Wet desulphurization parameter optimization method based on data processing

Abstract

The invention relates to the technical field of data processing, in particular to a wet desulphurization parameter optimization method based on data processing, which comprises the following steps: collecting the concentration value of limestone slurry in a desulphurization system at each moment and the pH value after pretreatment; recording the previous moment of any moment needing concentration interpolation as a target moment, and calculating the concentration prominence degree of the target moment; acquiring pH values corresponding to all concentration values in a time period to which the target moment belongs by utilizing a Pearson's correlation coefficient; calculating the data correlation of the target moment; calculating the data authenticity of the target moment; calculating a self-adaptive error factor at the moment needing concentration interpolation; and carrying out interpolation on the moment needing concentration interpolation by using the HTFE model so as to realize wet desulphurization parameter optimization based on data processing. According to the method, the authenticity of the concentration data at each moment is evaluated more accurately by calculating the concentration prominence degree and the data correlation, the influence of noise data on an interpolation result is reduced, and the interpolation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method for optimizing wet desulfurization parameters based on data processing. Background Art

[0002] The current environment is facing serious air pollution, among which sulfur dioxide not only causes acid rain, but is also an important component of haze, posing a threat to public health and the ecological environment. Therefore, it is particularly important to reduce sulfur dioxide emissions and improve air quality through effective wet desulfurization technology. In the desulfurization system, the concentration of limestone slurry directly affects the rate and efficiency of the desulfurization reaction. The appropriate concentration can ensure that sulfur dioxide fully reacts with the slurry, thereby improving the desulfurization efficiency. The proportional-integral-derivative (PID) control algorithm can optimize the desulfurization reaction process by accurately controlling the concentration of limestone slurry, ensuring that the concentration is maintained at the set value, thereby improving the desulfurization efficiency and system stability. However, in the process of transmitting the concentration data of limestone slurry, due to problems such as communication lines or network failures, some data points may be missing when the data is transmitted to the data recording system, that is, the concentration values ​​at the corresponding time of these data are also missing. There is a method for interpolating missing limestone slurry concentration data, which can effectively process missing data and provide more reliable and accurate concentration data.

[0003] In the 9th issue of Computer Age in 2023, there is an article entitled "Research on Time Series Forecasting Methods Based on Historical Trends and Forecast Errors". In this article, a time series adaptive forecasting method based on historical trends and forecast errors (Historical TrendsForecastErrors, HTFE) is proposed to address the problems of complex training and poor migration prediction ability of time series prediction methods. First, the range of the forecast value at the next moment is determined based on the forecast error and the current value, and then the final forecast value is determined based on the short-term historical trend. The current forecast value is substituted into the next cycle to continue the forecast, and the data is predicted through a continuous "forecast-correction-forecast" cycle. Finally, using time series data such as finance and wind power, classic time series prediction algorithms such as LSTM, SVM, ARIMA, and MA are compared in terms of prediction accuracy, migration prediction ability, and computing speed.

[0004] In the process of collecting limestone slurry concentration data, the electromagnetic interference generated by the surrounding equipment affects the quality of the sensor signal, resulting in noise data in the collected limestone slurry concentration data, which is often close to some real limestone slurry concentration data values. However, when the HTFE model interpolates missing data, it is based on the predicted value and actual value of the historical data before the data point at the missing moment. If there is noise data in the historical data, the actual value of the historical data will not be accurate enough, which will further lead to inaccurate prediction errors obtained from the predicted value and actual value of the historical data, which will subsequently lead to a decrease in the accuracy of the model's initial prediction value and final prediction value for the data point value. At the same time, the HTFE model will use the same error factor value when predicting and calculating the values ​​of all data points that need to be interpolated, resulting in a decrease in the accuracy of the HTFE model when interpolating missing data, and ultimately affecting the control of limestone slurry concentration. Summary of the invention

