A method for gas production energy efficiency optimization and carbon emission management

Abstract

The present application belongs to the technical field of gas production, and provides a gas production energy efficiency optimization and carbon emission management method, comprising: predicting the oxygen input change amount of the gasifier at different times in the future period through the constructed gasifier model, and monitoring the uniformity of the oxygen input amount of the sample inlet when adjusting the oxygen input amount, if the oxygen input is uniform, distributing and adjusting the oxygen input amount according to the current oxygen input proportion of each oxygen inlet, if the oxygen input is not uniform, combining the oxygen input amount deviation and the oxygen input proportion of the oxygen inlet to determine the oxygen input adjustment proportion, improving the uniformity of the oxygen input of the gasifier, and strengthening the optimization effect of the gas production energy efficiency and the carbon emission, and performing correlation analysis on the oxygen input amount of the oxygen inlet and the opening degree of the oxygen inlet, in the case that there is a proportional influence relationship between the oxygen input amount and the opening degree, adjusting the opening degree of each oxygen inlet combined with the oxygen input adjustment amount of each oxygen inlet, to ensure that the expected gas production energy efficiency and carbon emission optimization target is achieved.

Description

Technical Field

[0001] This invention belongs to the field of gas production technology, specifically a method for optimizing energy efficiency and managing carbon emissions in gas production. Background Technology

[0002] Current technologies lack predictive analysis of future oxygen intake changes in gasifiers, making it impossible to prepare for adjustments in advance. This leads to delayed adjustments, impacting the optimization of gas production efficiency and carbon emissions. For example, failure to adjust oxygen intake in a timely manner based on various changes during production prevents optimal gas production efficiency and may increase carbon emissions due to unreasonable oxygen intake. Furthermore, in cases of uneven oxygen intake at the inlet, current technologies may lack effective methods to comprehensively consider oxygen intake deviations and proportions to determine a reasonable adjustment ratio. This makes it difficult to improve oxygen intake uniformity while adjusting the oxygen intake, potentially leading to uneven reactions within the gasifier, affecting gas quality and production efficiency, and increasing equipment wear. Current technologies may also fail to fully consider the relationship between oxygen intake and inlet opening degree, making precise oxygen intake adjustment impossible through accurate adjustment of the inlet opening degree. This introduces errors in oxygen intake adjustment, making it difficult to accurately achieve the expected gas production efficiency and carbon emission optimization goals. For instance, the oxygen intake may be either too high or too low, failing to precisely meet production needs.

[0003] Therefore, the present invention provides a method for optimizing energy efficiency and managing carbon emissions in gas production. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for optimizing energy efficiency and managing carbon emissions in gas production, comprising: By using a gasifier model and combining it with the target optimization functions for energy efficiency optimization and carbon emissions, the oxygen intake of the gasifier is predicted, and the oxygen intake change sequence within the prediction period is obtained. The uniformity of oxygen intake at each oxygen inlet is monitored at the current moment to determine the overall oxygen intake status of the oxygen inlet at the current moment. If the overall oxygen intake status of the oxygen inlet at the current moment shows uniform oxygen intake, then the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period is determined based on the oxygen intake ratio of each oxygen inlet at the current moment and the oxygen intake change sequence within the prediction period. If the overall oxygen supply status of the oxygen inlet at the current moment shows uneven oxygen supply, then the oxygen supply adjustment ratio of each oxygen inlet is determined based on the oxygen supply deviation and oxygen supply ratio of each oxygen inlet at the current moment, and the oxygen supply adjustment amount of each oxygen inlet at different times in the prediction period is determined by combining the oxygen supply change sequence within the prediction period. A correlation analysis was conducted on the oxygen supply and opening / closing degree of the oxygen inlet to determine whether there is a proportional influence relationship between the oxygen supply and opening / closing degree. If so, the opening / closing degree of each oxygen inlet was adjusted according to the proportional influence relationship and the oxygen supply adjustment amount of each oxygen inlet.

