Intelligent management and control method and system for optimizing energy efficiency of boiler industry control

Abstract

The application discloses a boiler industrial control energy efficiency optimization intelligent management and control method and system, relates to the technical field of industrial automation and intelligent control, and realizes the fundamental change of boiler energy efficiency control from single-point parameter adjustment to global multi-target collaborative optimization by high-precision synchronization and processing of boiler operation parameters, utilizing a hierarchical decision architecture composed of a combustion efficiency optimization layer, a heat conduction matching layer and an emission constraint coordination layer, fusing multi-source sensing data, dynamically adjusting air-fuel ratio by adaptive fuzzy reasoning, optimizing feed water flow based on thermal efficiency function, and reversely correcting combustion instructions according to emission margin, so that the boiler can always maintain the optimal state of energy efficiency, environmental protection and safety in the whole working condition range, while supporting edge deployment and closed-loop feedback, continuously feeding back and rolling optimization, realizing the unity of energy efficiency maximization, emission minimization and operation stability of the boiler in the whole working condition, and significantly improving the comprehensive performance and economic and environmental benefits of the boiler.

Description

Technical Field

[0001] This application relates to the field of industrial automation and intelligent control technology, and in particular to intelligent management and control methods and systems for optimizing energy efficiency in boiler industry control. Background Technology

[0002] As a core piece of equipment in industrial production and energy supply, the boiler's operational efficiency and environmental performance directly affect a company's operating costs and carbon emission levels. Boiler systems not only need to maintain high thermal efficiency under complex variable load conditions, but also must strictly meet increasingly stringent emission standards for pollutants such as nitrogen oxides. Therefore, achieving synergistic optimal control of multiple objectives, including boiler combustion efficiency, heat transfer performance, and environmental emissions, has become an urgent need in the fields of industrial automation and energy conservation and carbon reduction.

[0003] Currently, existing practices rely on static models and preset parameters built upon classical thermodynamics to adjust key parameters such as furnace temperature, steam drum water level, and flue gas oxygen content at single points or locally through proportional, integral, and derivative control loops. However, the static models in these practices struggle to adapt to dynamic disturbances such as fuel composition fluctuations and sudden changes in load demand, leading to decreased control accuracy. Furthermore, most systems employ single-point feedback and independent loop control, neglecting the strong coupling relationships between subsystems such as combustion, heat transfer, and emissions, which can easily cause conflicts in control objectives. For example, pursuing high thermal efficiency may result in excessive emissions. Therefore, achieving intelligent control and optimization of boiler industrial control energy efficiency under complex and variable operating conditions has become an urgent problem to be solved.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method and system for intelligent control and management of boiler industrial energy efficiency optimization, aiming to solve the technical problem of how to achieve intelligent control and management of boiler industrial energy efficiency optimization under complex and ever-changing operating conditions.

[0006] To achieve the above objectives, this application proposes an intelligent control method for optimizing energy efficiency in boiler industrial control, the method comprising:

[0007] Boiler operating parameters are acquired through a distributed sensor network;

[0008] Based on the boiler operating parameters, a target state vector sequence under a unified time reference is determined;

[0009] Based on the target state vector sequence input hierarchical decision model, the fuel valve opening correction amount, fan speed adjustment amount, water pump inverter frequency adjustment value, and combustion intensity adjustment value are predicted. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. The combustion efficiency optimization sub-model, heat conduction matching sub-model, and emission constraint coordination sub-model are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer.

[0010] Based on at least one control command corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value, the boiler operating parameters are iteratively adjusted to determine the target boiler parameters;

[0011] The target boiler parameters are fed back to complete the intelligent management and control of boiler industrial control energy efficiency optimization.

[0012] In one embodiment, the step of determining the target state vector sequence under a unified time reference based on the boiler operating parameters includes:

[0013] Based on the master clock source, the sensor sampling data corresponding to the boiler operating parameters are synchronized to determine the original synchronization data;

[0014] The original synchronization data is filtered using a sliding window mid-range filtering algorithm to determine the filtered data;

[0015] Linear interpolation is used to fill in the missing data points of the filtered data to determine the interpolation data;

[0016] The interpolated data is rearranged according to timestamps to form a target state vector sequence arranged under a unified time reference, with each row corresponding to the sampling time and each column corresponding to the physical quantity.

[0017] In one embodiment, the hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model;

[0018] The steps of predicting the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment based on the target state vector sequence input hierarchical decision model include:

[0019] The fuel lower heating value, theoretical air volume coefficient and current load rate in the target state vector sequence are input into the combustion efficiency optimization sub-model to predict the ideal air-fuel ratio setpoint.

[0020] Using a fuzzy rule base, nonlinear compensation is performed on the air-fuel ratio deviation corresponding to the ideal air-fuel ratio setting value to adjust the corresponding fuel valve opening and fan speed, thereby determining the fuel valve opening correction amount and fan speed adjustment amount.

[0021] The steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence are input into the heat conduction matching sub-model to predict the efficiency.

[0022] The water flow rate setpoint that minimizes the efficiency of the pump is obtained by solving the gradient descent method, and the corresponding frequency adjustment value of the pump frequency converter is obtained.

[0023] The measured nitrogen oxide concentration, flue gas oxygen content, and denitrification agent injection margin in the target state vector sequence are input into the emission constraint coordination sub-model to predict emission values.

[0024] Based on the emission value, the combustion layer command attenuation factor is triggered to limit the corresponding combustion intensity within a predefined emission return safety range, thereby determining the combustion intensity adjustment value.

[0025] In one embodiment, the step of inputting the lower heating value of fuel, theoretical air-fuel ratio, and current load rate from the target state vector sequence into the combustion efficiency optimization sub-model to predict the ideal air-fuel ratio setpoint includes:

[0026] The fuel lower heating value, theoretical air volume coefficient and current load rate in the target state vector sequence are input into the combustion efficiency optimization sub-model to predict the fuel element analysis results.

[0027] The corresponding theoretical air volume is calculated online based on the fuel element analysis results.

[0028] Based on the theoretical air volume coefficient and the current load rate in the target state vector sequence, the corresponding target excess air coefficient is read from a predefined lookup table;

[0029] The target total air volume is determined based on the theoretical air volume and the target excess air coefficient.

[0030] The ideal air-fuel ratio setpoint is calculated based on the target total air volume and the instantaneous fuel flow rate in the target state vector sequence.

[0031] In one embodiment, the step of using a fuzzy rule base to perform nonlinear compensation on the air-fuel ratio deviation corresponding to the ideal air-fuel ratio setting value, adjusting the corresponding fuel valve opening and fan speed, and determining the fuel valve opening correction amount and fan speed adjustment amount includes:

[0032] Obtain the fuel valve opening increment and the fan speed percentage adjustment;

[0033] The air-fuel ratio deviation and load rate deviation corresponding to the ideal air-fuel ratio set value are classified as antecedent variables using a fuzzy rule base.

[0034] The fuel valve opening increment and the fan speed percentage adjustment are classified as consequent variables using a fuzzy rule base.

[0035] The antecedent variables are mapped to corresponding fuzzy sets, and the corresponding rules and rule applicability are determined according to the fuzzy sets. The consequent variables are weighted and averaged using the rules and rule applicability to determine the fuzzy output information. The fuzzy sets include negative large fuzzy sets, negative small fuzzy sets, zero fuzzy sets, positive small fuzzy sets, and positive large fuzzy sets.

[0036] The fuzzy output information is solved using the center of gravity method to obtain the fuel valve opening correction amount and the blower speed adjustment amount.

[0037] In one embodiment, the step of inputting the steam drum pressure change rate and the average wall temperature gradient of the evaporation section from the target state vector sequence into the heat conduction matching sub-model to predict the efficiency includes:

[0038] Obtain water supply flow rate and fuel mass flow rate;

[0039] The steam enthalpy rise is determined by querying a predefined table of steam thermodynamic properties based on the steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence.

[0040] The steam enthalpy rise, the feedwater flow rate, the fuel mass flow rate, and the corresponding mass fuel chemistry are input into the heat conduction matching sub-model to predict the efficiency.

[0041] In one embodiment, the step of obtaining the corresponding pump frequency adjustment value by solving for the water flow rate setpoint that minimizes the pump efficiency using the gradient descent method includes:

[0042] Obtain the gradient iteration step size, convergence condition, and sampling period;

[0043] The gradient descent method is used to calculate the change in efficiency corresponding to the increase and decrease of the water flow rate when the gradient iteration step size is reduced, and the gradient direction is determined.

[0044] Based on the convergence condition and the sampling period, the water flow rate setpoint is iteratively adjusted along the gradient ascent direction in the gradient direction to obtain the water flow rate setpoint that minimizes the efficiency and the corresponding water pump inverter frequency adjustment value.

[0045] In one embodiment, the step of determining the combustion intensity adjustment value by triggering a combustion layer command attenuation factor based on the emission value to limit the corresponding combustion intensity within a predefined emission return safety range includes:

[0046] Obtain standard limits and denitrification agent injection margin;

[0047] A safety margin is determined based on the standard limits and the emission values;

[0048] Based on the aforementioned safety margin, the combustion layer command attenuation factor is adjusted according to the corresponding control cycle decrease value to determine the combustion layer command correction attenuation factor.

[0049] The combustion intensity is adjusted by modifying the fuel valve opening correction amount and the fan speed adjustment amount of the corresponding combustion layer output according to the combustion layer instruction correction attenuation factor, and the combustion intensity is reduced in advance according to the denitrification agent injection margin, so as to limit the corresponding combustion intensity within the predefined emission return safety range, thus obtaining the combustion intensity adjustment value.

[0050] In one embodiment, the step of iteratively adjusting the boiler operating parameters based on at least one control command corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value to determine the target boiler parameters includes:

[0051] Obtain control strategy information;

[0052] When the control strategy information is the first type of strategy, the system only executes the control command corresponding to the combustion intensity adjustment value to iteratively adjust the boiler operating parameters to obtain the first target boiler parameters;

[0053] When the control strategy information is the second type of strategy, all control commands corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value and the combustion intensity adjustment value are suspended, and the proportional-integral-derivative emergency control loop is started, and the corresponding boiler operating parameters are used as the second target boiler parameters.

