack connectors, such as stainless steel. Long-term operation at excessive temperatures will lead to structural failure. The hydrogen refueling ratio constraint is set as a hard constraint of less than or equal to 30%; Based on the theories of flammability limits and flame propagation speed in combustion science, when the proportion of hydrogen exceeds 30%, the combustion speed of the mixed gas is too fast, which can easily cause backfire or violent oscillating combustion in the premixing chamber, threatening equipment safety. This limit is a key safety threshold in the design of gas appliances. The MPC algorithm uses the prediction-correction interior point method as the optimization method to solve the problem: Problem transformation: at each rolling time domain starting point, the nonlinear optimization problem, i.e. the objective function at the current operation point, is linearized and approximated, and transformed into the standard form of quadratic programming (QP). Constraint processing involves classifying and organizing the above constraints into hard constraints, soft constraints, and equality constraint matrices. The solution process involves iteratively solving the problem. After initializing the obstacle parameters, the search direction is calculated through a prediction step, and then numerical stability is improved through a correction step. This iterative process continues until the convergence tolerance of 10 is met. -3 Ultimately, the optimal control command sequence in the future time domain is obtained; The rolling implementation involves sending only the first control command in the optimal sequence to the actuator, and then recalculating the above optimization based on the new system measurements when the next sampling period arrives. Hard handling of safety constraints: The hydrogen ratio constraint must not be violated under any circumstances; Temperature constraint settings include a safety buffer zone (±5℃). Soft handling of performance constraints: Thermal comfort constraints allow for small deviations in the short term; Power constraints are set to gradually transition regions; Regarding handling solution failures, if the interior point method fails to converge, activate the quadratic programming backup solver; if consecutive solution failures occur, switch to conservative control mode. Real-time monitoring of key constraint fulfillment status; triggering emergency control strategies based on preset thresholds. Specifically, the multi-dimensional requirements affecting the operation of the fully premixed condensing heat exchanger of the wall-hung boiler, such as comfort and safety, are quantified into calculable mathematical constraints and applied to the rolling optimization framework of the MPC controller. By using the interior point method for efficient solution, it is ensured that the globally optimized control commands can still be generated in real time under complex constraint conditions, thus solving the problem of accurate control of the fully premixed condensing heat exchanger of the hydrogen-natural gas mixed combustion wall-hung boiler under dynamic load.

[0030] Furthermore, the parameters of the system efficiency model are collected and updated in real time through edge computing smart terminals deployed locally on the wall-hung boiler; The rolling optimization computation of the MPC controller is deployed on a cloud platform; Edge computing smart terminals and cloud platforms interact with each other and issue commands through wireless communication modules; The hardware configuration of the edge computing smart terminal adopts an industrial-grade microprocessor based on the ARM architecture, equipped with a digital signal processing unit, and integrates multiple analog / digital input / output interfaces, an RS-485 communication interface, and a 4G / 5G wireless communication module. Real-time collection of combustion power, water temperature, pressure, and flue gas parameters of the wall-hung boiler, as well as operating data such as electrolysis power, temperature, and hydrogen production of the SOEC system; The collected raw data is filtered, outliers are removed, and the format is standardized. Key performance indicators, such as real-time efficiency and energy consumption, are calculated. The system efficiency parameters of the local simplified model are updated in real time using the recursive least squares method to ensure that the system efficiency model can track changes in the dynamic characteristics of the system. The cloud platform uses cloud computing clusters, such as AWS EC2 or Alibaba Cloud ECS instances, configured with multi-core CPUs and large memory for complex optimization calculations; The MPC controller optimization engine is deployed based on MPC algorithms developed using MATLAB / Simulink or Python, and includes system prediction models, constraint handling, and optimization solvers. Establish a time-series database to store historical operational data, and use machine learning algorithms to analyze the system performance degradation trend to provide support for model calibration; The edge computing intelligent terminal sends pre-processed system status data, local model parameters, and prediction error indicators to the cloud platform every 5 minutes via the MQTT protocol. After the cloud platform completes the optimization calculation, it encrypts and sends the optimal control command set, including power setting value and flow setting value, to the edge computing smart terminal via HTTPS protocol; Edge computing smart terminals have data caching capabilities, storing data locally when the network is interrupted and automatically retransmitting it after the network is restored, ensuring data integrity. Data transmission is encrypted using the TLS 1.3 protocol, and two-way identity authentication is performed between the edge computing smart terminal and the cloud platform; When the cloud platform communication times out, such as no response for 30 seconds, the edge computing smart terminal automatically switches to the local backup control strategy to ensure the basic operation of the system. Edge computing smart terminal applications employ a watchdog mechanism, automatically restarting within 3 seconds of an abnormal crash; Specifically, by deploying data acquisition, preprocessing, and parameter identification at the edge, and deploying complex MPC controller optimization calculations on the cloud platform, the real-time advantages of edge computing and the computing power advantages of the cloud platform are fully utilized.

