Energy-saving boiler flue gas waste heat recovery control system for thermal power plant

Abstract

The present application relates to thermal power plant waste heat recovery technology field, disclose a kind of energy-saving type thermal power plant boiler flue gas waste heat recovery control system, including boiler and heat exchanger, the boiler smoke outlet is connected with the heat exchanger inlet by pipeline, the pipeline of the boiler smoke outlet and the heat exchanger inlet is equipped with dust collector and induced draft fan, the water inlet of the heat exchanger is equipped with water pump and valve, the water outlet of the heat exchanger is connected thermal power plant domestic water pipeline, the system flue gas parameter acquisition module real-time obtains the temperature distribution of flue gas in boiler tail flue, flow rate distribution data and flue gas composition detection result, generates flue gas thermal state matrix. Actuator collaborative control module resolves scheme instruction, dynamically adjusts heat exchanger valve opening and water pump speed parameter, generates equipment collaborative control signal set. Waste heat conversion monitoring module real-time collection heat medium working substance data, combined with control signal set execution state generates waste heat conversion process dynamic feedback table.

Description

Technical Field

[0001] This invention relates to the field of waste heat recovery technology in thermal power generation, specifically to an energy-saving control system for waste heat recovery from boiler flue gas in thermal power plants. Background Technology

[0002] Thermal power plants, as crucial energy conversion facilities, generate large amounts of high-temperature flue gas during power generation. This flue gas is typically discharged into the atmosphere through flue ducts, resulting in significant heat energy waste. Flue gas waste heat recovery within the boiler tail flue is a key aspect of improving the overall energy efficiency of power plants. Traditional flue gas waste heat recovery systems mostly employ stationary heat exchanger devices, transferring heat from the flue gas to the heat transfer medium (such as water or steam) through physical contact. This heat is used to preheat boiler feedwater or supply heating to the plant area, thereby reducing fuel consumption. However, existing technologies have many limitations in practical applications, resulting in low waste heat recovery efficiency and difficulty adapting to the dynamic changes in the operating conditions of thermal power plants. Specifically, these include the following problems: The control strategy for the heat exchange process is relatively crude. Most existing systems use preset fixed valve openings or pump speeds, which lack the ability to respond to flue gas conditions in real time. When flue gas parameters change, the control system adjusts lags and cannot quickly optimize the operating point of the heat exchanger. Some advanced systems have tried to introduce PID controllers, but PID parameters are usually tuned for specific operating conditions and perform poorly when the load changes significantly. Summary of the Invention

[0003] The purpose of this invention is to provide an energy-saving control system for waste heat recovery from flue gas in thermal power plant boilers, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, this invention provides an energy-saving waste heat recovery control system for boiler flue gas in thermal power plants, comprising a boiler and a heat exchanger. The boiler flue gas outlet and the heat exchanger flue gas inlet are connected by a pipeline. A dust collector and an induced draft fan are installed on the pipeline connecting the boiler flue gas outlet and the heat exchanger flue gas inlet. A water pump and a valve are installed at the heat exchanger water inlet. The heat exchanger water outlet is connected to the domestic water supply pipeline of the thermal power plant for inputting hot water from the heat exchanger into the domestic water supply pipeline of the thermal power plant. The system includes: The flue gas parameter acquisition module acquires real-time temperature and velocity distribution data of flue gas in the boiler tail flue, and generates a flue gas thermodynamic state matrix by combining the flue gas composition detection results. The heat exchange efficiency evaluation module calculates the theoretical heat exchange efficiency value of each flue section based on the temperature gradient distribution and flow velocity distribution in the flue gas thermodynamic state matrix, and generates a heat exchange potential distribution map. Based on the differences in heat exchange efficiency in different regions of the heat exchange potential distribution map, the heat recovery path optimization module identifies the optimal heat recovery path nodes and generates a graded heat recovery path planning scheme. The actuator collaborative control module parses the node operation instruction sequence in the staged heat recovery path planning scheme, dynamically adjusts the heat exchanger valve opening and water pump speed parameters, and generates a set of equipment collaborative control signals. The waste heat conversion monitoring module collects the temperature change rate and pressure fluctuation data of the heat transfer medium in real time, and generates a dynamic feedback table of the waste heat conversion process by combining the execution status of the equipment collaborative control signal set.

[0005] Preferably, the flue gas thermodynamic state matrix includes a longitudinal flue temperature gradient vector, a transverse velocity distribution vector, and a component concentration ratio table; the heat exchange potential distribution map specifically includes a zoned theoretical heat exchange efficiency thermodynamic map, a maximum recoverable heat label set, and a velocity constraint coefficient table; the graded heat recovery path planning scheme includes a priority path node list, a valve opening stage adjustment strategy, and a pump speed matching scheme; the equipment collaborative control signal set includes a valve opening command time sequence, a pump speed adjustment step size sequence, and an abnormal operating condition interruption identifier; and the waste heat conversion process dynamic feedback table includes a heat medium temperature rise rate log, a pressure fluctuation frequency statistics table, and an execution command delay compensation record.

[0006] Preferably, the flue gas parameter acquisition module includes: The multi-dimensional sensing submodule arranges an infrared temperature measurement array and a Pitot tube flow velocity sensor group along the flue axis to simultaneously collect raw flue gas temperature data and raw flow velocity data, and generate a real-time physical quantity dataset of the flue cross section. The component analysis submodule acquires data on oxygen content, carbon dioxide content and nitrogen oxide concentration through flue gas sampling probes, and calculates flue gas density correction coefficient by combining the temperature data in the real-time physical quantity dataset of the flue section, and generates a flue gas component-density correlation table. The matrix construction submodule sorts the real-time physical quantity dataset of the flue section according to spatial coordinates, and superimposes the density correction results from the flue gas composition-density correlation table to construct a three-dimensional flue gas thermodynamic state matrix.

[0007] Preferably, the heat exchange performance evaluation module includes: The theoretical calculation submodule calculates the convective heat transfer coefficient of each flue section based on the temperature gradient vector and velocity vector in the three-dimensional flue gas thermodynamic state matrix, using the heat transfer boundary layer theory, and generates a set of theoretical heat transfer efficiency values ​​for each section. The constraint analysis submodule identifies regions in the set of theoretical heat transfer efficiency values ​​for the partition that are lower than the average efficiency value, extracts the flow velocity constraint coefficient and composition corrosion factor at the corresponding locations, and generates a heat recovery constraint factor analysis table. The map generation submodule maps the set of theoretical heat exchange efficiency values ​​for the partition to the spatial grid nodes of the flue 3D model, and combines the negative markers in the heat recovery constraint analysis table to generate a visual heat exchange potential distribution map with constraints.

[0008] Preferably, the recycling path optimization module includes: The node filtering submodule traverses the theoretical heat exchange efficiency values ​​marked in the heat exchange potential distribution map, selects the center point of the continuous spatial region with an efficiency value higher than a set threshold as the primary recovery node, and generates a candidate node coordinate list. The path simulation submodule performs fluid network topology analysis based on the candidate node coordinate list, simulates the flue gas flow path changes under different valve opening combinations, and outputs the predicted total heat recovery value for each path. The scheme generation submodule compares the predicted total heat recovery value with the system energy consumption increment, selects the path node combination and corresponding equipment parameters with the maximum net benefit, and forms a graded heat recovery path planning scheme.

[0009] Preferably, the actuator coordination control module includes: The instruction decomposition submodule parses the valve opening stage adjustment strategy in the graded heat recovery path planning scheme, discretizes the continuous adjustment process into a time-opening instruction pair sequence, and generates a valve step control instruction set. The speed matching submodule calculates the speed-torque characteristic matching point of the drive motor based on the flow demand curve in the pump speed matching scheme, and generates a pump speed adjustment step sequence. The anomaly monitoring submodule compares the actual valve opening feedback signal with the expected value of the valve step control command set in real time. When the deviation exceeds the tolerance range, it triggers the abnormal working condition interruption flag in the equipment collaborative control signal set.