[0054] When the control strategy information is the third type of strategy, a stable combustion command is obtained, and the air-coal ratio of the boiler operating parameters is iteratively adjusted according to the stable combustion command to obtain the third target boiler parameters;

[0055] When the control strategy information is the fourth type of strategy, the control commands corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value and the combustion intensity adjustment value are executed in normal queue to iteratively adjust the boiler operating parameters and obtain the fourth target boiler parameters;

[0056] The target boiler parameters are obtained based on any one of the first target boiler parameters, the second target boiler parameters, the third target boiler parameters, and the fourth target boiler parameters.

[0057] Furthermore, to achieve the above objectives, this application also proposes an intelligent control system for optimizing energy efficiency in boiler industry control, the intelligent control system for optimizing energy efficiency in boiler industry control comprising:

[0058] A multi-source data acquisition module is used to acquire boiler operating parameters through a distributed sensor network;

[0059] The data preprocessing module is used to determine the target state vector sequence under a unified time reference based on the boiler operating parameters;

[0060] The hierarchical decision engine module is used to predict the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment based on the target state vector sequence input into the hierarchical decision model. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. The combustion efficiency optimization sub-model, heat conduction matching sub-model, and emission constraint coordination sub-model are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer.

[0061] The instruction arbitration and execution module is used to iteratively adjust the boiler operating parameters based on at least one control instruction corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value, and to determine the target boiler parameters.

[0062] The feedback update module is used to feed back the target boiler parameters to complete the intelligent management and control of boiler industrial control energy efficiency optimization.

[0063] One or more technical solutions proposed in this application have at least the following technical effects:

[0064] This embodiment proposes an intelligent control method for optimizing energy efficiency in boiler industrial control. It acquires boiler operating parameters through a distributed sensor network; determines a target state vector sequence under a unified time reference based on these parameters; inputs the target state vector sequence into a hierarchical decision model to predict fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. These sub-models are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. Based on at least one control command corresponding to the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment, the boiler operating parameters are iteratively adjusted to determine the target boiler parameters. The target boiler parameters are then fed back to complete the intelligent control method for optimizing energy efficiency in boiler industrial control. This application achieves a fundamental shift in boiler energy efficiency control from single-point parameter adjustment to global multi-objective collaborative optimization by performing high-precision synchronization and processing of boiler operating parameters and utilizing a hierarchical decision-making architecture composed of a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. This enables the boiler to maintain optimal energy efficiency, environmental protection, and safety across all operating conditions, while continuously providing feedback and rolling optimization to achieve a balance between maximizing energy efficiency, minimizing emissions, and maintaining operational stability under all operating conditions, significantly improving the boiler's overall performance and economic and environmental benefits. Attached Figure Description

[0065] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0066] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a schematic diagram of the intelligent control and management method for boiler industrial control energy efficiency optimization in this application;

[0068] Figure 2 This is a flowchart illustrating an embodiment of the intelligent control method for optimizing energy efficiency in boiler industrial control according to this application.

[0069] Figure 3 This is a flowchart illustrating Embodiment 2 of the intelligent control method for optimizing energy efficiency in boiler industrial control according to this application.

[0070] Figure 4 This is a schematic diagram of the module structure of the intelligent control system for boiler industrial control energy efficiency optimization, as described in an embodiment of this application.

[0071] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0072] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0073] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0074] The main solution of this application embodiment is as follows: Boiler operating parameters are acquired through a distributed sensor network; a target state vector sequence under a unified time reference is determined based on the boiler operating parameters; a hierarchical decision model is input based on the target state vector sequence to predict the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. These sub-models are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer; the boiler operating parameters are iteratively adjusted based on at least one control command corresponding to the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment to determine the target boiler parameters; and the target boiler parameters are fed back to complete the intelligent management and control of boiler industrial control energy efficiency optimization.

[0075] In this embodiment, for ease of description, the following description will focus on the intelligent control and management equipment for optimizing energy efficiency in boiler industrial control.

[0076] Because the static models in the existing technology are difficult to adapt to dynamic disturbances such as fuel composition fluctuations and sudden changes in load demand, the control accuracy is reduced. Moreover, most systems use single-point feedback and independent loop control, which ignores the strong coupling relationship between subsystems such as combustion, heat transfer and emission, which can easily lead to conflict of control objectives. For example, pursuing high thermal efficiency may lead to excessive emissions.

[0077] This application provides a solution, such as Figure 1 As shown, Figure 1This diagram illustrates the architecture of the intelligent control method for boiler industrial control energy efficiency optimization proposed in this application. It constructs a multi-source heterogeneous data fusion and sensing architecture to collect combustion parameters, thermal parameters, environmental parameters, and load demand parameters during boiler operation in real time, establishing a dynamically coupled energy efficiency state space model. Based on this dynamically coupled energy efficiency state space model, a hierarchical decision-making mechanism is introduced, decomposing the global energy efficiency target into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. These layers are linked in a closed loop through feedback correction channels. In the combustion efficiency optimization layer, an adaptive fuzzy inference engine is used to coordinately adjust the fuel supply rate, air supply volume, and induced draft volume, ensuring the air-fuel ratio remains within the theoretically optimal range. In the heat conduction matching layer, based on the real-time steam pressure gradient and heat exchange surface temperature distribution, the feedwater flow rate and drum water level setpoints are dynamically adjusted to ensure a bottleneck-free heat transfer path and minimized energy loss. In the emission constraint coordination layer, combined with online flue gas composition analysis results and environmental threshold boundary conditions, the combustion layer output commands are corrected in reverse, maximizing thermal efficiency while ensuring compliance with standards. At this point, the three-level decision results are converted into precise control signals for valve opening, motor speed, and baffle angle through the actuator drive module, enabling the boiler to achieve energy efficiency self-optimization and stable operation across the entire operating range.

[0078] As can be seen from the above embodiments, this application achieves a fundamental shift in boiler energy efficiency control from single-point parameter adjustment to global multi-objective collaborative optimization by performing high-precision synchronization and processing of boiler operating parameters and utilizing a hierarchical decision-making architecture composed of a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. This enables the boiler to maintain its optimal state of energy efficiency, environmental protection, and safety throughout the entire operating range, while continuously performing feedback and rolling optimization to achieve the unity of maximizing boiler energy efficiency, minimizing emissions, and maintaining operational stability under all operating conditions, significantly improving the overall performance and economic and environmental benefits of the boiler.

[0079] Based on this, embodiments of this application provide an intelligent control method for optimizing energy efficiency in boiler industrial control, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the intelligent control method for optimizing energy efficiency in boiler industrial control according to this application.

[0080] In this embodiment, the intelligent management and control method for optimizing energy efficiency in boiler industrial control includes steps S10 to S50:

[0081] Step S10: Obtain boiler operating parameters through a distributed sensor network;

[0082] It is understood that the boiler operating parameters are a multi-dimensional data set of the boiler's current operating status collected in real time through a distributed sensor network.

[0083] In a specific embodiment, the boiler operating parameters can be acquired through a distributed sensor network, including furnace temperature field distribution data, flue gas negative pressure data, economizer outlet water temperature data, superheater outlet steam pressure data, air preheater inlet and outlet temperature difference data, instantaneous fuel flow rate data, primary and secondary air ratio data, flue gas oxygen content data, nitrogen oxide concentration data, and current load command data. The furnace temperature field distribution data is acquired by three sets of thermocouple arrays arranged along the furnace height. Each array consists of nine measuring points forming a 3x3 grid structure to capture the three-dimensional temperature field distribution characteristics. A flue gas negative pressure sensor, located at the rear, collects flue gas negative pressure data. Two meters upstream of the bend in the vertical flue, to avoid the eddy interference zone and ensure that the measured value reflects the true flue gas flow resistance, a fuel instantaneous flow meter collects the instantaneous fuel flow data. A Coriolis mass flow meter is used and installed near the burner inlet end of the main fuel pipeline to directly measure the mass flow rate, which is not affected by density fluctuations. The flue gas oxygen content and nitrogen oxide concentration detection unit is integrated in the horizontal flue at the economizer outlet. The sampling probe extends into the central area of ​​the flue and is equipped with a self-cleaning purging device to prevent ash accumulation and blockage that could cause measurement drift. The current load command data is sent by the central dispatch system via industrial Ethernet and includes the predicted load change trend value for the next ten minutes for feedforward compensation control.

[0084] Step S20: Determine the target state vector sequence under a unified time reference based on the boiler operating parameters;

[0085] It should be noted that the target state vector sequence is a data set constructed under a unified high-precision time base.

[0086] It is understood that the target state vector sequence is formed after a series of preprocessing steps, such as time alignment, noise filtering, and missing value interpolation, on the original boiler operating parameters. The sequence is organized in matrix form, where each row represents a specific and unified sampling time, and each column corresponds to a processed boiler physical quantity, such as furnace temperature, steam pressure, and flue gas oxygen content. This integrates the boiler operating parameters into a sequence of high-dimensional state vectors that are strictly synchronized on the time axis and arranged in chronological order.

[0087] In a specific embodiment, after the current operating status data is acquired, the boiler operating parameters are obtained. These parameters undergo spatiotemporal alignment and noise suppression processing to form a high-dimensional state vector sequence under a unified time reference. Specifically, all sensor sampling trigger signals are emitted from a unified master clock source, and the hardware synchronization accuracy of each channel is controlled within twenty microseconds to ensure that different physical quantities are collected under the same time reference. Subsequently, a sliding window mid-range filtering algorithm is executed on the original data sequence, with a fixed window width of seven sampling points to effectively eliminate pulse-type interference such as sudden jumps caused by electromagnetic transients or mechanical vibrations. Then, linear interpolation is performed on the filtered data to fill in missing points. The interpolation is calculated based on adjacent effective values ​​to avoid introducing false trends. Finally, the processed data is reordered by timestamp to form a high-dimensional state vector sequence, i.e., the target state vector sequence. Each row corresponds to a sampling time, and each column corresponds to a physical quantity, with a total dimension of no less than fifteen, covering all the aforementioned operating parameters. This sequence serves as the unified input interface for the three-level hierarchical decision model, ensuring that the data used for subsequent model inference is strictly aligned on the time axis and numerically stable and reliable.

[0088] In one feasible implementation, step S20 may include steps A11 to A14:

[0089] Step A11: Synchronize the sensor sampling data corresponding to the boiler operating parameters according to the master clock source to determine the original synchronization data;

[0090] It should be noted that the original synchronization data is the initial dataset obtained by forcibly aligning the sampling times of all heterogeneous sensors in the distributed sensor network based on a unified master clock source.

