Low-emission high-efficiency boiler combustion control management system

Abstract

The embodiment of the invention provides a low-emission and high-efficiency boiler combustion control and management system, relates to the technical field of low-emission and high-efficiency boiler combustion control and management systems, and aims to solve the problems that the combustion efficiency is reduced and the emission of nitrogen oxides is increased due to poor air-coal matching synergism. The system comprises a sensor array, a data acquisition and preprocessing module, a multivariable coupling analysis engine, a combustion state collaborative decision center and an actuator driving array, and an optimal air-coal ratio set point is generated in real time through deep neural network modeling and a multi-target dynamic optimization algorithm. And the actuating mechanism is driven to cooperatively adjust air supply, fuel and flue gas recirculation, so that cooperative control of high efficiency and low emission is realized.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of boiler combustion control, and particularly relates to a low-emission high-efficiency boiler combustion control management system. BACKGROUND

[0002] In the field of energy power and heat energy engineering, as a core heat energy conversion equipment, the combustion efficiency and the pollution emission control level of the boiler are directly related to the energy utilization efficiency and environmental protection. Efficient and clean combustion technology is a key link to achieve the goal of energy saving and emission reduction, and has great significance for industrial production and central heating systems.

[0003] Among them, the boiler combustion control management system is the core technical direction to achieve the above-mentioned goal. The system aims to accurately adjust the key parameters such as fuel supply, air ratio, furnace temperature and air-coal ratio, so as to optimize the combustion process, thereby maximizing the combustion efficiency and inhibiting the generation of nitrogen oxides and other pollutants under the premise of ensuring stable heat output.

[0004] The prior art usually adopts an independent closed-loop control system based on the classical PID control theory to adjust a single or a few combustion parameters. However, such a system is difficult to cope with complex working conditions such as fuel property fluctuation and frequent load changes during boiler operation. There is a lack of cooperation between control loops, which often causes the air-coal ratio to deviate from the optimal interval, resulting in insufficient combustion or excessive air coefficient. This not only directly reduces the thermal efficiency of the boiler, but also significantly increases the emission concentration of nitrogen oxides, which cannot meet the dual requirements of high efficiency and low emission. Therefore, how to realize the comprehensive and collaborative optimization of combustion efficiency and pollutant emission under complex actual operating conditions has become a technical problem to be solved in the field. SUMMARY

[0005] The embodiment of the present application aims to at least solve one of the technical problems existing in the prior art, and provides a low-emission high-efficiency boiler combustion control management system.

[0006] The embodiment of the present application provides a low-emission high-efficiency boiler combustion control management system, comprising: a sensor array deployed on the site of the boiler, used to collect multi-parameter original data of the boiler combustion process; a data acquisition and preprocessing module connected to the sensor array, used to standardize the original signals uploaded by the sensor array, and generate a multi-parameter time series data set of the combustion process with a unified time stamp; The multivariate coupled analysis engine, which is connected to the data acquisition and preprocessing module, has a built-in combustion nonlinear mapping model based on a deep neural network. It is used to take the preprocessed combustion process multi-parameter time series dataset as input and generate the predicted values ​​of the theoretically optimal combustion efficiency index and nitrogen oxide emission concentration index under the current operating conditions through forward propagation calculation. The combustion state collaborative decision-making center, connected to the multivariate coupling analysis engine, integrates a dynamic multi-objective optimization algorithm. It uses the combustion efficiency and nitrogen oxide emission concentration prediction values ​​output by the multivariate coupling analysis engine as optimization objectives, the boiler operation safety boundary conditions as constraints, and performs Pareto optimal solution set search through a non-dominated sorting genetic algorithm. Based on preset weight preferences, it selects the optimal air-coal ratio setpoint combination from the Pareto front. The actuator drive array, controlled by the combustion state collaborative decision-making center, is used to receive the optimal setpoint combination command from the combustion state collaborative decision-making center and drive multiple actuators to perform precise actions, thereby achieving coordinated regulation of air volume, fuel supply and recirculated flue gas volume.

[0007] Furthermore, the sensor array consists of multiple high-precision sensors distributed in the boiler furnace, flue, air supply system, and fuel supply pipeline; The sensor array includes an infrared thermal imager array for real-time monitoring of the two-dimensional temperature field inside the furnace, a multi-component gas analyzer for measuring the oxygen and nitrogen oxide concentrations in the flue gas, a Coriolis mass flow meter for detecting the instantaneous flow rate of fuel, and a differential pressure transmitter for acquiring the air flow rate and pressure.

[0008] Furthermore, the data acquisition and preprocessing module performs standardization processing on the raw signals uploaded by the sensor array, including signal filtering, outlier removal, and dimensional normalization. The signal filtering uses a digital filtering algorithm to suppress high-frequency noise in the original signal; The outlier removal is based on the sliding window statistical principle, which calculates the mean and standard deviation of the data within the window. Data points that deviate from the mean by more than 3 times the standard deviation are identified as outliers and removed. The dimensional normalization transforms each physical quantity signal into the range of 0 to 1.