[0005] In order to solve the problem that when the HTFE model interpolates missing data, the data at the previous moment of reference may be noise data, and the error factor of the same size is used subsequently, which reduces the accuracy of the interpolation processing and ultimately affects the control of limestone slurry concentration, the present invention proposes a wet flue gas desulfurization parameter optimization method based on data processing, which includes the following steps: Collect the concentration value of limestone slurry in the desulfurization system at each moment and the pH value after pretreatment; The previous moment of any moment requiring concentration interpolation is recorded as the target moment, and the concentration prominence of the target moment is calculated based on the concentration value and concentration prediction value at the target moment, as well as the absolute value of the concentration numerical skewness and the concentration mean of the time period to which the target moment belongs; the Pearson correlation coefficient is used to obtain the pH values ​​corresponding to all concentration values ​​in the time period to which the target moment belongs, and the data relevance of the target moment is calculated based on the ranking of the concentration value at the target moment and the corresponding pH value in the respective time periods, the Pearson correlation coefficient of the concentration value in the time period to which the target moment belongs and the pH value corresponding to the concentration value, and the Pearson correlation coefficient of the remaining concentration values ​​in the time period to which the target moment belongs when the concentration value at the target moment is not included and the pH value corresponding to the remaining concentration values; the data authenticity of the target moment is calculated based on the concentration prominence and data relevance at the target moment; the preset error factor is corrected according to the data authenticity to obtain the adaptive error factor of the time period requiring concentration interpolation; according to the adaptive error factor, the HTFE model is used to interpolate the time period requiring concentration interpolation to obtain the interpolated concentration value at the time period requiring concentration interpolation, so as to realize the wet flue gas desulfurization parameter optimization based on data processing.

[0006] By calculating the concentration prominence and data correlation, the authenticity of the concentration data at each moment can be evaluated more accurately, thereby reducing the impact of noise data on the interpolation results and improving the accuracy of interpolation; the error factor can be dynamically corrected according to the authenticity of the data, and the adaptive mechanism can adjust the interpolation strategy according to the changes in the actual data to ensure the interpolation accuracy under different conditions, while making the interpolation process more flexible and reliable; the optimized concentration control can improve the operating stability of the wet flue gas desulfurization system and reduce system failures or efficiency reductions caused by concentration fluctuations.

[0007] Furthermore, the pre-processed pH value is a pH value interpolated using a simple moving average algorithm.

[0008] Furthermore, the concentration prediction value is a prediction value obtained through the HTFE model.

[0009] Furthermore, the concentration prominence satisfies the following relationship: ; In the formula, For the The concentration prominence of each target moment, For the The concentration value at the target moment, For the The mean concentration of the time period to which the target moment belongs, For the The predicted concentration value at the target time, For the The numerical skewness of the time period to which the target moment belongs, is a hyperparameter, is the absolute value symbol.

[0010] By comparing the deviations between actual concentration values, mean concentration values ​​and predicted values, the degree of data abnormality can be effectively captured, thereby more accurately evaluating the prominence of concentration data. By introducing numerical skewness, the absolute value of numerical skewness provides information about the distribution characteristics of concentration data, thereby helping to identify abnormal fluctuations and making the assessment of concentration prominence more comprehensive.

[0011] Furthermore, the data correlation satisfies the following relationship: ; In the formula, For the Data correlation at each target moment, and Respectively The concentration value at each target moment and the ranking of the pH value corresponding to the concentration value in their respective time periods, For the The Pearson correlation coefficient between the remaining concentration values ​​and the pH values ​​corresponding to the remaining concentration values ​​when the time period to which the target moment belongs does not contain the concentration value at the target moment. For the The Pearson correlation coefficient between the concentration value of the time period to which the target moment belongs and the pH value corresponding to the concentration value is is a hyperparameter, is the natural exponential function, is the normalization function, is the absolute value symbol.

[0012] By considering the ranking of concentration values ​​and pH values, the changing trend over time can be better reflected and more accurate interpolation results can be provided; through normalization processing, the influence of outliers can be effectively suppressed and the model's resistance to noise can be improved; through the two dimensions of concentration and pH value, the interpolation not only relies on a single factor, but is more comprehensive and can capture potential relationships.

[0013] Furthermore, the data authenticity satisfies the following relationship: ; In the formula, For the The data authenticity of the target moment, For the Data correlation at each target moment, For the The concentration prominence of each target moment, is the normalization function.