[0006] Furthermore, the standard deviation and mean of the oxygen intake at each oxygen inlet at the current moment are calculated and the ratio is processed to obtain the coefficient of variation of the oxygen intake at the oxygen inlet. If the oxygen inlet's coefficient of variation is greater than or equal to the oxygen inlet's coefficient of variation threshold, it indicates that the overall oxygen inlet status at the current moment is uneven. If the oxygen inlet's coefficient of variation is less than the threshold for the oxygen inlet's coefficient of variation, it indicates that the overall oxygen inlet status at the current moment is uniform.

[0007] Furthermore, based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times after the current time are obtained, and multiplied with the oxygen intake ratio of each oxygen inlet at the current time to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period.

[0008] Furthermore, the oxygen intake ratio of each oxygen inlet at the current moment is obtained by processing the ratio of the oxygen intake of each oxygen inlet at the current moment to the total oxygen intake at the current moment. The total oxygen intake at the current moment is obtained by summing the oxygen intake at each oxygen inlet at the current moment.

[0009] Furthermore, based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times from the current time are obtained, and multiplied with the oxygen intake adjustment ratio of each oxygen inlet to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period.

[0010] Furthermore, the oxygen intake deviation of the oxygen inlet is obtained by subtracting the oxygen intake amount from the average oxygen intake amount of the oxygen inlet, and the oxygen intake adjustment ratio of each oxygen inlet is calculated by the oxygen intake deviation of the oxygen inlet.

[0011] Furthermore, the adjustment coefficient for each oxygen inlet is obtained by calculating the ratio of the oxygen inlet deviation to the maximum oxygen inlet deviation. Based on the calculated adjustment coefficient, the oxygen intake ratio of each oxygen inlet is multiplied together with the current oxygen intake ratio of each oxygen inlet to determine the oxygen intake adjustment ratio of each oxygen inlet.

[0012] Furthermore, the opening and closing degree of the oxygen inlet under different oxygen intake levels during the historical period is obtained, and an oxygen intake data set and an opening and closing degree data set are constructed. The Pearson correlation coefficient between the oxygen intake data set and the opening and closing degree data set is calculated, and the absolute value is taken to obtain the linear correlation value. If the linear correlation value is greater than or equal to the linear correlation threshold, it indicates that there is a proportional relationship between the oxygen intake and the opening degree of the oxygen inlet.

[0013] Furthermore, the oxygen intake data set and the opening / closing degree data set are processed to obtain the influence relationship model between oxygen intake and opening / closing degree, obtain the oxygen intake adjustment amount of the oxygen inlet, input the influence relationship model between oxygen intake and opening / closing degree, and obtain the opening / closing degree adjustment amount of the oxygen inlet. The opening degree of the oxygen inlet is adjusted according to the adjustment amount of each oxygen inlet opening degree.

[0014] Furthermore, based on the oxygen intake data set and the opening / closing degree data set, the oxygen intake data points in the oxygen intake data set are marked in a two-dimensional rectangular coordinate system. The least squares method was used to fit the marked oxygen intake data points, and the functional equation of the fitted line was determined, thereby obtaining the influence relationship model between oxygen intake and opening degree.

[0015] The beneficial effects of this invention are as follows: 1. By constructing a gasifier model, the changes in oxygen intake at different times in the future cycle can be predicted, which helps to improve the timeliness of oxygen intake adjustment, thereby optimizing the energy efficiency and carbon emissions of gas production. When adjusting the oxygen intake, the uniformity of oxygen intake at the inlet is monitored. If the oxygen intake is uniform, the oxygen intake is distributed and adjusted according to the current oxygen intake ratio of each inlet to maintain the uniformity of oxygen intake in the gasifier. If the oxygen intake is uneven, the oxygen intake adjustment ratio is determined by combining the oxygen intake deviation and the oxygen intake ratio. While meeting the optimization of energy efficiency and carbon emissions in gas production, the oxygen intake uniformity of the gasifier is improved, thus enhancing the optimization effect of energy efficiency and carbon emissions in gas production.