[0010] Preferably, the waste heat conversion monitoring module includes: The thermodynamic acquisition submodule monitors the temperature rise rate of the heat transfer medium at the inlet and outlet of the heat exchange tube bundle through an embedded thermocouple array, and simultaneously collects the fluctuation frequency data of the pressure transmitter to generate a heat transfer medium state change log. The delay compensation submodule identifies the valve response lag time in the execution instruction delay compensation record, dynamically adjusts the sending timestamp of subsequent control instructions, and generates a compensated updated version of the equipment collaborative control signal set. The feedback integration submodule aligns the heat medium state change log with the updated version of the compensated equipment collaborative control signal set along the time axis to construct a dynamic feedback table for the waste heat conversion process with execution traceability markers.

[0011] Preferably, the system further includes: The dynamic reconfiguration module periodically reads the heat medium temperature rise rate log in the dynamic feedback table of the waste heat conversion process. When it detects that the rate decay exceeds a preset threshold, it triggers the heat exchange performance evaluation module to regenerate an updated heat exchange potential distribution map. The recovery path optimization module performs path node re-selection based on the updated heat exchange potential distribution map, generates secondary heat recovery path optimization schemes, and covers the original hierarchical heat recovery path planning schemes.

[0012] Preferably, the dynamic reconfiguration module includes: The attenuation analysis submodule calculates the sliding window mean change rate of the heat medium temperature rise rate log, and generates a heat exchange efficiency attenuation alarm signal when the change rate negatively exceeds the threshold. The map request submodule associates the heat exchange efficiency decay alarm signal with the current equipment collaborative control signal set and sends a local recalculation request with flue coordinates to the heat exchange efficiency evaluation module. The scheme replacement submodule receives the locally updated heat exchange potential distribution map returned by the heat exchange efficiency evaluation module, and regenerates the secondary heat recovery path optimization scheme only for the affected area, while retaining the original control parameters for the un-attenuated area.

[0013] Preferably, the system further includes: The energy efficiency closed-loop module calculates the cumulative heat absorption of the heat medium for N consecutive cycles in the dynamic feedback table of the waste heat conversion process, and divides it by the total energy consumption value of the water pump and valve adjustment during the same period to generate the system energy efficiency ratio trend curve. The recycling path optimization module dynamically adjusts the priority path node selection weight coefficients in the graded heat recovery path planning scheme according to the slope change direction of the system energy efficiency ratio trend curve.

[0014] Compared with the prior art, the beneficial effects of the present invention are: In this invention, the recovery path optimization module plans graded heat recovery paths based on the differences in the heat map, avoiding the limitations of a single path and prioritizing heat recovery for high-potential nodes. The actuator collaborative control module dynamically analyzes the path scheme and adjusts valve and pump parameters in real time to ensure that equipment response is synchronized with changes in operating conditions. The waste heat conversion monitoring module forms a closed-loop control by feeding back heat medium data and continuously optimizes the operating status. Overall, the system enhances the adaptability and precision of waste heat recovery, reduces heat waste, and improves the energy efficiency of power plants. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the working process of the energy-saving thermal power plant boiler flue gas waste heat recovery control system described in this invention. Figure 2 A flowchart defining the core data structure of the system; Figure 3 A flowchart illustrating the operation of the flue gas parameter acquisition module; Figure 4 The dynamic adjustment curve of valve opening and water pump speed; Figure 5This is a schematic diagram illustrating the working principle of the energy-saving thermal power plant boiler flue gas waste heat recovery control system described in this invention.

[0016] In the diagram: 1. Boiler; 2. Heat exchanger; 3. Dust collector; 4. Exhaust fan; 5. Water pump; 6. Valve. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 and Figure 5 This invention provides an energy-saving waste heat recovery control system for boiler flue gas in thermal power plants, including a boiler 1 and a heat exchanger 2. The flue gas outlet of the boiler 1 and the flue gas inlet of the heat exchanger 2 are connected by a pipeline. A dust collector 3 and an induced draft fan 4 are installed on the pipeline connecting the flue gas outlet of the boiler 1 and the flue gas inlet of the heat exchanger 2. The induced draft fan is used to introduce the flue gas into the heat exchanger 2. A water pump 5 and a valve 6 are installed at the water inlet of the heat exchanger 2. The water outlet of the heat exchanger 2 is connected to the domestic water pipeline of the thermal power plant. The system includes a flue gas parameter acquisition module that collects temperature distribution data and flow velocity distribution data of the flue gas in real time through a sensor array arranged in the flue gas duct at the tail end of the boiler. The flue gas composition detection results are combined with these data to generate a flue gas thermodynamic state matrix. A heat exchange efficiency evaluation module receives the flue gas thermodynamic state matrix, analyzes the temperature gradient distribution and flow velocity distribution, calculates the theoretical heat exchange efficiency value of each flue gas duct section, and forms a heat exchange potential distribution map. A recovery path optimization module processes the heat exchange potential distribution map, identifies the heat exchange efficiency differences in different areas, determines the optimal heat recovery path nodes, and outputs a graded heat recovery path planning scheme. The actuator collaborative control module parses the instruction sequence in the staged heat recovery path planning scheme, dynamically adjusts the opening degree of heat exchanger valve 6 and the speed parameters of water pump 5, and generates a set of equipment collaborative control signals. The waste heat conversion monitoring module monitors the temperature change rate and pressure fluctuation data of the heat medium, and generates a dynamic feedback table of the waste heat conversion process by combining the execution status of the equipment collaborative control signal set, thereby realizing closed-loop control of the system.

[0019] Example 1: See Figure 2The construction of the flue gas thermodynamic state matrix relies on the fusion processing of multi-source data. The longitudinal flue temperature gradient vector is acquired by an infrared temperature sensor array installed at different elevations in the boiler tail flue. Each sensor node acquires the instantaneous flue gas temperature value at a fixed sampling frequency, and the temperature data is transmitted to the data concentrator via a fieldbus. The transverse velocity distribution vector is measured using a cross-sectional Pitot tube array. The array probes are arranged along the grid points of the flue cross-section, and a differential pressure transmitter converts the dynamic pressure signal into a digital velocity signal, keeping the velocity data synchronized with the temperature data. The component concentration ratio table is obtained by sampling flue gas using a high-temperature sampling probe. A non-dispersive infrared gas analyzer detects the oxygen and carbon dioxide concentrations in real time, and a chemiluminescence analyzer monitors the nitrogen oxide concentration. The concentration percentage normalization calculation is performed on the three component data in the data preprocessing unit. The generation of the longitudinal flue temperature gradient vector requires solving the spatial interpolation problem. A cubic spline interpolation algorithm is used to construct a continuous temperature field between adjacent temperature measurement nodes. The interpolation algorithm considers the boundary effect correction caused by heat dissipation from the flue wall. The processing of the transverse velocity distribution vector includes Reynolds stress compensation. Fluctuation data provided by the turbulence intensity sensor is used to correct the time-averaged velocity value, and the compensated velocity distribution better reflects the actual flow conditions. The component concentration ratio table is coupled with physical parameters for calculation. The flue gas density correction coefficient is derived based on the ideal gas law. Real-time changes in temperature and pressure data are incorporated into the density calculation process, and the component concentration ratio table is updated every five minutes.