[0009] Furthermore, the deep neural network contains three hidden layers; The first hidden layer contains 128 neurons and uses the ReLU activation function; The second hidden layer contains 64 neurons and uses the ReLU activation function; The third hidden layer contains 32 neurons and uses the Sigmoid activation function.

[0010] Furthermore, the dynamic multi-objective optimization algorithm initializes a population of 100 individuals in each control cycle; Each individual is coded as a set of wind-coal ratio decision variables; The algorithm generates the offspring population by simulating binary crossover and polynomial mutation operations, with the crossover probability set to 0.9 and the mutation probability set to 0.1. Perform a fast non-dominated sort on the merged parent and offspring populations and calculate the crowding degree of individuals in each non-dominated layer. Elite selection is performed based on non-dominance level and crowding, retaining the 100 best individuals for the next generation of evolution.

[0011] Furthermore, the actuator drive array adopts a hierarchical control architecture; The top layer is a setpoint tracking controller, which uses a proportional-integral-derivative control algorithm to calculate the theoretical control quantity of each actuator; The middle layer is the actuator dynamic compensator, which performs feedforward compensation on the theoretical control quantity based on the measured dynamic characteristics of each actuator; The bottom layer is a safety interlock protection unit that continuously monitors the working status of each actuator and key safety parameters of the boiler. Once signs of overheating, overpressure, or flameout are detected, it immediately overrides the upper-level command and forcibly drives the relevant actuators into a preset safety state.

[0012] Furthermore, it also includes an adaptive learning and update module; The adaptive learning and update module periodically evaluates the prediction accuracy of the multivariate coupled analysis engine and the optimization effect of the combustion state collaborative decision-making center. When the evaluation metric is below the preset threshold for five consecutive running days, the model retraining process is automatically triggered. The model retraining process utilizes recently accumulated runtime data to perform incremental learning on the deep neural network model.

[0013] Furthermore, the model retraining process employs an incremental learning strategy; Incremental learning uses recently accumulated operational data to fine-tune the parameters of existing deep neural network models; The fine-tuning process uses a small learning rate to prevent catastrophic forgetting of existing knowledge.

[0014] Furthermore, the system operates within a multi-timescale coordinated control framework; The coordination and control framework includes a fast adjustment loop with a second-level response, a steady-state optimization loop with a minute-level response, and a strategy adjustment loop with an hour-level response. The rapid adjustment circuit acts directly on the actuator drive array to respond to instantaneous disturbances in the combustion process; The steady-state optimization loop periodically executes the computational tasks of the multivariate coupled analysis engine and the combustion state collaborative decision-making center; The strategy adjustment loop slowly adjusts high-level parameters such as weight preferences of the optimization algorithm based on long-term performance evaluation results.

[0015] Furthermore, the training process of the deep neural network model is as follows: Construct a training sample library containing historical operating data and corresponding optimal operating points. The training samples cover typical operating conditions of the boiler from 30% to 100% of its rated load. An adaptive moment estimation algorithm was used to iteratively optimize the network weights and biases. The initial learning rate was set to 0.001, the batch size was set to 64, and the number of training epochs was set to 500. The objective function for model training is defined as the weighted sum of the mean square error between the predicted combustion efficiency and the actual combustion efficiency and the mean square error between the predicted nitrogen oxide concentration and the actual nitrogen oxide concentration, where the weight of the combustion efficiency error is set to 0.6 and the weight of the nitrogen oxide concentration error is set to 0.4.

[0016] Compared with existing technologies, the low-emission, high-efficiency boiler combustion control and management system of this invention achieves deep fusion and intelligent decision-making of multiple parameters in the boiler combustion process by constructing a collaborative control system integrating a sensor array, data acquisition and preprocessing module, multivariate coupling analysis engine, combustion state collaborative decision-making center, and actuator drive array. The multivariate coupling analysis engine utilizes deep neural networks to accurately model the complex nonlinear relationship between combustion efficiency and pollutant emissions, providing a reliable basis for optimization decisions. The combustion state collaborative decision-making center employs a dynamic multi-objective optimization algorithm, capable of finding the optimal balance point between combustion efficiency and nitrogen oxide emissions in real time while meeting safety constraints, thus maintaining the air-coal ratio within the optimal range even under complex and variable operating conditions. The hierarchical control and safety interlocking design of the actuator drive array ensures the accurate execution of control commands and the safety of system operation. The adaptive learning and update module ensures the adaptability and robustness of the system in long-term operation. The multi-timescale coordinated control framework achieves comprehensive optimization from instantaneous adjustment to long-term strategies. This system fundamentally solves the problems of incomplete combustion and excessive pollutant emissions caused by the lack of coordination in traditional independent control loops, significantly improves the overall operating efficiency of the boiler, and achieves a synergistic unity of high efficiency and low emissions. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall technical solution architecture of the low-emission, high-efficiency boiler combustion control and management system proposed in this invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] Unless otherwise specifically stated, the technical or scientific terms used in the embodiments of this invention should be understood in their ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," as used in the embodiments of this invention, do not limit the shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof mentioned, nor do they exclude the appearance or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof, or the inclusion of these.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale, and techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail; however, where appropriate, the illustrated techniques, methods, and apparatus should be considered part of the specification. In all the examples shown and discussed herein, any other specific example may have different values. It should be noted that similar symbols and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] In the description of the embodiments of the present invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of the present invention, as well as the features of different embodiments or examples.

