A coal-fired unit SO2 over-standard early warning method, system, device and medium

Abstract

The application discloses a coal-fired unit SO2 over-standard early warning method, system, device and medium, and the method comprises the following steps: obtaining unit historical operation data; preprocessing the unit historical operation data to obtain an SO2 over-standard probability sample library; constructing an SO2 over-standard probability prediction model according to the SO2 over-standard probability sample library; calculating the SO2 over-standard probability of each period in the future according to the SO2 over-standard probability prediction model and issuing a warning; and giving the slurry circulating pump current adjustment suggestion of each period in the future according to the change trend of the SO2 over-standard probability of each period in the future and the total slurry circulating pump current. The SO2 over-standard risk caused by various uncertain factors is comprehensively considered, the prediction accuracy is high, and the operation personnel can be helped to adjust the desulfurization operation condition to a more reasonable condition, so that the economic loss caused by the excessive output of the slurry circulating pump is avoided.

Description

Technical Field

[0001] This invention belongs to the field of environmental protection technology, specifically relating to a method, system, device and medium for early warning of SO2 exceeding the standard in coal-fired power units. Background Technology

[0002] my country's current energy installed capacity structure is still dominated by thermal power. Thermal power plants emit large amounts of flue gas during operation, which contains SO2 and NO. X With rapid economic development, the requirements for air pollution control, including pollutants such as soot and particulate matter, are becoming increasingly stringent, and the task of controlling pollutant emissions is becoming increasingly challenging. Among these, SO2 is a key pollutant that thermal power plants need to remove.

[0003] Currently, the main SO2 removal process in coal-fired power plants is wet desulfurization (FGD). The principle of FGD is that limestone slurry in the absorption tower reacts with sulfur dioxide to form calcium sulfite. Calcium sulfite then reacts with oxygen to form gypsum, thus reducing the SO2 content in the flue gas. After ultra-low emission retrofitting, the SO2 emission limit for clean flue gas in coal-fired power plants is 35 mg / Nm³. 3 Therefore, the range of SO2 concentration adjustment at the export level is relatively small. In addition, frequent occurrences of load fluctuations, coal quantity changes, and coal blending at thermal power plants have led to significant fluctuations in the SO2 concentration of the raw flue gas, which has increased the risk to environmental protection work.

[0004] The slurry circulation pump is a major power-consuming device in the FGD (Fuel Dioxide Desulfurization) system of thermal power plants. Currently, the operation of slurry circulation pumps in the desulfurization systems of domestic thermal power plants is mainly controlled manually by operators. To ensure that the outlet sulfur dioxide concentration meets environmental protection requirements, operators generally increase the output of the slurry circulation pump to improve desulfurization efficiency and reduce the risk of SO2 exceeding standards due to various uncertainties. However, excessive output of the slurry circulation pump will increase desulfurization power consumption, leading to an increase in plant power consumption and affecting the economics of the unit. At the same time, if operators fail to make timely judgments, resulting in delayed adjustments, it will also increase the risk of SO2 exceeding standards.

[0005] Existing methods for early warning of SO2 exceedances in thermal power plants mainly involve analyzing the impact of single factors on SO2 levels, such as coal feed rate and sulfur content in the coal. An early warning is issued when the operating data of this factor exceeds the limit. In addition, there are methods that use historical operating data to construct SO2 concentration prediction models to forecast SO2 concentrations for future periods.

[0006] In actual operation of coal-fired power units, the SO2 concentration in the raw flue gas is affected not only by the coal feed rate and sulfur content of the coal, but also by many uncertain factors such as unit load fluctuations and combustion methods. Existing technologies do not comprehensively consider the SO2 concentration fluctuations caused by these factors, thus failing to account for the risk of SO2 exceeding standards. Furthermore, the accuracy of SO2 concentration predictions for future periods based on SO2 prediction models is limited, and they cannot quantitatively assess the risk of SO2 exceeding standards in future periods. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for early warning of SO2 exceeding the standard in coal-fired power units. It comprehensively considers the risk of SO2 exceeding the standard caused by various uncertain factors, has high prediction accuracy, and can help operators adjust the desulfurization operation to a more reasonable condition, avoiding economic losses caused by excessive output of the slurry circulation pump.