[0091] It is understandable that the original synchronization data is a unified and precise timestamp assigned to various boiler operating parameters that originally had microsecond-level deviations in time, marking that all physical quantities are placed under the same time reference for the first time. However, its values ​​still contain impulse noise, random interference and possible missing points introduced by the original measurement process.

[0092] Step A12: Filter the original synchronization data using a sliding window mid-range filtering algorithm to determine the filtered data;

[0093] It should be noted that the filtered data is the dataset obtained by applying a sliding window midpoint filtering algorithm to the original synchronization data.

[0094] It is understood that the filtered data has effectively identified and eliminated instantaneous and abrupt pulse noise caused by electromagnetic interference, mechanical vibration, etc., while retaining the true trend of parameter changes relatively well.

[0095] Step A13: Use linear interpolation to fill in the missing data points of the filtered data and determine the interpolation data;

[0096] It should be noted that the interpolated data is a complete, continuous and uninterrupted data sequence obtained by filling in the missing data points that still exist after filtering using a linear interpolation algorithm.

[0097] It is understood that the interpolated data can ensure that each sampling moment under a unified time base has a corresponding valid value, thereby eliminating data gaps caused by instantaneous sensor failure or data transmission packet loss.

[0098] Step A14: The interpolated data is rearranged according to timestamps to form a target state vector sequence arranged under a unified time reference, with each row corresponding to the sampling time and each column corresponding to the physical quantity.

[0099] It is understandable that arranging the data in the form of each row corresponding to the sampling time and each column corresponding to the physical quantity is to organize the processed interpolated data into a standard two-dimensional data matrix, in which each row represents a unique timestamp.

[0100] Step S30: Based on the target state vector sequence, input the hierarchical decision model to predict the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. The combustion efficiency optimization sub-model, heat conduction matching sub-model, and emission constraint coordination sub-model are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer.

[0101] It should be noted that the fuel valve opening correction amount is a control command value calculated by the combustion efficiency optimization sub-model and used to adjust the fuel supply valve; the fan speed adjustment amount is a control command value generated by the combustion efficiency optimization sub-model to adjust the speed of the forced draft fan or induced draft fan; the water pump inverter frequency adjustment value is a command value for controlling the operating frequency of the water pump motor optimized by the heat conduction matching sub-model through the gradient descent method; and the combustion intensity adjustment value is a control command value for actively limiting the combustion process generated by the emission constraint coordination sub-model when the risk of exceeding emission standards is predicted.

[0102] It is understood that the fuel valve opening correction amount is obtained after nonlinear compensation based on the deviation between the ideal air-fuel ratio setpoint and the actual air-fuel ratio using a fuzzy rule base. That is, it represents the percentage or absolute amount that needs to be increased or decreased based on the current valve opening. This is used to precisely adjust the fuel flow rate into the furnace, bringing the air-fuel ratio closer to the optimal value, thereby directly improving combustion efficiency. The fan speed adjustment amount is calculated in conjunction with the fuel valve opening correction amount. That is, it represents the percentage or absolute amount that needs to be increased or decreased in the current fan speed. By changing the air supply volume, it matches the change in fuel quantity, ensuring that the air-fuel ratio is dynamically maintained in the high-efficiency combustion zone. The optimal air-fuel ratio can be achieved. The frequency adjustment value of the water pump inverter aims to maximize the efficiency of the boiler system. That is, the output operating frequency of the water pump inverter needs to be set. By changing this frequency, the water flow rate is precisely adjusted to match the heat absorption capacity of the boiler evaporation section in real time, thereby minimizing heat loss in the heat transfer process. The combustion intensity adjustment value is a scaling factor that adjusts the fuel valve opening correction and fan speed adjustment of the combustion layer output as a whole. By reducing the total fuel and air volume input, the combustion intensity is forced to be limited within the predefined emission safety range, which is used to coordinate energy efficiency and environmental protection goals.

[0103] In a specific embodiment, after processing is complete, the target state vector sequence is input into a pre-constructed hierarchical decision model. This hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. Data is simultaneously fed into three parallel processing channels: the combustion efficiency optimization sub-model, the heat conduction matching sub-model, and the emission constraint coordination sub-model. Each sub-model operates independently without interference, interacting and arbitrating only at the output stage. Specifically, the combustion efficiency optimization sub-model analyzes the fuel's lower heating value, theoretical air volume coefficient, and current load rate to calculate the ideal air-fuel ratio setpoint. It also performs nonlinear compensation for actual air-fuel ratio deviations using a built-in fuzzy rule base. The heat conduction matching sub-model receives the steam drum pressure change rate, the average wall temperature gradient of the evaporator section, and the feedwater enthalpy, constructs a performance evaluation function, and solves for the feedwater flow setpoint that minimizes performance loss using the gradient descent method. The emission constraint coordination sub-model establishes an emission margin safety boundary function based on measured nitrogen oxide concentration, flue gas oxygen content, and denitrification agent injection margin, determining whether to trigger the combustion layer command attenuation factor.

[0104] The combustion efficiency optimization sub-model, heat conduction matching sub-model, and emission constraint coordination sub-model are all deployed on the large-core processors of the industrial edge computing node. A dual-buffer mechanism is used to receive new data, ensuring that the model inference process is not interrupted by data updates, with a single inference time not exceeding three milliseconds. The hierarchical decision engine module is deployed on the industrial edge computing node. Specifically, this node adopts a dual-core heterogeneous architecture: the large core is a quad-core ARM Cortex-A72 with a clock speed of 2.0 GHz, responsible for model inference computation; the small core is a dual-core Cortex-R5 with a clock speed of 800 MHz, responsible for real-time data stream scheduling and interrupt response. The two cores exchange intermediate results through a shared memory area, with communication latency controlled within five milliseconds. The operating system uses a real-time Linux kernel, with a task scheduling cycle of one millisecond, ensuring that control tasks are executed with priority. Model parameters and historical data are stored in a solid-state drive array, using a RAID five-redundancy structure, supporting hot-swappable replacement. The power module is configured with dual redundant power supplies: the main power supply comes from the plant's AC bus, and the backup power supply is a DC battery pack, with a switching time of less than twenty milliseconds. This deployment scheme ensures that the system operates stably for a long time in harsh industrial environments, with a mean time between failures (MTBF) of more than 100,000 hours.

[0105] Step S40: Based on at least one control command corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value, iteratively adjust the boiler operating parameters to determine the target boiler parameters;

[0106] It should be noted that the target boiler parameters are the set of parameters corresponding to the stable operating state of the boiler system that meets the global energy efficiency optimization target after iterative adjustment and closed-loop control of the control commands generated by the hierarchical decision model.

[0107] In a specific embodiment, the control strategies corresponding to the control commands are divided into four categories. The target boiler parameters can be obtained using these four control strategies. The first category is mandatory priority for exceeding emission standards. When the emission safety margin is less than 5%, the output of the combustion and heat conduction layers is immediately frozen, and only the emission layer attenuation command is executed. The second category is emergency response for exceeding the steam drum water level limit. When the water level deviates from the set value by more than 10%, all optimization commands are suspended, and the proportional-integral-derivative emergency control loop is activated. The third category is queued execution of combustion oscillation suppression commands. When a furnace pressure fluctuation frequency exceeds 2 Hz and the amplitude is greater than 500 Pa, a stable combustion command is inserted to adjust the air-fuel ratio and suppress oscillation. The fourth category is queued execution of conventional energy efficiency optimization commands, which are processed according to the order of receipt when there are no emergency events. Interlocking flags are set between the various strategies to prevent command superposition from causing equipment overload. The arbitration module outputs at a frequency of 20 times per second to ensure that emergency commands take effect within 50 milliseconds.

[0108] In one feasible implementation, step S40 may include steps B11 to B16:

[0109] Step B11: Obtain control strategy information;

[0110] It is understood that the control strategy information refers to which preset control strategy should be prioritized in the current control cycle, that is, to select, combine, modify or suspend multiple control commands output by the hierarchical decision model.

[0111] Step B12: When the control strategy information is the first type of strategy, the system only executes the control command corresponding to the combustion intensity adjustment value to iteratively adjust the boiler operating parameters to obtain the first target boiler parameters.

[0112] It is understandable that the first target boiler parameter is the stable operating state achieved by the system after iteratively adjusting the boiler operating parameters when the control strategy information is the first type of strategy, by suspending all adjustment instructions of the combustion efficiency optimization layer and the heat conduction matching layer, and only executing the control instruction corresponding to the combustion intensity adjustment value output by the emission constraint coordination layer. That is, under emergency environmental constraints, the boiler actively reduces the combustion intensity to bring pollutant emissions back to the safe range.

[0113] Step B13: When the control strategy information is the second type of strategy, suspend all control commands corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value and the combustion intensity adjustment value, and start the proportional-integral-derivative emergency control loop, and use the corresponding boiler operating parameters as the second target boiler parameters.

[0114] It is understandable that the second target boiler parameters are the set of boiler operating parameters obtained by the system after pausing the optimized control commands generated by the hierarchical decision model and switching to the bottom-level proportional-integral-derivative emergency control loop for rapid intervention when the control strategy information is the second type of strategy. This ensures that the core safety of the boiler is the highest priority, indicating that when the system detects that parameters such as the steam drum water level are seriously exceeded, it abandons the global energy efficiency optimization target and uses the control algorithm to bring the boiler back to a safe and stable operating state.

[0115] Step B14: When the control strategy information is the third type of strategy, obtain the stable combustion command, and iteratively adjust the air-coal ratio of the boiler operating parameters according to the stable combustion command to obtain the third target boiler parameters.

[0116] It is understood that the third target boiler parameter is the operating state in which the combustion process tends to be stable and the furnace pressure fluctuation is effectively suppressed after the system acquires and executes a special stable combustion command and dynamically iterates the air-coal ratio in the boiler operating parameters when the control strategy information is the third type of strategy. That is, when the system detects signs of unstable combustion, it executes the interim control action before the conventional energy efficiency optimization command, thereby quickly calming the combustion oscillation and restoring the stability of boiler combustion.

[0117] Step B15: When the control strategy information is the fourth type of strategy, the control commands corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value and the combustion intensity adjustment value are executed in normal queue to iteratively adjust the boiler operating parameters and obtain the fourth target boiler parameters.

