System for determining the amount of sludge added to the boiler based on a coupled neural BP network

Abstract

System for determining the amount of sludge added to the boiler based on a coupled neural BP network, characterized in that it comprises the following: a creation module for the networked model of the combustion chamber, which serves to obtain a detailed structure of the denitrification system and the desulfurization system in the boiler, and to create a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; a measurement module for the temperature field of the combustion chamber, which serves to create a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system, and to introduce CFD simulation values ​​into the relevant model and to simulate in order to obtain a temperature field of the combustion chamber; a denitrification efficiency acquisition module that serves to combine the detailed structure of the denitrification system with the temperature field of the combustion chamber and to obtain the denitrification efficiency and the NOx emission index within the framework of the 3D model; a measurement module for desulfurization efficiency, which serves to combine the detailed structure of the desulfurization system with the temperature field of the combustion chamber and to obtain the desulfurization efficiency and the SO2 emission index within the framework of the 3D model; a NOx dosing quantity module that serves to obtain the criterion for the minimum NOx emission value during boiler denitrification and the critical criterion for the NOx emission value with increasing sludge dosing quantity based on the denitrification efficiency and the NOx emission index within the framework of the 3D model, and to compare the different temperatures of the boiler chamber with the criterion of the minimum NOx emission value and the critical criterion of the NOx emission value with increasing sludge dosing quantity and to obtain the maximum regulated sludge dosing quantity corresponding to the NOx emission value; an SO2 dosing quantity module, which serves to obtain the criterion for the minimum SO2 emission value during the denitrification of the boiler and the critical criterion for the SO2 emission value with increasing sludge dosing quantity based on the desulfurization efficiency and the SO2 emission index within the framework of the 3D model, and to compare the different temperatures of the boiler chamber with the criterion of the minimum SO2 emission value and the critical criterion of the SO2 emission value with increasing sludge dosing quantity, and to obtain the maximum regulated sludge dosing quantity corresponding to the SO2 emission value; a model processing module that serves to train the maximum regulated sludge dosing quantity corresponding to the NOx emission value and the maximum regulated sludge dosing quantity corresponding to the SO2 emission value through a neural BP network and to obtain the maximum sludge dosing quantity of the boiler.

Description

TECHNICAL FIELD

[0001] The present invention relates to the field of calculating the sludge doping amount of the boiler, in particular to a system for determining a sludge doping amount of the boiler based on a coupled neural BP network. STATE OF THE ART

[0002] However, among current sludge treatment methods, boiler doping can fully utilize the organic components in the sludge, with a fast and thorough treatment rate. Coal-fired boiler doping treatment has the dual advantages of requiring low conversion investment and reducing coal consumption, making it a very advantageous treatment method. Coal-fired boiler doping results in a reduction in boiler efficiency, an increase in the capacity of the denitrification system for NOx treatment, an increase in the capacity of the desulfurization system for SO2 treatment, and an increase in the capacity of the dust removal system. The flue gas after treatment contains dioxins and other toxic gases in the sludge, among other problems. These problems limit the scope of coal-fired boiler doping.

[0003] In the past, the maximum sludge doping amount was determined based on the size of the plant and the processing capacity of the auxiliary system and other experience, and there is a certain blindness, so the actual maximum sludge doping amount is often significantly lower than the capacity of the coal-fired boiler can withstand.

[0004] Patent document CN113790456B provides a method and system for calculating the maximum sludge addition amount of coal-fired boilers. This patent is based on the relevant parameters of the coal-fired boiler, coal mill, denitrification system, desulfurization system, dust removal system, sewage treatment system, and the characteristics of the sludge under typical loads, so as to maximize the amount of sludge that can be added under typical loads under the premise of ensuring the safe operation of the coal-fired boiler. In addition, this patent can correct the maximum sludge addition amount in real time according to the target power and the actual power of the coal-fired power plant. However, this patent has the following disadvantages: 1.The calculation method can only calculate the maximum sludge dosing amount under typical load and design conditions and coal qualities, which is difficult to adapt to the situation of greater coal mixing in today's power plants and may lead to an overestimation of the maximum sludge dosing amount under conditions with poorer coal quality.