[0023] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention; it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0024] Example 1 Please refer to the attached document. Figure 1 This embodiment details the specific technical implementation of a low-emission, high-efficiency boiler combustion control and management system. The system consists of five core components: a sensor array deployed at the boiler site; a data acquisition and preprocessing module connected to the sensor array; a multivariate coupled analysis engine communicating with the data acquisition and preprocessing module; a combustion state collaborative decision-making center connected to the multivariate coupled analysis engine; and an actuator drive array controlled by the combustion state collaborative decision-making center. These five components, through tight logical connections and data flow, constitute a closed-loop intelligent control system, aiming to achieve synergistic optimization of high efficiency and low emissions in the boiler combustion process.

[0025] The sensor array, serving as the system's sensing layer, is physically deployed across the boiler's critical operating areas. Specifically, an infrared thermal imager array is arranged in a grid pattern along the four walls of the boiler furnace and at the front and rear arches. This array consists of no fewer than sixteen high-resolution infrared temperature probes, each responsible for monitoring the two-dimensional temperature distribution of a specific area within the furnace. The sampling frequency of the infrared thermal imager array is set to 10 Hz, enabling real-time capture of the dynamic changes in flame morphology, high-temperature region distribution, and temperature gradient within the furnace. A multi-component gas analyzer is installed in the boiler's tail flue. This analyzer uses non-dispersive infrared spectroscopy to measure the oxygen concentration in the flue gas and chemiluminescence to measure the nitrogen oxide concentration. The multi-component gas analyzer is equipped with a high-temperature sampling probe and a fully heated pipeline to ensure accurate flue gas samples; its measurement data update cycle is two seconds. A Coriolis mass flow meter is installed on the fuel supply pipeline near the burner inlet. This flow meter directly measures the instantaneous mass flow rate of the fuel, with a measurement accuracy of ±0.5%, sensitively reflecting fluctuations in fuel supply. Differential pressure transmitters are installed in the main air duct and each secondary air branch duct of the air supply system. These transmitters measure the pressure difference before and after the throttling element and calculate the real-time air volumetric flow rate using fluid dynamics formulas, while simultaneously monitoring the pressure status of the air supply system. All sensors transmit the acquired raw analog or digital signals to the data acquisition and preprocessing module via industrial-grade shielded cables or fiber optic networks.

[0026] The data acquisition and preprocessing module is the system's data hub, responsible for receiving and processing massive amounts of raw data from the sensor array. Hardware-wise, this module utilizes a high-performance industrial computer equipped with a multi-channel synchronous acquisition card. On the software side, the module runs a real-time data preprocessing program. This program first performs digital filtering on the input raw signal using a fourth-order Butterworth low-pass filter with a cutoff frequency set to 1.5 times the highest effective frequency of the signal to suppress high-frequency noise. Subsequently, the program executes an outlier removal algorithm based on the sliding window statistical principle, calculating the mean and standard deviation of the data within the window. Data points deviating from the mean by more than three times the standard deviation are identified as outliers and removed. For data gaps caused by outlier removal, linear interpolation is used to fill in the missing data. After filtering and outlier processing, the program performs dimensional normalization. Specifically, it converts each physical quantity signal to the range of zero to one. The conversion formula is based on the upper and lower limits of the range of each physical quantity. For temperature signals, the lower limit is set to 0 degrees Celsius, and the upper limit is set to 1200 degrees Celsius depending on the boiler type. For oxygen concentration signals, the lower limit is set to 0 percent, and the upper limit is set to 25 percent. For nitrogen oxide concentration signals, the lower limit is set to 0 milligrams per cubic meter, and the upper limit is set to 500 milligrams per cubic meter. For fuel flow and air flow signals, the lower limit is set to 0, and the upper limits correspond to the maximum design flow of their respective pipelines. Finally, the data acquisition and preprocessing module adds a uniform, high-precision timestamp to each processed data point, with a timestamp accuracy of one millisecond, generating a multi-parameter time-series dataset of the combustion process with strict temporal consistency. This dataset is continuously sent to the multivariate coupling analysis engine via a high-speed industrial Ethernet interface at 100-millisecond intervals.

