Flue gas NO based on thermal parameter optimization x Emission control methods
By constructing a mathematical model for optimizing thermal parameters, the problem of predicting and controlling NOx emissions during pellet roasting was solved, achieving precise NOx concentration regulation, reducing energy consumption and equipment corrosion risks, and achieving environmental protection and energy conservation effects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN IRON & STEEL RESOURCES GRP CHENGCHAO MINING CO LTD
- Filing Date
- 2026-02-27
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies cannot quantify and control the thermal parameters during pellet roasting, resulting in unpredictable NOx emissions. Traditional end-of-pipe treatment technologies also pose risks of high energy consumption and secondary pollution.
By measuring the thermal parameters of each stage of pellet roasting online, a multiple linear regression mathematical model was constructed to predict the NOx concentration trend at the SCR denitrification inlet. A univariate linear regression model was used to regulate relevant single factors to ensure that the NOx concentration is within the standard range.
It achieves accurate prediction and dynamic control of NOx concentration at the SCR denitrification inlet, reduces energy consumption, reduces equipment maintenance costs, and achieves the goals of environmental protection and energy conservation.
Smart Images
Figure CN122151635A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of metallurgical technology, specifically referring to a method for optimizing flue gas NO based on thermal parameters. x Emission control methods. Background Technology
[0002] With increasingly stringent environmental regulations and the worsening of environmental pollution problems, reducing nitrogen oxides (NOx) in industrial processes is becoming increasingly important. x NO emissions have become an important research area globally. x The main source is oxides produced by the reaction of nitrogen and oxygen during combustion, with excess NO being a significant contributor. x Emissions not only cause environmental problems such as acid rain and photochemical smog, but also pose serious threats to human health.
[0003] Traditional NO control x Emission control methods largely rely on end-of-pipe treatment technologies, such as selective catalytic reduction (SCR) and selective non-catalytic reduction (SNCR). Although these methods are effective in removing NO from flue gas in practical applications... x However, it also has some drawbacks. For example, SCR technology requires high-temperature operating conditions and has high equipment requirements, resulting in high energy consumption and increased production costs; SNCR technology, on the other hand, is less suitable for processing large flow rates of high-concentration NO. x It is inefficient and the generation of byproducts may cause secondary pollution problems.
[0004] Therefore, most current approaches focus on reducing NO at its source. x The formation of NO. During pellet roasting, NO... x Emissions are affected by various thermal parameters such as roasting temperature, fuel type, and nitrogen content in pulverized coal.
[0005] However, existing technologies lack methods for controlling NO at its source. x Emissions are largely determined by adjusting thermal parameters based on experience, making it impossible to quantify the specific adjustments to these parameters, let alone predict NO emissions in advance. x The changing trend of emissions. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention proposes a method for optimizing flue gas NO based on thermal parameters. x Emission control methods can predict NO at the SCR denitrification inlet. x The concentration trend can be accurately guided and used in conjunction with the NO inlet of the SCR denitrification system. x The adjustment of the single factor most relevant to the concentration affects the NO concentration at the SCR denitrification inlet. x The concentration does not exceed the set standard.
[0007] To achieve the above objectives, this invention designs a method for controlling NO in flue gas based on optimized thermal parameters. x The emission control method is characterized by including the following steps: S1) Online measurement of thermal parameters for each stage of pellet roasting; The thermal parameters include the pellet feed ratio and the NO at the SCR denitrification inlet. x Concentration and NO x Flow rate, flue gas temperature in each process stage, and also the nitrogen content of pulverized coal, fuel type and ratio, and combustion air volume in the rotary kiln; S2) The collected real-time thermal parameter data are preprocessed and normality tested, and then the thermal parameters of each process section are analyzed in relation to the NO inlet of the SCR denitrification system. x The correlation between concentrations was determined to establish the relationship between each process stage and the NO inlet of the SCR denitrification system. x Concentration-dependent thermal parameters; S3) For each process segment, establish significantly correlated thermal parameters and SCR denitrification inlet NO. x A linear relationship between concentrations was established to solve for the NO inlet of the SCR denitrification system. x A multiple linear regression mathematical model for NO concentration was used to predict the NO concentration at the SCR denitrification inlet. x The trend of concentration change; S4) If the NO at the SCR denitrification inlet is predicted to be... x If the concentration exceeds the set standard, analyze the multiple linear regression mathematical model of each process segment to identify the correlation with the NO inlet of the SCR denitrification system. x The single factor most relevant to the concentration was established, and the relationship between the single factor and the NO inlet of the SCR denitrification system was established. x A linear relationship between concentrations was established to adjust the NO concentration at the SCR denitrification inlet. x A univariate linear regression mathematical model for NO concentration is used to regulate NO. x concentration.