[0005] Patent document CN115951012A provides a method for determining the maximum mixing rate of coal-fired coupled sludge. This patent establishes a method and system for stepwise determining the maximum sludge doping amount through a doping test based on theoretical calculations, taking the operating conditions of the coal mill, the operating conditions of the boiler burner, the actual boiler efficiency, and pollutant emissions as boundary conditions. However, this patent has the following disadvantages: 1. The patent requires field measurements through the doping test and multiple measurements under different operating conditions, which are only representative of the coal quality and operating conditions at that time, and the maximum doping amount is not accurately estimated. CONTENTS OF THE PRESENT UTILITY MODEL

[0006] The purpose of the present invention is to overcome the above problem of high-temperature corrosion in the combustion chamber caused by the slight wall brushing of large cut-circle flames, and to provide a method and a system for determining a sludge doping amount of the boiler based on a coupled BP neural network.

[0007] To achieve the above purpose, the following technical solutions were used in the present invention: In a first aspect, the present invention provides a method for determining a sludge doping amount of the boiler based on a coupled neural BP network, comprising the following steps: Obtaining a detailed structure of the denitrification system and the desulfurization system in the boiler, and creating a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; Creating a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system, incorporating CFD simulation values ​​into the relevant model and simulating to obtain a temperature field of the combustion chamber; Combining the detailed structure of the denitrification system with the temperature field of the combustion chamber and obtaining the denitrification efficiency and NOx emission index within the 3D model; Combining the detailed structure of the desulfurization system with the temperature field of the combustion chamber and obtaining the desulfurization efficiency and SO2 emission index within the 3D model; Obtaining the minimum NOx emission value criterion in boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, comparing the different temperatures of the boiler chamber with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount, and obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value; Obtaining the minimum SO2 emission criterion for boiler denitrification and the critical SO2 emission criterion with increasing sludge doping amount based on the desulfurization efficiency and the SO2 emission index within the 3D model; comparing different boiler chamber temperatures with the minimum SO2 emission criterion and the critical SO2 emission criterion with increasing sludge doping amount, and obtaining the maximum regulated sludge doping amount corresponding to the SO2 emission value; training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network and obtaining the maximum sludge doping amount of the boiler.

[0008] A further improvement of the present invention is that the specific method for obtaining a detailed structure of the denitrification system and the desulfurization system in the boiler, and for creating a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system is as follows: Obtaining the reactor structure, injection system, catalyst arrangement, and flue gas parameters in the denitrification system as parameter information, and obtaining the absorber tower structure, spray system, packing properties, and flue gas flow parameters in the desulfurization system as boundary information; Creating a 3D geometric model based on the obtained parameter information and performing mesh distribution, inputting the boundary information into the 3D geometric model and obtaining the meshed model of the combustion chamber. A further improvement of the present invention is that the specific method for creating a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system and for introducing CFD simulation values ​​into the relevant model and simulating to obtain a temperature field of the combustion chamber is as follows: Obtaining the parameters of combustion properties of different coal grades and establishing a preliminary combustion model based on the parameters of combustion properties of different coal grades using thermodynamics and combustion model; Importing the parameters of combustion properties of different coal qualities into the networked model of the combustion chamber and simulating the combustion process of the different coal qualities in the combustion chamber;

[0009] Obtaining the combustion chamber temperature field using a CFD simulation after coupling the preliminary combustion model and the networked combustion chamber model. A further improvement of the present invention is that the specific method for combining the detailed structure of the denitrification system with the combustion chamber temperature field and obtaining the denitrification efficiency and NOx emission index within the 3D model is as follows: Determine the temperature distribution at different locations in the combustion chamber according to the temperature field of the combustion chamber, combine the combustion characteristics of different coal grades with the NOx generation rate, and simulate to obtain the generation process and concentration distribution of NOx in the combustion chamber; Simulate the injection process of the reducing agent into the combustion chamber based on the generation process and concentration distribution of NOx in the combustion chamber and in combination with the detailed structure of the denitrification system, simulate the mixing and diffusion of the reducing agent in the flue gas by means of CFD simulation, and calculate the NOx reduction efficiency of each injection point based on the reaction rate and reduction properties of the reducing agent in each temperature layer; Obtain the denitrification efficiency of each injection point according to the NOx reduction efficiency of each injection point, summarize the denitrification efficiency of each injection point, and obtain the denitrification removal efficiency and NOx emission index within the 3D model.

[0010] A further improvement of the present invention is that the specific method for combining the detailed structure of the desulfurization system with the temperature field of the combustion chamber and obtaining the desulfurization efficiency and the SO2 emission index within the 3D model is as follows: Determine the generation amount and concentration distribution of SO2 in the flue gas according to the temperature field of the combustion chamber and analyze the generation amount and concentration distribution in the flue gas and obtain the SO2 distribution data in the flue gas; Simulate the contact and reaction process between SO2 and the absorbent by CFD simulation and obtain the local desulfurization efficiency of each spray layer according to the distribution data of SO2 in the flue gas and in combination with the detailed structure of the desulfurization system; Summarize and obtain the desulfurization removal efficiency and SO2 emission index corresponding to the local desulfurization efficiency of each spray layer.