[0020] The visualization of the heat exchange potential distribution map is based on a 3D mesh model of the flue. The zoned theoretical heat transfer efficiency heat map uses infrared thermal imaging technology for color coding, with warm colors representing high-temperature areas and cool colors representing low-temperature areas. The maximum recoverable heat set is achieved through integral calculation, where the flue gas enthalpy curve is numerically integrated within each grid cell, and the upper and lower limits of the integration are jointly determined by the inlet flue gas temperature and dew point temperature. The flow velocity constraint coefficient table establishes a correlation model between flow velocity and heat transfer coefficient. Low flow velocity areas are marked with heat transfer deterioration warnings, and high flow velocity areas are marked with wear risk warnings. The warning thresholds are dynamically adjusted according to the pipe material characteristics. The generation of the zoned theoretical heat transfer efficiency heat map requires multi-parameter coupled calculation. The theoretical heat transfer efficiency value of each grid cell is solved using the Nusselt number correlation formula, and the correlation coefficients are individually calibrated according to the flue gas composition characteristics. The maximum recoverable heat set considers actual operating constraints, with the marked values ​​deducting system heat dissipation losses and measurement error margins. The marked data is refreshed every thirty seconds. The flow rate constraint coefficient table includes a dynamic adjustment mechanism. When the flue gas composition changes, the coefficient table automatically updates the correction factor, which is obtained through regression analysis of historical data.

[0021] The generation of the graded heat recovery path planning scheme adopts a multi-objective optimization algorithm. The priority path node list is initially screened using a greedy algorithm, and the node selection criteria comprehensively consider heat exchange efficiency and equipment accessibility. The valve 6 opening stage adjustment strategy is designed using fuzzy control theory. The adjustment process is divided into a rapid adjustment stage and a fine adjustment stage, and the stage switching condition is automatically determined based on the flue gas temperature change rate. The pump 5 speed matching scheme is derived based on similarity theory. The relationship between speed and flow rate is obtained by fitting the pump characteristic curve, and the fitting curve coefficients are corrected online according to the degree of equipment wear. The priority path node list is maintained using a dynamic update mechanism. When the flue gas parameters change significantly, the node list is reordered, and the sorting weight considers the urgency of heat recovery and the frequency of equipment operation. The valve 6 opening stage adjustment strategy includes anti-saturation measures, automatically introducing small amplitude disturbances under long-term stable operating conditions. The disturbance amplitude is adaptively adjusted according to the valve positioning accuracy. The pump 5 speed matching scheme sets safety constraints, limiting the speed operating range to the pump's efficient operating range, and the constraint boundary is calibrated in real time according to the inlet pressure.

[0022] The generation of the equipment collaborative control signal set requires solving the timing synchronization problem. The valve 6 opening command time sequence is marked with hardware timestamps, and each command includes the execution time point and the expected opening value, achieving millisecond-level time synchronization accuracy. The pump 5 speed adjustment step size sequence adopts a variable step size design, using large step size for rapid adjustment during system startup and small step size for fine adjustment during stable operation. The abnormal operating condition interruption indicator includes a multi-level early warning mechanism, divided into three levels according to the severity of deviation: general early warning, severe early warning, and emergency shutdown, each corresponding to a different contingency plan. The optimization of the valve 6 opening command time sequence adopts a model predictive control method, predicting the optimal timing of valve operation based on the inertia of flue gas flow, with the prediction time domain adaptively adjusted according to the flue length. The generation of the pump 5 speed adjustment step size sequence considers the motor thermal effect, automatically inserting a cooling interval during continuous adjustment, with the cooling time dynamically calculated based on the motor winding temperature. The implementation of the abnormal operating condition interruption indicator relies on a redundancy check mechanism; important signals use a two-out-of-three voting method to determine the abnormal state, and the voting result is output through a safety relay. The construction of the dynamic feedback table for the waste heat conversion process employs multi-dimensional data fusion technology. The heat medium temperature rise rate log is collected through a high-precision temperature transmitter, and the temperature measurements undergo thermocouple cold junction compensation and signal filtering. The pressure fluctuation frequency statistics table is generated using a peak detection algorithm, with the statistical period synchronized with the system control cycle. Detailed data recording is automatically triggered when the fluctuation frequency exceeds the limit. The execution command delay compensation record is collected using a hardware interrupt method, accurately measuring the time difference between the issuance of the control command and the receipt of the field feedback signal. The measurement results are used for control system timing calibration.

[0023] The analysis of the heat medium temperature rise rate log employs time series analysis. Sliding window standard deviation calculation is used to identify abnormal temperature rise patterns, and a mapping relationship is established between these abnormal patterns and specific operating conditions. The pressure fluctuation frequency statistics table is correlated with equipment vibration data; high-frequency fluctuations correspond to pump cavitation conditions, while low-frequency fluctuations correspond to valve oscillation conditions. The correlation rules are obtained through machine learning training. The implementation of execution command delay compensation records adopts a feedforward compensation strategy, predicting the optimal lead time for the next command based on historical delay data. The prediction model is updated using an adaptive filtering algorithm. The generation of the system energy efficiency ratio trend curve is based on energy balance calculations. The cumulative heat absorption of the heat medium is obtained by integrating the flow rate and temperature difference, with the integration period consistent with the equipment inspection cycle. The total energy consumption value of pump and valve regulation is measured using an energy quality analyzer. Energy consumption data distinguishes between useful and useless components; only the useful component is included in the calculation. The analysis of the system energy efficiency ratio trend curve uses least squares fitting. The point of change in the sign of the fitted slope serves as the trigger condition for control strategy adjustment, with the adjustment magnitude proportional to the rate of change of the slope. The calculation of the cumulative heat absorption of the heat transfer medium takes into account phase change. When the working medium vaporizes during heat exchange, the latent heat calculation mode is automatically activated. The phase change point is determined based on a combined criterion of pressure and temperature. The measurement of total energy consumption includes harmonic loss compensation. The harmonic power generated by the nonlinear load is separated by Fourier analysis, and the separated fundamental power is used for energy efficiency calculation. The system energy efficiency ratio trend curve is stored using a circular cache mechanism, saving historical data for the most recent 1,000 periods. Data retrieval supports combined queries based on time range and energy efficiency threshold.

[0024] The weight coefficient adjustment of the recycling path optimization module adopts a reinforcement learning mechanism. The weight coefficients are dynamically updated based on the shape characteristics of the system's energy efficiency ratio trend curve, and the update strategy is implemented based on the Q-learning algorithm. The weight coefficient adjustment process involves a balance between exploration and utilization. During the stable operation phase of the system, existing experience is prioritized, while the proportion of exploration is increased during the phase of changing operating conditions. The weight coefficient value range is normalized, and different weight coefficients remain independent of each other to avoid control conflicts caused by parameter coupling. The initial values ​​of the weight coefficients are derived from offline simulation results. The simulation model is built based on design parameters, and model verification uses factory acceptance test data. The trigger condition for weight coefficient adjustment is set with dead zone protection. Adjustment is not triggered when the energy efficiency ratio fluctuation is within the normal range, and the dead zone width is adaptively set according to the measurement error. The weight coefficients are linked to equipment life management. Under high load conditions, the weight of frequently operated equipment is automatically reduced, and the weight adjustment amount is positively correlated with the cumulative operating time of the equipment.