[0008] Further, in S1), the flue gas at the SCR denitrification inlet is extracted by a sampling pump with a rated negative pressure of 50 kPa. The extracted flue gas passes sequentially through a corrosion-resistant and heat-resistant stainless steel sampling probe and a matching cooling pretreatment unit, and is then sent to a flue gas analyzer for online determination of NO at the SCR denitrification inlet. x Concentration and flow rate data.
[0009] Furthermore, in S2), the data preprocessing includes removing outliers.
[0010] Furthermore, in S2), SPSS software is used to perform a normality test to ensure that the data meets the basic premise of correlation analysis.
[0011] Furthermore, in S2), the various thermal parameters are related to the NO inlet of the SCR denitrification system. x The strength of the correlation between concentrations is determined by the Pearson correlation coefficient, which is calculated using the following formula. In the formula, ρ xy This represents the Pearson correlation coefficient. X Indicates thermal parameters, This represents the sample mean of thermal parameters. Y Indicates NOx concentration. This represents the sample mean of NOx concentration.
[0012] Furthermore, in S3), the calculation formula for the multiple linear regression mathematical model is as follows: Y=β0+β1X1+β2X2+β3X3+...+β n X n In the formula, Y indicates the NO at the SCR denitrification inlet. x concentration, β0 represents the intercept. β1, β2, β3, β n Represents the partial regression coefficient. X1, X2, X3, X n Indicates significantly relevant thermal parameters.
[0013] Furthermore, in S4), the calculation formula for the univariate linear regression mathematical model is as follows: Y=β0+β1X1 In the formula, Y indicates the NO at the SCR denitrification inlet. x concentration, β0 represents the intercept. β1 represents the partial regression coefficient. X1 represents the most relevant single factor.
[0014] The advantages of this invention are: This invention performs real-time monitoring of various thermal parameters in the pellet production process and constructs a solution for the NO inlet of the SCR denitrification system. x A multiple linear regression mathematical model of concentration was used to predict the NO concentration at the SCR denitrification inlet. x The trend of concentration change; If the NO at the SCR denitrification inlet predicted by this invention... xWhen the concentration exceeds the set standard, the most relevant single factor and the NO at the SCR denitrification inlet are then constructed. x A univariate linear regression mathematical model for NO concentration was used to regulate the temperature of the corresponding process stage, ensuring the NO concentration at the SCR denitrification inlet. x浓度 The dynamic control can be flexibly applied in actual production, providing a basis for on-site operation and for parameter adjustment in subsequent SCR processes.
[0015] This invention is based on the optimization of flue gas NO. x Emission control methods not only predict NO at the SCR denitrification inlet x The trend of NO concentration changes, and the ability to precisely control NO x Concentration, effectively achieving NO concentration in pellet roasting x Emissions are controlled throughout the process to achieve the goals of energy conservation and environmental protection. Attached Figure Description
[0016] Figure 1 This is a flowchart of the present invention. Detailed Implementation
[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0018] In the description of this invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention.
[0019] like Figure 1 As shown, in this embodiment, the pellets are preheated in a chain grate mill, calcined in a rotary kiln, and then cooled in an annular cooler. The burner nozzles are located at the kiln head, and the burner temperature diffuses from the kiln tail to the PH2 section of the chain grate mill. NO in the rotary kiln... x The flue gas enters the chain grate machine from the kiln head and kiln tail.
[0020] The chain grate denitrification system consists of four sections from inlet to outlet: DDD1, DDD2, TPH, PH1, and PH2. Each section is equipped with a fume hood and an air box (including a south air box and a north air box). The selective catalytic reduction (SCR) denitrification system has its inlet located at the air box of the PH1 section of the chain grate and its outlet located at the fume hood of the DDD2 section.
[0021] In this embodiment, during the experiment, NO in the chain grate machine... xWhen the gas enters the pre-SCR denitrification system, the pressure of the SCR system becomes too high, which requires a large amount of ammonia to be injected. This leads to an increase in ammonia escape rate, causing equipment corrosion and increasing many unnecessary maintenance costs. Therefore, process denitrification (the process of reducing the formation of nitrogen oxides in combustion flue gas) is particularly important.