[0008] This invention provides the following technical solution:

[0009] Firstly, a method for early warning of SO2 exceeding standards in coal-fired power units is provided, including:

[0010] Obtain historical operating data of the generating unit;

[0011] Preprocessing of historical operating data of the unit yields a sample library of SO2 exceedance probability.

[0012] Based on the SO2 exceedance probability sample library, construct an SO2 exceedance probability prediction model;

[0013] Based on the SO2 exceedance probability prediction model, calculate the probability of SO2 exceeding the standard in each future period and issue an early warning.

[0014] Based on the trend of SO2 exceedance probability in future periods as a function of the total current of the slurry circulation pump, suggestions for adjusting the current of the slurry circulation pump in future periods are given.

[0015] Furthermore, when acquiring historical operating data of the unit, the sampling range is 30% to 100% of the rated load of the coal-fired unit, and the data is obtained from the SIS database within a preset sampling time period and sampling cycle. The acquired historical operating data of the unit includes: unit load, ambient temperature, sulfur content of coal, SO2 concentration in raw flue gas, SO2 concentration in clean flue gas, and current of each slurry circulation pump.

[0016] Further preprocessing of the unit's historical operating data includes:

[0017] Calculate the SO2 removal concentration and the total current of the slurry circulation pump, remove outliers, and form a sample set;

[0018] Cluster analysis was performed on the sample set based on the total current of the slurry circulation pumps as the classification standard. Then, cluster analysis was performed on each subclass to calculate the minimum SO2 removal concentration under the total current of the slurry circulation pumps for each class.

[0019] Calculate the critical value of SO2 concentration in the original flue gas under the sum of the currents of the circulating pumps for each type of slurry based on the minimum SO2 removal concentration.

[0020] Under each subclass of the total current of the slurry circulation pump, cluster analysis is performed based on the unit load, ambient temperature, and sulfur content of the coal as classification criteria to form multiple subclass sample datasets;

[0021] Based on the analysis of the subclass sample dataset, the distribution pattern of SO2 concentration in the original flue gas of each subclass was analyzed, and the probability density function of SO2 concentration distribution in the original flue gas was constructed.

[0022] The probability of SO2 exceeding the standard for each subclass is calculated based on the critical value of SO2 concentration in the original flue gas and the probability density function of SO2 concentration distribution in the original flue gas, forming a sample library of SO2 exceeding the standard probability.

[0023] Furthermore, the SO2 removal concentration is the difference between the original flue gas SO2 concentration and the clean flue gas SO2 concentration, and the total current of the slurry circulation pumps is the sum of the currents of each slurry circulation pump.

[0024] The resulting sample set includes: unit load, ambient temperature, sulfur content of coal, SO2 concentration in raw flue gas, SO2 concentration in clean flue gas, SO2 removal concentration, and total current of slurry circulation pump.

[0025] Furthermore, using the total current of the slurry circulation pumps as the classification standard, the K-means clustering algorithm was used to classify the sample set. Under the total current of the slurry circulation pumps in each category, the SO2 removal concentration fluctuated within a certain range. Then, K-means clustering was used to perform cluster analysis on the sample set for each category of the total current of the slurry circulation pumps, and the centroid data of each category was calculated. The SO2 removal concentration value of the centroid data with the minimum SO2 removal concentration was taken as the minimum SO2 removal concentration under the total current of the slurry circulation pumps in that category. Then, the critical value of the original flue gas SO2 concentration is the sum of the minimum SO2 removal concentration and the SO2 emission limit.