[0011] A further improvement of the present invention is that the specific method for obtaining the minimum NOx emission value criterion in the denitrification of the boiler and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, and for comparing the various temperatures of the boiler chamber with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount and obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value is as follows: Obtaining the combustion properties of the sludge and creating a preliminary doping model based on the combustion properties of the sludge; Simulate the NOx generation process based on the preliminary doping model and in combination with the combustion chamber temperature field and the flue gas flow model, and obtain the NOx generation amount and NOx concentration distribution for each doping ratio; Obtain the final NOx emission value for each sludge doping ratio according to the NOx generation amount and NOx concentration distribution for each doping ratio and in combination with the reaction efficiency of the denitrification system.

[0012] A further improvement of the present invention is that the specific method for obtaining the minimum SO2 emission value criterion in the denitrification of the boiler and the critical SO2 emission value criterion with increasing sludge doping amount based on the desulfurization efficiency and the SO2 emission index within the 3D model, and for comparing the various temperatures of the boiler chamber with the minimum SO2 emission value criterion and the critical SO2 emission value criterion with increasing sludge doping amount and for obtaining the maximum regulated sludge doping amount corresponding to the SO2 emission value is as follows: Obtaining the sulfur content and combustion properties in the sludge as well as the release properties of sulfur in the combustion chamber at different doping ratios and creating a preliminary doping model; Simulate the SO2 generation process at different sludge doping ratios using a CFD model based on the preliminary doping model and in combination with the combustion chamber temperature field and flue gas flow characteristics, and obtain the effects of different doping amounts on the combustion temperature and SO2 generation rate; Calculate the final SO2 emission value at different sludge dosing ratios based on the effects of different dosing levels on the combustion temperature and SO2 production rate and in combination with the desulfurization system efficiency model.

[0013] A further improvement of the present invention is that the specific method for training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value by a BP neural network and obtaining the maximum sludge doping amount of the boiler is as follows: Obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value and performing a treatment of normalization and standardization; Constructing a BP neural network model and training the BP neural network model based on the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value and obtaining a final BP neural network model; Input the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value into the final BP neural network model and obtain a maximum sludge doping amount of the boiler.

[0014] In a second aspect, the present invention provides a system for determining a sludge doping amount of the boiler based on a coupled BP neural network, comprising: a module for creating the networked model of the combustion chamber, which is used to obtain a detailed structure of the denitrification system and the desulfurization system in the boiler, and is used to create a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; a combustion chamber temperature field acquisition module used to create a relevant model for different coal qualities in the combustion chamber according to the networked combustion chamber model and in combination with the real-time data of the denitrification system and the desulfurization system, and to input CFD simulation values ​​into the relevant model and simulate to obtain a combustion chamber temperature field; a denitrification efficiency acquisition module, which is used to combine the detailed structure of the denitrification system with the temperature field of the combustion chamber and to obtain the denitrification efficiency and NOx emission index within the 3D model; A desulfurization efficiency acquisition module, which combines the detailed structure of the desulfurization system with the combustion chamber temperature field and obtains the desulfurization efficiency and the SO2 emission index within the 3D model; A NOx doping amount module, which obtains the minimum NOx emission value criterion for boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, and compares different boiler chamber temperatures with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount and obtains the maximum regulated sludge doping amount corresponding to the NOx emission value; an SO2 doping quantity module used to obtain the minimum SO2 emission value criterion for boiler denitrification and the critical SO2 emission value criterion with increasing sludge doping quantity based on the desulfurization efficiency and the SO2 emission index within the 3D model, and to compare the different boiler chamber temperatures with the minimum SO2 emission value criterion and the critical SO2 emission value criterion with increasing sludge doping quantity and to obtain the maximum regulated sludge doping quantity corresponding to the SO2 emission value; a model processing module for training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network and obtaining the maximum sludge doping amount of the boiler.

[0015] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method for determining a sludge doping amount of the boiler based on a coupled BP neural network when executing the computer program.