[0014] By combining data correlation and concentration prominence, the authenticity of the data can be effectively quantified, ensuring that when calculating authenticity, not only the relationship between the data is reflected, but also the actual changes in the data can be reflected.

[0015] Furthermore, the adaptive error factor satisfies the following relationship: ; In the formula, For the An adaptive error factor at the time when concentration interpolation is required, For the The data authenticity of the target moment, is the preset error factor.

[0016] Furthermore, the wet desulfurization parameter optimization based on data processing includes: combining the concentration value at each moment and the concentration value after interpolation at the moment when concentration interpolation is required to form a complete concentration data sequence, and according to the complete concentration data sequence, using a PID control algorithm to control the limestone slurry concentration to complete the wet desulfurization parameter optimization based on data processing.

[0017] Through the complete concentration data sequence, the limestone slurry concentration can be controlled more accurately to ensure the optimal reaction conditions during the desulfurization process, thereby improving the desulfurization efficiency and reducing sulfur dioxide emissions; using the PID control algorithm, the slurry concentration can be dynamically adjusted according to the real-time concentration data to achieve rapid response and ensure the stability and efficiency of the system under different working conditions; by effectively controlling the desulfurization process, the emission of harmful gases can be significantly reduced, environmental protection can be promoted, and a more sustainable production method can be achieved.

[0018] The present invention has the following beneficial effects: By analyzing the changing characteristics of the concentration data and pH value data of the limestone slurry in the desulfurization system, the authenticity of the data at each moment can be effectively evaluated, thereby realizing the adaptive error factor adjustment at the interpolation moment. The adjustment of the adaptive error factor can dynamically optimize the response of the model according to the actual situation, improve the flexibility and accuracy of data interpolation, thereby improving the overall performance of the model, and ultimately improving the desulfurization efficiency and stability, reducing the economic losses caused by operational errors or system failures, and reducing the overall operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals are the same or corresponding parts, wherein: Figure 1 It is a flow chart of the steps of a method for optimizing wet desulfurization parameters based on data processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] The core purpose of this solution is to use the HTFE model to interpolate the missing data points in the limestone slurry concentration data before using the PID control algorithm to intelligently control the limestone slurry concentration data. In the process, the error factor at each moment that needs to be interpolated will be adapted, which is mainly based on the analysis of the concentration data of the limestone slurry at each moment and the numerical change characteristics of the pH value data.

[0022] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] See also Figure 1 , which shows a flow chart of the steps of a method for optimizing wet desulfurization parameters based on data processing provided by an embodiment of the present invention, the method comprising the following steps: S1: Collect the concentration value of the limestone slurry in the desulfurization system at each moment and the pH value after pretreatment.

[0024] The limestone slurry concentration data and slurry pH value data in the desulfurization system are collected through online concentration sensors and pH sensors, and then the collected data are digitized using analog-to-digital conversion equipment.

[0025] Implementers can set the collection duration and frequency according to the specific implementation situation. For example, the collection duration is 3 hours and the collection frequency is 2 seconds / time. Since there may be missing values ​​at the first moment, in order to prevent the lack of historical data for reference in this case, pre-collection can be performed as historical data. For example, the pre-collection time is 1 minute.

[0026] It should be noted that, since the present invention is to optimize the parameters of the limestone slurry concentration data, and there may be data loss in the limestone slurry concentration data, the present invention will use the analysis of the change characteristics of the limestone slurry concentration data itself and its correlation with the pH value data at the same time. Then, when analyzing the correlation between the two data, the pH value data may also be distorted or lost, but since the pH value data is an auxiliary reference in this solution, and considering the influence of the algorithm complexity, before analyzing the correlation between the two data, we can use the Simple Moving Average (SMA) algorithm to perform simple preprocessing on the pH value data, so that the pH value data can be more comprehensive and accurate, to ensure the subsequent analysis of the correlation between the two data.

[0027] Specifically, the pre-processed pH value is a pH value interpolated using a simple moving average algorithm.

[0028] S2: Record the previous moment of any moment requiring concentration interpolation as the target moment, and calculate the concentration prominence degree of the target moment.