[0026] Furthermore, under each subcategory of the total current of the slurry circulation pump, cluster analysis was performed using the K-means algorithm with unit load, ambient temperature, and sulfur content of coal as classification criteria. Centroid data were calculated to obtain subcategories with the total current of the slurry circulation pump, the critical value of SO2 concentration in raw flue gas, unit load, ambient temperature, and sulfur content of coal as classification criteria. The sample data of each subcategory included: SO2 concentration in raw flue gas and SO2 concentration in clean flue gas.

[0027] Furthermore, the probability density function of the SO2 concentration distribution in the original flue gas is:

[0028]

[0029] In the formula, σ represents the standard deviation of the original flue gas SO2 concentration, and x represents the original flue gas SO2 concentration. σ represents the mean SO2 concentration in the original flue gas. 2 This represents the variance of the original flue gas SO2 concentration;

[0030] The formula for calculating the probability of SO2 exceeding the standard is:

[0031]

[0032] In the formula, p is the probability of SO2 exceeding the standard, and ε0 represents the critical value of SO2 concentration in the original flue gas;

[0033] The resulting sample library of SO2 exceedance probability includes: total current of slurry circulation pumps, unit load, ambient temperature, sulfur content of coal, and SO2 exceedance probability.

[0034] Furthermore, the methods for constructing SO2 exceedance probability prediction models include:

[0035] Based on a sample database of SO2 exceedance probabilities, an artificial neural network algorithm was used to train a pre-built model with the total current of the slurry circulation pump, unit load, ambient temperature, and sulfur content of coal as input parameters and the SO2 exceedance probability as output parameter, thus obtaining an SO2 exceedance probability prediction model.

[0036] Furthermore, methods for calculating the probability of SO2 exceeding the standard in future periods and issuing early warnings include:

[0037] The model inputs the planned unit load values ​​for each future time period, the current ambient temperature, the current sulfur content of the coal, and the total current of the currently operating slurry circulation pump into the SO2 exceedance probability prediction model. It then calculates and outputs the range of SO2 exceedance probability for each future time period and issues warnings of different levels.

[0038] Secondly, a warning system for SO2 exceeding the standard in coal-fired power units is provided, including:

[0039] The data acquisition module is used to acquire historical operating data of the unit;

[0040] The preprocessing module is used to preprocess the historical operating data of the unit and obtain a sample library of SO2 exceedance probability.

[0041] The model building module is used to build a prediction model for SO2 exceedance probability based on the SO2 exceedance probability sample library;

[0042] The calculation and early warning module is used to calculate the probability of SO2 exceeding the standard in the future period and issue an early warning based on the SO2 exceedance probability prediction model.

[0043] Thirdly, a coal-fired power unit SO2 exceeding early warning device is provided, comprising a processor and a storage medium, wherein the storage medium is used to store instructions, and the processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

[0044] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] (1) This invention obtains historical operating data of the unit, preprocesses the historical operating data of the unit, analyzes the SO2 concentration distribution, and the SO2 concentration distribution comprehensively reflects the influence of various uncertain factors during operation. By calculating the probability of SO2 exceeding the standard, the uncertainty of operating parameters is used to quantify the risk of SO2 exceeding the standard.

[0047] (2) This invention constructs a SO2 exceedance probability prediction model based on a sample library of SO2 exceedance probabilities, and calculates the SO2 exceedance probability for each period in the future in combination with the unit load plan, and issues a short-term exceedance warning to remind operators to adjust the desulfurization operation conditions in advance. The prediction accuracy is high.

[0048] (3) This invention analyzes the trend of SO2 exceedance probability in future time periods with the total current of slurry circulation pump, and provides suggestions for adjusting the current of slurry circulation pump in future time periods. This helps operators adjust the desulfurization operation to a more reasonable condition and avoid unnecessary economic losses caused by excessive output of slurry circulation pump. Attached Figure Description

[0049] Figure 1 This is a flowchart of the SO2 exceeding early warning method for coal-fired power units in Embodiment 1 of the present invention;

[0050] Figure 2 This is a distribution diagram of SO2 concentration in the original flue gas in Embodiment 2 of the present invention. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a method for early warning of SO2 exceeding the standard in coal-fired power units, including the following steps:

[0054] Step 1: Obtain historical operating data of the unit.