[0016] Compared with the prior art, the present invention has the following advantageous effects: By precisely adjusting the sludge doping amount, the present invention avoids excessive local temperatures and effectively reduces the phenomenon of high-temperature corrosion caused by flame wall brushing of large intersecting circle flames. The BP neural network model is capable of predicting the temperature field distribution at different doping amounts, controlling it within a safe temperature range, and extending the boiler service life. The present invention optimizes the sludge doping amount based on real-time models of denitrification and desulfurization systems to reduce NOx and SO2 emissions below critical levels.By predicting and controlling the emission value, the sludge doping amount is achieved to meet environmental standards while reducing pollutant emissions. The method of the present invention is capable of increasing the maximum sludge doping amount under the premise of safety and environmental protection, thus achieving greater recycling of sludge resources and reducing fuel costs. This optimized doping amount not only improves combustion efficiency but also promotes the recycling of solid waste, thus contributing to promoting sustainable development. By coupling the denitrification and desulfurization system with the temperature field of the combustion chamber, the optimized sludge doping amount contributes to forming a more stable combustion temperature field and improving combustion efficiency.This optimization process can reduce coal consumption, lessen dependence on coal resources, and save fuel costs. In summary, the present invention can not only solve the problem of high-temperature corrosion of the wall-type sectional boiler, but also reduce pollutant emissions, improve the sludge doping amount, and improve combustion efficiency, thus realizing the economic efficiency and environmental protection of boiler operation. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 shows a flowchart of the present invention; Fig. 2 shows a system diagram of the present invention; Fig. 3 shows a system diagram of Embodiment 10. DETAILED DESCRIPTION

[0017] For a better understanding of the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are intended to illustrate the present invention only and are not intended to limit it. Embodiment 1

[0018] In Fig. 1, a method for determining a sludge doping amount of the boiler based on a coupled neural BP network is available, comprising the following steps: S1: Obtain a detailed structure of the denitrification system and the desulfurization system in the boiler, and create a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; S2: Create a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system, introduce CFD simulation values ​​into the relevant model, and simulate to obtain a temperature field of the combustion chamber; S3: Combining the detailed structure of the denitrification system with the temperature field of the combustion chamber and obtaining the denitrification efficiency and NOx emission index within the 3D model; S4: Combining the detailed structure of the desulfurization system with the temperature field of the combustion chamber and obtaining the desulfurization efficiency and SO2 emission index within the 3D model; S5: Obtaining the minimum NOx emission value criterion in boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and NOx emission index within the 3D model, comparing the different boiler chamber temperatures with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount, and obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value; S6: Obtain the minimum SO2 emission criterion for boiler denitrification and the critical SO2 emission criterion with increasing sludge doping amount based on the desulfurization efficiency and the SO2 emission index within the 3D model, compare the different boiler chamber temperatures with the minimum SO2 emission criterion and the critical SO2 emission criterion with increasing sludge doping amount, and obtain the maximum regulated sludge doping amount corresponding to the SO2 emission value; S7: Train the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network, and obtain the maximum sludge doping amount of the boiler. Embodiment 2

[0019] In Fig. 2, a system for determining a sludge doping amount of the boiler based on a coupled neural BP network is available, which includes: a module for creating the networked model of the combustion chamber, which is used to obtain a detailed structure of the denitrification system and the desulfurization system in the boiler, and is used to create a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; a combustion chamber temperature field acquisition module used to create a relevant model for different coal qualities in the combustion chamber according to the networked combustion chamber model and in combination with the real-time data of the denitrification system and the desulfurization system, and to input CFD simulation values ​​into the relevant model and simulate to obtain a combustion chamber temperature field; a denitrification efficiency acquisition module, which is used to combine the detailed structure of the denitrification system with the temperature field of the combustion chamber and to obtain the denitrification efficiency and NOx emission index within the 3D model; A desulfurization efficiency acquisition module, which combines the detailed structure of the desulfurization system with the combustion chamber temperature field and obtains the desulfurization efficiency and the SO2 emission index within the 3D model; A NOx doping amount module, which obtains the minimum NOx emission value criterion for boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, and compares different boiler chamber temperatures with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount and obtains the maximum regulated sludge doping amount corresponding to the NOx emission value; an SO2 doping quantity module used to obtain the minimum SO2 emission value criterion for boiler denitrification and the critical SO2 emission value criterion with increasing sludge doping quantity based on the desulfurization efficiency and the SO2 emission index within the 3D model, and to compare the different boiler chamber temperatures with the minimum SO2 emission value criterion and the critical SO2 emission value criterion with increasing sludge doping quantity and to obtain the maximum regulated sludge doping quantity corresponding to the SO2 emission value; a model processing module for training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network and obtaining the maximum sludge doping amount of the boiler. Embodiment 3

[0020] The specific procedure for obtaining a detailed structure of the denitrification system and the desulfurization system in the boiler, and for creating a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system is as follows: Obtain detailed structural information of the denitrification and desulfurization system, including the physical and chemical parameters of key components such as the reactor, injection system, catalyst, absorption tower, spray system, and so on. This data provides an accurate basis for subsequent model construction. The collected detailed information was input into a 3D modeling tool to create a fine-grained geometric model and perform the networked distribution. Boundary conditions such as flue gas flow rate and temperature were precisely specified in the model, allowing the model to realistically simulate the operating environment of the denitrification and desulfurization system.