[0031] Furthermore, in step S4, the specific process of the dynamic air-fuel ratio control algorithm is as follows: The dynamic air-fuel ratio control algorithm achieves precise control of the hydrogen-natural gas blend combustion process through four steps: real-time monitoring, theoretical calculation, closed-loop feedback, and execution regulation. Real-time monitoring of fuel parameters: Key parameters are collected in real time using high-precision sensors, and the volumetric flow rate VH2 of the reflux hydrogen is monitored using a thermal mass flow meter with a range of 0-5 cubic meters per hour. A thermal conductivity concentration sensor was used to monitor the purity of hydrogen (CH2), and a turbine flow meter was used to monitor the volumetric flow rate of natural gas (VNG). The zirconia oxygen sensor installed in the flue gas exhaust duct is responsible for real-time detection of the residual oxygen content in the flue gas, with a measurement range of 0-21%. Theoretical air volume calculation: Based on the monitored fuel parameters, calculate the theoretical air volume required for the blended fuel, and then calculate the overall characteristics of the blended fuel: The calorific value of the mixed gas, LHVmix, is obtained by weighting the calorific value of hydrogen (10.8 MJ / m³) and natural gas (34.0 MJ / m³) by their volume ratio. Based on the principles of stoichiometry, calculate the theoretical air requirement Vair:

[0032] Among them, 2.38 and 9.52 are the theoretical air volume coefficients for the complete combustion of hydrogen and natural gas, respectively.

[0033] PID control algorithm feedback correction: The actual residual oxygen level measured by the oxygen sensor is compared with the target residual oxygen level, which is usually set to 3%, to obtain the deviation value e; An incremental digital PID controller is used to process the deviation value e and output the correction amount ΔV for the theoretical air volume. The proportional gain Kp, integral time Ti, and derivative time Td of the PID control algorithm are tuned on-site to ensure a balance between fast response and stability, ultimately yielding the target combustion air flow rate.

[0034] Actuator adjustment: Based on the calculated target combustion air flow value The combustion air supply of the fully premixed burner is adjusted by changing the PWM control signal of the brushless DC fan to precisely adjust its speed, thereby controlling the air volume; The wall-mounted boiler's fully premixed condensing heat exchanger has a pre-stored fan characteristic curve, establishing a correspondence between speed and air volume. At the same time, as an auxiliary means, the air volume can also be finely adjusted by adjusting the opening of the electric damper. Security and optimization mechanisms: The wall-mounted boiler's fully premixed condensing heat exchanger also includes comprehensive safety protection logic. When an oxygen sensor malfunction is detected, it automatically switches to a feedforward control mode based on fuel quantity. When the hydrogen supply is interrupted, it immediately adjusts the control parameters and switches to a pure natural gas combustion mode. Continuously monitor combustion noise and flame signals. If combustion is determined to be unstable, automatically relax the air-fuel ratio control target to prioritize combustion safety. Specifically, by real-time and precise monitoring of fuel composition and flow rate, calculating basic air demand based on chemical principles, and using feedback on residual oxygen in flue gas for fine correction of the PID control algorithm, the control command is finally executed by adjusting the fan speed or damper opening to ensure stable, efficient, and safe clean combustion of hydrogen-natural gas blended fuel under various operating conditions.