[0022] like Figure 1 As shown, the present invention provides a method for controlling NOx emissions from flue gas based on thermal parameter optimization, comprising the following steps: S1) Online measurement of thermal parameters for each stage of pellet roasting; The thermal parameters include the pellet feed ratio and the NO at the SCR denitrification inlet. x Concentration and NO x The flow rate, flue gas temperature in each process stage, and also the nitrogen content of pulverized coal, fuel type and ratio, and combustion air volume in the rotary kiln.
[0023] Preferably, the flue gas at the SCR denitrification inlet is extracted by a sampling pump with a rated negative pressure of 50 kPa. The extracted flue gas passes sequentially through a 310S corrosion-resistant and heat-resistant stainless steel sampling probe and a matching cooling pretreatment unit before being sent to a flue gas analyzer for online determination of NO at the SCR denitrification inlet. x Concentration and flow rate data.
[0024] Specifically, the system collects the temperatures of each cooling section of the annular cooler online, as well as the temperatures at the kiln head and tail of the rotary kiln, and the temperatures at the hoods / wind boxes of the DDD1, DDD2, TPH, PH1, and PH2 sections of the chain grate machine. It also collects the raw material ratio of the rotary kiln pellets and collects the N content of pulverized coal, fuel type and ratio, and combustion air volume of the burner nozzles in real time in the central control room, providing basic data for subsequent optimized control.
[0025] Monitoring the nitrogen content in pulverized coal is equivalent to measuring the nitrogen content in the pulverized coal and analyzing its effect on NO. x Impact of emissions. Monitoring fuel type and blending involves recording the various fuels used and their proportions to ensure the impact of different fuels on NO emissions. x The impact on emissions is minimized. Monitoring the combustion air volume means adjusting the combustion air volume to optimize the combustion process and reduce NO. x The generation of .
[0026] The data is uploaded to the data center in real time through the data acquisition system, ensuring that every step of the production process can be accurately monitored.
[0027] In this embodiment, thermal parameters are collected every two seconds. Since the temperatures in the rotary kiln and annular cooler cannot be collected, the following analysis is based solely on other thermal parameters from the rotary kiln and annular cooler, as well as the thermal parameters from the chain grate. S2) The collected real-time thermal parameter data are preprocessed and subjected to normality testing, and then the thermal parameters of each process segment are analyzed in relation to the NO inlet temperature of the SCR denitrification system. x The correlation between concentrations was determined to establish the relationship between each process stage and the NO inlet of the SCR denitrification system. x Concentration-dependent thermal parameters.
[0028] Specifically, data preprocessing included outlier removal. SPSS software was used to perform a normality test to ensure the data met the basic prerequisites for correlation analysis, thus providing stable and reliable data for subsequent analysis.
[0029] In this embodiment, some results of the normality test for each thermal parameter are shown in Table 1 below.
[0030] Table 1. Normality test results for various thermal parameters As shown in Table 1, when the sample size is large and the data is stable, the data of each thermal parameter are highly significant, that is, they follow a normal distribution and have good statistical significance.
[0031] Preferably, the correlation between various thermal parameters and the NO inlet temperature of the SCR denitrification system is analyzed using Pearson correlation coefficient. x Correlation analysis helps determine the mutual influence between various thermal parameters, clarify the changing influence patterns of these parameters, and provide data support for the subsequent construction of linear regression mathematical models.
[0032] Specifically, the thermal parameters and the NO inlet temperature of the SCR denitrification system... x The strength of the correlation between concentrations is determined by the Pearson correlation coefficient, which is calculated using the following formula. In the formula, ρ xy This represents the Pearson correlation coefficient. X Indicates thermal parameters, This represents the sample mean of thermal parameters. Y Indicates NOx concentration. This represents the sample mean of NOx concentration.
[0033] In this embodiment, after correlation analysis, the NO at the SCR denitrification inlet was found to be... x Temperature is the thermal parameter that is significantly correlated with concentration. Since the temperatures in the rotary kiln and annular cooler cannot be collected in this embodiment, the temperatures at the fume hood / air box of sections DDD1, DDD2, TPH, PH1, and PH2 in the chain grate are used as the basis for the following analysis. Therefore, a correlation is established between the temperature of each section in the chain grate and the NO inlet temperature at the SCR denitrification inlet of the chain grate. x The correlation between concentrations is shown in Table 2 below.
[0034] Table 2. Correlation between various thermal parameters and NOx concentration at the SCR denitrification inlet. In this embodiment, as shown by the Pearson correlation coefficient (Table 2), when the temperature of the main control system rises, the NO at the SCR denitrification inlet... x The concentration also increases significantly, especially the DDD2 fume hood temperature, TPH fume hood temperature, and PH2 fume hood temperature, with correlation indices reaching 0.506, 0.483, and 0.431, respectively. This indicates that the concentration of nitrogen oxides is highly correlated with these three temperatures.