[0029] It should be noted that the prominence of the data at each target moment is obtained by analyzing the concentration value characteristics at each target moment and the concentration value change characteristics in the time period to which it belongs. When analyzing this indicator, the more prominent the concentration value at each target moment, the greater the difference between the concentration value at each target moment and the concentration prediction value, and the more discrete the numerical distribution of the concentration value in the time period to which each target moment belongs, the greater the prominence of this target moment.

[0030] The concentration prominence at the target moment is calculated based on the concentration value and concentration prediction value at the target moment, as well as the absolute value of the concentration numerical skewness and the concentration mean value of the time period to which the target moment belongs.

[0031] Specifically, the concentration prediction value is a prediction value obtained through the HTFE model.

[0032] Specifically, the concentration prominence satisfies the following relationship: ; In the formula, For the The concentration prominence of each target moment, For the The concentration value at the target moment, For the The mean concentration of the time period to which the target moment belongs, For the The predicted concentration value at the target time, For the The numerical skewness of the time period to which the target moment belongs, is a hyperparameter, is the absolute value symbol.

[0033] The implementer can set the number of moments and hyperparameters included in the time period according to the specific implementation situation. For example, the time period contains 20 moments and the hyperparameter is 0.01. The existence of the hyperparameter is to prevent and The value of 0 occurs.

[0034] in, The larger the The more prominent the concentration value at a target moment is compared to the data in the time period to which it belongs, the greater the prominence of the data will be; The larger the The more likely the concentration value at a target moment is to undergo a sudden change, the greater the possibility that it is a potential noise data point, and the greater the corresponding prominence; Indicates The absolute value of the skewness of the time period to which the target moment belongs is larger. The more discrete the distribution of the values ​​of the time period to which the target moment belongs, the The greater the credibility of the existence of a mutation at a target moment, the more prominent the corresponding data will be.

[0035] S3: Use the Pearson correlation coefficient to obtain the pH values ​​corresponding to all concentration values ​​within the time period to which the target moment belongs.

[0036] Since there is a time difference between the changes in limestone slurry concentration data and pH value data, it is necessary to align the two types of data on the time axis before analyzing the correlation between the limestone slurry concentration data and the pH value data. Specifically, the Pearson correlation coefficient of the limestone slurry concentration sequence and the pH value data sequence in the same time period is calculated, and then the pH value concentration data sequence is moved back one moment at a time to obtain the Pearson correlation coefficient between the pH value data sequence and the initial limestone slurry concentration data sequence. Similarly, multiple groups of Pearson correlation coefficients are obtained, for example, 60 groups, and then the two data sequences with the largest Pearson correlation coefficients are selected, which are the concentration values ​​corresponding to the pH values.

[0037] S4: Calculate the data correlation at the target time.

[0038] It should be noted that according to the scenario survey, in the desulfurization system, limestone slurry is mainly used to neutralize acidic gases (such as sulfur dioxide). In this process, limestone (CaCO3) reacts with acidic substances to produce calcium bicarbonate (Ca(HCO3)2). As the concentration of limestone slurry increases, the pH value in the reaction will also change, usually manifested as an increase in pH. Therefore, in this step, the correlation between the limestone slurry concentration data and the pH value data at each target moment will be analyzed.

[0039] The concentration values ​​at the target moment are ranked by numerical value in the time period to which the target moment belongs, and the pH values ​​corresponding to the concentration values ​​at the target moment are ranked by data size in the time period to which they belong, and the rankings of the concentration values ​​at the target moment and the corresponding pH values ​​in their respective time periods are obtained respectively. The data correlation at the target moment is calculated based on the rankings of the concentration values ​​at the target moment and the corresponding pH values ​​in their respective time periods, the Pearson correlation coefficient between the concentration values ​​in the time period to which the target moment belongs and the pH values ​​corresponding to the concentration values, and the Pearson correlation coefficient between the remaining concentration values ​​in the time period to which the target moment belongs when the concentration value at the target moment is not included and the pH values ​​corresponding to the remaining concentration values.