[0025] Example 2: See Figure 3The multi-dimensional sensing submodule of the flue gas parameter acquisition module adopts a distributed sensor network architecture. The infrared temperature measurement array is composed of a combination of K-type armored thermocouples and infrared radiation thermometers. The thermocouple measuring points are welded to specific elevation positions on the flue wall, and the infrared radiation thermometer measures the core temperature of the flue gas non-contactly through an observation window. The Pitot tube flow velocity sensor group adopts a combination structure of total pressure tube and static pressure tube. The measuring rod penetrates the flue cross-section and is equipped with a purging device to prevent ash accumulation. The differential pressure transmitter converts the differential pressure signal into a 4-20mA standard signal output. Raw temperature and flow velocity data are transmitted via PROFIBUS-DP fieldbus. Each data packet includes a timestamp and device identifier, generating a real-time physical quantity dataset of the flue cross-section containing a triplet of spatial coordinates, temperature value, and flow velocity value. The flue gas sampling probe of the composition analysis submodule is equipped with a high-temperature filtration and condensation dehumidification unit. The quartz fiber filter cartridge captures solid particles, and the Peltier condenser controls the flue gas dew point temperature below 5 degrees Celsius. Oxygen content detection uses a zirconia sensor, carbon dioxide content detection uses a non-dispersive infrared sensor, and nitrogen oxide concentration detection uses a chemiluminescence analyzer. These three analyzers are integrated within a cabinet and share a sample gas processing system. The flue gas composition-density correlation table is generated using the ideal gas equation of state. Molar mass is calculated based on the percentage of flue gas components, and a density correction factor incorporates a compressibility factor to compensate for deviations between the actual and ideal gas composition.

[0026] The spatial coordinates of the matrix construction submodule are divided using a Cartesian coordinate system, with the X-axis corresponding to the flue width, the Y-axis to the flue height, and the Z-axis to the flue length. Each voxel is 100mm × 100mm × 200mm in size. The real-time physical quantity dataset of the flue cross-section is processed using a Kriging spatial interpolation algorithm, with interpolation parameters including a variation function model and search radius settings to generate a continuous spatial distribution field. The data structure of the three-dimensional flue gas thermodynamic state matrix adopts a hierarchical storage format: the base layer stores the original measured values, the correction layer stores the density-compensated physical parameters, and the correlation layer stores the correspondence between components and physical quantities. The theoretical calculation submodule of the heat exchange efficiency evaluation module implements boundary layer theory calculations. The flat plate turbulent heat transfer formula introduces a roughness correction term, and the roughness parameter is dynamically updated based on ash accumulation monitoring data. The convective heat transfer coefficient calculation distinguishes between parallel and cross-flow conditions. The angle between the flue gas velocity vector and the heat transfer tube bundle is used as the basis for flow pattern judgment, and the influence of the tube bundle arrangement is differentiated through staggered and in-line correlation. The set of theoretical heat transfer efficiency values ​​for each partition is stored using a hash table structure, with spatial coordinates as keys and heat transfer efficiency values ​​as mapping results, achieving constant time complexity for querying.

[0027] The efficiency threshold setting in the constraint analysis submodule employs an adaptive algorithm, with the average efficiency value calculated using a sliding window whose size is automatically adjusted based on the load change rate. The velocity constraint coefficient is derived through dimensionless analysis, with the ratio of local velocity to design velocity serving as the primary parameter and secondary flow intensity as an auxiliary correction parameter. The calculation of the compositional corrosion factor incorporates the time-cumulative effect; the influence of the product of nitrogen oxide concentration and temperature on the corrosion rate is described using the Arrhenius equation, generating a heat recovery constraint analysis table containing both immediate risk values ​​and long-term predicted values. The spatial mesh mapping in the map generation submodule utilizes isoparametric element transformation, and the surface boundaries of the flue 3D model are represented using non-uniform rational B-splines. Mesh node numbering follows the right-hand rule. The rendering of the visualized heat exchange potential distribution map employs gamma correction technology, the color mapping table is optimized based on human visual characteristics, and high-contrast areas are compressed using Huffman coding. The constrained visualized heat exchange potential distribution map supports multi-dimensional interactive queries; mouse hover displays node coordinates and attribute data, and box selection generates regional statistical reports. The data verification of the multi-dimensional sensing submodule adopts a redundant measurement mechanism, with dual sets of sensors arranged at key sections for cross-verification, triggering an automatic calibration program when the deviation exceeds the limit. The installation position of the Pitot tube flow velocity sensor group has been optimized through flow field simulation to avoid the eddy flow region and boundary layer transition region, and the measurement uncertainty is controlled within ±2%.

[0028] The calibration cycle of the composition analysis submodule is synchronized with the unit overhaul. Standard gas calibration covers a range of 20%-100%, and zero-point drift compensation uses automatic zeroing technology. The update trigger condition for the flue gas composition-density correlation table includes fuel switching events. Density calculation models are established separately for coal-fired and gas-fired conditions, with a model switching delay of less than 10 seconds. Real-time calculation of the density correction coefficient incorporates gas pressure compensation. An atmospheric pressure sensor is installed at the top of the chimney, and the sampling frequency is consistent with the composition analysis cycle. The voxel size of the matrix construction submodule is configurable, and the user interface provides mesh refinement and coarsening options. Mesh reconstruction time is displayed in the operation log. The storage format of the three-dimensional flue gas thermodynamic state matrix supports the HDF5 standard. Matrix slice data can be published externally via the OPC-UA protocol, achieving a data compression rate of 70%. The origin of the spatial coordinate system is set at the center of the induced draft fan inlet flange, and the coordinate transformation matrix supports converting measured values ​​to the global coordinate system. The boundary layer calculation of the theoretical calculation submodule considers unsteady-state effects. The transient heat transfer model is solved using an implicit difference scheme, and the time step is automatically adjusted according to the Courant number. The calculated convective heat transfer coefficients underwent dimensionality consistency testing. The dimensionless number group includes the Nusselt number, Reynolds number, and Prandtl number, and the correlation coefficients were obtained through fitting to an experimental database. The persistent storage of the set of theoretical heat transfer efficiency values ​​for each zone utilizes a time-series database, with each data point accompanied by a quality code identifier, and outliers are automatically marked as unavailable.

[0029] The velocity constraint coefficient model in the constraint analysis submodule incorporates Reynolds number correction, and interpolation smoothing is used in the transition zone between laminar and turbulent flow. The influence of the velocity gradient is quantified through differential analysis. The compositional corrosion factor calculation incorporates material corrosion test data, and corrosion rate mapping tables are established separately for different pipe materials, with material information sourced from the equipment ledger system. The output format of the heat recovery constraint factor analysis table adopts a JSON architecture, with fields including constraint type, severity level, impact range, and recommended measures. The lightweight 3D model processing in the map generation submodule utilizes LOD technology, employing a simplified mesh for distant views and a fine mesh for close-up views, maintaining a rendering frame rate above 30fps. The color mapping scheme for the heat exchange potential distribution map considers the needs of colorblind users, providing both red-green and blue-yellow color schemes. The visualization and interactive functions support 3D cross-sectional analysis, with the cross-sectional position adjustable arbitrarily, and the contour maps and vector maps updated synchronously.

[0030] Example 3: The node screening submodule of the heat recovery path optimization module implements spatial clustering analysis. The theoretical heat transfer efficiency values ​​marked in the heat exchange potential distribution map are smoothed by Gaussian filtering, and outliers are identified and removed using density clustering algorithm for discrete points. A dynamic adjustment mechanism is used to set the threshold, with the initial threshold set as the upper quartile of the theoretical heat transfer efficiency value distribution, and subsequent thresholds scaled proportionally according to the system load rate. A flood filling algorithm is used to determine continuous spatial regions, and the region boundary is defined based on the gradient change of efficiency values. The gradient threshold is obtained through training with historical data. Pareto optimization is introduced in the candidate node coordinate list generation process. Coordinate point selection considers both heat transfer efficiency and equipment maintenance accessibility, and the maintenance accessibility weight factor is dynamically updated based on maintenance records. The optimization of the candidate node coordinate list uses a multi-objective genetic algorithm. The fitness function includes three objective terms: total heat recovery, pipeline investment cost, and operating energy consumption. The non-dominated sorting genetic algorithm-II is used to solve for the Pareto optimal solution set. Fluid network topology analysis is based on graph theory modeling, abstracting the flue system as a directed graph structure. Nodes represent measurement points, edges represent fluid paths, and edge weights include pressure drop coefficients and heat transfer characteristics.