[0055] Step 2: Preprocess the historical operating data of the unit to obtain a sample library of SO2 exceedance probability.

[0056] Step 2.1: Calculate the SO2 removal concentration and the total current of the slurry circulation pump, remove outlier data, and form a sample set;

[0057] Step 2.2: Perform cluster analysis on the sample set based on the total current of the slurry circulation pumps as the classification standard, and then perform cluster analysis on each subclass to calculate the minimum SO2 removal concentration under the total current of the slurry circulation pumps for each class.

[0058] Step 2.3: Calculate the critical value of SO2 concentration in the original flue gas under the sum of the currents of the circulating pumps for each type of slurry, based on the minimum SO2 removal concentration.

[0059] Step 2.4: Under each subclass of the total current of the slurry circulation pump, perform cluster analysis based on unit load, ambient temperature, and sulfur content of coal as classification criteria to form multiple subclass sample datasets;

[0060] Step 2.5: Analyze the distribution pattern of SO2 concentration in the raw flue gas of each subclass based on the subclass sample dataset, and construct the probability density function of SO2 concentration distribution in the raw flue gas;

[0061] Step 2.6: Calculate the SO2 exceedance probability of each subclass based on the critical value of SO2 concentration in the original flue gas and the probability density function of SO2 concentration distribution in the original flue gas, and form a sample library of SO2 exceedance probability.

[0062] Step 3: Based on the SO2 exceedance probability sample library, construct an SO2 exceedance probability prediction model using a neural network algorithm.

[0063] Step 4: Calculate the probability of SO2 exceeding the standard in each future period based on the SO2 exceedance probability prediction model and issue an early warning.

[0064] Step 5: Based on the trend of SO2 exceedance probability with the total current of slurry circulation pump in future time periods, determine the total current of slurry circulation pump when the SO2 exceedance probability drops to 0, and give suggestions for adjusting the current of slurry circulation pump in future time periods.

[0065] Example 2

[0066] This embodiment provides a method for early warning of SO2 exceeding the standard in coal-fired power units, including the following steps:

[0067] Step 1: Using 30% to 100% of the rated load of the coal-fired unit as the sampling range, obtain the historical operating data of the unit from the SIS database within the preset sampling time period and sampling cycle. The obtained historical operating data of the unit includes: unit load, ambient temperature, sulfur content of coal, SO2 concentration of raw flue gas, SO2 concentration of clean flue gas, and current of each slurry circulation pump.

[0068] Among them, the original flue gas SO2 concentration refers to the SO2 concentration at the inlet of the wet desulfurization equipment of the coal-fired power unit, that is, the SO2 concentration in the flue gas before SO2 removal; the clean flue gas SO2 concentration is the SO2 concentration at the outlet of the wet desulfurization equipment of the coal-fired power unit, that is, the SO2 concentration in the flue gas after SO2 removal.

[0069] Step 2: Calculate the SO2 removal concentration and the total current of the slurry circulation pump for each data set to form a dataset, including: unit load, ambient temperature, sulfur content of coal, SO2 concentration in raw flue gas, SO2 concentration in clean flue gas, SO2 removal concentration, and total current of the slurry circulation pump. Wherein, SO2 removal concentration = raw flue gas SO2 concentration - clean flue gas SO2 concentration, and the total current of the slurry circulation pump is the sum of the currents of each slurry circulation pump.

[0070] Step 3: Remove outlier data from the dataset in Step 2 to form a sample set. The outlier data to be removed includes:

[0071] ① Samples in the dataset where any parameter is negative;

[0072] ② Use the box plot method to screen outliers in the original flue gas SO2 concentration and the total current of the slurry circulation pump, and remove the sample from that group.