[0118] It is understood that the fourth target boiler parameter is the ideal operating state achieved by the system after iteratively adjusting the boiler operating parameters through normal queuing and execution of all control commands output by the hierarchical decision model when the control strategy information is the fourth type of strategy, resulting in optimal global energy efficiency, compliance with environmental protection standards, and stable operation.

[0119] Step B16: Obtain the target boiler parameters based on any one of the first target boiler parameters, the second target boiler parameters, the third target boiler parameters, and the fourth target boiler parameters.

[0120] It is understood that the target boiler parameters are the system steady-state operating points that are optimal in terms of energy efficiency, environmental protection and safety, achieved through active intervention and feedback correction of boiler operating parameters. These parameters will be collected and fed back to the system input as the benchmark and evaluation for the next round of rolling optimization cycle.

[0121] Step S50: Feed back the target boiler parameters to complete the intelligent management and control of boiler industrial control energy efficiency optimization.

[0122] It is understandable that the feedback of the target boiler parameters is achieved by the system collecting and transmitting the actual values ​​reached after regulation through the sensor network after a control cycle ends. These values ​​are then used as the state input for the new cycle and compared and evaluated with the state sequence and optimization target of the previous cycle, thus forming a closed-loop control loop. This loop can continuously correct the control commands for the next cycle based on the actual response of the system, thereby realizing continuous adaptive and rolling optimization of intelligent control of boiler energy efficiency.

[0123] In a specific embodiment, the execution signals corresponding to the target boiler parameters can be connected to the fieldbus after photoelectric isolation to drive the fuel regulating valve, the forced draft fan frequency converter, the feedwater pump motor, and the flue gas damper actuator. The fuel regulating valve uses an electric actuator with built-in closed-loop position feedback, achieving a positioning accuracy better than 0.5% and a response time of less than one second. The forced draft fan frequency converter receives speed percentage commands, internally converts them into frequency output, and has an adjustment range of 20% to 120% of the rated speed, with adjustable acceleration and deceleration times. The feedwater pump motor is also driven by a frequency converter; the frequency command is linearly related to the feedwater flow rate setpoint, and the slope is calibrated based on the pump characteristic curve. The flue gas damper actuator is an angular stroke electric device that receives opening percentage commands and uses mechanical limits to prevent overtravel.

[0124] After all actuators have moved, their actual position or speed is fed back to the control system via feedback channels for the next round of state evaluation, forming a complete closed-loop control. The actuator positioning confirmation signal serves as a prerequisite for starting a new round of optimization, ensuring that the control action is truly completed before making the next decision. After closed-loop control is complete, new state data is periodically collected. Specifically, each control cycle is fixed at 500 milliseconds, triggered by a hardware timer. At the start of the cycle, the system freezes all sensor readings, performs spatiotemporal alignment and noise suppression processing, and generates a new high-dimensional state vector sequence. At this time, multiple sub-models reason in parallel, generating a new round of control commands. The arbitration module integrates the commands and issues them for execution. During the actuator movement, the system continuously monitors the positioning status. Once all key actuators confirm their positioning, or after a three-second timeout, the system forcibly enters the next cycle, regardless of whether the action is fully completed. New state data is collected at the start of the next cycle, ensuring the optimization process progresses continuously without time gaps. The feedback update module is equipped with an abnormal state interception mechanism. When any sensor data fluctuation exceeds three times the standard deviation of the historical mean, the corresponding channel data is automatically frozen and nearest neighbor interpolation is enabled, while a diagnostic alarm signal is triggered. This mechanism ensures that even if some sensors fail, the system can still maintain basic operation, avoiding a global crash due to a single point of failure.

[0125] Furthermore, intelligent management and control of boiler industrial control energy efficiency optimization can be achieved using a boiler industrial control energy efficiency optimization intelligent management and control system. This system supports offline training. Specifically, during boiler shutdown and maintenance, a historical operating dataset is imported. This dataset contains state vectors and corresponding control commands recorded every 500 milliseconds for at least three months. The training process consists of three stages: The first stage calibrates the membership function parameters of the fuzzy rule base using a genetic algorithm with a population size of 50, a crossover probability of 0.7, a mutation probability of 0.1, and a fitness function that is a weighted sum of efficiency and emission compliance rate under the corresponding operating condition. The second stage adjusts the gradient descent learning rate by searching for the optimal value in the range of 0.01 to 0.1 using a grid search method with a step size of 0.01. The third stage corrects the safety boundary threshold by back-calculating the critical margin value based on historical exceedance events and taking the lower limit of the 90% confidence interval as the new threshold. After training, the new parameters are written to solid-state storage and automatically loaded upon the next startup. This mode improves the model's generalization ability and adapts to operating condition drift caused by fuel characteristics or equipment aging.

[0126] The boiler industrial control energy efficiency optimization intelligent management and control system is equipped with a human-machine interface. Specifically, operators can manually set the load tracking weight coefficient, emission tolerance level, and energy efficiency optimization aggressiveness parameter. The load tracking weight coefficient ranges from zero to one; the larger the value, the more the system tends to respond quickly to load changes, sacrificing some steady-state efficiency. The emission tolerance level is divided into three levels: strict, standard, and lenient, corresponding to safety margin thresholds of 10%, 5%, and 2%, respectively. The energy efficiency optimization aggressiveness parameter controls the gradient descent step size, ranging from 0.01 to 0.1; the larger the value, the faster the optimization speed but the more prone to oscillation. The interface synchronously displays the real-time output command values ​​of the three decision layers, the current boiler efficiency value, the emission safety margin percentage, and the status of the main actuators. It supports historical curve playback, allowing users to view the parameter evolution process for any time period. The energy efficiency benchmarking analysis function automatically calculates the relative energy efficiency index, defined as the ratio of the local unit's efficiency to the industry benchmark unit's efficiency multiplied by 100. When the index is below 90, a highlight is given and optimization directions are recommended.

[0127] The intelligent management and control system for boiler industrial control energy efficiency optimization also supports multi-boiler group control mode. Specifically, the central coordinator collects the marginal energy efficiency contribution rate of each boiler, where marginal energy efficiency is defined as the steam efficiency increment corresponding to a unit increase in fuel. The total load command is allocated using the Lagrange multiplier method, with the objective function being to maximize the overall plant efficiency, and the constraints being the upper and lower limits of each boiler's output and the upper limit of total emissions. The solution process uses a sequential quadratic programming algorithm, updating the allocation scheme every five minutes. Each boiler maintains its local three-layer optimization architecture and operates independently, receiving only load commands from the central coordinator as the top-level input. The central coordinator and each boiler's local controller use deterministic network communication, with a fixed message transmission period of 500 milliseconds. Data packets include timestamps and sequence numbers, and the receiving end reorders packets based on the sequence number. When the packet loss rate exceeds 5%, a degraded operation mode is activated, each boiler switches to local independent optimization, the group control coordination function is disabled, and the local emission constraint layer safety margin threshold is increased by 5 percentage points, sacrificing some energy efficiency for operational stability.

[0128] The boiler industrial control energy efficiency optimization intelligent management system activates a preheating guidance program during the cold start-up phase. Specifically, it calculates the minimum warm-up rate based on the ambient temperature and boiler metal wall thickness using the formula: Warm-up rate equals the allowable thermal stress of the material divided by the elastic modulus and then multiplied by the coefficient of thermal expansion, with the result in degrees Celsius per minute. The upper limit is set at 5 degrees Celsius per minute, and the lower limit is 1 degree Celsius per minute. The actual rate is dynamically adjusted based on the wall temperature difference monitoring value. When the temperature difference between any two points exceeds 40 degrees Celsius, the heating is paused, and forced ventilation is activated to equalize the temperature until the temperature difference drops below 30 degrees Celsius. During the preheating guidance program, the combustion intensity gradually increases, with the initial fuel valve opening set at 5%, increasing by 0.5% per minute until the main steam parameters reach 80% of the rated value, at which point it switches to normal optimization mode. This process prevents thermal stress concentration from damaging pressure-bearing components and extends the service life of the equipment.

[0129] The intelligent control system for boiler industrial control and energy efficiency optimization activates a transient compensation mechanism under conditions of rapid load changes. Specifically, it predicts the load change trend for the next 30 seconds in advance, uses a first-order inertial element to simulate the dynamic response characteristics of the boiler, and identifies the time constant online based on the current load rate using a recursive least squares algorithm with a forgetting factor of 0.98. The prediction model takes the load command sequence of the past 10 seconds as input and outputs the predicted load value for the next 30 seconds. Based on the predicted value, it adjusts the setpoints of the combustion layer and heat conduction layer in advance, with the adjustment magnitude proportional to the predicted rate of change. The proportionality coefficient is calibrated based on historical transient data. This mechanism suppresses parameter fluctuations by more than 30%, shortens the transient time by 50%, and improves the system's dynamic response performance.

[0130] The intelligent management and control system for boiler industrial control energy efficiency optimization is equipped with an energy efficiency audit and tracking module. Specifically, it can record the status data, model output instructions, actuator response results, and final energy efficiency indicators based on each optimization decision, forming a complete operation log. The data storage period is ninety days, and data exceeding this period is automatically archived to an external storage device. The monthly energy efficiency report statistically analyzes key performance indicators such as average efficiency, emission compliance rate, and instruction execution success rate. The report supports exporting to a standardized format file, including boiler number, statistical period, average load rate, comprehensive efficiency, average nitrogen oxide emissions, number of abnormal events, and year-on-year and month-on-month change rates. The energy cost accounting unit converts the energy efficiency optimization results into economic benefit values ​​based on real-time fuel prices, electricity unit prices, and pollution discharge fee standards, intuitively displaying the direct economic benefits brought by energy conservation and consumption reduction. The economic weight can be dynamically adjusted. When the predicted electricity price increase exceeds 20% in the next two hours, the weight automatically increases to 0.8, prioritizing energy efficiency maximization to offset the impact of electricity prices.