[0021] After the initial model creation, the model's accuracy is verified by comparing it with the system's actual operating data or CFD simulation results. Based on the comparison results, any necessary optimization of the model parameters is performed to ensure that the model can accurately predict the temperature field, efficiency, and pollutant emissions of the denitrification and desulfurization system and provide a reliable simulation basis for subsequent analyses. Embodiment 4

[0022] The specific procedure for establishing a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system, and for introducing CFD simulation values ​​into the relevant model and simulating to obtain a temperature field of the combustion chamber is as follows: Collect and analyze the key combustion characteristics parameters of different coal grades, including volatile matter content, ash content, fixed carbon content, combustion calorific value, ignition temperature, and so on. These parameters can directly affect the intensity and rate of the combustion reaction and ultimately influence the distribution of the temperature field in the combustion chamber. Based on this data, a preliminary combustion model is constructed using thermodynamic and combustion models to ensure that the model can accurately reproduce the combustion behavior of different coal grades and provide the basis for subsequent modeling of the temperature field in the combustion chamber.

[0023] The combustion characteristic parameters of different coal grades are imported into the meshed model of the combustion chamber based on the preliminary combustion model. The combustion chamber interior is divided into fine meshes and boundary conditions such as coal grade, air flow rate, and temperature are specified based on different mesh positions, so that the model can accurately simulate the combustion process of different coal grades in the combustion chamber. This step allows the thermal characteristics of combustion of different coal grades to be represented in the model in the combustion chamber, which provides the basis for further analysis of the temperature field and emission characteristics.

[0024] Coupling the combustion model created in the first two steps with the networked model and numerically calculating the three-dimensional temperature field of the combustion chamber for different coal grades using computational fluid dynamics (CFD) simulation. Determining the temperature distribution characteristics of the individual coal grades in the combustion chamber by analyzing the temperature field distribution and further comparing the efficiency and emission characteristics of the denitrification and desulfurization systems. The results of this step can provide the necessary temperature and reaction basis for subsequent NOx and SO2 emission analyses. Embodiment 5

[0025] The specific procedure for combining the detailed structure of the denitrification system with the temperature field of the combustion chamber and obtaining the denitrification efficiency and NOx emission index within the 3D model is as follows: Determine the temperature distribution at various locations in the combustion chamber using the previously created three-dimensional temperature field model of the combustion chamber. Incorporate the combustion characteristics and NOx generation rates of different coal grades into the model based on the temperature field analysis and a NOx generation model (e.g., thermal NOx, fast NOx, or fuel-based NOx generation mechanism), and simulate the NOx generation process and concentration distribution in the combustion chamber. This step allows the NOx generation source and concentration distribution in each region of the combustion chamber to be derived, providing a basis for the reductant injection strategy.

[0026] Simulate the injection process of reducing agents (e.g., ammonia or urea) in the combustion chamber in conjunction with the injection system structure of the denitrification system after obtaining the NOx generation distribution. Input parameters such as injection position, nozzle type, injection angle, and flow rate into the model, and simulate the mixing and diffusion of the reducing agent in the high-temperature flue gas using computational fluid dynamics (CFD). Calculate the NOx reduction efficiency at each injection point based on the reaction rate and reduction properties of the reducing agent in each temperature layer. This step combines the reducing agent distribution with the NOx generation distribution and can accurately predict the spatial efficiency of the denitrification process.

[0027] Calculate the overall denitrification efficiency based on the obtained NOx reduction efficiency. Summarize the reduction effects at each injection point and in different temperature layers, calculate the overall NOx removal efficiency of the denitrification system, and obtain the NOx emission index based on the difference in NOx concentration before and after removal. Compare the NOx emission index with the relevant emission standards and determine the performance of the denitrification system and its compliance with emission regulations under these operating conditions. This step integrates the temperature field, reductant distribution, and NOx generation and reduction characteristics, providing accurate data for optimizing the design and operating parameters of the denitrification system. Embodiment 6