[0031] The path simulation submodule incorporates transient effect compensation in calculating the total heat recovery prediction. Flue gas temperature fluctuations are predicted using an autoregressive integral moving average model, with the prediction time domain matched to the valve adjustment time constant. The total heat recovery prediction for each path is verified using energy conservation checks, with cross-validation between the inlet enthalpy difference method and the surface heat flow method. Model reconstruction is triggered when the deviation exceeds 5%. Uncertainty quantification of simulation results employs the Monte Carlo method, with the input parameter disturbance range determined based on the accuracy of the measuring instruments. Output results are presented in confidence interval form. The scheme generation submodule's net benefit calculation model includes full lifecycle cost analysis, with equipment depreciation based on a 20-year period and a discount rate referencing industry benchmark rates of return. The path node combination with the highest net benefit is selected using a branch-and-bound method, and the search tree pruning rule is based on a benefit growth rate threshold, which is adaptively adjusted with search depth. The output format of the graded heat recovery path planning scheme adopts industrial basic standards, and the scheme includes three types of entity objects: geometric information, topological relationships, and control parameters, supporting direct import from BIM software.

[0032] The instruction decomposition submodule of the actuator collaborative control module implements time series discretization. The continuous curve of the valve opening stage adjustment strategy is discretized using cubic spline interpolation, and the density of discrete points is determined according to the valve actuator resolution. The time-opening instruction sequence is optimized using the critical path method, identifying the time constraints of valve linkage operations, and parallel task scheduling considers actuator resource conflicts. The valve step control instruction set is verified using a formal verification method, with linear time-series logic formulas describing safety constraints, and model checking tools verifying the instruction sequence's deadlock-free characteristics. The flow demand curve fitting of the speed matching submodule uses piecewise polynomial regression, with inflection point identification based on curvature change detection, and the number of segment intervals adaptively determined according to the frequency of operating condition changes. The speed-torque characteristic matching point of the drive motor is solved using a quasi-Newton method, with the objective function including the product of motor efficiency and pump efficiency, and constraints considering the NPSH safety boundary. The generation of the pump speed adjustment step sequence introduces anti-surge control, with acceleration limits calculated based on the pump rotor dynamics characteristics, and a rapid pass-through strategy used in the critical speed range.

[0033] The real-time comparison algorithm of the anomaly monitoring submodule employs sliding window dynamic programming. The window size is adjusted according to the actuator response time, and the dynamic programming cost function includes a weighted sum of the absolute value of the deviation and the rate of change. The filtering of the valve's actual opening feedback signal uses a Kalman filter. The state variables include the opening value and its rate of change, and the process noise covariance is estimated online based on the actuator wear level. The abnormal operating condition interruption trigger conditions in the equipment collaborative control signal set have multi-level thresholds. General anomalies are logged using a soft interrupt, while severe anomalies trigger a hardware watchdog reset circuit. The spatial clustering parameters of the node filtering submodule are optimized using contour coefficients. The number of clusters is determined using the elbow rule, with the optimal number of clusters corresponding to the region with the maximum contour coefficient. The center point location of continuous spatial regions uses a weighted centroid method, with weighting factors combining heat transfer efficiency and region area. The region area is calculated using Green's formula for polygon areas. The candidate node coordinate list is stored using a quadtree index, achieving a spatial query efficiency of O(logn) complexity, and supporting radius search and rectangular region queries.

[0034] The graph theory model for fluid network topology analysis enhances dynamic weight updates, with edge weights dynamically corrected based on real-time differential pressure measurements. Correction coefficients are fitted to the pressure drop curve using the least squares method. In the valve opening combination simulation, adaptive mesh refinement is employed, with the Jacobian coefficient of the near-wall region mesh controlled above 0.8. Mesh independence verification is achieved through three-level mesh refinement. The correction of the total heat recovery prediction considers the impact of ash accumulation; the ash pollution coefficient is monitored online using laser scattering, and the monitoring data is used to correct for thermal resistance. The lifecycle cost analysis of the scheme generation submodule incorporates carbon trading costs; carbon dioxide emissions are calculated based on fuel consumption, and the carbon unit price is predicted using forward contract prices. The search efficiency optimization of the branch and bound method employs heuristic rules; the initial solution is quickly generated using a greedy algorithm, and the bounding threshold is gradually tightened according to the search progress. Data mapping for industrial basic standards uses the EXPRESS language to define entity relationships, and attribute verification is implemented through XML schema definition for syntax checking. The discretization process of the instruction decomposition submodule considers the mechanical inertia of the actuator; a smooth transition segment is inserted into the time-opening instruction sequence, with the transition time set according to the technical parameters of the actuator model. The formally verified linear timing logic formula includes safety and activity requirements. The safety requirement prohibits valve over-opening, while the activity requirement ensures that all instructions are eventually executed. The communication protocol for the valve step control instruction set adopts the IEEE 1588 precise time protocol, with clock synchronization accuracy reaching the microsecond level, and the instruction transmission uses a redundant frame structure.

[0035] The piecewise multinomial regression of the speed matching submodule uses the Bayesian information criterion to determine the optimal number of segments to prevent overfitting, and the regularization parameter is selected through cross-validation. The Hessian matrix update of the quasi-Newton method uses the BFGS algorithm, with the step size selection satisfying the Wolfe condition, and the convergence criterion based on the gradient norm threshold. The acceleration limiting curve for anti-surge control is generated based on the pump's rotor dynamics test data, and critical speed identification is achieved through vibration spectrum analysis. The sliding window dynamic programming state transition equation of the anomaly monitoring submodule includes a deviation integral term, with the integral weight increasing with duration to prevent long-term deviation persistence. The observation matrix of the Kalman filter is determined based on the sensor installation location, and the observation noise covariance is calibrated through Allan variance analysis. The hardware watchdog reset circuit adopts a dual-redundant design, and the watchdog feeding signal is verified by both hardware logic and software instructions. Net profit is calculated using the following formula: in: Represents the present value of net income. Represents the heat recovered in year t. This indicates the price per kilocalorie. Let be the system energy consumption in year t. It's about energy prices. The discount rate is... It is the initial investment cost. The calculation period is specified. All parameters in the formula are updated using real-time data, including the unit price of heat. Based on steam parameter-based pricing, energy prices... It adopts a linkage with the futures price index.

[0036] See Figure 4 This graph, with control time as the horizontal axis, simultaneously presents the dynamic relationship between valve opening and pump speed, intuitively demonstrating the core function of the actuator collaborative control module. As seen in the graph, the valve opening and pump speed exhibit a precise coordinated adjustment trend. This dynamic linkage is a direct manifestation of the actuator collaborative control module's analysis of the graded heat recovery path planning scheme. Through the instruction decomposition submodule, the valve opening strategy is discretized into a time-opening instruction sequence, and the speed matching submodule derives the pump speed adjustment step sequence. This achieves time synchronization and parameter matching between the heat exchanger valves and the pump, ensuring efficient collaborative operation of the heat recovery system under different operating conditions. Ultimately, it provides precise equipment execution layer support for the waste heat recovery process, providing a visual representation of the actuator collaborative control module's dynamic adjustment and guarantee of waste heat recovery efficiency.