[0035] As can be seen from the Sig. index, the data analysis results are true and reliable, and the correlation of the data is not caused by accidental factors, but has a very strong positive correlation.
[0036] S3) For each process segment, establish significantly correlated thermal parameters and SCR denitrification inlet NO. x A linear relationship between concentrations was established to solve for the NO inlet of the SCR denitrification system. x A multiple linear regression mathematical model for NO concentration was used to predict the NO concentration at the SCR denitrification inlet. x The trend of concentration change.
[0037] Preferably, the calculation formula for the multiple linear regression mathematical model is as follows: Y=β0+β1X1+β2X2+β3X3+...+β n X n In the formula, Y indicates the NO at the SCR denitrification inlet. x concentration, β0 represents the intercept. β1, β2, β3, β n Represents the partial regression coefficient. X1, X2, X3, X n Indicates significantly relevant thermal parameters.
[0038] In this embodiment, let the dependent variable be the NO at the SCR denitrification inlet.x The concentration is Y, and the independent variables for the DDD1, DDD2, TPH, PH1, and PH2 sections of the chain grate fan / fume hood are temperature X, respectively. 1~ The multiple linear regression mathematical models constructed for X5, specifically for the north bellows, south bellows, and smoke hood, are shown in Table 3 below.
[0039] Table 3. Linear Regression Mathematical Models for Chain Grate Machine Processes The dependent variable for Y1, Y2, and Y3 is the NO at the SCR denitrification inlet. x The concentration is X1~X5, which represents the temperature of the bellows / fume hood in sections DDD1~PH2. By monitoring the bellows / fume hood temperature in sections DDD1~PH2 in real time, the NO concentration at the SCR denitrification inlet can be predicted. x concentration.
[0040] S4) If the NO at the SCR denitrification inlet is predicted to be... x If the concentration exceeds the set standard, analyze the multiple linear regression mathematical model of each process segment to identify the correlation with the NO inlet of the SCR denitrification system. x The single factor most relevant to the concentration was established, and the relationship between the single factor and the NO inlet of the SCR denitrification system was established. x A linear relationship between concentrations was established to adjust the NO concentration at the SCR denitrification inlet. x A univariate linear regression mathematical model for NO concentration is used to regulate NO. x concentration.
[0041] Preferably, the calculation formula for the univariate linear regression mathematical model is as follows: Y=β0+β1X1 In the formula, Y indicates the NO at the SCR denitrification inlet. x concentration, β0 represents the intercept. β1 represents the partial regression coefficient. X1 represents the most relevant single factor.
[0042] In this embodiment, the most relevant parameters for single factors are the temperatures of the DDD2, TPH, and PH2 fume hoods and the temperatures of the north-side TPH, PH1, and PH2 wind boxes. The constructed univariate linear regression mathematical model is shown in Table 4 below.
[0043] Table 4. Mathematical Model of Univariate Linear Regression In Table 4, the dependent variable for Y4, Y5, Y6, Y7, Y8, and Y9 is the NO at the SCR denitrification inlet. x concentration.
[0044] During the experiment in this embodiment, the NO from the pellet plant x When the nitrogen oxides enter the pre-SCR denitrification system, excessive pressure in the SCR system necessitates the injection of large amounts of ammonia, leading to increased ammonia escape rate, equipment corrosion, and unnecessary maintenance costs. Therefore, denitrification during the cooling process is crucial. The univariate linear regression mathematical model in Table 4 above is used to modify the thermal parameters to control the NO inlet nitrogen oxides at the SCR denitrification system. x At certain concentrations, the NO content of Y5, Y6, Y7, Y8, and Y9 increases with increasing temperature. x As the concentration increases, NO in Y4 x As the concentration decreases, a reasonable temperature range needs to be found in the fume hood system within the DDD1~PH2 range to control NO concentration. x concentration.
[0045] In this embodiment, the SCR denitrification inlet NO of the pellet plant before optimization x The concentration reached 226 mg / m³ 3 To ensure it meets local policy requirements after denitrification, its SCR denitrification inlet NO... x The concentration needs to be reduced to 200 mg / m³ 3 Based on the calculations obtained using the aforementioned univariate linear regression mathematical model, the DDD2 section fume hood temperature increased by 10℃, the TPH section fume hood temperature decreased by 20℃, and the PH2 section fume hood temperature decreased by 20℃. Finally, the NO at the SCR denitrification inlet was monitored. x Concentration from 226 mg / m 3 Reduced to 197 mg / m 3 This has enabled low-nitrogen emissions in the pellet production process.