[0016] 2. Conduct a correlation analysis on the oxygen intake and opening / closing degree of the oxygen inlet. Given the proportional relationship between the oxygen intake and opening / closing degree, adjust the opening / closing degree of each oxygen inlet based on the oxygen intake adjustment amount. This will allow for more precise adjustment of the oxygen intake and ensure that the expected goals for optimizing gas production energy efficiency and carbon emissions are achieved. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart illustrating the steps of a gas production energy efficiency optimization and carbon emission management method according to an embodiment of the present invention; Figure 2 This is a flowchart of a gas production energy efficiency optimization and carbon emission management system according to an embodiment of the present invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0020] Example 1 Please see Figure 1 As shown in the embodiment of the present invention, a method for optimizing energy efficiency and managing carbon emissions in gas production includes the following steps: S1: Construct a gasifier model and combine it with the target optimization functions of energy efficiency optimization and carbon emission to predict the oxygen intake of the gasifier and obtain the oxygen intake change sequence within the prediction period. The process of constructing the gasifier model is as follows: A1, Historical Data Collection: During the historical period, the oxygen intake was accurately measured in real time using a flow sensor installed in the oxygen inlet pipe at the bottom of the gasifier. The amount of coal fed per unit time, F, is obtained through a weighing sensor. coal The average temperature T inside the gasifier is determined by temperature sensors placed at different locations within the gasifier, and the pressure P inside the gasifier is measured by pressure sensors. The composition of the flammable gas is then determined using a gas analyzer, including but not limited to the volume fractions of carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), and methane (CH4). , , , In addition, industrial and elemental analyses are performed on the coal fed into the furnace in advance. The industrial analysis includes obtaining the mass fractions of moisture (M), ash (A), volatile matter (V), and fixed carbon (FC). The elemental analysis includes determining the mass fractions of elements such as carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and sulfur (S). A2, Data Preprocessing: Moving average filtering and other methods are used to denoise the collected data and eliminate abnormal fluctuations caused by sensor noise. For missing data, linear interpolation or statistical models based on historical data are used to fill in the missing data based on the trend of data changes before and after the missing data. After filling in the missing data, the historical data is normalized to the range of [0, 1] for different units of data. For example, for oxygen intake, the specific normalization formula is as follows:

[0021] in, and These represent the maximum and minimum values ​​of historical oxygen intake data. The normalized oxygen intake; A3, Gasifier Model Construction: Based on artificial neural networks, the inputs are oxygen intake (FO2) and coal feed rate (F).coal The data includes the moisture content (M), ash content (A), volatile matter (V), fixed carbon (FC) mass fraction of the coal fed into the gasifier, the average temperature (T) inside the gasifier, and the pressure (P) inside the gasifier. The output includes the composition of the gasification products (volume fractions of carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), and methane (CH4). , , , wait); The objective optimization function for energy efficiency optimization and carbon emission optimization is constructed as follows: The objective optimization function aims to reduce carbon emissions and improve gasification efficiency. For carbon emissions, it incorporates carbon monoxide and carbon dioxide emissions, specifically:

[0022] Where Np represents the prediction time domain within the prediction period, and This represents the production of carbon monoxide and carbon dioxide gases at time k. and Weighting coefficients representing the relative importance of carbon monoxide and carbon dioxide in carbon emissions; Gasification efficiency is reflected by increasing the production of effective gases such as hydrogen and methane, specifically by including the production of hydrogen and methane:

[0023] Where Np represents the prediction time domain within the prediction period, and This represents the production of hydrogen and methane at time k. and Indicates the weighting coefficients for hydrogen and methane; The objective function J is then constructed as follows:

[0024] in, This represents the weighting coefficients used to balance carbon emissions and gasification efficiency. It should be noted that the weight coefficients recorded in the objective optimization function are... , , , as well as All of these were set up by those skilled in the art based on industry standards and the need for carbon emission energy efficiency optimization; Based on the constructed gasifier model, the objective optimization function is solved using a quadratic programming algorithm, yielding the oxygen intake change sequence within the prediction period that minimizes the objective optimization function, as follows:

[0025] Where Nc represents the control time domain within the prediction period, Nc≤Np, This is to predict the change in oxygen intake at the k+Nc-1th time after the current time in the cycle. S2: Monitor the uniformity of oxygen intake at each oxygen inlet at the current moment to determine the overall oxygen intake status of the oxygen inlet at the current moment. The process of monitoring the uniformity of oxygen intake at each oxygen inlet at the current moment and determining the overall oxygen intake status of the oxygen inlet at the current moment includes: The oxygen inlet flow rate FO2, i (i=1, 2, 3...n) of each oxygen inlet is obtained at the current moment by the flow sensor installed on each oxygen inlet pipe, where n represents the number of oxygen inlets; Calculate the average oxygen intake rate at each oxygen inlet. The specific calculation formula is as follows:

[0026] Then calculate the standard deviation of the oxygen intake at each oxygen inlet. The specific calculation formula is as follows:

[0027] The standard deviation of the oxygen intake at each oxygen inlet The average oxygen intake at each oxygen inlet The ratio is processed to obtain the oxygen inlet variation coefficient; In some preferred embodiments, the oxygen inlet coefficient of variation is compared with an oxygen inlet coefficient of variation threshold. If the oxygen inlet's coefficient of variation is greater than or equal to the oxygen inlet's coefficient of variation threshold, it indicates that the overall oxygen intake at the oxygen inlet is uneven at the current moment. If the oxygen inlet's coefficient of variation is less than the oxygen inlet's coefficient of variation threshold, it means that the overall oxygen inlet is uniform at the current moment. It should be noted that the purpose of obtaining the oxygen intake variation coefficient is as follows: Function 1: By comparing the oxygen inlet variation coefficient, we can determine whether the oxygen inlet of the gasifier is uniform, which helps us to grasp the oxygen inlet operation status of the gasifier and facilitates subsequent operation monitoring, adjustment and maintenance of the gasifier. Function 2: By comparing the oxygen inlet variation coefficient, we can determine whether the oxygen inlet of the gasifier is uniform, which makes it easier to select an appropriate oxygen inlet adjustment ratio based on whether the oxygen inlet of the gasifier is uniform, thereby improving the energy efficiency of gas production and reducing carbon emissions. S3: If the overall oxygen intake status of the oxygen inlet at the current moment shows uniform oxygen intake, then determine the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period based on the oxygen intake ratio of each oxygen inlet at the current moment and the oxygen intake change sequence within the prediction period. The process of determining the oxygen adjustment amount for each oxygen inlet based on the current oxygen ratio of each inlet and the oxygen quantity change sequence within the prediction period is as follows: Based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times after the current time are obtained, and multiplied with the oxygen intake ratio of each oxygen inlet at the current time to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period. The oxygen intake ratio of each oxygen inlet at the current moment is obtained by processing the ratio of the oxygen intake of each oxygen inlet at the current moment to the total oxygen intake at the current moment. The total oxygen intake at the current moment is obtained by summing the oxygen intake at each oxygen inlet at the current moment; For example, assuming there are three oxygen inlets, 1, 2, and 3, with oxygen intake volumes of jy1, jy2, and jy3 respectively; then the oxygen intake ratios of inlets 1, 2, and 3, jyb1, jyb2, and jyb3, are respectively:

[0028]

[0029]

[0030] Assume the change in oxygen intake from the current moment to the next moment is Then the oxygen adjustment amounts jyt1, jyt2, and jyt3 for oxygen inlets 1, 2, and 3 are respectively:

[0031]

[0032]

[0033] S4: If the overall oxygen intake status of the oxygen inlet at the current moment shows uneven oxygen intake, then determine the oxygen intake adjustment ratio of each oxygen inlet based on the oxygen intake deviation and oxygen intake ratio of each oxygen inlet at the current moment, and determine the oxygen intake adjustment amount of each oxygen inlet at different times in the prediction period by combining the oxygen intake change sequence within the prediction period. The process of determining the oxygen adjustment ratio of each oxygen inlet based on the current oxygen intake deviation and oxygen intake ratio of each oxygen inlet includes: The oxygen inlet flow rate FO2,i is compared with the average oxygen inlet flow rate. The difference was calculated to obtain the oxygen intake deviation at the oxygen inlet. The adjustment coefficient ki for each oxygen inlet is calculated by comparing it with the deviation of the maximum oxygen intake at the oxygen inlet. The specific calculation is as follows:

[0034] Where n represents the number of oxygen inlets; It should be noted that the above formula for calculating ki reflects proportional control. Proportional control can respond quickly to deviations. Here, by using the ratio of the flow deviation to the maximum deviation as part of the adjustment coefficient, the oxygen supply can be adjusted according to the relative magnitude of the flow deviation of each oxygen inlet. The larger the deviation, the larger the adjustment range, which helps to quickly correct the problem of uneven oxygen supply. Based on the calculated adjustment coefficient ki, the oxygen intake ratio of each oxygen inlet at the current moment is multiplied to determine the oxygen intake adjustment ratio jti of each oxygen inlet. The specific calculation is as follows:

[0035] Where jybi represents the oxygen intake ratio of the i-th oxygen inlet at the current moment; It should be noted that the above formula for calculating the oxygen adjustment ratio jti of each oxygen inlet reflects that the proportion of each oxygen inlet in the total oxygen intake is taken into account, and the increase is determined in combination with its flow deviation, so as to achieve the purpose of both increasing the total oxygen intake and improving the uniformity of oxygen intake. Based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times from the current time are obtained, and multiplied with the oxygen intake adjustment ratio of each oxygen inlet to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period. The technical solution of this invention is as follows: By constructing a gasifier model, the change in oxygen intake at different times in the future cycle is predicted, which helps to improve the timeliness of oxygen intake adjustment, thereby optimizing the energy efficiency and carbon emissions of gas production. When adjusting the oxygen intake, the uniformity of oxygen intake at the oxygen inlet is monitored. When the oxygen intake at the oxygen inlet is uniform, the oxygen intake is distributed and adjusted according to the current oxygen intake ratio of each oxygen inlet to maintain the uniformity of oxygen intake in the gasifier. When the oxygen intake at the oxygen inlet is uneven, the oxygen intake adjustment ratio is determined by combining the oxygen intake deviation and the oxygen intake ratio. While the oxygen intake adjustment meets the optimization of energy efficiency and carbon emissions in gas production, it improves the uniformity of oxygen intake in the gasifier and enhances the optimization effect of energy efficiency and carbon emissions in gas production.

[0036] Example 2 Please see Figure 1 As shown, based on Example 1, this embodiment of the invention considers that after understanding the oxygen adjustment amount of each oxygen inlet, the relationship between the oxygen intake amount and the opening degree of the oxygen inlet can be analyzed. The opening degree of the oxygen inlet can be precisely controlled according to the required oxygen adjustment amount, thereby achieving more accurate adjustment of the oxygen intake amount and ensuring that the expected gas production energy efficiency and carbon emission optimization targets are met. Therefore, the gas production energy efficiency optimization and carbon emission management method described in this embodiment of the invention includes the following steps: S5: Perform a correlation analysis on the oxygen supply and opening degree of the oxygen inlet to determine whether there is a proportional influence relationship between the oxygen supply and opening degree. If so, adjust the opening degree of each oxygen inlet according to the proportional influence relationship and the oxygen supply adjustment amount of each oxygen inlet. The process of performing correlation analysis on the oxygen inlet volume and the opening / closing degree of the oxygen inlet includes: By using the gasifier operation report, the opening and closing degree of the oxygen inlet under different oxygen intake rates during the historical period is obtained, and an oxygen intake rate data group and an opening and closing degree data group are constructed. It should be noted that each oxygen intake data point in the oxygen intake data group has a corresponding opening / closing degree data point in the opening / closing degree data group. The Pearson correlation coefficient between the oxygen intake data set and the opening / closing degree data set was calculated using the Pearson correlation algorithm, and the absolute value was taken to obtain the linear correlation value. For example, suppose there exists an injection volume data set (jyl1, jyl2, jyl3...jylm), where jylm represents the m-th oxygen injection volume in the oxygen injection data set, and m represents the number of the oxygen injection volume; suppose there exists an opening degree data set (kh1, kh2, kh3...khm), where khm represents the m-th opening degree in the opening degree data set, and m represents the number of the opening degree; the specific formula for calculating the Pearson correlation coefficient p is:

[0037] Where m is the number of oxygen intake or opening degree, jyli represents the i-th oxygen intake in the oxygen intake data group, and khi represents the i-th opening degree in the opening degree data group. This represents the mean of the oxygen intake data set. This represents the mean of the opening / closing degree data set; In some preferred embodiments, the linear correlation value is compared with the linear correlation threshold; If the linear correlation value is greater than or equal to the linear correlation threshold, it indicates that there is a proportional relationship between the oxygen intake and the opening degree of the oxygen inlet. If the linear correlation value is less than the linear correlation threshold, it means that there is no proportional relationship between the oxygen intake and the opening degree of the oxygen inlet, and no operation is performed. The process of adjusting the opening degree of each oxygen inlet based on the proportional influence relationship and in conjunction with the oxygen adjustment amount of each oxygen inlet includes: Based on the oxygen intake data set and the opening degree data set, a two-dimensional rectangular coordinate system is constructed with the X-axis representing the opening degree and the Y-axis representing the oxygen intake. Data points of the oxygen intake in the oxygen intake data set are marked in the two-dimensional rectangular coordinate system. After marking the data points of oxygen intake, the least squares method is used to fit the marked oxygen intake data points to obtain a fitted straight line. Based on the slope and intercept of the fitted straight line, the function equation of the fitted straight line is determined, thereby obtaining the influence relationship model between oxygen intake and opening degree, specifically: jy=k*kh+b, where k represents the slope, b represents the intercept, jy represents the oxygen intake, and kh represents the opening degree. It should be noted that the basis for fitting the labeled oxygen intake data points using the least squares method is that there is a proportional relationship between oxygen intake and opening degree. Based on any oxygen inlet, obtain the oxygen inlet adjustment amount, input the influence relationship model between oxygen inlet amount and opening degree, and obtain the opening degree adjustment amount of oxygen inlet. The opening degree of the oxygen inlet is adjusted according to the adjustment amount of each oxygen inlet opening degree; The technical solution of this invention is as follows: a correlation analysis is performed on the oxygen intake and the opening degree of the oxygen inlet. When there is a proportional influence relationship between the oxygen intake and the opening degree of the oxygen inlet, the opening degree of each oxygen inlet is adjusted in combination with the oxygen intake adjustment amount of each oxygen inlet, so as to more accurately adjust the oxygen intake and ensure that the expected gas production energy efficiency and carbon emission optimization targets are achieved.

[0038] Example 3 Please see Figure 2 As shown in the embodiment of the present invention, a gas production energy efficiency optimization and carbon emission management system includes the following modules: Oxygen Intake Prediction Module: Constructs a gasifier model and combines it with the target optimization functions of energy efficiency optimization and carbon emission to predict the oxygen intake of the gasifier, obtaining the oxygen intake change sequence within the prediction period; Oxygen intake status monitoring module: Monitors the uniformity of oxygen intake at each oxygen inlet at the current moment and determines the overall oxygen intake status of the oxygen inlet at the current moment. In this embodiment of the invention, the process for determining the overall oxygen intake status of the oxygen inlet at the current moment is as follows: The oxygen inlet flow rate FO2, i (i=1, 2, 3...n) of each oxygen inlet is obtained at the current moment by the flow sensor installed on each oxygen inlet pipe, where n represents the number of oxygen inlets; Calculate the average oxygen intake rate at each oxygen inlet. The specific calculation formula is as follows:

[0039] Then calculate the standard deviation of the oxygen intake at each oxygen inlet. The specific calculation formula is as follows:

[0040] The standard deviation of the oxygen intake at each oxygen inlet The average oxygen intake at each oxygen inlet The ratio is processed to obtain the oxygen inlet variation coefficient; Compare the oxygen inlet's coefficient of variation with the oxygen inlet's coefficient of variation threshold; If the oxygen inlet's coefficient of variation is greater than or equal to the oxygen inlet's coefficient of variation threshold, it indicates that the overall oxygen intake at the oxygen inlet is uneven at the current moment. If the oxygen inlet's coefficient of variation is less than the oxygen inlet's coefficient of variation threshold, it means that the overall oxygen inlet is uniform at the current moment. Uniform oxygen intake adjustment module: If the overall oxygen intake status of the oxygen inlet at the current moment shows uniform oxygen intake, then the oxygen intake adjustment amount of each oxygen inlet at different moments in the prediction period is determined based on the oxygen intake ratio of each oxygen inlet at the current moment and the oxygen intake change sequence within the prediction period. In this embodiment of the invention, the process of determining the oxygen adjustment amount of each oxygen inlet at different times within the prediction period based on the current oxygen ratio of each oxygen inlet and the oxygen quantity change sequence within the prediction period is as follows: Based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times after the current time are obtained, and multiplied with the oxygen intake ratio of each oxygen inlet at the current time to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period. The oxygen intake ratio of each oxygen inlet at the current moment is obtained by processing the ratio of the oxygen intake of each oxygen inlet at the current moment to the total oxygen intake at the current moment. The total oxygen intake at the current moment is obtained by summing the oxygen intake at each oxygen inlet at the current moment; Non-uniform oxygen intake adjustment module: If the overall oxygen intake status of the oxygen inlet at the current moment shows that the oxygen intake is uneven, the oxygen intake adjustment ratio of each oxygen inlet is determined according to the oxygen intake deviation and oxygen intake ratio of each oxygen inlet at the current moment, and the oxygen intake adjustment amount of each oxygen inlet at different times in the prediction period is determined by combining the oxygen intake change sequence within the prediction period. In this embodiment of the invention, the process of determining the oxygen adjustment amount at each oxygen inlet at different times within the prediction period includes: The oxygen inlet flow rate FO2,i is compared with the average oxygen inlet flow rate. The difference was calculated to obtain the oxygen intake deviation at the oxygen inlet. The adjustment coefficient ki for each oxygen inlet is calculated by comparing it with the deviation of the maximum oxygen intake at the oxygen inlet. The specific calculation is as follows:

[0041] Where n represents the number of oxygen inlets; Based on the calculated adjustment coefficient ki, the oxygen intake ratio of each oxygen inlet at the current moment is multiplied to determine the oxygen intake adjustment ratio jti of each oxygen inlet. The specific calculation is as follows:

[0042] Where jybi represents the oxygen intake ratio of the i-th oxygen inlet at the current moment; Based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times from the current time are obtained, and multiplied with the oxygen intake adjustment ratio of each oxygen inlet to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period. Oxygen Inlet Opening and Closing Adjustment Module: Performs correlation analysis on the oxygen inlet quantity and opening degree of the oxygen inlet to determine whether there is a proportional influence relationship between the oxygen inlet quantity and opening degree. If so, adjusts the opening degree of each oxygen inlet according to the proportional influence relationship and the oxygen inlet adjustment amount of each oxygen inlet. In this embodiment of the invention, the process of adjusting the opening degree of each oxygen inlet includes: By using the gasifier operation report, the opening and closing degree of the oxygen inlet under different oxygen intake rates during the historical period is obtained, and an oxygen intake rate data group and an opening and closing degree data group are constructed. The Pearson correlation coefficient between the oxygen intake data set and the opening / closing degree data set was calculated using the Pearson correlation algorithm, and the absolute value was taken to obtain the linear correlation value. Compare the linear correlation value with the linear correlation threshold; If the linear correlation value is greater than or equal to the linear correlation threshold, it indicates that there is a proportional relationship between the oxygen intake and the opening degree of the oxygen inlet. Based on the oxygen intake data set and the opening degree data set, a two-dimensional rectangular coordinate system is constructed with the X-axis representing the opening degree and the Y-axis representing the oxygen intake. Data points of the oxygen intake in the oxygen intake data set are marked in the two-dimensional rectangular coordinate system. After marking the data points of oxygen intake, the least squares method is used to fit the marked oxygen intake data points to obtain a fitted straight line. Based on the slope and intercept of the fitted straight line, the function equation of the fitted straight line is determined, thereby obtaining the influence relationship model between oxygen intake and opening degree, specifically: jy=k*kh+b, where k represents the slope, b represents the intercept, jy represents the oxygen intake, and kh represents the opening degree. Based on any oxygen inlet, obtain the oxygen inlet adjustment amount, input the influence relationship model between oxygen inlet amount and opening degree, and obtain the opening degree adjustment amount of oxygen inlet. The opening degree of the oxygen inlet is adjusted according to the adjustment amount of each oxygen inlet opening degree.