[0073] Step 4: Using the total current of the slurry circulation pumps as the classification standard, the K-means clustering algorithm is used to classify the sample set. Under the total current of the slurry circulation pumps in each category, the SO2 removal concentration fluctuates within a certain range. Then, the K-means clustering algorithm is used to perform cluster analysis on the sample set for each category of the total current of the slurry circulation pumps, and the centroid data of each category is calculated. The SO2 removal concentration value of the centroid data with the minimum SO2 removal concentration is taken as the minimum SO2 removal concentration under the total current of the slurry circulation pumps in that category.

[0074] Step 5: Based on the results of Step 4, there exists a minimum SO2 removal concentration under the total output current of each type of slurry circulation pump. For the SO2 concentration in the clean flue gas to meet emission standards, it must be less than the national SO2 emission limit. According to the SO2 removal concentration calculation method, the following definition applies:

[0075] The critical value of SO2 concentration in raw flue gas = the minimum SO2 removal concentration + the SO2 emission limit.

[0076] When the SO2 concentration in the raw flue gas exceeds the critical value, the SO2 concentration in the net flue gas may exceed the standard under the total output current of the slurry circulation pump.

[0077] Step 6: Under each subcategory of the total current of the slurry circulation pump, cluster analysis is performed using the K-means algorithm based on the classification criteria of unit load, ambient temperature, and sulfur content of coal. Centroid data are calculated to obtain each subcategory based on the classification criteria of the total current of the slurry circulation pump, the critical value of SO2 concentration in raw flue gas, unit load, ambient temperature, and sulfur content of coal. The sample data of each subcategory includes: SO2 concentration in raw flue gas and SO2 concentration in clean flue gas.

[0078] Step 7: Analyze the SO2 concentration distribution patterns of each sub-category of raw flue gas and draw a SO2 concentration distribution map of the raw flue gas, such as... Figure 2 As shown, the SO2 concentration distribution in the original flue gas generally follows a normal distribution.

[0079] Calculate the mean SO2 concentration in the raw flue gas. The formula is as follows:

[0080]

[0081] In the formula, x i This represents the SO2 concentration of the i-th original flue gas, and n represents the number of original flue gas SO2 concentrations.

[0082] Calculate the variance σ of the SO2 concentration in the raw flue gas. 2 The formula is as follows:

[0083]

[0084] Since the original flue gas SO2 concentration follows a normal distribution, the probability density function of the original flue gas SO2 concentration distribution is:

[0085]

[0086] In the formula, σ represents the standard deviation of the original flue gas SO2 concentration, and x represents the original flue gas SO2 concentration.

[0087] Step 8: Each subcategory, categorized by total slurry circulation pump current, critical SO2 concentration in raw flue gas, unit load, ambient temperature, and sulfur content of coal, represents a different operating condition. The probability of SO2 exceeding the standard for each subcategory is the probability that the SO2 concentration in the raw flue gas exceeds the critical SO2 concentration. The formula for calculating the SO2 exceeding probability p is:

[0088]

[0089] In the formula, ε0 represents the critical value of SO2 concentration in the original flue gas.

[0090] Based on the SO2 exceedance probability of each subclass, a new SO2 exceedance probability sample library is formed using each subclass as a sample, including: total current of slurry circulation pump, unit load, ambient temperature, sulfur content of coal, and SO2 exceedance probability.

[0091] Step 9: Based on the SO2 exceedance probability sample library obtained in Step 8, use an artificial neural network algorithm to train the pre-built model with the total current of the slurry circulation pump, unit load, ambient temperature, and sulfur content of coal as input parameters and the SO2 exceedance probability as output parameter, to obtain the SO2 exceedance probability prediction model.