[0028] The specific procedure for combining the detailed structure of the desulfurization system with the temperature field of the combustion chamber and obtaining the desulfurization efficiency and SO2 emission index within the framework of the 3D model is as follows: Determine the generation amount and concentration distribution of SO2 in the flue gas using the three-dimensional temperature field of the combustion chamber and the flue gas flow model. Analyze the flue gas flow, temperature, and initial SO2 concentration to obtain the SO2 concentration distribution of the flue gas at the inlet of the absorber tower, and correct for the sulfur content and combustion conditions of different coal grades. This step provides the initial SO2 distribution data for the desulfurization reaction, which helps simulate the reaction between the absorber and SO2 in subsequent steps. Simulate the contact and reaction process between SO2 and the absorbent (e.g., limestone slurry) based on the structure of the absorbent spray system in the absorber tower after obtaining the initial SO2 concentration in the flue gas.Import parameters such as the number of spray layers, nozzle distribution, spray flow rate, and absorbent concentration into the model and simulate the distribution, flow, and chemical reaction of the absorbent with SO2 using computational fluid dynamics (CFD). Calculate the local desulfurization efficiency of each spray layer based on the reaction rate and contact time between the spray liquid and SO2. In this step, the characteristics of the flue gas flow and the spray system were combined to form the basis for the final calculation of the overall desulfurization efficiency.

[0029] Summarize the overall desulfurization efficiency in the absorber tower based on the obtained local desulfurization efficiency of each spray layer and determine the SO2 emission index based on the SO2 concentration at the absorber tower outlet. Calculate the overall SO2 removal effect of the absorption tower by comparing the SO2 concentrations at the inlet and outlet and comparing the SO2 emission index with the emission standards, and evaluate the effectiveness of the desulfurization system under this operating condition. This step integrates the flue gas characteristics, SO2 generation, and local desulfurization efficiency from the first two steps and provides data for optimizing absorbent consumption and spray system design. Embodiment 7

[0030] The specific method for obtaining the minimum NOx emission value criterion for boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, and comparing the different boiler chamber temperatures with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount and obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value is as follows: Investigate the key combustion properties of sludge, including volatile matter, nitrogen content, water content, ash content, and combustion calorific value. The high nitrogen content of the sludge can lead to increased NOx generation during combustion, while the volatile matter and water content can influence the combustion chamber temperature field. Create a preliminary doping model based on these properties. The relationship between the sludge doping ratio and the combustion chamber temperature field, the flue gas flow characteristics, and the initial NOx generation amount serve as input data to provide basic data for simulating the NOx generation amount in subsequent steps.

[0031] Simulate the NOx generation process at different sludge doping ratios based on the preliminary doping model and in combination with the combustion chamber temperature field and flue gas flow model. Import the doping ratio, sludge combustion characteristics, and initial NOx generation characteristics, and simulate and analyze the effects of increasing the doping ratio on the NOx generation rate using computational fluid dynamics (CFD). The effects of different doping levels on the combustion temperature and NOx generation rate were analyzed, and the NOx generation amount and concentration distribution were calculated for each doping ratio, which forms the basis for calculating the denitrification efficiency in the next step.

[0032] Calculate the final NOx emission value of each sludge doping ratio based on the NOx generation amount from the simulation and in combination with the reaction efficiency of the denitrification system. By comparing the NOx emission value of different doping ratios with the environmental standards, the critical NOx emission value of the sludge doping is determined, that is, the maximum doping ratio that meets the emission requirements. At the same time, the relationship curve between the doping ratio and the NOx emission value is established to provide a basis for optimal control of the boiler system. This step integrates the characteristics of sludge doping, NOx generation amount, and denitrification system efficiency, enabling a comprehensive evaluation of the NOx emission value for different doping amounts. Embodiment 8

[0033] The specific method for obtaining the minimum SO2 emission value criterion for boiler denitrification and the critical SO2 emission value criterion with increasing sludge doping amount based on the desulfurization efficiency and the SO2 emission index within the 3D model, and for comparing the different boiler chamber temperatures with the minimum SO2 emission value criterion and the critical SO2 emission value criterion with increasing sludge doping amount, and for obtaining the maximum regulated sludge doping amount corresponding to the SO2 emission value, is as follows: Analyze the sulfur content and combustion characteristics of the sludge and determine the release characteristics of sulfur elements in the combustion chamber at different doping ratios. The sludge sulfur content directly affects the amount of SO2 generated, while the sludge water and ash content also influences the temperature field of the combustion chamber and the flue gas composition. Create a preliminary doping model based on this data. The relationship between the sludge doping ratio, the sulfur content, and the amount of SO2 generated serves as an input parameter to provide a basis for accurate simulation of SO2 generation in subsequent steps.