[0131] The boiler industrial control energy efficiency optimization intelligent management system is equipped with a carbon footprint tracking module. Specifically, it can calculate the total carbon dioxide equivalent emissions based on the fuel carbon content and actual consumption. The calculation formula is: carbon emissions equal fuel consumption multiplied by carbon content multiplied by 44 divided by 12. Indirect emissions are calculated based on the grid emission factor, which is dynamically obtained from the national database. A daily carbon emission report is generated, and digitally signed emission data packets are uploaded to the national carbon emission monitoring platform daily. The blockchain notarization adopts a consortium blockchain architecture, with nodes jointly maintained by regulatory departments, verification agencies, and key emission units. The consensus mechanism uses a practical Byzantine fault-tolerant algorithm to ensure data immutability. The carbon asset digital management module registers carbon emission quotas on the blockchain ledger, supporting one-click order placement for selling surplus quotas or buying shortage quotas, with automatic transaction settlement. The smart contract execution engine presets carbon trading trigger conditions, such as "automatic price inquiry when quota utilization reaches 90%." When the conditions are met, the buy and sell orders are automatically executed, improving carbon asset management efficiency.

[0132] The boiler industrial control energy efficiency optimization intelligent management and control system has a reserved online evolution interface for artificial intelligence models. Specifically, it allows incremental learning of new operating data during operation, integrating new experiences into existing decision-making models through knowledge distillation technology. The teacher model is a large-scale neural network trained offline, while the student model is a lightweight version running online. The distillation loss function includes dual constraints of output soft label cross-entropy and mean squared error of intermediate features. The lightweight student model's parameter size is compressed to less than 5% of the teacher model, increasing inference speed by eight times and reducing memory usage by 90%. The self-evolutionary kernel incorporates a genetic algorithm optimizer, periodically mutating and selecting the structural parameters of the three-layer hierarchical decision model. The fitness function is a weighted sum of the average efficiency and emission compliance rate over 24 consecutive hours. The evolutionary process runs in an independent sandbox environment. Candidate models are first validated on a digital twin, and only those that meet the standards are deployed to the physical system, ensuring zero risk in the evolutionary process.

[0133] The boiler industrial control energy efficiency optimization intelligent management system is equipped with a predictive maintenance module. Specifically, it constructs an equipment health assessment model based on vibration spectrum analysis, lubricating oil quality testing, and infrared thermal imaging data. Vibration spectrum analysis uses a sampling frequency of 10,000 Hz, extracting characteristic frequency amplitudes through fast Fourier transform and comparing them with a baseline spectrum. An early wear alarm is triggered when the energy increase in a specific frequency band exceeds 50%. Lubricating oil quality testing employs an online viscometer and moisture sensor. When the viscosity deviates from the nominal value by 15% or the moisture content exceeds 0.3%, oil deterioration is determined, and replacement is indicated. Infrared thermal imaging data is collected by a fixed thermal imager, scanning the surface temperature field of key equipment every hour. Hot spot tracking diagnosis is initiated when the local temperature rise rate exceeds two degrees Celsius per minute. Before the annual overhaul, a maintenance recommendation list is automatically generated, recommending priority replacements for sensors, actuators, or vulnerable components based on equipment operating time, fault history, and performance degradation trends, assisting in the development of a precise maintenance plan.

[0134] The boiler industrial control energy efficiency optimization intelligent management and control system is equipped with a network security shield, specifically deploying a triple defense line of an industrial firewall, an intrusion detection system, and a data encryption gateway. The industrial firewall policy is based on a whitelist mechanism, allowing only predefined control commands and data query messages to pass. The intrusion detection system employs a dual-engine approach of signature-based matching and abnormal behavior analysis, achieving a false alarm rate of less than one in a thousand. The data encryption gateway implements SM quad encryption using the national cryptographic algorithm for all outgoing data, with a key length of 256 bits, and the session key is rotated hourly. A quantum key distribution interface is reserved, allowing future access to a quantum communication network to obtain unconditional secure keys, replacing the existing symmetric encryption system. This protection system meets the Level 3 compliance requirements of the Information Security Protection System, ensuring the long-term security of core industrial data.

[0135] The intelligent management and control system for boiler industrial control and energy efficiency optimization supports a federated learning collaborative model. Specifically, while protecting the data privacy of each power plant, a global optimization model is jointly trained. Each participant only uploads model gradients, not the original data. The aggregation server uses secure multi-party computation (MPC) technology to synthesize the global model. Based on the principle of secret sharing, the MPC splits the gradient vector into multiple random partitions and distributes them to different participants. Weighted averaging can be completed during aggregation without reconstructing the original gradients. Each training cycle is 24 hours. Local models at each power plant are updated daily, and the global model is aggregated weekly. The convergence speed is comparable to centralized training, and the model accuracy difference is less than one percent. This model achieves cross-enterprise knowledge sharing, improves the overall energy efficiency level of the industry, and ensures that trade secrets are not leaked.

[0136] This embodiment proposes an intelligent control method for optimizing boiler industrial energy efficiency, which solves the technical problem of how to achieve intelligent control of boiler industrial energy efficiency optimization under complex and ever-changing operating conditions. Compared with the prior art, this application realizes the fundamental transformation of boiler energy efficiency control from single-point parameter adjustment to global multi-objective collaborative optimization by performing high-precision synchronization and processing of boiler operating parameters and utilizing a hierarchical decision-making architecture composed of a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. This enables the boiler to maintain the optimal state of energy efficiency, environmental protection, and safety throughout the entire operating range, while continuously performing feedback and rolling optimization to achieve the unity of maximizing boiler energy efficiency, minimizing emissions, and maintaining operational stability under all operating conditions, significantly improving the overall performance and economic and environmental benefits of the boiler.

[0137] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the first embodiment can be referred to the above description, and will not be repeated hereafter.

[0138] In this embodiment, refer to Figure 3 , Figure 3 This is a flowchart illustrating Embodiment 2 of the intelligent control method for optimizing energy efficiency in boiler industrial control according to this application. Step S30 specifically includes steps S31 to S36:

[0139] Step S31: Input the fuel lower heating value, theoretical air volume coefficient and current load rate from the target state vector sequence into the combustion efficiency optimization sub-model to predict the ideal air-fuel ratio setpoint.

[0140] It should be noted that the ideal air-fuel ratio setting is the ratio of air to fuel mass flow rate that can theoretically achieve the highest combustion efficiency under the current load and fuel characteristics, determined by the system based on the calculated target total air volume and the real-time measured instantaneous fuel flow rate.

[0141] In a specific embodiment, in the combustion efficiency optimization sub-model, the ideal air-fuel ratio setpoint is calculated. Specifically, the theoretical air volume coefficient is calculated online based on the fuel element analysis results. The calculation formula is: theoretical air volume equals carbon content multiplied by 11.3% plus hydrogen content multiplied by 34.2% minus oxygen content multiplied by 4.3%, then divided by 100. This theoretical air volume coefficient is automatically updated with each fuel batch switch and stored in local non-volatile memory. Each time the fuel is switched, the material management system pushes a new value and, combined with the current load rate, reads the corresponding optimal excess air coefficient from a pre-stored lookup table. This table is calibrated based on historical best operating data and covers the load range of 30% to 120%. The theoretical air volume is multiplied by the optimal excess air coefficient to obtain the target total air volume. Subsequently, the ideal air-fuel ratio setpoint is calculated based on the ratio of the instantaneous fuel flow rate to the target total air volume.

[0142] In one feasible implementation, step S31 may include steps C11 to C15:

[0143] Step C11: Input the fuel lower heating value, theoretical air volume coefficient and current load rate from the target state vector sequence into the combustion efficiency optimization sub-model to predict the fuel element analysis results;

[0144] It should be noted that the fuel element analysis results are obtained by using a combustion efficiency optimization sub-model to calculate or predict the corresponding fuel chemical composition data online based on real-time parameters such as the input fuel lower heating value, theoretical air volume coefficient, and current load rate. The data mainly includes the mass percentage of elements such as carbon, hydrogen, oxygen, and sulfur, and are used to accurately calculate the theoretical air volume and ideal air-fuel ratio.

[0145] Step C12: Calculate the corresponding theoretical air volume online based on the fuel element analysis results;

[0146] It should be noted that the theoretical air volume is the minimum air volume or mass required for the complete combustion of a unit of fuel, calculated based on the results of fuel element analysis and through theoretical combustion chemical reaction equations.

[0147] It is understood that the theoretical air quantity represents the amount of air required for all combustible components in a fuel to react completely with oxygen under ideal chemical equilibrium conditions.

[0148] Step C13: Based on the theoretical air volume coefficient and the current load rate in the target state vector sequence, read the corresponding target excess air coefficient from a predefined lookup table;

[0149] It should be noted that the target excess air coefficient is an optimized coefficient greater than 1 that the system dynamically reads from a predefined lookup table based on the current load rate.

[0150] It is understood that the target excess air coefficient is the proportion of additional air required to ensure that the fuel can burn fully, safely and efficiently under actual combustion conditions, based on the theoretical air volume. It can convert the theoretical air volume into the target total air volume pursued in actual operation, so as to balance combustion efficiency and boiler safety and prevent heat loss caused by incomplete combustion or excessive air supply.

[0151] Step C14: Determine the target total air volume based on the theoretical air volume and the target excess air coefficient;

[0152] It should be noted that the target total air volume is the total air volume that the boiler should supply during actual operation, obtained by multiplying the calculated theoretical air volume by the target excess air coefficient determined from the lookup table based on the current load rate.

[0153] It is understandable that the target total air volume takes into account both the theoretical requirement for complete fuel combustion and the actual air margin required for the boiler to operate safely and efficiently under specific loads. The system will calculate the final ideal air-fuel ratio setpoint based on this target total volume and the measured fuel flow rate, thereby accurately guiding the adjustment of the air supply system.

[0154] Step C15: Calculate the ideal air-fuel ratio setpoint based on the target total air volume and the instantaneous fuel flow rate in the target state vector sequence.

[0155] It is understood that the ideal air-fuel ratio setpoint can be used as a dynamically updated optimal control reference input to the controller, which is then compared with the actual air-fuel ratio measurement to generate a deviation signal, thereby guiding the subsequent precise adjustment of the fuel valve and blower.

[0156] Step S32: Using a fuzzy rule base, nonlinear compensation is performed on the air-fuel ratio deviation corresponding to the ideal air-fuel ratio setting value to adjust the corresponding fuel valve opening and fan speed, and the fuel valve opening correction amount and fan speed adjustment amount are determined.