[0037] Example 4: The thermodynamic acquisition submodule of the waste heat conversion monitoring module adopts a distributed temperature and pressure sensor network. The embedded thermocouple array uses a mixed arrangement of K-type armored thermocouples and T-type thermocouples. The K-type thermocouples cover the high-temperature range (0-1200℃), and the T-type thermocouples cover the low-temperature range (-200-350℃). The installation positions of the thermocouples are determined by thermodynamic simulation to identify key measurement points. The pressure transmitter uses sputtered thin-film technology, with the measuring diaphragm in direct contact with the heat transfer medium. The pressure interface uses a tapered pipe thread sealing structure. The pressure fluctuation frequency data is analyzed using FFT to extract characteristic frequencies. The heat transfer medium state change log is recorded using a cyclic storage strategy, with the latest data overwriting the oldest data. The storage depth maintains the most recent 1000 sets of sampled values. Each set of data includes a timestamp, temperature gradient, and pressure spectrum feature vector. The temperature sampling frequency of the thermodynamic acquisition submodule is dynamically adjusted according to the phase state of the heat transfer medium. A base sampling rate of 1Hz is used for liquid conditions, increased to 10Hz for gas-liquid two-phase conditions, and 5Hz for pure gas phase conditions. Pressure fluctuation frequency statistics are performed using short-time Fourier transform, with the window function length set according to the system inertial time constant and the overlap sampling rate set to 75%. The data structure of the heat medium state change log adopts layered storage: the raw data layer stores unprocessed signals, the feature extraction layer stores statistical indicators, and the application layer stores operating condition diagnostic results.

[0038] The valve response lag time measurement of the delay compensation submodule uses hardware timestamp comparison. The control command issuance time is recorded on the FPGA hardware clock, and the field feedback signal reception time is captured via interrupt, achieving microsecond-level time difference measurement accuracy. Subsequent control command transmission timestamp adjustments employ a predictive compensation algorithm, establishing an ARIMA predictive model based on historical lag time series, with model parameters retrained every 24 hours. The updated version of the compensated equipment collaborative control signal set is managed using version numbers. Major version numbers correspond to control strategy changes, minor version numbers to parameter fine-tuning, and a version rollback mechanism retains the 10 most recent historical versions. The lag time statistical analysis of the delay compensation submodule uses Weibull distribution fitting; shape parameters reflect actuator wear status, and scale parameters characterize average response speed. The rolling time-domain optimization of the predictive compensation algorithm includes stability constraints, limiting the compensation change rate to ±5% per second to avoid system oscillation caused by overcompensation. Verification of the updated version of the equipment collaborative control signal set uses digital signature technology; the RSA algorithm ensures data transmission integrity, and the signature key is automatically changed every 7 days.

[0039] The timeline alignment of the feedback integration submodule adopts the IEEE 1588 precision time protocol, and the master clock source uses a GPS-disciplined atomic clock, achieving sub-microsecond synchronization accuracy. The encoding of the traceability markers uses a composite structure: the first 16 bits identify the batch of control commands, the middle 32 bits record the device address, and the last 16 bits store the checksum. The dynamic feedback table for the waste heat conversion process is stored in a columnar database, using timestamps as the primary key index, supporting fast range queries by time range. Data compression uses Delta encoding combined with the Snappy compression algorithm. Data alignment in the feedback integration submodule uses a dynamic time warping algorithm to solve the timing matching problem for devices with different sampling rates, and the warping path constraint uses Sakoe-Chiba banding constraints. Decoding the traceability markers requires an authorization key, which is stored using a hardware security module, and access logs record all query operations. The backup of the dynamic feedback table for the waste heat conversion process adopts an off-site disaster recovery architecture, with asynchronous replication between the master and slave databases, and an RPO (Recovery Point Objective) of less than 5 minutes. The periodic reading interval of the dynamic reconfiguration module is set according to system inertia. Under normal operating conditions, a fixed period of 60 seconds is used, which is automatically shortened to 10 seconds under variable load conditions. The sliding window analysis of the heat medium temperature rise rate log uses a weighted moving average, with more recent data having higher weight. The window size is proportional to the system's thermal inertia time constant. The preset threshold adopts a dual-threshold hysteresis design, where the threshold value for entering an abnormal state is higher than the threshold value for returning to a normal state, preventing frequent state reversals.

[0040] The dynamic reconfiguration module's periodic adjustments consider controller load balancing; when CPU utilization exceeds 80%, the read interval is automatically extended to avoid system overload. The heat transfer medium temperature rise rate decay detection uses a CUSUM (cumulative sum) control chart algorithm; an early warning is triggered when the cumulative deviation exceeds the control limit, which is calculated based on the standard deviation of historical data. The graded push of heat exchange performance decay alarm signals uses a publish-subscribe model; alarms of different severity levels are sent to the corresponding responsible personnel, with emergency alarms simultaneously sent via SMS and email. The local recalculation requests in the heat exchange performance evaluation module include spatial range descriptions; flue coordinates use an octree spatial index, and the level of detail is dynamically adjusted according to computational accuracy requirements. The locally updated heat exchange potential distribution map uses an incremental update mechanism, transmitting only grid data for changed areas, reducing network bandwidth usage by over 60%. The generation of secondary heat recovery path optimization schemes uses a tabu search algorithm; the tabu table size is set to three times the dimension of the solution space, and the amnesty criterion is based on the improvement in solution quality. Priority management of local recalculation requests uses a weighted fair queue; computational resource allocation weights consider regional importance and urgency, with weight coefficients set by the expert system. The incremental update mechanism ensures data consistency through multi-version concurrency control, ensuring read operations do not block write operations, and setting the transaction isolation level to read committed. The tabu search algorithm uses a 2-opt local search for neighborhood structure, a greedy random adaptive search process for candidate solution generation, and a restart strategy for diversification. Refer to Table 1 for the dynamic feedback table of the waste heat conversion process, which monitors the heat medium status.

[0041] Table 1: Data Structure Table for Heating Medium Status Change Log The data verification of the heat medium status change log adopts a redundant verification mechanism, with a CRC32 checksum attached to each record. Data that fails verification is automatically retransmitted. The temperature change rate is calculated using the five-point central difference method, with forward / backward difference used at boundary points. The difference step size is adaptively adjusted according to the signal noise level. The encoding rule of the operating condition identification code adopts a bit-field structure, with the highest bit indicating the phase state (0-liquid / 1-gas), the middle 4 bits indicating the flow rate area, and the lowest 3 bits indicating the pressure level.

[0042] The delay compensation submodule uses a Kalman filter for its lag time prediction model. State variables include lag time and its first derivative, and the observation matrix is ​​adjusted based on the actuator health score. The adjustment of the compensation timestamp is limited to ±200 milliseconds; exceeding this range triggers an actuator performance check. The deployment of updated versions of the equipment collaborative control signal set employs a blue-green deployment strategy, with the old and new versions running in parallel for comparison. Once the effect is verified, traffic is gradually switched. The feedback integration submodule uses a dynamic time warping algorithm for time-series data association. The search for warped paths uses dynamic programming, and the cost function includes the difference between Euclidean distance and time-series derivatives. Parsing the execution traceability markers requires tiered access permissions; ordinary operators can only view the marker summary, while maintenance engineers have full decoding permissions. The analysis tool for the dynamic feedback table of the waste heat conversion process integrates a machine learning library, supporting functions such as cluster analysis, anomaly detection, and trend prediction. The algorithm interface conforms to the PMML standard. The local update triggering conditions of the dynamic reconfiguration module include spatial correlation analysis. When adjacent areas simultaneously experience performance degradation, recalculated areas are automatically merged to reduce redundant calculations. The verification of the secondary heat recovery path optimization scheme adopts digital twin technology. The virtual model runs 10 times faster than real-time and can predict the implementation effect of the scheme in advance. The rolling optimization of the scheme replacement process adopts model predictive control, and the optimization time domain covers the next maintenance cycle. The objective function includes energy efficiency benefits and equipment life loss.