[0027] The multivariate coupling analysis engine is the intelligent core of the system, and its core component is a combustion nonlinear mapping model built based on a deep neural network. This model is deployed on a dedicated graphics processing unit (GPU) server to ensure real-time inference. The number of nodes in the input layer of the combustion nonlinear mapping model strictly corresponds to the feature dimensions of the multi-parameter time-series dataset of the combustion process. These features include, but are not limited to, temperature values ​​in multiple regions of the furnace, flue gas oxygen concentration, flue gas nitrogen oxide concentration, instantaneous fuel flow rate, total air flow rate, flow rates of each secondary air branch, and air pressure. The input features are 12-dimensional: the average temperature of the four regions of the furnace front wall / rear wall / left wall / right wall (4-dimensional), flue gas oxygen concentration (1-dimensional), flue gas nitrogen oxide concentration (1-dimensional), total fuel mass flow rate (1-dimensional), primary air / secondary air A / secondary air B / secondary air C flow rates (4-dimensional), and main air duct pressure (1-dimensional). The time-series window length is 10 seconds, and the sampling interval is 100ms. The model's network structure contains three hidden layers. The first hidden layer contains 128 neurons, which perform high-order nonlinear transformations on the input features, and its activation function uses a linear rectified unit. The mathematical expression for the linear rectified function is that the output equals the maximum value between the input and zero. This function effectively alleviates the vanishing gradient problem and accelerates model training. The second hidden layer contains sixty-four neurons, further extracting and compressing feature information, and also uses a linear rectified unit as the activation function. The third hidden layer contains thirty-two neurons, which are responsible for mapping features to a representation space more suitable for the prediction task. Its activation function uses an sigmoid growth curve. The sigmoid growth curve compresses the neuron output to between zero and one, and its mathematical expression is that the output equals one divided by one plus the negative power of the natural constant e. The model's output layer contains two neurons, corresponding to the theoretically optimal combustion efficiency index prediction value and the theoretically optimal nitrogen oxide emission concentration index prediction value under the current operating conditions, respectively. The combustion efficiency index is a dimensionless number between zero and one; the closer the value is to one, the more complete the combustion. The unit of the nitrogen oxide emission concentration prediction value is milligrams per cubic meter. This deep neural network model is fully trained using historical data. The specific training process is as follows: First, a large-scale training sample library is constructed from the boiler historical database. The sample library needs to cover all typical operating conditions of the boiler from 30% to 100% rated load. Each sample contains a set of preprocessed multi-parameter combustion process data as input features, and the actual optimal combustion efficiency and actual optimal nitrogen oxide concentration values ​​obtained through thermal calculations and emission monitoring under the corresponding operating condition as target labels. The training uses an adaptive moment estimation algorithm to iteratively optimize all weight and bias parameters of the network. The adaptive moment estimation algorithm combines the advantages of the momentum method and the root mean square propagation method, and can adaptively adjust the learning rate of each parameter. The training hyperparameters are set as follows: initial learning rate of 0.001, batch size of 64, and total number of training cycles of 500.The objective function for model training is defined as the combined error between the predicted and actual values, which is a weighted sum of the mean square error of combustion efficiency prediction and the mean square error of nitrogen oxide concentration prediction. The weighting coefficient for the mean square error of combustion efficiency prediction is set to 0.6, and the weighting coefficient for the mean square error of nitrogen oxide concentration prediction is set to 0.4. By minimizing this objective function, the trained model can accurately capture the complex, nonlinear coupling relationship between combustion efficiency and nitrogen oxide emission concentration. During online system operation, the multivariate coupling analysis engine periodically receives the latest multi-parameter time-series dataset of the combustion process from the data acquisition and preprocessing module, inputs it into the trained combustion nonlinear mapping model for forward propagation calculation, and outputs the theoretical optimal combustion efficiency prediction and the theoretical optimal nitrogen oxide emission concentration prediction for the current moment within fifty milliseconds. These two predictions form the basis for subsequent multi-objective optimization.