[0157] Understandably, due to the complex nonlinear characteristics of the combustion system, directly using the calculated air-fuel ratio deviation for control would lead to poor results. Therefore, the system uses a preset fuzzy rule library to perform intelligent nonlinear compensation processing on the deviation, thereby generating smooth and reasonable control commands, determining the amount of adjustment required for the fuel valve opening and blower speed, so as to achieve precise coordinated adjustment of the air-fuel ratio and ensure that the combustion process is always maintained in the high-efficiency range.

[0158] In a specific embodiment, the ideal air-fuel ratio setpoint can be used as the reference input of the fuzzy controller, compared with the actual measured air-fuel ratio, and a deviation signal is generated for subsequent nonlinear compensation. At this point, nonlinear compensation for the actual air-fuel ratio deviation is performed using a fuzzy rule base. Specifically, the fuzzy rule base contains thirty-seven regular rules, where the antecedent variable is the absolute difference between the load rate deviation percentage and the actual air-fuel ratio deviation from the theoretical value, and the consequent variable is the fuel valve opening increment and the fan speed percentage adjustment. The rule inference is defuzzified using the centroid method, outputting a continuous adjustment amount instead of a step command. Specifically, the load rate deviation and air-fuel ratio deviation can be mapped to five fuzzy sets: negative large, negative small, zero, positive small, and positive large. The membership function adopts a triangular distribution. Based on the current input value, relevant rules are activated, and the applicability of each rule is calculated. The consequent variables of all activated rules are weighted and averaged, and the weight is the applicability. The final output value is calculated by the centroid method and used as the fuel valve opening correction amount and the blower speed adjustment amount. This process ensures that the adjustment action is smooth and continuous, avoiding control jitter caused by rule switching. The output frequency of the fuzzy controller is ten times per second, which matches the response speed of the underlying actuator.

[0159] In one feasible implementation, step S32 may include steps D11 to D15:

[0160] Step D11: Obtain the fuel valve opening increment and the fan speed percentage adjustment.

[0161] It should be noted that the fuel valve opening increment is a basic adjustment amount defined as a consequent variable in the fuzzy rule base. It is used to characterize the theoretical range that the fuel regulating valve opening needs to be changed in the fuzzy inference stage to compensate for a specific degree of air-fuel ratio deviation. It is expressed as a percentage or in opening units.

[0162] It is understood that the fan speed percentage adjustment is a basic adjustment amount defined as a consequent variable in the fuzzy rule base. It represents the relative magnitude of the fan speed change that needs to be initially determined in the fuzzy inference stage to match fuel changes and correct the air-fuel ratio. It is expressed as a percentage of the rated speed and can be calculated in conjunction with the fuel valve opening increment.

[0163] Step D12: Use a fuzzy rule base to classify the air-fuel ratio deviation and load rate deviation corresponding to the ideal air-fuel ratio setting value as antecedent variables;

[0164] It is understood that the antecedent variable is a variable in the rule base of the fuzzy logic control system, located in the IF part, used to express the input conditions. That is, the measured parameters of the air-fuel ratio deviation and load rate deviation are input into the fuzzy rule base as the preconditions for triggering the control rules. They will be mapped to the corresponding fuzzy set to evaluate the current operating condition and activate the corresponding control rules, thereby determining how the system should adjust its output.

[0165] Step D13: Use a fuzzy rule base to classify the fuel valve opening increment and the fan speed percentage adjustment as consequent variables;

[0166] It is understood that the consequent variable is a variable in the rule base of the fuzzy logic control system, located in the THEN part, used to express the output result. That is, the fuel valve opening increment and the fan speed percentage adjustment are the preliminary control quantities derived from the fuzzy rules. As the conclusion or output action corresponding to the rule after it is activated, they need to be converted into accurate fuel valve opening correction and fan speed adjustment quantities that can directly drive the actuator after weighted averaging and defuzzification calculation based on the rule applicability.

[0167] Step D14: Map the antecedent variables to the corresponding fuzzy sets respectively, determine the corresponding rules and rule applicability based on the fuzzy sets, and use the rules and rule applicability to weighted average the consequent variables to determine the fuzzy output information. The fuzzy sets include negative large fuzzy sets, negative small fuzzy sets, zero fuzzy sets, positive small fuzzy sets, and positive large fuzzy sets.

[0168] It should be noted that the fuzzy output information is a fuzzy set or membership function distribution that has not yet been solved, obtained by weighting the consequent variables of all activated rules according to their respective rule applicability during the fuzzy inference process. It contains a weighted combination of the output information of all relevant control rules and is an intermediate result between fuzzy inference and precise output. It still needs to be processed by defuzzification methods such as the centroid method.

[0169] Understandably, in a fuzzy control system, to accurately describe the state of the input variables, their actual numerical range can be divided into five linguistic variable levels with clear semantics. That is, the fuzzy output information can correspond to negative large, indicating that it is much lower than the target value; negative small, indicating that it is slightly lower than the target value; zero, indicating that it is equal to or very close to the target value; positive small, indicating that it is slightly higher than the target value; and positive large, indicating that it is much higher than the target value. Each level is defined by a specific membership function, such as a triangular distribution, to define its numerical boundary and fuzzy transition region, which is used for intelligent reasoning and nonlinear compensation of the system.

[0170] Step D15: The fuzzy output information is solved using the centroid method to obtain the fuel valve opening correction amount and the blower speed adjustment amount.

[0171] It is understood that the fuel valve opening correction amount is a specific control command that can be directly issued to the fuel regulating valve actuator after nonlinear compensation of the air-fuel ratio deviation by the fuzzy rule base and defuzzification by the centroid method. It is a precise value that represents the specific percentage or absolute opening value that needs to be increased or decreased immediately based on the current valve opening. It is used to eliminate the air-fuel ratio deviation by precisely adjusting the fuel supply, thereby stabilizing the combustion process in the highest efficiency range.

[0172] Additionally, it should be noted that the fan speed adjustment amount is a specific control command that can be directly sent to the fan inverter after being generated in conjunction with the fuel valve opening correction amount and after undergoing fuzzy inference and defuzzification processing. It is a precise value that represents the specific percentage or absolute speed value that the fan speed needs to change. It is used to synchronously adjust the air supply volume so that it is precisely matched with the corrected fuel quantity, and together ensure that the air-fuel ratio is dynamically maintained at the ideal set value to achieve efficient combustion.

[0173] Step S33: Input the steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence into the heat conduction matching sub-model to predict the efficiency.

[0174] Understandably, efficiency can be taken as the objective function to be minimized, and the gradient descent method, an optimization algorithm, can be used to automatically and iteratively find the water flow setpoint that maximizes efficiency, i.e. minimizes flow loss.

[0175] In a specific embodiment, within the heat conduction matching sub-model, a performance evaluation function is constructed, whereby performance efficiency is defined as the effective output performance divided by the input fuel performance. The effective output performance is determined by the product of steam enthalpy rise and feedwater flow rate. Steam enthalpy rise is obtained by consulting a table of steam thermodynamic properties. The input parameters are the superheater outlet steam pressure and temperature. Feedwater flow rate is measured in real-time by a mass flow meter. The input fuel performance is obtained by multiplying the fuel mass flow rate by the fuel chemical performance per unit mass. The chemical performance value is read from a database based on the fuel type; for coal, it is approximately 33,000 kJ / kg, and for natural gas, approximately 55,000 kJ / kg. The evaluation function expression is: Performance efficiency equals steam enthalpy rise multiplied by feedwater flow rate, divided by fuel mass flow rate multiplied by chemical performance.

[0176] In one feasible implementation, step S33 may include steps E11 to E13:

[0177] Step E11: Obtain the water supply flow rate and fuel mass flow rate;

[0178] It should be noted that the feedwater flow rate is the mass of water pumped into the boiler economizer and steam-water system per unit time, which is measured in real time by a mass flow meter installed on the feedwater pipeline. It characterizes the boiler's evaporation capacity, is used to calculate the current actual heat load and efficiency, and is also the target object that the optimization algorithm directly adjusts.

[0179] It is understood that the fuel mass flow rate is the mass of fuel fed into the boiler furnace for combustion per unit time, which is directly measured by high-precision instruments such as Coriolis mass flow meters installed on the main fuel pipeline, and is used together with the feedwater flow rate to determine the energy balance of the boiler system.

[0180] Step E12: Determine the steam enthalpy rise by querying a predefined table of steam thermodynamic properties based on the steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence.

[0181] It should be noted that the steam enthalpy rise refers to the increase in enthalpy per unit mass of the working fluid, i.e., the steam, when the feedwater absorbs heat in the boiler's evaporative heating surface and transforms into saturated steam or superheated steam.

[0182] It is understood that the steam enthalpy rise is the total enthalpy difference between the feedwater inlet state and the superheater outlet steam state, determined by consulting the steam thermodynamic property table based on parameters such as the steam drum pressure change rate and the average wall temperature gradient of the evaporation section. It is used to characterize the effective heat absorption of the boiler's evaporation heating surface.

[0183] Step E13: Input the steam enthalpy rise, the feedwater flow rate, the fuel mass flow rate, and the corresponding mass fuel chemistry into the heat conduction matching sub-model to predict the efficiency.

[0184] It is understood that the efficiency is the ratio of the effective output steam efficiency of the boiler system to the chemical efficiency of the input fuel. The effective output efficiency is determined by the product of the steam enthalpy rise and the feedwater flow rate, while the input fuel efficiency is obtained by multiplying the fuel mass flow rate by its unit mass chemical efficiency.

[0185] Step S34: Solve for the water flow rate setpoint that minimizes the pump efficiency using the gradient descent method, and determine the corresponding water pump frequency adjustment value based on the water flow rate setpoint.

[0186] Understandably, the system can solve for the set value of the feedwater flow rate that minimizes the efficiency of the boiler, and combine it with the characteristic curve of the feedwater pump to calculate the specific operating frequency that the pump frequency converter needs to be set, thereby matching the real-time evaporation and heat absorption capacity of the boiler by precisely controlling the feedwater flow rate.