[0035] Furthermore, the flue gas and condensation waste heat generated by the wall-hung boiler are recovered through heat exchange pipelines connected between the wall-hung boiler and the SOEC system, and connected to the SOEC system to provide the heat required for electrolysis of the wall-hung boiler. Based on this, the method further includes the following steps: The heat exchange pipeline network is designed to establish a waste heat recovery path based on temperature gradient. High-temperature flue gas is preferentially transported to the SOEC system through high-temperature resistant pipelines. The cooled flue gas and the generated condensate are then subjected to deep waste heat recovery through corresponding pipelines. Primary waste heat recovery channel (high-temperature sensible heat recovery): The heat source is the high-temperature flue gas produced by the wall-hung boiler after combustion. The path, the flue gas duct—guides the high-temperature flue gas from the combustion chamber of the wall-hung boiler to the high-temperature heat exchanger of the SOEC system; The recovered heat is mainly the sensible heat of the flue gas, which has the highest temperature and the highest quality, and is directly used to meet the high-temperature reaction heat required by the SOEC electrolysis stack. Secondary waste heat recovery channel (low-temperature latent heat recovery): The heat source is the high-temperature flue gas, which decreases in temperature after primary heat exchange, including the latent heat released by the condensation of water vapor and the sensible heat of the condensate itself. The path consists of two parts: The condensate pipeline collects the condensate produced inside the wall-hung boiler. The low-temperature flue gas duct guides the partially cooled flue gas containing condensable water vapor to the deep condensation heat exchanger. The recovered heat is mainly the latent heat of condensation of water vapor, which is at a low temperature and of low quality. It can be used to preheat the feed of SOEC system or other low-temperature heat requirements. An integrated temperature and pressure sensor is installed at the inlet of the SOEC system to monitor the parameters of the waste heat medium in real time. Install an adjustable flow circulation pump to control the flow rate of the waste heat medium in the pipeline; A three-way regulating valve is installed at the branch point of the heat exchange pipeline to precisely control the waste heat distribution ratio. The waste heat distribution control strategy is as follows: The valve actuator adopts an intelligent electric regulating valve, with a valve position opening control accuracy of ±0.5%. The valve actuator is equipped with a position feedback module, which uploads the actual opening degree to the controller in real time; The valve is equipped with a soft limit function to prevent system shock caused by excessive adjustment; The dynamic allocation algorithm calculates the target flow rate of each branch based on the waste heat allocation coefficient in the MPC optimization instruction; The valve opening is dynamically adjusted based on real-time monitoring of waste heat temperature and pressure parameters. Establish valve opening-flow characteristic curves to achieve precise flow control; Safety protection mechanisms include minimum flow protection to ensure that the SOEC system always receives a basic heat source supply. When abnormal pipeline pressure is detected, it automatically switches to safe operation mode; Establish a valve fault self-diagnosis function to promptly detect and alarm abnormalities in the actuator; Receive and parse commands: The MPC controller receives the optimal control instruction set generated by the MPC algorithm in real time. The waste heat distribution parameters in the parsing command are converted into specific valve control commands. The feasibility of the control commands is verified based on the actual state of the system. The actual inlet temperature of the SOEC system is monitored using a temperature sensor. Compare the measured temperature with the target temperature and fine-tune the valve opening accordingly. The control parameters are updated every 30 seconds to ensure that the temperature control accuracy is within ±2℃. The operating mode is set to three modes: In normal mode, dynamic waste heat distribution is performed according to MPC optimization instructions; Energy-saving mode prioritizes meeting the thermal demand of the SOEC system to maximize energy utilization. Security mode: When a system anomaly is detected, it automatically switches to a preset security allocation ratio. Specifically, by establishing recovery paths for flue gas and condensing waste heat, constructing heat exchange pipeline systems, and implementing a dynamic waste heat distribution control method based on optimized commands, the system achieves refined management and dynamic distribution of flue gas and condensing waste heat, improving the utilization efficiency of waste heat resources. Through deep integration with MPC algorithm-based optimization control, the system enables coordinated optimization of waste heat distribution and the overall operating status of the wall-hung boiler, providing reliable heat protection for the stable operation of the SOEC system while ensuring the satisfaction of users' heating needs, embodying the concept of energy cascade utilization and system coordinated control.