[0028] The combustion state collaborative decision-making center is the command center of the system, integrating a dynamic multi-objective optimization algorithm. This algorithm uses the theoretical optimal combustion efficiency prediction and the theoretical optimal nitrogen oxide emission concentration prediction output by the multivariate coupled analysis engine as optimization objectives, while using the boiler's safe boundary conditions as hard constraints. These safe boundary conditions include, but are not limited to, the maximum furnace temperature limit, the lower limit of flue gas oxygen content, the minimum stable combustion load of the burner, and the safe range of boiler drum water level. The dynamic multi-objective optimization algorithm uses a non-dominated sorting genetic algorithm to search for the Pareto optimal solution set. In each control cycle, typically thirty seconds, the algorithm starts and initializes a population of one hundred individuals. Each individual represents a set of air-fuel ratio decision variables using real-number encoding. These decision variables typically include the total fuel flow setpoint, the total forced air flow setpoint, the secondary air ratio, and the flue gas recirculation flow setpoint. The algorithm generates the offspring population by simulating binary crossover and polynomial mutation operations. The simulated binary crossover operation is an extension of the binary string crossover process on real-number encoding, with a crossover probability set to 0.9. The multinomial mutation operation perturbs individual codes using a multinomial distribution to maintain population diversity, with a mutation probability set to 0.1. Subsequently, the algorithm merges the parent and offspring populations and performs a fast non-dominated sort on the merged population. The fast non-dominated sort divides the population into multiple non-dominated levels based on the individual's objective function value. Individuals in the first non-dominated level are not dominated by any other individuals; that is, they are no worse than other individuals in all objectives and are better than others in at least one objective. Individuals in the second non-dominated level are dominated only by individuals in the first non-dominated level, and so on. Within each non-dominated level, the algorithm further calculates the crowding degree of each individual. Crowding degree measures how densely an individual is with other individuals in its non-dominated level; a higher crowding degree indicates a sparser solution set around the individual, which helps maintain the diversity of the solution set. Finally, the algorithm performs elite selection based on the non-dominated level and crowding degree. Individuals with higher non-dominated levels are prioritized, and for individuals in the same non-dominated level, those with higher crowding degrees are selected. Through elite selection, the one hundred best individuals are retained to form a new parent population, which then enters the next generation of evolution. The optimization process is iterative, with a maximum of fifty iterations. The algorithm also includes a convergence criterion: if the change in the leading edge position of the Pareto optimal solution set over ten consecutive generations is less than one-thousandth, it is considered convergent and terminates early. After the optimization process, the algorithm outputs the Pareto optimal solution set found in the current control cycle. The decision-maker built into the combustion state collaborative decision-making center selects a unique combination of optimal air-fuel ratio setpoints from the Pareto leading edge based on preset weight preferences. These weight preferences are set by the operator according to actual operational needs; for example, when pursuing maximum efficiency, a higher weight can be assigned to combustion efficiency; when environmental protection requirements are stringent, a lower weight can be assigned to nitrogen oxide emission concentrations.The final optimal air-coal ratio setpoint combination is sent to the actuator drive array via a high-speed communication network.

[0029] The actuator drive array is the system's execution terminal, responsible for translating the optimization instructions issued by the combustion state collaborative decision-making center into precise physical actions of each actuator. The actuator drive array adopts a hierarchical control architecture consisting of three levels. The top level is the setpoint tracking controller, which receives the optimal air-coal ratio setpoint combination instruction and compares it with the actual feedback values ​​of each actuator to generate a control error signal. The setpoint tracking controller uses a proportional-integral-derivative (PID) control algorithm to calculate the theoretical control quantity for each actuator. The PID algorithm includes a proportional element, an integral element, and a derivative element. The proportional element outputs the control quantity based on the current error magnitude, the integral element accumulates historical errors to eliminate steady-state error, and the derivative element provides anticipatory adjustment based on the error change trend. The controller's proportional coefficient, integral time constant, and derivative time constant are all field-tuned to ensure fast and stable setpoint tracking performance. The middle level consists of actuator dynamic compensators. Because each actuator, such as the variable frequency fan, fuel regulating valve, and flue gas recirculation valve, exhibits varying degrees of mechanical inertia, response lag, and nonlinearity, the actuator dynamic compensator, based on the pre-identified dynamic models of each actuator, performs feedforward compensation on the theoretical control quantity output by the setpoint tracking controller. The compensator effectively counteracts the adverse effects of actuator dynamic lag on control performance by introducing a leading element or inverse model compensation. The bottom layer is the safety interlock protection unit. This unit operates independently of the upper-level control loop, continuously and in real-time monitoring the operating status of each actuator, including valve position feedback, motor current, and actuator alarm signals. It also monitors key safety parameters of the boiler operation, such as furnace pressure, drum water level, and main steam temperature and pressure. The safety interlock protection unit has comprehensive pre-set safety logic. Once any condition endangering the safe operation of the boiler, such as over-temperature, over-pressure, excessive negative pressure, or signs of flameout, is detected, the safety interlock protection unit will immediately override the upper-level control commands, cut off normal control logic, and forcibly drive the relevant actuators to a preset safe state. For example, when signs of flameout appear, the fuel supply is immediately cut off while maintaining the purge airflow; when the furnace pressure is too high, the induced draft fan opening is quickly reduced. This layered architecture ensures both the precise execution of control commands and provides the highest level of safety for the system. Over-temperature: Temperature in any area of ​​the furnace > 1200℃; Over-pressure: Furnace pressure > +500Pa; Signs of flameout: Furnace bottom temperature < 600℃ and flue gas oxygen concentration > 18% for 5 seconds.