[0043] Example 5: The attenuation analysis submodule of the dynamic reconfiguration module implements sliding window mean change rate calculation. The window width is set to 30 sampling periods, with each sampling period corresponding to 5 seconds of real-time data acquisition. Preprocessing of the heat medium temperature rise rate log uses median filtering to remove pulse interference. Outlier detection within the window uses the Tukeyfences method; data points exceeding 1.5 times the interquartile range are automatically replaced with linear interpolation results. The change rate calculation uses the central difference method, dividing the relative change between the current window mean and the previous window mean by the time interval, with the result expressed as a percentage. A dynamic adjustment mechanism is used for threshold setting: the base threshold is set to -5%, automatically widening to -3% when the system load rate is greater than 90%, and tightening to -7% when the load rate is less than 50%. The sliding window of the attenuation analysis submodule uses an overlapping design, with each new window containing 2 / 3 of the data points from the previous window; overlapping data reuse improves computational efficiency. The trend judgment of the mean change rate uses the Mann-Kendall test, with a significance level set at 0.05 to avoid false alarms caused by random fluctuations. The generation of heat exchange efficiency degradation alarm signals includes three levels of classification: Level 1 alarms correspond to a change rate in the range of -5% to -10%, Level 2 alarms correspond to a range of -10% to -15%, and Level 3 alarms correspond to a sharp degradation below -15%. The alarm signal includes metadata in four dimensions: timestamp, spatial location identifier, degradation magnitude, and duration. The alarm signal association in the map request submodule adopts a content-based addressing method. The instruction sequence of the device collaborative control signal set is used to generate a 64-bit fingerprint through hash calculation, achieving a fingerprint matching accuracy of over 99.99%. The spatial range definition of the local recalculation request uses a convex hull algorithm, expanding outwards from the alarm point until a normal monitoring point is encountered. A buffer zone is set at the expansion boundary to prevent boundary effects. The flue coordinates are encoded using Morton code space-filling curves. The three-dimensional coordinates are converted into a one-bit code to achieve fast neighborhood lookup. The lookup radius is set to 3 meters based on the system response speed requirements.

[0044] The recalculation priority allocation of the graph request submodule adopts a weighted round-robin algorithm, with weighting factors considering alarm level, affected area, and device importance coefficient. The local recalculation request transmission protocol uses a reliable datagram protocol, with a packet loss retransmission mechanism to ensure data integrity, and the transmission timeout is dynamically adjusted based on network latency. The response result of the heat exchange efficiency evaluation module includes incremental update data packets, with the packet header containing version number, checksum, and data length fields, and the payload using Protocol Buffers serialization format. The local update region identification of the scheme replacement submodule uses a region growing algorithm, with seed points set at alarm coordinates, and the growth criterion based on the spatial correlation of heat exchange efficiency values. The generation of the secondary heat recovery path optimization scheme uses a simulated annealing algorithm, with an initial temperature set to 1000℃, an annealing rate using a logarithmic descent strategy, and a Markov chain length fixed at 1000 iterations. Parameter retention in the undecayed region uses copy-on-write technology; the original control parameters remain read-only in memory, and modification operations are performed on the copy until verification is successful. The transition process for replacing the sub-module in the solution employs smooth interpolation, with a 10-minute transition zone during the transition between the old and new solutions. Control parameters are switched gradually according to the S-curve pattern. Solution verification uses an A / B testing framework, dividing the system into experimental and control groups for parallel operation, and shortening the key performance indicator acquisition cycle to 15 seconds. A conservative strategy is adopted for equipment control in the affected area, limiting the adjustment range to within 50% of the rated range and reducing the adjustment frequency to one-third of the normal value.

[0045] The cumulative heat absorption of the heat medium in the energy efficiency closed-loop module is calculated using trapezoidal numerical integration, with the integration interval corresponding to N complete operating cycles. The number of cycles N is set to 24 cycles (i.e., 2 hours) based on system inertia. The total energy consumption of pumps and valve regulation is collected using a power quality analyzer, achieving an active power measurement accuracy of 0.5 class. Harmonic power components are separated using FFT analysis. The system energy efficiency ratio trend curve is generated using moving average smoothing, with the window width consistent with the integration period, and data point time labels aligned to the hour. Energy consumption allocation in the energy efficiency closed-loop module uses the activity-based costing method, with pump energy consumption allocated according to flow rate and valve regulation energy consumption allocated according to the number of actions. Outlier removal from the system energy efficiency ratio trend curve uses the Grubbs test, with a significance level set at 0.01, and removal points are supplemented using linear interpolation. The trend curve is stored using a piecewise linear approximation, and inflection point identification is based on the Douglas-Pock algorithm, with compression error controlled within 0.1%. The weight coefficients of the recycling path optimization module are adjusted using the gradient descent method, with a learning rate set to 0.001 and a momentum factor set to 0.9 to prevent oscillations. The update cycle of the priority path node selection weight coefficients is synchronized with the energy efficiency ratio trend curve, and the weights are recalculated at the end of each cycle.

[0046] The weight coefficient constraint of the recycling path optimization module adopts the projection gradient method, with weight values ​​limited to the [0,1] interval, and the sum of weights remaining a constant of 1. The adjustment range of the weight coefficient for priority path node selection is proportional to the absolute value of the slope, with the maximum single adjustment limited to within 0.1. Historical versions of weight coefficients are saved using snapshot technology, retaining weight change records for the most recent 30 days, supporting weight rollback to any historical moment. The attenuation detection of the dynamic reconfiguration module and the collaborative work of the energy efficiency closed-loop module adopt an event-driven architecture. Attenuation alarm signals are published to the message queue as system events, and the energy efficiency closed-loop module subscribes to relevant events to trigger weight adjustments. The message queue is implemented using RabbitMQ, with message persistence ensuring no data loss in the event of system failure, and a round-robin allocation strategy for consumer load balancing. Data consistency between the two modules is guaranteed through distributed transactions, with the transaction isolation level set to read committed, and a timeout rollback mechanism to prevent deadlock. In a specific example, when the boiler load increases from 300MW to 350MW, the heat medium temperature rise rate log shows that the change rate at measuring point #12 decreased from -2.1% to -8.7% within 5 minutes. The sliding window mean calculation in the attenuation analysis submodule uses an exponentially weighted moving average, with a weighting coefficient of 0.3 for recent data and 0.7 for historical data. The rate of change calculation results show that the rate exceeds the -5% threshold for three consecutive periods, triggering a level-two alarm signal. The alarm number includes the timestamp "2025-06-15T14:30:00Z" and coordinates "X=35.6,Y=12.8,Z=8.2".

[0047] After receiving the alarm signal, the graph request submodule analyzed the command sequence at the corresponding moment in the equipment collaborative control signal set and found a 2.3-second delay in the opening command of the regulating valve V-12A. A local recalculation request specified a cylindrical region with center coordinates (35.6, 12.8, 8.2), a radius of 2.5 meters, and a height of 4 meters. The incremental update data packet returned by the heat exchange efficiency evaluation module was 1.2MB in size and contained three data segments: grid point temperature gradient vector, flow velocity distribution vector, and component concentration ratio table. The scheme replacement submodule adopted a parallel computing architecture, starting four worker threads to process the grid data in different quadrants. The initial solution of the simulated annealing algorithm used the current running scheme, and after 856 iterations, a better solution was found, improving the objective function value by 7.3%. During the transition period, the opening of the regulating valve in region #12 was gradually adjusted from 65% to 72%, and the pump speed was increased from 1450 rpm to 1520 rpm. The transition time of 8 minutes did not cause system oscillation. Data from the energy efficiency closed-loop module over 24 periods showed that the slope of the system's energy efficiency ratio trend curve changed from -0.03 to +0.02. The recovery path optimization module adjusted its weighting coefficients accordingly: the weight for heat exchange efficiency increased from 0.6 to 0.65, the weight for equipment lifespan decreased from 0.3 to 0.25, and the weight for energy cost remained unchanged at 0.1. After the new weighting coefficients were applied, the priority path node list was reordered, and the priority of the #12 area node increased from 8th to 3rd.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An energy-saving waste heat recovery control system for boiler flue gas in a thermal power plant, comprising a boiler and a heat exchanger, wherein the boiler flue gas outlet and the heat exchanger flue gas inlet are connected by a pipeline, a dust collector and an induced draft fan are installed on the pipeline connecting the boiler flue gas outlet and the heat exchanger flue gas inlet, a water pump and a valve are installed at the heat exchanger water inlet, and the heat exchanger water outlet is connected to the domestic water pipeline of the thermal power plant, characterized in that... The system also includes: The flue gas parameter acquisition module acquires real-time temperature and velocity distribution data of flue gas in the boiler tail flue, and generates a flue gas thermodynamic state matrix by combining the flue gas composition detection results. The heat exchange efficiency evaluation module calculates the theoretical heat exchange efficiency value of each flue section based on the temperature gradient distribution and flow velocity distribution in the flue gas thermodynamic state matrix, and generates a heat exchange potential distribution map. Based on the differences in heat exchange efficiency in different regions of the heat exchange potential distribution map, the heat recovery path optimization module identifies the optimal heat recovery path nodes and generates a graded heat recovery path planning scheme. The actuator collaborative control module parses the node operation instruction sequence in the staged heat recovery path planning scheme, dynamically adjusts the heat exchanger valve opening and water pump speed parameters, and generates a set of equipment collaborative control signals. The waste heat conversion monitoring module collects the temperature change rate and pressure fluctuation data of the heat transfer medium in real time, and generates a dynamic feedback table of the waste heat conversion process by combining the execution status of the equipment collaborative control signal set.

2. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 1, characterized in that, The flue gas thermodynamic state matrix includes a longitudinal flue temperature gradient vector, a transverse velocity distribution vector, and a component concentration ratio table. The heat exchange potential distribution map specifically includes a zoned theoretical heat exchange efficiency thermogram, a maximum recoverable heat label set, and a velocity constraint coefficient table. The graded heat recovery path planning scheme includes a priority path node list, a valve opening stage adjustment strategy, and a pump speed matching scheme. The equipment collaborative control signal set includes a valve opening command time sequence, a pump speed adjustment step size sequence, and an abnormal operating condition interruption identifier. The waste heat conversion process dynamic feedback table includes a heat medium temperature rise rate log, a pressure fluctuation frequency statistics table, and an execution command delay compensation record.

3. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 1, characterized in that, The flue gas parameter acquisition module includes: The multi-dimensional sensing submodule arranges an infrared temperature measurement array and a Pitot tube flow velocity sensor group along the flue axis to simultaneously collect raw flue gas temperature data and raw flow velocity data, and generate a real-time physical quantity dataset of the flue cross section. The component analysis submodule acquires data on oxygen content, carbon dioxide content and nitrogen oxide concentration through flue gas sampling probes, and calculates flue gas density correction coefficient by combining the temperature data in the real-time physical quantity dataset of the flue section, and generates a flue gas component-density correlation table. The matrix construction submodule sorts the real-time physical quantity dataset of the flue section according to spatial coordinates, and superimposes the density correction results from the flue gas composition-density correlation table to construct a three-dimensional flue gas thermodynamic state matrix.

4. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 3, characterized in that, The heat exchange performance evaluation module includes: The theoretical calculation submodule calculates the convective heat transfer coefficient of each flue section based on the temperature gradient vector and velocity vector in the three-dimensional flue gas thermodynamic state matrix, using the heat transfer boundary layer theory, and generates a set of theoretical heat transfer efficiency values ​​for each section. The constraint analysis submodule identifies regions in the set of theoretical heat transfer efficiency values ​​for the partition that are lower than the average efficiency value, extracts the flow velocity constraint coefficient and composition corrosion factor at the corresponding locations, and generates a heat recovery constraint factor analysis table. The map generation submodule maps the set of theoretical heat exchange efficiency values ​​for the partition to the spatial grid nodes of the flue 3D model, and combines the negative markers in the heat recovery constraint analysis table to generate a visual heat exchange potential distribution map with constraints.

5. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 4, characterized in that, The recycling path optimization module includes: The node filtering submodule traverses the theoretical heat exchange efficiency values ​​marked in the heat exchange potential distribution map, selects the center point of the continuous spatial region with an efficiency value higher than a set threshold as the primary recovery node, and generates a candidate node coordinate list. The path simulation submodule performs fluid network topology analysis based on the candidate node coordinate list, simulates the flue gas flow path changes under different valve opening combinations, and outputs the predicted total heat recovery value for each path. The scheme generation submodule compares the predicted total heat recovery value with the system energy consumption increment, selects the path node combination and corresponding equipment parameters with the maximum net benefit, and forms a graded heat recovery path planning scheme.

6. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 5, characterized in that, The actuator coordination control module includes: The instruction decomposition submodule parses the valve opening stage adjustment strategy in the graded heat recovery path planning scheme, discretizes the continuous adjustment process into a time-opening instruction pair sequence, and generates a valve step control instruction set. The speed matching submodule calculates the speed-torque characteristic matching point of the drive motor based on the flow demand curve in the pump speed matching scheme, and generates a pump speed adjustment step sequence. The anomaly monitoring submodule compares the actual valve opening feedback signal with the expected value of the valve step control command set in real time. When the deviation exceeds the tolerance range, it triggers the abnormal working condition interruption flag in the equipment collaborative control signal set.

7. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 6, characterized in that, The waste heat conversion monitoring module includes: The thermodynamic acquisition submodule monitors the temperature rise rate of the heat transfer medium at the inlet and outlet of the heat exchange tube bundle through an embedded thermocouple array, and simultaneously collects the fluctuation frequency data of the pressure transmitter to generate a heat transfer medium state change log. The delay compensation submodule identifies the valve response lag time in the execution instruction delay compensation record, dynamically adjusts the sending timestamp of subsequent control instructions, and generates a compensated updated version of the equipment collaborative control signal set. The feedback integration submodule aligns the heat medium state change log with the updated version of the compensated equipment collaborative control signal set along the time axis to construct a dynamic feedback table for the waste heat conversion process with execution traceability markers.

8. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 7, characterized in that, The system also includes: The dynamic reconfiguration module periodically reads the heat medium temperature rise rate log in the dynamic feedback table of the waste heat conversion process. When it detects that the rate decay exceeds a preset threshold, it triggers the heat exchange performance evaluation module to regenerate an updated heat exchange potential distribution map. The recovery path optimization module performs path node re-selection based on the updated heat exchange potential distribution map, generates secondary heat recovery path optimization schemes, and covers the original hierarchical heat recovery path planning schemes.

9. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 8, characterized in that, The dynamic reconfiguration module includes: The attenuation analysis submodule calculates the sliding window mean change rate of the heat medium temperature rise rate log, and generates a heat exchange efficiency attenuation alarm signal when the change rate negatively exceeds the threshold. The map request submodule associates the heat exchange efficiency decay alarm signal with the current equipment collaborative control signal set and sends a local recalculation request with flue coordinates to the heat exchange efficiency evaluation module. The scheme replacement submodule receives the locally updated heat exchange potential distribution map returned by the heat exchange efficiency evaluation module, and regenerates the secondary heat recovery path optimization scheme only for the affected area, while retaining the original control parameters for the un-attenuated area.

10. The energy-saving waste heat recovery control system for boiler flue gas in thermal power plants according to claim 9, characterized in that, The system also includes: The energy efficiency closed-loop module calculates the cumulative heat absorption of the heat medium for N consecutive cycles in the dynamic feedback table of the waste heat conversion process, and divides it by the total energy consumption value of the water pump and valve adjustment during the same period to generate the system energy efficiency ratio trend curve. The recycling path optimization module dynamically adjusts the priority path node selection weight coefficients in the graded heat recovery path planning scheme according to the slope change direction of the system energy efficiency ratio trend curve.

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