[0036] Furthermore, step S3, which involves sending the SOEC system electrolysis power setpoint to the SOEC system, specifically includes: The SOEC system receives the electrolysis power setpoint and performs the following cooperative operations accordingly: In terms of power response, the DC power rectifier of the SOEC system adjusts its output power to match the set value; The DC power rectifier adjusts its output power based on the received electrolytic power setting, ranging from 1-15kW. It uses pulse width modulation (PWM) technology to control the IGBT switching frequency and regulates the DC output voltage from 0-100V. The power adjustment formula is:

[0037] in, This represents the actual output power. For output voltage, For output current; The actual power value is sampled every 100ms and compared with the set value. A PID control algorithm is used to eliminate the deviation. When a power deviation exceeding ±3% is detected, an automatic calibration program is initiated to readjust the control parameters. Thermal management: The SOEC system's thermal management module distributes waste heat recovered by the fully premixed condensing heat exchanger of the wall-hung boiler to maintain the optimal operating temperature of the electrolysis stack. The thermal management module dynamically allocates waste heat based on changes in electrolysis power, establishing a heat demand model:

[0038] in, For the required calories, The electrolysis power setpoint is given by k and b, which are empirical coefficients. Real-time monitoring of waste heat temperature recovered from the fully premixed condensing heat exchanger of the wall-hung boiler. and traffic ; Calculate available heat :

[0039] Where cp is the specific heat capacity of the waste heat medium, and ρ is the density of the waste heat medium. The volumetric flow rate of the waste heat medium. The temperature at which the waste heat is recovered. The target temperature required by the SOEC system; By adjusting the opening of the three-way valve, heat is distributed to ensure that the inlet temperature of the electrolytic stack is maintained within the optimal range of 750±10℃. In the gas generation process, the gaseous mixture generated by the electrolysis stack is separated and purified by the hydrogen separation and purification module of the SOEC system; The gaseous mixture produced by the electrolysis stack, including hydrogen and unreacted water vapor, enters the hydrogen separation and purification module: Gas-liquid separation is performed using temperature difference condensation method, cooling the mixture to below the dew point, and the condensate is discharged through an automatic drain valve; The purity of hydrogen is improved by using palladium membrane separation technology; Establish a purity feedback mechanism to automatically adjust purification parameters when the purity of the outlet hydrogen is detected to be lower than 99.9%. Real-time monitoring of separation efficiency:

[0040] in, This represents the volume of purified hydrogen gas. This represents the theoretical hydrogen production volume. The three modules work together through real-time data exchange: When adjusting the power, the thermal management module calculates the changes in heat load in advance and adjusts the waste heat distribution ratio accordingly. When the thermal management module detects insufficient residual heat, it sends a power reduction request to the power control module. The gas production processing module feeds back the purity data to the power control module as a basis for optimizing the electrolysis parameters; Establish a unified timing control system: power adjustment → thermal management response → gas generation processing, with a time interval of less than 5 seconds. Specifically, by controlling power response, heat management and gas production, the system ensures the efficient and stable operation of the electrolytic hydrogen production process. Through deep collaboration between modules, the system improves the overall energy efficiency of the wall-hung boiler and provides key technical support for the control of hydrogen-natural gas hybrid energy.

[0041] Furthermore, a fully premixed condensing heat exchanger for a wall-hung boiler, used to implement a SOEC system coupled waste heat recovery control method for the fully premixed condensing heat exchanger of the wall-hung boiler, includes: The primary heat exchange section adopts a stainless steel spiral finned tube structure and is located in the high-temperature flue at the combustion chamber outlet to directly recover the sensible heat of the flue gas. The secondary condensing heat exchange section, located downstream of the primary heat exchange section, adopts a corrosion-resistant stainless steel plate heat exchange structure to further recover the latent heat of the low-temperature flue gas and reduce the flue gas temperature to below the dew point, generating condensate. The core of the phase change heat exchanger is a sealed vacuum tube filled with an organic working fluid with a phase change temperature of 60-80℃. Its evaporation section is embedded in the condensate collection tank of the secondary condensation heat exchanger, where the working fluid vaporizes after absorbing heat from the condensate. The SOEC heating channel is made of copper pipes, which connect to the condensation section (heat release end) of the phase change heat exchanger. It uses the latent heat released by the condensation of the working fluid to provide a stable high-temperature heat source for the SOEC system. The heating return flow channel uses stainless steel pipes to connect to the outlet of the secondary condensing heat exchanger and deliver the recovered sensible heat to the user's heating system. The heating return water interface is a standard G3 / 4 threaded interface, equipped with a temperature sensor socket, which facilitates connection to the user's heating system and monitoring of the return water temperature. The oxygen sensor, using a zirconium oxide probe, is vertically inserted into the middle of the flue gas channel to monitor the residual oxygen content in the flue gas after combustion in real time. The hydrogen regeneration flow regulating valve is a proportional valve controlled by a stepper motor. It is installed at the end of the hydrogen supply pipeline to precisely control the regeneration hydrogen flow rate. Each component is connected through a pipeline system to form a complete heat energy recovery and distribution network. The control system generates optimized instructions based on the SOEC system of the wall-hung boiler's fully premixed condensing heat exchanger coupled with the waste heat recovery control method. It coordinates and adjusts the valve opening and combustion parameters to achieve the cascade utilization and dynamic distribution of waste heat. Specifically, through a multi-stage heat exchange structure and an intelligent distribution system, efficient recovery and precise distribution of waste heat from flue gas are achieved. The application of phase change heat transfer technology improves the utilization efficiency of latent heat at low temperatures, while the adjustable flow channel design enables the system to dynamically optimize energy flow according to real-time demand. This close integration of structural design and control methods provides a reliable hardware foundation for the efficient coupling operation of the SOEC system and the wall-hung boiler, realizing the cascade utilization of energy and maximizing the overall energy efficiency of the system.