[0092] Step 10: Using the SO2 exceedance probability prediction model, issue early warnings for SO2 exceedances in the unit during short-term periods such as 15 minutes, 30 minutes, 1 hour, and 2 hours. Specifically, input the planned unit load value for each future period, the current ambient temperature, the current sulfur content of the coal, and the sum of the current current of the currently operating slurry circulation pump into the SO2 exceedance probability prediction model obtained in Step 9. After calculation, output the range of SO2 exceedance probabilities for each future period and issue early warnings of different levels.

[0093] Step 11: Analyze the trend of SO2 exceedance probability in future time periods obtained in Step 10 with the total current of slurry circulation pump, determine the total current of slurry circulation pump when the SO2 exceedance probability drops to 0, and give suggestions for adjusting the current of slurry circulation pump in future time periods.

[0094] Example 3

[0095] This embodiment provides an early warning system for SO2 exceeding the standard in coal-fired power units, employing the method described in Embodiment 1 or 2, specifically including:

[0096] The data acquisition module is used to acquire historical operating data of the unit;

[0097] The preprocessing module is used to preprocess the historical operating data of the unit and obtain a sample library of SO2 exceedance probability.

[0098] The model building module is used to build a prediction model for SO2 exceedance probability based on the SO2 exceedance probability sample library;

[0099] The calculation and early warning module is used to calculate the probability of SO2 exceeding the standard in the future period and issue an early warning based on the SO2 exceedance probability prediction model.

[0100] Example 4

[0101] This embodiment provides an early warning device for SO2 exceeding the standard in a coal-fired power unit, including a processor and a storage medium. The storage medium is used to store instructions, and the processor is used to operate according to the instructions to execute the steps of the method described in Embodiment 1 or 2.

[0102] Example 5

[0103] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1 or 2.

[0104] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A coal-fired unit SO2 over-standard early warning method, characterized in that, The method comprises the following steps: acquiring historical operation data of a unit; preprocessing the historical operation data of the unit to obtain a SO2 over-standard probability sample library; constructing a SO2 over-standard probability prediction model according to the SO2 over-standard probability sample library; calculating the SO2 over-standard probability of each future period and issuing a warning according to the SO2 over-standard probability prediction model; providing adjustment suggestions for the slurry circulating pump current in each future period according to the variation trend of the SO2 over-standard probability of each future period with the total slurry circulating pump current; the preprocessing of the historical operation data of the unit comprises the following steps: calculating the SO2 removal concentration and the total slurry circulating pump current, eliminating abnormal data, and forming a sample set; performing cluster analysis on the sample set according to the total slurry circulating pump current as a classification standard, and then performing cluster analysis on each subclass to calculate the minimum SO2 removal concentration under each total slurry circulating pump current; calculating the critical value of the original flue gas SO2 concentration under each total slurry circulating pump current according to the minimum SO2 removal concentration; performing cluster analysis on the sample set according to the unit load, ambient temperature and coal sulfur content under each subclass of the total slurry circulating pump current to form multiple subclass sample data sets; analyzing the distribution rule of the original flue gas SO2 concentration of each subclass based on the subclass sample data set, and constructing a probability density function of the original flue gas SO2 concentration distribution; calculating the SO2 over-standard probability of each subclass according to the critical value of the original flue gas SO2 concentration and the probability density function of the original flue gas SO2 concentration distribution to form the SO2 over-standard probability sample library; the construction method of the SO2 over-standard probability prediction model comprises the following steps: based on the SO2 over-standard probability sample library, using an artificial neural network algorithm, taking the total slurry circulating pump current, unit load, ambient temperature and coal sulfur content as input parameters, and taking the SO2 over-standard probability as output parameter to train a pre-built model to obtain the SO2 over-standard probability prediction model.