[0030] The system also integrates an adaptive learning and update module, which is crucial for maintaining long-term system performance. This module runs independently in the background, typically 24 hours a day. Its core task is to periodically evaluate the prediction accuracy of the multivariate coupled analysis engine and the optimization effect of the combustion state co-decision center. When evaluating the prediction accuracy of the multivariate coupled analysis engine, the module collects the model's predicted values ​​for combustion efficiency and nitrogen oxide concentration within an evaluation period and compares them with the validated actual optimal values ​​after that time, calculating the root mean square error and mean absolute percentage error. When evaluating the optimization effect of the combustion state co-decision center, the module statistically analyzes the average combustion efficiency and average nitrogen oxide emission concentration of the boiler under optimization commands and compares them with the baseline levels before optimization. The module has multiple preset evaluation index thresholds. When an evaluation index, such as the root mean square error of the prediction model or the average actual emission concentration, falls below its corresponding preset threshold for five consecutive operating days, the adaptive learning and update module automatically triggers a model retraining process. The retraining process does not train the model from scratch but uses an incremental learning strategy. Incremental learning utilizes recently accumulated boiler operation data, such as the past thirty days, to fine-tune the parameters of existing deep neural network models. The fine-tuning process employs a small learning rate, such as 0.0001, to prevent catastrophic forgetting of existing knowledge. Through incremental learning, the deep neural network model can gradually adapt to changes in fuel characteristics, such as fluctuations in coal calorific value and volatile matter content, as well as drift in boiler dynamic characteristics caused by equipment aging, such as ash accumulation on heat exchanger surfaces and decreased fan efficiency, ensuring the predictive accuracy and optimization effectiveness of the system's long-term operation.

[0031] The system operates within a multi-timescale coordinated control framework. This framework divides the control task into three loops with different response time scales, working collaboratively to achieve comprehensive optimization. The fast adjustment loop, with a second-level response, directly acts on the setpoint tracking controller layer of the actuator drive array. This loop primarily addresses instantaneous disturbances during combustion, such as instantaneous combustion fluctuations caused by changes in fuel particle size or minute pulsations in the air supply system pressure. Through rapid calculations by the proportional-integral-derivative (PID) controller, the fast adjustment loop adjusts the actuators within seconds to suppress disturbances and maintain instantaneous combustion stability. The steady-state optimization loop, with a minute-level response, periodically executes calculations between the multivariate coupled analysis engine and the combustion state collaborative decision-making center, typically every thirty seconds to two minutes. Under relatively stable operating conditions, this loop makes more refined adjustments to the air-fuel ratio, seeking the optimal balance between efficiency and emissions under current conditions, achieving continuous improvement in steady-state operating quality. The strategy adjustment loop, with an hour-level response, has the longest operating cycle, typically ranging from several hours to twenty-four hours. This loop slowly adjusts the system's high-level parameters based on the long-term operational performance evaluation results of the adaptive learning and update module. These high-level parameters include, but are not limited to, weight preferences in the multi-objective optimization algorithm of the combustion state collaborative decision center, boundary values ​​of safety constraints, and even the trigger threshold for model retraining. The strategy adjustment loop ensures that the system's optimization strategy remains dynamically consistent with the boiler's long-term operational goals, such as annual energy efficiency targets and total environmental emission targets.

[0032] In summary, this embodiment fully reveals the specific implementation details of the low-emission, high-efficiency boiler combustion control and management system by detailing the precise deployment and data acquisition of the sensor array, the efficient cleaning and standardization of the data acquisition and preprocessing module, the construction and forward inference of the deep neural network model in the multivariate coupled analysis engine, the iterative search and decision-making of the dynamic multi-objective optimization algorithm in the combustion state collaborative decision-making center, the hierarchical precise control and safety interlocking of the actuator-driven array, the periodic evaluation and incremental learning of the adaptive learning and update module, and the division of labor and cooperation of the multi-timescale coordinated control framework. Through deep data fusion and intelligent decision-making, this system achieves real-time and precise optimization of the air-coal ratio under complex and variable operating conditions, thereby significantly improving combustion efficiency and effectively reducing nitrogen oxide emissions while ensuring safe boiler operation.

[0033] Example 2 This embodiment provides an alternative implementation scheme for a low-emission, high-efficiency boiler combustion control and management system. Its core architecture is consistent with that of Embodiment 1, but different technical paths are adopted in the model selection of the multivariate coupling analysis engine and the optimization algorithm of the combustion state collaborative decision center to cope with specific application scenarios or computing resource limitations.

[0034] In the multivariate coupling analysis engine section, this embodiment uses a machine learning model based on gradient boosting decision tree ensemble to replace the deep neural network in Embodiment 1, for establishing the nonlinear mapping relationship of combustion. Gradient boosting decision tree is an iterative decision tree algorithm that constructs multiple decision trees sequentially, with each tree's learning objective being to correct the prediction residual of the previous tree. In specific implementation, this ensemble model consists of one hundred decision trees, with a maximum depth limit of six layers for each tree to prevent overfitting. The model's input features are the same as in Embodiment 1, consisting of a multi-parameter time-series dataset of the combustion process standardized by the data acquisition and preprocessing module. The model outputs are also the theoretical optimal combustion efficiency prediction and the theoretical optimal nitrogen oxide emission concentration prediction. When training the model, the same historical sample library as in Embodiment 1 is used, and the optimization algorithm employs gradient descent with a learning rate set to 0.1. The loss function is also defined as the weighted sum of the combustion efficiency prediction error and the nitrogen oxide concentration prediction error, with the weight allocation consistent with Embodiment 1. Gradient boosting decision tree models typically exhibit excellent performance when processing tabular data and are relatively insensitive to hyperparameter adjustments, making them more advantageous for deployment on edge devices with limited computing resources. After the model training is completed, during the online inference phase, the multivariate coupling analysis engine inputs the preprocessed data into the gradient boosting decision tree model. The model quickly provides prediction results by traversing the rule paths of each decision tree, and the inference latency can be controlled within 20 milliseconds.