[0034] Simulate the SO2 generation process at different sludge doping ratios based on the preliminary model and in combination with the combustion chamber temperature field and flue gas flow characteristics. Import parameters such as the sludge sulfur content and doping ratio into the CFD (Computational Fluid Dynamics) model and analyze the effects of different doping amounts on the combustion temperature and SO2 generation rate. Through the CFD simulation, the SO2 concentration distribution and generation rate at each doping ratio were obtained, which form the basis for calculating the desulfurization efficiency in the next step. This step can provide accurate SO2 generation data for further analyzing the adaptability of the desulfurization system and desulfurization efficiency.

[0035] Calculate the final SO2 emission value under different sludge doping ratios based on the SO2 generation and concentration distribution obtained from the simulation and by combining it with the FGD system efficiency model. Evaluate the SO2 removal effect at each doping ratio based on the absorption efficiency of the desulfurization system and calculate the SO2 concentration at the desulfurization system outlet. Compare the SO2 emission value of each doping ratio with the environmental standards at each doping ratio and establish the critical SO2 emission value for sludge doping, i.e., the maximum sludge doping level at which the emission standards were met. Establish the relationship curve between the doping ratio and the SO2 emission value to obtain a decision basis for the optimal operation of the boiler system. Embodiment 9

[0036] The specific method for training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network and obtaining the maximum sludge doping amount of the boiler is as follows: collecting and compiling the data on the maximum sludge doping amount corresponding to the NOx and SO2 emission values ​​within the framework of the 3D model, and normalizing or standardizing this data to meet the input requirements of the BP neural network. At the same time, the input characteristics and output targets of the BP neural network are set, where the input characteristics include the threshold data of the NOx and SO2 emission values, and the output target is the maximum sludge doping amount of the boiler.

[0037] The BP neural network model was created based on the preprocessed data and the appropriate number of layers, nodes, and activation functions were determined. The model was trained using the NOx and SO2 emission data sets to gradually learn the nonlinear relationship between NOx and SO2 emission values ​​and the sludge doping amount. During the training process, the network weights were continuously adjusted using the backpropagation algorithm to reduce the prediction error and optimize model performance.

[0038] The trained BP neural network model was validated on an independent dataset to ensure the model's good generalization ability and prediction accuracy. The actual operating data of NOx and SO2 emission values ​​were input into the model to predict the maximum sludge doping amount for the boiler. The final model was used to control the boiler's sludge doping operation in real time to optimize the doping amount while meeting emission standards. Embodiment 10

[0039] As in the Fig.3, the present invention also provides an electronic device 100 for a method for determining a sludge doping amount of the boiler based on a coupled BP neural network, the electronic device 100 comprising a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0040] The memory 101 may be used to store the computer program 103, and the processor 102 executes the steps of a method for monitoring and controlling high-temperature corrosion of the water-cooled wall of a wall-type sectional-circuit boiler, as described in Embodiment 1, by executing the computer program stored in the memory 101 and retrieving the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (e.g., a sound reproduction function, an image reproduction function, etc.), and the like; the memory data area may store data (e.g., audio data), etc., generated during use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such asa hard disk, RAM, a pluggable hard disk, a smart media card (SMC), a secured digital card (SD), a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0041] The at least one processor 102 may be a central processing unit (CPU), but also other general purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or the processor 102 may also be any conventional processor, etc., where the processor 102 is the control center of the electronic device 100 and uses various interfaces and lines to connect various parts of the overall electronic device 100.

[0042] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for determining a sludge doping amount of the boiler based on a coupled BP neural network, and the processor 102 can execute the plurality of instructions: Obtaining a detailed structure of the denitrification system and the desulfurization system in the boiler, and creating a networked model of the combustion chamber based on the detailed structure of the denitrification system and the desulfurization system; Creating a relevant model for different coal qualities in the combustion chamber according to the networked model of the combustion chamber and in combination with the real-time data of the denitrification system and the desulfurization system, incorporating CFD simulation values ​​into the relevant model and simulating to obtain a temperature field of the combustion chamber; Combining the detailed structure of the denitrification system with the temperature field of the combustion chamber and obtaining the denitrification efficiency and NOx emission index within the 3D model; Combining the detailed structure of the desulfurization system with the temperature field of the combustion chamber and obtaining the desulfurization efficiency and SO2 emission index within the 3D model; Obtaining the minimum NOx emission value criterion in boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, comparing the different temperatures of the boiler chamber with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount, and obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value; Obtaining the minimum SO2 emission criterion for boiler denitrification and the critical SO2 emission criterion with increasing sludge doping amount based on the desulfurization efficiency and the SO2 emission index within the 3D model; comparing different boiler chamber temperatures with the minimum SO2 emission criterion and the critical SO2 emission criterion with increasing sludge doping amount, and obtaining the maximum regulated sludge doping amount corresponding to the SO2 emission value; training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network and obtaining the maximum sludge doping amount of the boiler. Embodiment 11