2. The coal-fired unit SO2 over-standard early warning method according to claim 1, characterized in that, When acquiring the historical operation data of the unit, 30% to 100% of the rated load of the coal-fired unit is taken as the sampling range, and the sampling time period and sampling cycle are preset to acquire the data from the SIS database; the acquired historical operation data of the unit comprises the unit load, ambient temperature, coal sulfur content, original flue gas SO2 concentration, net flue gas SO2 concentration and each slurry circulating pump current.

3. The coal-fired unit SO2 over-standard early warning method according to claim 1, characterized in that, The SO2 removal concentration is the difference between the original flue gas SO2 concentration and the net flue gas SO2 concentration, and the total slurry circulating pump current is the sum of each slurry circulating pump current; the formed sample set comprises the unit load, ambient temperature, coal sulfur content, original flue gas SO2 concentration, net flue gas SO2 concentration, SO2 removal concentration and total slurry circulating pump current.

4. The coal-fired unit SO2 over-limit early warning method according to claim 1, characterized in that, With the total current of the slurry circulating pump as the classification standard, the sample set is classified by using the K-means clustering algorithm, and under the total current of the slurry circulating pump in each classification, the SO2 removal concentration fluctuates within a certain range. Then, the sample set is clustered and analyzed by using the K-means clustering algorithm under the total current of the slurry circulating pump in each classification, the centroid data of each classification is calculated, and the SO2 removal concentration value of the centroid data with the minimum SO2 removal concentration is taken as the minimum SO2 removal concentration under the total current of the slurry circulating pump in the classification. The critical value of the SO2 concentration of the original flue gas is the sum of the minimum SO2 removal concentration and the SO2 emission limit value.

5. The coal-fired unit SO2 over-limit early warning method according to claim 1, characterized in that, Under the total current of the slurry circulating pump in each sub-class, the unit load, the ambient temperature and the sulfur content of the coal are taken as the classification standards, the K-means algorithm is used for clustering analysis, and the centroid data is calculated to obtain each sub-class with the total current of the slurry circulating pump, the critical value of the SO2 concentration of the original flue gas, the unit load, the ambient temperature and the sulfur content of the coal. The sample data of each sub-class includes the SO2 concentration of the original flue gas and the SO2 concentration of the clean flue gas.

6. The coal-fired unit SO2 over-limit early warning method according to claim 1, characterized in that, The distribution probability density function of the SO2 concentration of the original flue gas is: ； wherein x represents the original flue gas SO2 concentration, x represents the original flue gas SO2 concentration, x represents the original flue gas SO2 concentration, The calculation formula of the SO2 over-standard probability is: ； In the formula, p is the SO2 over-standard probability, represents the critical value of SO2 concentration of the original flue gas; The formed SO2 over-standard probability sample library includes the total current of the slurry circulating pump, the unit load, the ambient temperature, the sulfur content of the coal and the SO2 over-standard probability.

7. The coal-fired unit SO2 over-limit early warning method according to claim 1, characterized in that, The method for calculating the SO2 over-standard probability in the future period and issuing a warning includes: The unit load plan value in each period in the future, the current ambient temperature, the current sulfur content of the coal and the total current of the slurry circulating pump currently running are input into the SO2 over-standard probability prediction model, the range of the SO2 over-standard probability in each period in the future is calculated and output, and different levels of warnings are issued.

8. A coal-fired unit SO2 over-limit early warning system, characterized in that, For implementing the method in any one of claims 1-7, comprising: a data acquisition module for acquiring unit historical operation data; a preprocessing module for preprocessing the unit historical operation data and obtaining an SO2 over-standard probability sample library; a model construction module for constructing an SO2 over-standard probability prediction model according to the SO2 over-standard probability sample library; a calculation and warning module for calculating the SO2 over-standard probability in the future period and issuing a warning according to the SO2 over-standard probability prediction model.

9. A coal-fired unit SO2 over-standard early warning device, characterized in that, comprising a processor and a storage medium, the storage medium being used to store instructions, the processor being used to operate according to the instructions to perform the steps of the method in any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method in any one of claims 1-7.

Images