[0035] In the collaborative decision-making center for combustion state, this embodiment uses a decomposition-based multi-objective evolutionary algorithm instead of the non-dominated sorting genetic algorithm in Embodiment 1. The core idea of ​​the decomposition-based multi-objective evolutionary algorithm is to decompose a multi-objective optimization problem into several scalarized subproblems and simultaneously optimize these subproblems. Specifically, the algorithm first decomposes the bi-objective optimization problem—maximizing combustion efficiency and minimizing nitrogen oxide emissions—into one hundred scalarized subproblems using a set of uniformly distributed weight vectors. Each subproblem corresponds to a specific weight combination, thus forming a uniform search direction in the objective space. The algorithm initializes a population of one hundred individuals, each associated with a specific subproblem. During the evolutionary process, for each subproblem, the algorithm typically selects ten individuals corresponding to adjacent subproblems within its neighborhood and performs crossover and mutation operations to generate new candidate solutions. The crossover operation uses simulated binary crossover, and the mutation operation uses polynomial mutation, with parameter settings identical to Embodiment 1. The newly generated candidate solutions are used to update the current optimal solutions for their respective subproblems and other subproblems within their neighborhood. This neighborhood-based cooperative mechanism enables the population to collaboratively search the entire Pareto front. The optimization process is iterative, with a maximum of fifty iterations. Because the decomposition-based multi-objective evolutionary algorithm breaks down the search task, its convergence speed may outperform the non-dominated sorting genetic algorithm on some problems. After optimization, the combustion state collaborative decision-making center directly selects the corresponding individuals from the solutions to these one hundred optimized sub-problems, based on preset weight preferences, as the optimal air-coal ratio setpoint combination, making the decision-making process more direct.

[0036] The basic hierarchical control architecture and safety interlocking logic of the actuator drive array are exactly the same as in Embodiment 1, ensuring control accuracy and safety. However, at the setpoint tracking controller level, this embodiment introduces a Smith predictor to further improve control performance for actuators with slower response characteristics, such as large flue gas recirculation valves. The Smith predictor is a compensatory control method for objects with large time lag. When controlling the flue gas recirculation valve, the setpoint tracking controller embeds an approximate dynamic model of the valve, including its gain, time constant, and pure time lag. The controller uses this model to predict the future output of the controlled object under the action of the control quantity and compares this predicted value with the set value, thereby generating control actions in advance and effectively overcoming the adverse effects of large time lag on system stability. The control of other actuators still adopts the proportional-integral-derivative algorithm.

[0037] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A low-emission, high-efficiency boiler combustion control and management system, characterized in that, include: A sensor array deployed at the boiler site is used to collect raw data on multiple parameters of the boiler combustion process; The data acquisition and preprocessing module connected to the sensor array is used to standardize the raw signals uploaded by the sensor array and generate a multi-parameter time series dataset of the combustion process with a unified timestamp. The multivariate coupled analysis engine, which is connected to the data acquisition and preprocessing module, has a built-in combustion nonlinear mapping model based on a deep neural network. It is used to take the preprocessed combustion process multi-parameter time series dataset as input and generate the predicted values ​​of the theoretically optimal combustion efficiency index and nitrogen oxide emission concentration index under the current operating conditions through forward propagation calculation. The combustion state collaborative decision-making center, connected to the multivariate coupling analysis engine, integrates a dynamic multi-objective optimization algorithm. It uses the combustion efficiency and nitrogen oxide emission concentration prediction values ​​output by the multivariate coupling analysis engine as optimization objectives, the boiler operation safety boundary conditions as constraints, and performs Pareto optimal solution set search through a non-dominated sorting genetic algorithm. Based on preset weight preferences, it selects the optimal air-coal ratio setpoint combination from the Pareto front. The actuator drive array, controlled by the combustion state coordination decision center, is used to receive the optimal setpoint combination command from the combustion state coordination decision center and drive multiple actuators to perform precise actions, thereby achieving coordinated regulation of air volume, fuel supply and recirculated flue gas volume.