[0040] Specifically, the data correlation satisfies the following relationship: ; In the formula, For the Data correlation at each target moment, and Respectively The concentration value at each target moment and the ranking of the pH value corresponding to the concentration value in their respective time periods, For the The Pearson correlation coefficient between the remaining concentration values ​​and the pH values ​​corresponding to the remaining concentration values ​​when the time period to which the target moment belongs does not contain the concentration value at the target moment. For the The Pearson correlation coefficient between the concentration value of the time period to which the target moment belongs and the pH value corresponding to the concentration value is is a hyperparameter, is the natural exponential function, is the normalization function, is the absolute value symbol.

[0041] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.01. The existence of hyperparameters is to prevent situation occurs.

[0042] in, Indicated in The difference between the limestone slurry concentration data and the corresponding pH value data at the target time in their respective time periods, that is, the difference in relative size. The larger the value, the greater the difference in the ranking of the limestone slurry concentration data at the target time. The correlation between the limestone slurry concentration data and the corresponding pH value data at each target moment will be lower; It represents the normalized result of the difference in correlation coefficient between all concentration values ​​and corresponding pH values ​​before and after the concentration value of the target time period. The larger the value, the better the result. The greater the influence of the limestone slurry concentration data at the target time on the Pearson correlation coefficient between the two data, the more it can be further explained that at the The correlation between the limestone slurry concentration data at a target moment and the corresponding pH value data will be lower.

[0043] S5: Calculate the data authenticity at the target moment.

[0044] It should be noted that the authenticity of the data at each target moment is obtained by combining the data prominence and data relevance at each target moment. The smaller the data relevance at each target moment is, the greater the possibility that the limestone slurry concentration data at each target moment is noise data, and the lower the authenticity will be.

[0045] The data authenticity at the target moment is calculated based on the concentration prominence and data correlation at the target moment.

[0046] Specifically, the data authenticity satisfies the following relationship: ; In the formula, For the The data authenticity of the target moment, For the Data correlation at each target moment, For the The concentration prominence of each target moment, is the normalization function.

[0047] in, The smaller it is, the greater the possibility that the limestone slurry concentration data at the target time is noise data, and the lower its authenticity; The larger the The greater the possibility that the limestone slurry concentration data at a target moment belongs to noise data, the lower its authenticity will be.

[0048] S6: Calculate the adaptive error factor at the time when concentration interpolation is required.

[0049] It should be noted that in this step, the preset error factor for each moment requiring concentration interpolation needs to be adapted according to the authenticity of the data; wherein, the lower the data authenticity of each target moment, the lower the error factor is required to ensure that the error between the concentration value at each target moment and the predicted concentration value has a lower impact on the prediction result of this moment requiring concentration interpolation.

[0050] The preset error factor is corrected according to the authenticity of the data to obtain an adaptive error factor at the time when concentration interpolation is required.

[0051] Specifically, the adaptive error factor satisfies the following relationship: ; In the formula, For the An adaptive error factor at the time when concentration interpolation is required, For the The data authenticity of the target moment, is the preset error factor.

[0052] Implementers can set the error factor according to specific implementation circumstances, for example, 0.9.

[0053] S7: Use the HTFE model to interpolate the time when concentration interpolation is required to achieve wet flue gas desulfurization parameter optimization based on data processing.

[0054] According to the adaptive error factor, the HTFE model is used to perform interpolation processing on the time when concentration interpolation is required to obtain the interpolated concentration value at the time when concentration interpolation is required, so as to achieve wet flue gas desulfurization parameter optimization based on data processing.

[0055] Implementers can set the number of historical moments that the HTFE model refers to when performing interpolation predictions and the value of the prediction interval error factor according to specific implementation circumstances. For example, the number of historical moments is 20 and the prediction interval error factor is 0.95.

[0056] Specifically, the method for realizing wet desulfurization parameter optimization based on data processing includes: The concentration values ​​at each moment and the interpolated concentration values ​​at the moment when concentration interpolation is required are combined into a complete concentration data sequence. Based on the complete concentration data sequence, the concentration of the limestone slurry is controlled using the PID control algorithm to complete the wet flue gas desulfurization parameter optimization based on data processing.