[0187] In a specific embodiment, efficiency can be used as the optimization objective, and maximizing it is equivalent to minimizing efficiency loss. The gradient descent method uses a fixed iteration step size of 0.05, and the convergence threshold is set to a minimum change in the objective function after three consecutive iterations of less than one-thousandth. Each iteration requires recalculating the steam enthalpy rise and chemical efficiency to ensure the evaluation function is dynamically updated according to operating conditions. The feedwater flow rate setpoint that minimizes efficiency loss can be solved using the gradient descent method. Specifically, the feedwater flow rate setpoint is initialized to the current measured value, and the efficiency corresponding to the current setpoint is calculated. The change in efficiency when the feedwater flow rate increases or decreases by a small step is then calculated to approximate the gradient direction. The feedwater flow rate setpoint is adjusted along the gradient direction with a step size of 0.05 multiplied by the maximum allowable flow rate. This process is repeated until the change in the objective function after three consecutive iterations is less than one-thousandth, at which point convergence is determined. The final output feedwater flow rate setpoint serves as the reference for the frequency adjustment command of the feedwater pump inverter. To prevent oscillation, a mandatory wait of two sampling cycles is performed after each adjustment before the next iteration. This process ensures that the feedwater flow rate always tracks the changes in the heat absorption capacity of the evaporation section, avoids local dry burning or flooding of the heat exchange surface, and maintains the optimal efficiency of the complete heat conduction path.

[0188] In one feasible implementation, step S34 may include steps F11 to F13:

[0189] Step F11: Obtain the gradient iteration step size, convergence condition, and sampling period;

[0190] It is understood that the gradient iteration step size is the magnitude of the change in the setpoint of the water flow rate adjusted along the gradient direction each time the gradient descent method is used for iterative optimization. If the step size is too large, the optimization process will oscillate or even diverge near the optimal solution. If the step size is too small, the convergence speed will be slow. The convergence condition is the criterion used to determine whether the gradient descent iterative optimization process has ended. It is set to a sufficiently small threshold, for example, the change in the efficiency of the objective function in three consecutive iterations is less than one-thousandth. When the change in the objective function during the optimization process meets this condition, it is considered that the algorithm has found an approximate optimal solution, the iteration can be stopped, and the current setpoint of the water flow rate can be output. The sampling period specifically refers to the time interval between two adjacent gradient descent iterations in this context, or the period during which the system reads sensor data to update the input parameters of the optimization model. Setting an appropriate sampling period is to ensure that the effect of the previous water flow rate adjustment is fully reflected in the system, and to avoid misjudgment or oscillation caused by system response lag or data update too quickly.

[0191] Step F12: Calculate the change in efficiency corresponding to the increase and decrease of the gradient iteration step size when the water flow rate increases or decreases, respectively, using the gradient descent method, and determine the gradient direction;

[0192] It should be noted that the gradient direction is determined when optimizing the feedwater flow rate setpoint using the gradient descent method. This is achieved by calculating the change in efficiency after increasing or decreasing the setpoint by a small step size, where the small step size is the gradient iteration step size. This yields the parameter adjustment direction that maximizes the increase or decrease of the objective function. In the problem of maximizing efficiency, the algorithm actually seeks the gradient ascent direction. This direction can represent the trend of change in the feedwater flow rate setpoint at the current moment to minimize efficiency loss, and is used to determine the search path at each step during the iterative optimization process.

[0193] Step F13: Iteratively adjust the water flow rate setpoint according to the convergence condition and the sampling period along the gradient rising direction in the gradient direction to obtain the water flow rate setpoint that minimizes the efficiency and the corresponding water pump inverter frequency adjustment value.

[0194] Understandably, when solving for the optimal feedwater flow rate, with the goal of maximizing efficiency, the feedwater flow rate setpoint is continuously updated iteratively along the calculated gradient direction with a specific step size, based on the preset convergence conditions and sampling period, until the convergence conditions are met, thereby determining the most accurate feedwater flow rate setpoint that enables the boiler heat transfer process to achieve the highest efficiency.

[0195] Step S35: Input the measured nitrogen oxide concentration, flue gas oxygen content, and denitrification agent injection margin from the target state vector sequence into the emission constraint coordination sub-model to predict the emission value;

[0196] Understandably, emission values ​​are assessment indicators used to quantitatively characterize the current or recent pollutant emission levels and the risk of exceeding standards of boilers, representing the instantaneous concentration of pollutants.

[0197] In a specific embodiment, an emission margin safety boundary function is established in the emission constraint coordination sub-model. Specifically, the safety margin can be equal to the standard limit minus the current measured value and then divided by the standard limit. The standard limit is preset, for example, the nitrogen oxide emission limit is 50 milligrams per standard cubic meter. The current measured value is provided in real time by the flue gas analyzer, after noise suppression and time alignment processing. When the safety margin is less than 5%, a command attenuation factor is activated, with an initial value of 0.95, decreasing by 0.01 each control cycle until the emission value falls back to a safety margin greater than 10%.

[0198] Step S36: Based on the emission value, trigger the combustion layer command attenuation factor to limit the corresponding combustion intensity within the predefined emission return safety range, and determine the combustion intensity adjustment value.

[0199] Understandably, when the predicted values ​​indicate that emissions are about to exceed the limits, the model will immediately trigger a combustion layer command attenuation factor. This factor actively and forcibly reduces the combustion intensity of the boiler by proportionally reducing the fuel valve and fan adjustment commands output by the combustion efficiency optimization layer, thereby limiting the combustion process to a predefined emission return safety range.

[0200] In a specific embodiment, a combustion layer command attenuation factor can be triggered based on the emission values. This attenuation factor acts on the fuel valve opening correction and fan speed adjustment output by the combustion efficiency optimization sub-model, multiplying them by the attenuation factor before outputting. This mechanism ensures that when emissions approach the threshold, the system proactively reduces combustion intensity, reserving a reaction time window to avoid exceeding limits due to lag in the denitrification system response. The denitrification agent injection margin is calculated from the difference between the tank level gauge and the cumulative injection amount. When the margin is below 20% of the rated capacity, the combustion intensity is reduced in advance, further enhancing emission safety.

[0201] In one feasible implementation, step S36 may include steps G11~G14:

[0202] Step G11: Obtain the standard limit and denitrification agent injection margin;

[0203] It should be noted that the standard limit is the highest permissible value for the concentration of pollutants in boiler flue gas that meets the standard. It serves as a preset boundary condition for the emission constraint coordination sub-model, used to calculate the safety margin of the current emission status and trigger combustion restriction commands.

[0204] It is understood that the denitrification agent injection margin is the remaining usable processing capacity of the boiler denitrification system at the current moment. It can be estimated by the tank level, injection pump capacity and cumulative consumption. It represents the system's buffer capacity to cope with the risk of future emission increases. When the margin is insufficient, the emission constraint coordination sub-model will intervene in advance and actively reduce the combustion intensity to reserve a sufficient time window for the denitrification reaction.

[0205] Step G12: Determine the safety margin based on the standard limit and the emission value;

[0206] It is understood that the safety margin is a risk assessment indicator used to quantify the current emission status approaching the standard limit. Its calculation formula is (standard limit - current measured emission value) / standard limit. The calculation result is expressed as a percentage, thereby representing the remaining emission excess buffer space before the system triggers control actions. When the safety margin is lower than the preset threshold, the system will automatically activate preventive measures such as the command attenuation factor.

[0207] Step G13: Based on the safety margin, adjust the combustion layer command attenuation factor according to the corresponding control cycle decrease value to determine the combustion layer command correction attenuation factor.

[0208] It is understood that the combustion layer command correction attenuation factor is a proportional coefficient between 0 and 1 that is dynamically calculated based on the size of the safety margin when the emission constraint coordination sub-model detects insufficient emission safety margin. The combustion layer command correction attenuation factor achieves precise limitation of combustion intensity by scaling the fuel valve opening correction amount and fan speed adjustment amount of the original output of the combustion efficiency optimization layer in real time. Its value decreases as the safety margin decreases, and it is used to flexibly correct the energy efficiency optimization command under the premise of ensuring emission compliance, so as to prioritize environmental protection goals.

[0209] Step G14: Adjust the corresponding combustion layer output fuel valve opening correction amount and fan speed adjustment amount according to the combustion layer instruction correction attenuation factor, and reduce the combustion intensity in advance according to the denitrification agent injection margin, so as to limit the corresponding combustion intensity within the predefined emission return safety range, and obtain the combustion intensity adjustment value.

[0210] Understandably, the system can use the calculated combustion layer command to correct the attenuation factor, proportionally reducing the fuel valve opening correction amount and fan speed adjustment amount originally output by the combustion efficiency optimization layer, thereby directly reducing the current combustion power. At the same time, the system will refer to the denitrification agent injection margin indicator and proactively further reduce the combustion intensity before the denitrification treatment capacity approaches saturation, thereby constraining the boiler's real-time operating status within the predefined emission return safety range.

[0211] This embodiment proposes an intelligent control method for optimizing energy efficiency in boiler industrial control, which solves the technical problem of how to achieve intelligent control for optimizing energy efficiency in boiler industrial control under complex and ever-changing operating conditions. Compared with the prior art, this application realizes global multi-objective collaborative optimization of the boiler system by constructing a hierarchical decision-making architecture that optimizes combustion efficiency, matches heat conduction, and coordinates emission constraints. The combustion layer calculates the ideal air-fuel ratio based on real-time fuel characteristics and load dynamics, and adjusts the air-fuel ratio precisely through fuzzy reasoning. The heat conduction layer predicts efficiency based on the steam drum pressure and wall temperature gradient, and optimizes the feedwater flow rate using the gradient descent method. The emission layer predicts risks based on pollutant concentration and denitrification capacity, and dynamically constrains combustion intensity through a command attenuation mechanism, so that the boiler always maintains the optimal state of highest energy efficiency, lowest emissions, and most stable operation under complex operating conditions.

[0212] This application also provides an intelligent control system for optimizing energy efficiency in boiler industrial control. Please refer to [link / reference]. Figure 4 The boiler industrial control energy efficiency optimization intelligent management and control system includes:

[0213] The multi-source data acquisition module 10 is used to acquire boiler operating parameters through a distributed sensor network;

[0214] Data preprocessing module 20 is used to determine the target state vector sequence under a unified time reference based on the boiler operating parameters;

[0215] The hierarchical decision engine module 30 is used to predict the fuel valve opening correction amount, fan speed adjustment amount, water pump inverter frequency adjustment value and combustion intensity adjustment value based on the target state vector sequence input into the hierarchical decision model. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model and an emission constraint coordination sub-model. The combustion efficiency optimization sub-model, heat conduction matching sub-model and emission constraint coordination sub-model are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer and an emission constraint coordination layer.