2. The low-emission, high-efficiency boiler combustion control and management system according to claim 1, characterized in that, The sensor array consists of multiple high-precision sensors distributed in the boiler furnace, flue, air supply system and fuel supply pipeline. The sensor array includes an infrared thermal imager array for real-time monitoring of the two-dimensional temperature field inside the furnace, a multi-component gas analyzer for measuring the oxygen and nitrogen oxide concentrations in the flue gas, a Coriolis mass flow meter for detecting the instantaneous flow rate of fuel, and a differential pressure transmitter for acquiring the air flow rate and pressure.

3. The low-emission, high-efficiency boiler combustion control and management system according to claim 1, characterized in that, The data acquisition and preprocessing module performs standardization processing on the raw signals uploaded by the sensor array, including signal filtering, outlier removal, and dimension normalization. The signal filtering uses a digital filtering algorithm to suppress high-frequency noise in the original signal; The outlier removal is based on the sliding window statistical principle, which calculates the mean and standard deviation of the data within the window. Data points that deviate from the mean by more than 3 times the standard deviation are identified as outliers and removed. The dimensional normalization transforms each physical quantity signal into the range of 0 to 1.

4. A low-emission, high-efficiency boiler combustion control and management system according to any one of claims 1 to 3, characterized in that, The deep neural network contains three hidden layers; The first hidden layer contains 128 neurons and uses the ReLU activation function; The second hidden layer contains 64 neurons and uses the ReLU activation function; The third hidden layer contains 32 neurons and uses the Sigmoid activation function.

5. A low-emission, high-efficiency boiler combustion control and management system according to any one of claims 1 to 3, characterized in that, The dynamic multi-objective optimization algorithm initializes a population of 100 individuals in each control cycle; Each individual is coded as a set of wind-coal ratio decision variables; The algorithm generates the offspring population by simulating binary crossover and polynomial mutation operations, with the crossover probability set to 0.9 and the mutation probability set to 0.

1. Perform a fast non-dominated sort on the merged parent and offspring populations and calculate the crowding degree of individuals in each non-dominated layer. Elite selection is performed based on non-dominance level and crowding, retaining the 100 best individuals for the next generation of evolution.

6. A low-emission, high-efficiency boiler combustion control and management system according to any one of claims 1 to 3, characterized in that, The actuator drive array adopts a hierarchical control architecture; The top layer is a setpoint tracking controller, which uses a proportional-integral-derivative control algorithm to calculate the theoretical control quantity of each actuator; The middle layer is the actuator dynamic compensator, which performs feedforward compensation on the theoretical control quantity based on the measured dynamic characteristics of each actuator; The bottom layer is a safety interlock protection unit that continuously monitors the working status of each actuator and key safety parameters of the boiler. Once signs of overheating, overpressure, or flameout are detected, it immediately overrides the upper-level command and forcibly drives the relevant actuators into a preset safety state.

7. A low-emission, high-efficiency boiler combustion control and management system according to any one of claims 1 to 3, characterized in that, It also includes an adaptive learning and update module; The adaptive learning and update module periodically evaluates the prediction accuracy of the multivariate coupled analysis engine and the optimization effect of the combustion state collaborative decision-making center. When the evaluation metric is below the preset threshold for five consecutive running days, the model retraining process is automatically triggered. The model retraining process utilizes recently accumulated runtime data to perform incremental learning on the deep neural network model.

8. The low-emission, high-efficiency boiler combustion control and management system according to claim 7, characterized in that, The model retraining process employs an incremental learning strategy. Incremental learning uses recently accumulated operational data to fine-tune the parameters of existing deep neural network models; The fine-tuning process uses a small learning rate to prevent catastrophic forgetting of existing knowledge.

9. A low-emission, high-efficiency boiler combustion control and management system according to any one of claims 1 to 3, characterized in that, The system operates within a multi-timescale coordinated control framework; The coordination and control framework includes a fast adjustment loop with a second-level response, a steady-state optimization loop with a minute-level response, and a strategy adjustment loop with an hour-level response. The rapid adjustment circuit acts directly on the actuator drive array to respond to instantaneous disturbances in the combustion process; The steady-state optimization loop periodically executes the computational tasks of the multivariate coupled analysis engine and the combustion state collaborative decision-making center; The strategy adjustment loop slowly adjusts high-level parameters such as weight preferences of the optimization algorithm based on long-term performance evaluation results.

10. A low-emission, high-efficiency boiler combustion control and management system according to any one of claims 1 to 3, characterized in that, The training process of the deep neural network model is as follows: Construct a training sample library containing historical operating data and corresponding optimal operating points. The training samples cover typical operating conditions of the boiler from 30% to 100% of its rated load. An adaptive moment estimation algorithm was used to iteratively optimize the network weights and biases. The initial learning rate was set to 0.001, the batch size was set to 64, and the number of training epochs was set to 500. The objective function for model training is defined as the weighted sum of the mean square error between the predicted combustion efficiency and the actual combustion efficiency and the mean square error between the predicted nitrogen oxide concentration and the actual nitrogen oxide concentration, where the weight of the combustion efficiency error is set to 0.6 and the weight of the nitrogen oxide concentration error is set to 0.4.

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