[0057] The implementer can set the proportional gain, integral time and differential time of the PID control algorithm according to the specific implementation situation. For example, the proportional gain is 0.6, and the integral time and differential time are both 5.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing wet flue gas desulfurization parameters based on data processing, characterized in that: include: Collect the concentration value of limestone slurry in the desulfurization system at each moment and the pH value after pretreatment; The previous moment of any moment requiring concentration interpolation is recorded as the target moment, and the concentration prominence of the target moment is calculated based on the concentration value and concentration prediction value at the target moment, as well as the absolute value of the concentration numerical skewness and the concentration mean of the time period to which the target moment belongs; the Pearson correlation coefficient is used to obtain the pH values ​​corresponding to all concentration values ​​in the time period to which the target moment belongs, and the data correlation of the target moment is calculated based on the ranking of the concentration value at the target moment and the corresponding pH value in the respective time periods, the Pearson correlation coefficient of the concentration value in the time period to which the target moment belongs and the pH value corresponding to the concentration value, and the Pearson correlation coefficient of the remaining concentration values ​​in the time period to which the target moment belongs when the concentration value at the target moment is not included and the pH value corresponding to the remaining concentration values; Calculate the data authenticity at the target time based on the concentration prominence and data relevance at the target time; According to the authenticity of the data, the preset error factor is corrected to obtain an adaptive error factor at the time when concentration interpolation is required; according to the adaptive error factor, the HTFE model is used to interpolate the time when concentration interpolation is required to obtain the interpolated concentration value at the time when concentration interpolation is required, so as to realize wet flue gas desulfurization parameter optimization based on data processing.

2. The method for optimizing wet desulfurization parameters based on data processing according to claim 1 is characterized in that: The pre-processed pH value is a pH value obtained by interpolation using a simple moving average algorithm.

3. The method for optimizing wet desulfurization parameters based on data processing according to claim 1 is characterized in that: The concentration prediction value is a prediction value obtained through the HTFE model.

4. The method for optimizing wet desulfurization parameters based on data processing according to claim 1 is characterized in that: The concentration prominence satisfies the following relationship: ； In the formula, For the The concentration prominence of each target moment, For the The concentration value at the target moment, For the The mean concentration of the time period to which the target moment belongs, For the The predicted concentration value at the target time, For the The numerical skewness of the time period to which the target moment belongs, is a hyperparameter, is the absolute value symbol.

5. The method for optimizing wet flue gas desulfurization parameters based on data processing according to claim 1 is characterized in that: The data correlation satisfies the following relationship: ； In the formula, For the Data correlation at each target moment, and Respectively The concentration value at each target moment and the ranking of the pH value corresponding to the concentration value in their respective time periods, For the The Pearson correlation coefficient between the remaining concentration values ​​and the pH values ​​corresponding to the remaining concentration values ​​when the time period to which the target moment belongs does not contain the concentration value at the target moment. For the The Pearson correlation coefficient between the concentration value of the time period to which the target moment belongs and the pH value corresponding to the concentration value is is a hyperparameter, is the natural exponential function, is the normalization function, is the absolute value symbol.

6. The method for optimizing wet desulfurization parameters based on data processing according to claim 1 is characterized in that: The data authenticity satisfies the following relationship: ； In the formula, For the The data authenticity of the target moment, For the Data correlation at each target moment, For the The concentration prominence of each target moment, is the normalization function.

7. The method for optimizing wet desulfurization parameters based on data processing according to claim 1 is characterized in that: The adaptive error factor satisfies the following relationship: ； In the formula, For the An adaptive error factor at the time when concentration interpolation is required, For the The data authenticity of the target moment, is the preset error factor.

8. The method for optimizing wet desulfurization parameters based on data processing according to claim 1 is characterized in that: The method for realizing wet desulfurization parameter optimization based on data processing includes: The concentration values ​​at each moment and the interpolated concentration values ​​at the moment when concentration interpolation is required are combined into a complete concentration data sequence. Based on the complete concentration data sequence, the concentration of the limestone slurry is controlled using the PID control algorithm to complete the wet flue gas desulfurization parameter optimization based on data processing.

Images