[0043] The module / unit integrated into the electronic device 100 may be stored in a computer-readable storage medium if implemented as a software functional unit and sold or used as a standalone product. Based on this understanding, all or part of the process of the present invention for implementing all or part of the process in the method of the above embodiment may also be performed by means of a computer program for controlling the corresponding hardware; the computer program may be stored in a computer-readable storage medium that, when executed by a processor, implements the steps of each of the above embodiments of the method.If the computer program comprises computer program code, the computer program code may be in source code form, object code form, executable file form, or an intermediate form or the like. The computer-readable medium may include any device or apparatus capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk drive, a magnetic disk, a compact disk, computer memory, and read-only memory (ROM).

[0044] Those skilled in the art should appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining aspects of software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) containing computer-usable program code.

[0045] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It is understood that each of the processes and / or boxes in the flowchart and / or block diagram, and the combination of processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions.These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce an apparatus for performing the functions specified in the one or more processes of the flowchart and / or the one or more boxes of the block diagram.

[0046] These computer program instructions may also be stored in a computer-readable memory capable of instructing the computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in this computer-readable memory produce an article of manufacture comprising an instruction device that implements a function specified in one or more processes of the flowchart and / or one or more boxes of the block diagram.

[0047] These computer program instructions may also have been loaded onto a computer or other programmable data processing device such that a series of operational steps were performed on the computer or other programmable device to produce computer-implemented processing such that the instructions executed on the computer or other programmable device provide steps for implementing the functionality specified in the flowchart, process or plurality of processes and / or the block diagram, box or plurality of boxes.

[0048] Finally, it should be noted that the above embodiments are only illustrative of the technical solutions of the present invention and are not intended to be limiting thereof; Although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present invention may still be modified or equivalently substituted, and any modification or equivalent substitution that does not deviate from the spirit and scope of the present invention is covered by the scope of the claims of the present invention. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] CN 113790456B

[0004] CN 115951012A

[0005]

Claims

[1] System for determining a sludge doping amount of the boiler based on a coupled neural BP network, characterized by that it includes: a combustion chamber network model creation module for obtaining a detailed structure of the denitrification and desulfurization systems in the boiler and creating a network model of the combustion chamber based on the detailed structure of the denitrification and desulfurization systems; a combustion chamber temperature field acquisition module used to create a relevant model for different coal qualities in the combustion chamber according to the networked combustion chamber model and in combination with the real-time data of the denitrification system and the desulfurization system, and to input CFD simulation values ​​into the relevant model and simulate to obtain a combustion chamber temperature field; a denitrification efficiency acquisition module, which is used to combine the detailed structure of the denitrification system with the temperature field of the combustion chamber and to obtain the denitrification efficiency and NOx emission index within the 3D model; a desulfurization efficiency acquisition module used to combine the detailed structure of the desulfurization system with the temperature field of the combustion chamber and obtain the desulfurization efficiency and the SO2 emission index within the 3D model; a NOx doping amount module for obtaining the minimum NOx emission value criterion for boiler denitrification and the critical NOx emission value criterion with increasing sludge doping amount based on the denitrification efficiency and the NOx emission index within the 3D model, and for comparing different boiler chamber temperatures with the minimum NOx emission value criterion and the critical NOx emission value criterion with increasing sludge doping amount and obtaining the maximum regulated sludge doping amount corresponding to the NOx emission value; an SO2 doping quantity module used to obtain the minimum SO2 emission value criterion for boiler denitrification and the critical SO2 emission value criterion with increasing sludge doping quantity based on the desulfurization efficiency and the SO2 emission index within the 3D model, and to compare the different boiler chamber temperatures with the minimum SO2 emission value criterion and the critical SO2 emission value criterion with increasing sludge doping quantity and to obtain the maximum regulated sludge doping quantity corresponding to the SO2 emission value; a model processing module for training the maximum regulated sludge doping amount corresponding to the NOx emission value and the maximum regulated sludge doping amount corresponding to the SO2 emission value through a BP neural network and obtaining the maximum sludge doping amount of the boiler.

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