[0043] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing energy efficiency and managing carbon emissions in gas production, characterized in that: include: By constructing a gasifier model, the sequence of oxygen intake changes within the predicted cycle is obtained; The uniformity of oxygen intake at each oxygen inlet is monitored at the current moment to determine whether the overall oxygen intake status of the oxygen inlet is uniform at the current moment. Calculate the standard deviation and mean of the oxygen intake at each oxygen inlet at the current moment, and calculate the ratio to obtain the coefficient of variation of oxygen intake at the oxygen inlet. If the oxygen inlet's coefficient of variation is greater than or equal to the oxygen inlet's coefficient of variation threshold, it indicates that the overall oxygen inlet status at the current moment is uneven. If the oxygen inlet's coefficient of variation is less than the oxygen inlet's coefficient of variation threshold, it means that the overall oxygen inlet status at the current moment is uniform. If uniform, the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period is determined based on the current oxygen intake ratio of each oxygen inlet and the oxygen intake change sequence within the prediction period. If the oxygen intake is uneven, the oxygen intake adjustment ratio of each oxygen inlet is determined based on the current oxygen intake deviation and oxygen intake ratio of each oxygen inlet, and the oxygen intake adjustment amount of each oxygen inlet at different times in the prediction period is determined by combining the oxygen intake change sequence within the prediction period. By analyzing the correlation, it is determined whether there is a proportional relationship between the oxygen supply and the opening degree of the oxygen inlet. If so, the opening degree of each oxygen inlet is adjusted according to the proportional relationship and the oxygen supply adjustment amount of each oxygen inlet. Obtain the opening and closing degree of the oxygen inlet under different oxygen intake levels during the historical period, and construct an oxygen intake data set and an opening and closing degree data set. Calculate the Pearson correlation coefficient between the oxygen intake data set and the opening and closing degree data set, and take the absolute value to obtain the linear correlation value. If the linear correlation value is greater than or equal to the linear correlation threshold, it indicates that there is a proportional relationship between the oxygen intake and the opening degree of the oxygen inlet. The oxygen intake data set and the opening / closing degree data set are processed to obtain the influence relationship model between oxygen intake and opening / closing degree. The oxygen intake adjustment amount of the oxygen inlet is used as input to obtain the opening / closing degree adjustment amount of the oxygen inlet. The opening degree of the oxygen inlet is adjusted according to the adjustment amount of each oxygen inlet opening degree.

2. The method for optimizing energy efficiency and managing carbon emissions in gas production according to claim 1, characterized in that: Based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times after the current time are obtained, and multiplied with the oxygen intake ratio of each oxygen inlet at the current time to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period.

3. The method for optimizing energy efficiency and managing carbon emissions in gas production according to claim 2, characterized in that: The oxygen intake ratio of each oxygen inlet at the current moment is the ratio between the oxygen intake of each oxygen inlet at the current moment and the total oxygen intake at the current moment. The total oxygen intake at the current moment is the sum of the oxygen intake from each oxygen inlet at the current moment.

4. The method for optimizing energy efficiency and managing carbon emissions in gas production according to claim 1, characterized in that: Based on the oxygen intake change sequence within the prediction period, the changes in oxygen intake at different times from the current time are obtained, and multiplied with the oxygen intake adjustment ratio of each oxygen inlet to obtain the oxygen intake adjustment amount of each oxygen inlet at different times within the prediction period.

5. The method for optimizing energy efficiency and managing carbon emissions in gas production according to claim 4, characterized in that: The deviation between the oxygen inlet flow rate and the average oxygen inlet flow rate is calculated to obtain the oxygen inlet flow rate deviation. The oxygen adjustment ratio of each oxygen inlet is then calculated based on the oxygen inlet flow rate deviation.

6. The method for optimizing energy efficiency and managing carbon emissions in gas production according to claim 4, characterized in that: Calculate the ratio of the oxygen intake deviation at the oxygen inlet to the maximum oxygen intake deviation at the oxygen inlet to obtain the adjustment coefficient for each oxygen inlet; combine the adjustment coefficient with the current oxygen intake ratio of each oxygen inlet to determine the oxygen intake adjustment ratio for each oxygen inlet.

7. The method for optimizing energy efficiency and managing carbon emissions in gas production according to claim 1, characterized in that: Based on the oxygen intake data set and the opening / closing degree data set, the oxygen intake data points in the oxygen intake data set are marked in a two-dimensional rectangular coordinate system. The least squares method was used to fit the marked oxygen intake data points, and the functional equation of the fitted line was determined, thereby obtaining the influence relationship model between oxygen intake and opening degree.

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