Claims

1. A method for controlling waste heat recovery coupled in a fully premixed condensing heat exchanger system of a wall-hung boiler, characterized in that, The wall-hung boiler fully premixed condensing heat exchanger includes an oxygen sensor installed on its flue gas passage and a hydrogen recirculation flow regulating valve installed on the hydrogen supply pipeline of the wall-hung boiler fully premixed condensing heat exchanger. The method includes the following steps: S1, based on historical operating data and real-time environmental data, predicts user heat load demand through an LSTM neural network model; S2, based on the predicted user heat load demand, real-time electricity price signal, natural gas price signal and SOEC hydrogen production concentration, the optimal control instruction set for the current control cycle is generated through the system efficiency model predictive control MPC algorithm; The optimal control command set includes: combustion power setpoint, water pump flow rate setpoint, SOEC system electrolysis power setpoint, and hydrogen flow rate setpoint returned to the fully premixed condensing heat exchanger of the wall-hung boiler. S3, the hydrogen flow rate setting value is sent to the hydrogen recirculation flow regulating valve, the combustion power setting value and the water pump flow rate setting value are sent to the MPC controller executing the MPC algorithm, and the SOEC system electrolysis power setting value is sent to the SOEC system; S4. Based on the hydrogen flow rate setpoint and the residual oxygen content in the flue gas fed back by the oxygen sensor, the combustion air flow rate on the fully premixed burner of the fully premixed condensing heat exchanger of the wall-hung boiler is calculated and adjusted in real time through a dynamic air-fuel ratio control algorithm to achieve stable and efficient combustion of hydrogen-natural gas mixed fuel.

2. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, The method further includes step S0: The operating data of the fully premixed condensing heat exchanger and SOEC coupling system of the wall-hung boiler are acquired through the data acquisition and sensing module. The operational data includes: Outlet water temperature, return water temperature, flue gas temperature, condensate temperature, gas flow rate, water and electricity metering data, and hydrogen flow rate and concentration data of the SOEC system; Provide data input for steps S1 and S2.

3. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, In step S1, the historical operating data includes historical heat load curves and historical weather data; The real-time environmental data includes outdoor temperature, indoor set temperature, and solar irradiance. The LSTM neural network model is trained on data that has undergone denoising and normalization.

4. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, In step S2, the optimization process of the predictive control MPC algorithm depends on the system efficiency model, which is established in the following way: Establish a sub-model of the thermal efficiency of the wall-hung boiler, whose inputs include combustion power, air-fuel ratio, and return water temperature, and whose output is the real-time thermal efficiency. A sub-model for SOEC electrolysis efficiency is established, with inputs including electrolysis power and waste heat temperature from the wall-hung boiler, and outputs hydrogen production efficiency and hydrogen production concentration. Establish a sub-model for the economic operation of the system, whose inputs include real-time electricity price, natural gas price, and hydrogen value, and whose output is the operating cost; The MPC controller performs rolling optimization with the objective function of maximizing the overall system efficiency. The objective function is as follows: ； in, This is the overall system efficiency index, where a, b, and c are weighting coefficients. For the thermal efficiency of wall-hung boilers, The value represents SOEC electrolysis efficiency, and Cost represents operating costs.

5. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 4, characterized in that, In step S2, the MPC algorithm generates the optimal control instruction set by processing the following constraints based on the prediction of the system efficiency model: The user thermal comfort constraints, the minimum and maximum combustion power constraints of the wall-hung boiler, the safe operating temperature and power constraints of the SOEC system, and the hydrogen regeneration ratio safety constraints are used as the boundary conditions of the optimization problem. The MPC algorithm employs interior-point optimization to search within the feasible region that satisfies all the above constraints, thereby finding the optimal overall system efficiency index. The largest optimal solution is the optimal control instruction set.

6. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, The parameters of the system efficiency model are collected and updated in real time through an edge computing smart terminal deployed locally on the wall-hung boiler. The rolling optimization calculation of the MPC controller is deployed on a cloud platform; The edge computing smart terminal and the cloud platform interact with each other and issue commands through a wireless communication module.

7. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, In step S4, the specific process of the dynamic air-fuel ratio control algorithm is as follows: Real-time monitoring of the flow rate and concentration of reflux hydrogen, as well as the flow rate of natural gas; Calculate the theoretical air volume required for the current fuel mixture based on the volumetric flow rate, calorific value, and stoichiometric ratio of hydrogen and natural gas. Based on the residual oxygen content in the flue gas fed back by the oxygen sensor, a PID control algorithm is used to correct the theoretical air volume to obtain the target combustion air flow rate. Adjust the fan speed or damper opening of the fully premixed burner to match the target combustion air flow rate.

8. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, The flue gas and condensation waste heat generated by the wall-hung boiler are recovered through heat exchange pipelines connected between the wall-hung boiler and the SOEC system, and are connected to the SOEC system to provide the heat required for electrolysis of the wall-hung boiler. Based on this, the method further includes the following steps: According to the optimal control instruction set, the valve opening in the heat exchange pipeline is dynamically adjusted to distribute the recovered waste heat and adjust the proportion of waste heat flowing to the SOEC system and the user-end heating system.

9. The SOEC system coupled waste heat recovery control method for a wall-mounted boiler with a fully premixed condensing heat exchanger according to claim 1, characterized in that, The step S3 of sending the SOEC system electrolysis power setting value to the SOEC system specifically includes: The SOEC system receives the electrolysis power setpoint and performs the following cooperative operations accordingly: In terms of power response, the DC power rectifier of the SOEC system adjusts its output power to match the set value; Thermal management: The thermal management module of the SOEC system distributes the waste heat recovered by the fully premixed condensing heat exchanger of the wall-hung boiler to maintain the optimal operating temperature of the electrolysis stack. The gaseous mixture generated by the electrolysis stack is separated and purified by the hydrogen separation and purification module of the SOEC system.

10. A fully premixed condensing heat exchanger for a wall-hung boiler, characterized in that, A method for controlling the coupled waste heat recovery of a fully premixed condensing heat exchanger system for a wall-hung boiler as described in any one of claims 1-9 includes: The primary heat exchange section is used to recover the sensible heat of the flue gas; The secondary condenser heat exchange section is used to recover the latent heat of the low-temperature flue gas and the sensible heat of the condensate. The phase change heat exchanger is filled with a phase change working fluid. The heat absorption end of the phase change heat exchanger is located in the condensate collection channel of the secondary condensation heat exchanger to absorb the latent heat of the condensate. The SOEC heating channel is connected to the heat release end of the phase change heat exchanger, and is used to transport the recovered high-quality latent heat to the SOEC system. The heating return flow channel is connected to the secondary condensing heat exchange section; The heating return water interface is connected to the heating return flow channel and is used to connect to the return water pipe of the user's heating system. Oxygen sensor, installed in the smoke exhaust duct; A hydrogen recirculation flow regulating valve is installed on the hydrogen supply line of the fully premixed burner; The SOEC heating channel and the heating return channel are respectively equipped with regulating valves. The regulating valves are configured to be controlled by a control system that implements the SOEC system coupled waste heat recovery control method of the wall-mounted boiler fully premixed condensing heat exchanger according to any one of claims 1-9, so as to dynamically allocate the proportion of waste heat flowing to the SOEC system and the user-end heating system according to the optimal control instruction set.

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