[0046] When it is necessary to adjust the on-site process parameters, the NO can be adjusted according to the model. x Predicting trends. For example, under current operating conditions, the NO inlet temperature of the SCR denitrification system... x The concentration is 186 mg / m³ 3 When the temperature of the PH2 stage is increased by 10℃, the NO at the SCR denitrification inlet... x The concentration will be increased to 191 mg / m³ 3 .
[0047] This invention is based on the optimization of flue gas NO. x Emission control methods not only predict NO at the SCR denitrification inlet x The trend of NO concentration changes, and the ability to precisely control NO x Concentration, effectively achieving NO concentration in pellet roasting x Emissions are controlled throughout the process to achieve the goals of energy conservation and environmental protection.
[0048] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.
Claims
1. A method for optimizing flue gas NO based on thermal parameters x The emission control method is characterized by, Includes the following steps: S1) Online measurement of thermal parameters for each stage of pellet roasting; The thermal parameters include the pellet feed ratio and the NO at the SCR denitrification inlet. x Concentration and NO x Flow rate, flue gas temperature in each process section, and also the nitrogen content of pulverized coal, fuel type and ratio, and combustion air volume in the rotary kiln; S2) The collected real-time thermal parameter data are preprocessed and normality tested, and then the thermal parameters of each process section are analyzed in relation to the NO inlet of the SCR denitrification system. x The correlation between concentrations was determined to establish the relationship between each process stage and the NO inlet of the SCR denitrification system. x Concentration-dependent thermal parameters; S3) For each process segment, establish significantly correlated thermal parameters and SCR denitrification inlet NO. x A linear relationship between concentrations was established to solve for the NO inlet of the SCR denitrification system. x A multiple linear regression mathematical model for NO concentration was used to predict the NO concentration at the SCR denitrification inlet. x The trend of concentration change; S4) If the NO at the SCR denitrification inlet is predicted to be... x If the concentration exceeds the set standard, analyze the multiple linear regression mathematical model of each process segment to identify the correlation with the NO inlet of the SCR denitrification system. x The single factor most relevant to the concentration was established, and the relationship between the single factor and the NO inlet of the SCR denitrification system was established. x A linear relationship between concentrations was established to adjust the NO concentration at the SCR denitrification inlet. x A univariate linear regression mathematical model for NO concentration is used to regulate NO. x concentration.
2. The flue gas NO based on thermal parameter optimization as described in claim 1 x The emission control method is characterized by: In S1), a sampling pump with a rated negative pressure of 50 kPa extracts the flue gas from the SCR denitrification inlet. The extracted flue gas passes sequentially through a corrosion-resistant and heat-resistant stainless steel sampling probe and a matching cooling pretreatment unit before being sent to a flue gas analyzer for online determination of NO at the SCR denitrification inlet. x Concentration and flow rate data.
3. The flue gas NO based on thermal parameter optimization as described in claim 2 x The emission control method is characterized by: In S2), the data preprocessing includes removing outliers.
4. The flue gas NO based on thermal parameter optimization as described in claim 3 x The emission control method is characterized by: In S2), SPSS software was used to perform a normality test to ensure that the data met the basic premise of correlation analysis.
5. The flue gas NO based on thermal parameter optimization according to claim 4 x The emission control method is characterized by: In S2), the thermal parameters and the NO inlet of the SCR denitrification system are related. x The strength of the correlation between concentrations is determined by the Pearson correlation coefficient, which is calculated using the following formula. In the formula, ρ xy This represents the Pearson correlation coefficient. X Indicates thermal parameters, This represents the sample mean of thermal parameters. Y Indicates NOx concentration. This represents the sample mean of NOx.
6. The flue gas NO based on thermal parameter optimization according to claim 1 x The emission control method is characterized by: In S3), the calculation formula for the multiple linear regression mathematical model is as follows: Y=β0+β1X1+β2X2+β3X3+...+β n X n In the formula, Y indicates the NO at the SCR denitrification inlet. x concentration, β0 represents the intercept. β1, β2, β3, β n Represents the partial regression coefficient. X1, X2, X3, X n Indicates significantly relevant thermal parameters.
7. The flue gas NO based on thermal parameter optimization according to claim 1 x The emission control method is characterized by: In S4), the calculation formula for the univariate linear regression mathematical model is as follows: Y=β0+β1X1 In the formula, Y indicates the NO at the SCR denitrification inlet. x concentration, β0 represents the intercept. β1 represents the partial regression coefficient. X1 represents the most relevant single factor.