[0216] The instruction arbitration and execution module 40 is used to iteratively adjust the boiler operating parameters based on at least one control instruction corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value, and to determine the target boiler parameters.

[0217] The feedback update module 50 is used to feed back the target boiler parameters to complete the intelligent management and control of boiler industrial control energy efficiency optimization.

[0218] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for intelligent control and management of boiler industrial energy efficiency optimization, characterized in that, The method includes: Boiler operating parameters are acquired through a distributed sensor network; Based on the boiler operating parameters, a target state vector sequence under a unified time reference is determined; Based on the target state vector sequence input hierarchical decision model, the fuel valve opening correction amount, fan speed adjustment amount, water pump inverter frequency adjustment value, and combustion intensity adjustment value are predicted. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. The combustion efficiency optimization sub-model, heat conduction matching sub-model, and emission constraint coordination sub-model are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. Based on at least one control command corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value, the boiler operating parameters are iteratively adjusted to determine the target boiler parameters; The target boiler parameters are fed back to complete the intelligent management and control of boiler industrial control energy efficiency optimization. The steps of predicting the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment based on the target state vector sequence input hierarchical decision model include: The fuel lower heating value, theoretical air volume coefficient and current load rate in the target state vector sequence are input into the combustion efficiency optimization sub-model to predict the ideal air-fuel ratio setpoint. The fuzzy rule base is used to perform nonlinear compensation on the air-fuel ratio deviation corresponding to the ideal air-fuel ratio setting value, and the corresponding fuel valve opening and fan speed are adjusted to determine the fuel valve opening correction amount and fan speed adjustment amount. The steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence are input into the heat conduction matching sub-model to predict the efficiency. The water flow rate setpoint that minimizes the efficiency of the pump is obtained by solving the gradient descent method, and the corresponding frequency adjustment value of the pump frequency converter is obtained. The measured nitrogen oxide concentration, flue gas oxygen content, and denitrification agent injection margin in the target state vector sequence are input into the emission constraint coordination sub-model to predict emission values. Based on the emission value, the combustion layer command attenuation factor is triggered to limit the corresponding combustion intensity within a predefined emission return safety range, thereby determining the combustion intensity adjustment value.

2. The method as described in claim 1, characterized in that, The step of determining the target state vector sequence under a unified time reference based on the boiler operating parameters includes: Based on the master clock source, the sensor sampling data corresponding to the boiler operating parameters are synchronized to determine the original synchronization data; The original synchronization data is filtered using a sliding window mid-range filtering algorithm to determine the filtered data; Linear interpolation is used to fill in the missing data points of the filtered data to determine the interpolation data; The interpolated data is rearranged according to timestamps to form a target state vector sequence arranged under a unified time reference, with each row corresponding to the sampling time and each column corresponding to the physical quantity.

3. The method as described in claim 1, characterized in that, The step of inputting the fuel lower heating value, theoretical air volume coefficient, and current load rate from the target state vector sequence into the combustion efficiency optimization sub-model to predict the ideal air-fuel ratio setpoint includes: The fuel lower heating value, theoretical air volume coefficient and current load rate in the target state vector sequence are input into the combustion efficiency optimization sub-model to predict the fuel element analysis results. The corresponding theoretical air volume is calculated online based on the fuel element analysis results. Based on the theoretical air volume coefficient and the current load rate in the target state vector sequence, the corresponding target excess air coefficient is read from a predefined lookup table; The target total air volume is determined based on the theoretical air volume and the target excess air coefficient. The ideal air-fuel ratio setpoint is calculated based on the target total air volume and the instantaneous fuel flow rate in the target state vector sequence.

4. The method as described in claim 1, characterized in that, The steps of using a fuzzy rule base to perform nonlinear compensation on the air-fuel ratio deviation corresponding to the ideal air-fuel ratio setpoint, adjusting the corresponding fuel valve opening and fan speed, and determining the fuel valve opening correction amount and fan speed adjustment amount include: Obtain the fuel valve opening increment and the fan speed percentage adjustment; The air-fuel ratio deviation and load rate deviation corresponding to the ideal air-fuel ratio set value are classified as antecedent variables using a fuzzy rule base. The fuel valve opening increment and the fan speed percentage adjustment are classified as consequent variables using a fuzzy rule base. The antecedent variables are mapped to corresponding fuzzy sets, and the corresponding rules and rule applicability are determined according to the fuzzy sets. The consequent variables are weighted and averaged using the rules and rule applicability to determine the fuzzy output information. The fuzzy sets include negative large fuzzy sets, negative small fuzzy sets, zero fuzzy sets, positive small fuzzy sets, and positive large fuzzy sets. The fuzzy output information is solved using the center of gravity method to obtain the fuel valve opening correction amount and the blower speed adjustment amount.

5. The method as described in claim 1, characterized in that, The step of inputting the steam drum pressure change rate and the average wall temperature gradient of the evaporation section from the target state vector sequence into the heat conduction matching sub-model to predict the efficiency includes: Obtain water supply flow rate and fuel mass flow rate; The steam enthalpy rise is determined by querying a predefined table of steam thermodynamic properties based on the steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence. The steam enthalpy rise, the feedwater flow rate, the fuel mass flow rate, and the corresponding mass fuel chemistry are input into the heat conduction matching sub-model to predict the efficiency.

6. The method as described in claim 1, characterized in that, The step of obtaining the corresponding pump frequency adjustment value by solving for the water flow rate setpoint that minimizes the pump efficiency using the gradient descent method includes: Obtain the gradient iteration step size, convergence condition, and sampling period; The gradient descent method is used to calculate the change in efficiency corresponding to the increase and decrease of the water flow rate when the gradient iteration step size is reduced, and the gradient direction is determined. Based on the convergence condition and the sampling period, the water flow rate setpoint is iteratively adjusted along the gradient ascent direction in the gradient direction to obtain the water flow rate setpoint that minimizes the efficiency and the corresponding water pump inverter frequency adjustment value.

7. The method as described in claim 1, characterized in that, The step of determining the combustion intensity adjustment value by triggering the combustion layer command attenuation factor based on the emission value to limit the corresponding combustion intensity within a predefined emission return safety range includes: Obtain standard limits and denitrification agent injection margin; A safety margin is determined based on the standard limits and the emission values; Based on the aforementioned safety margin, the combustion layer command attenuation factor is adjusted according to the corresponding control cycle decrease value to determine the combustion layer command correction attenuation factor. The combustion intensity is adjusted by modifying the fuel valve opening correction amount and the fan speed adjustment amount of the corresponding combustion layer output according to the combustion layer instruction correction attenuation factor, and the combustion intensity is reduced in advance according to the denitrification agent injection margin, so as to limit the corresponding combustion intensity within the predefined emission return safety range, thus obtaining the combustion intensity adjustment value.

8. The method as described in claim 1, characterized in that, The step of iteratively adjusting the boiler operating parameters based on at least one control command corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value to determine the target boiler parameters includes: Obtain control strategy information; When the control strategy information is the first type of strategy, the system only executes the control command corresponding to the combustion intensity adjustment value to iteratively adjust the boiler operating parameters to obtain the first target boiler parameters; When the control strategy information is the second type of strategy, all control commands corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value and the combustion intensity adjustment value are suspended, and the proportional-integral-derivative emergency control loop is started, and the corresponding boiler operating parameters are used as the second target boiler parameters. When the control strategy information is the third type of strategy, a stable combustion command is obtained, and the air-coal ratio of the boiler operating parameters is iteratively adjusted according to the stable combustion command to obtain the third target boiler parameters; When the control strategy information is the fourth type of strategy, the control commands corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value and the combustion intensity adjustment value are executed in normal queue to iteratively adjust the boiler operating parameters and obtain the fourth target boiler parameters; The target boiler parameters are obtained based on any one of the first target boiler parameters, the second target boiler parameters, the third target boiler parameters, and the fourth target boiler parameters.

9. A smart control system for optimizing energy efficiency in boiler industrial control, characterized in that, The system is used to implement the method as described in claim 1, the system comprising: A multi-source data acquisition module is used to acquire boiler operating parameters through a distributed sensor network; The data preprocessing module is used to determine the target state vector sequence under a unified time reference based on the boiler operating parameters; The hierarchical decision engine module is used to predict the fuel valve opening correction, fan speed adjustment, water pump inverter frequency adjustment, and combustion intensity adjustment based on the target state vector sequence input into the hierarchical decision model. The hierarchical decision model includes a combustion efficiency optimization sub-model, a heat conduction matching sub-model, and an emission constraint coordination sub-model. The combustion efficiency optimization sub-model, heat conduction matching sub-model, and emission constraint coordination sub-model are obtained by decomposing the global energy efficiency objective function corresponding to the boiler operating parameters into a combustion efficiency optimization layer, a heat conduction matching layer, and an emission constraint coordination layer. The instruction arbitration and execution module is used to iteratively adjust the boiler operating parameters based on at least one control instruction corresponding to the fuel valve opening correction amount, the fan speed adjustment amount, the water pump frequency converter frequency adjustment value, and the combustion intensity adjustment value, and to determine the target boiler parameters. The feedback update module is used to feed back the target boiler parameters to complete the intelligent management and control of boiler industrial control energy efficiency optimization. The hierarchical decision engine module is also used to input the fuel lower heating value, theoretical air volume coefficient and current load rate in the target state vector sequence into the combustion efficiency optimization sub-model to predict the ideal air-fuel ratio setpoint. The fuzzy rule base is used to perform nonlinear compensation on the air-fuel ratio deviation corresponding to the ideal air-fuel ratio setting value, and the corresponding fuel valve opening and fan speed are adjusted to determine the fuel valve opening correction amount and fan speed adjustment amount. The steam drum pressure change rate and the average wall temperature gradient of the evaporation section in the target state vector sequence are input into the heat conduction matching sub-model to predict the efficiency. The water flow rate setpoint that minimizes the efficiency of the pump is obtained by solving the gradient descent method, and the corresponding frequency adjustment value of the pump frequency converter is obtained. The measured nitrogen oxide concentration, flue gas oxygen content, and denitrification agent injection margin in the target state vector sequence are input into the emission constraint coordination sub-model to predict emission values. Based on the emission value, the combustion layer command attenuation factor is triggered to limit the corresponding combustion intensity within a predefined emission return safety range, thereby determining the combustion intensity adjustment value.

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