Digital energy efficiency improvement method for blast furnace gas overpressure turbine power generation device

Abstract

The present application relates to the technical field of iron smelting, and particularly relates to a digital energy efficiency improvement method for a blast furnace gas overpressure turbine power generation device, comprising the following steps: S1: obtaining historical operation data, obtaining a normal operation data set according to offline data analysis and screening, calculating an energy efficiency index value, and determining an initial value of an important parameter value range, an energy efficiency benchmark value, and an energy efficiency advanced value; S2: obtaining real-time data on site, judging whether main parameters and production efficiency are abnormal according to the important parameter value range, the energy efficiency benchmark value, and the energy efficiency advanced value; S3: reading process data in a database, judging whether optimization is performed through linear fitting of the energy efficiency index; S4: judging whether the energy efficiency advanced value and the energy efficiency benchmark value are replaced, outputting and alarming; and S5: obtaining historical operation data of a model device and determining a value range of a second important parameter, performing multivariate linear regression analysis on the second important parameter to obtain an energy efficiency influence law, and calculating a new energy efficiency benchmark value.

Description

Technical Field

[0001] This invention relates to the field of iron and steel metallurgy and ironmaking technology, and in particular to a digital energy efficiency improvement method for a blast furnace gas residual pressure turbine power generation device. Background Technology

[0002] The iron and steel metallurgy industry is a high-energy-consuming industry. Two important documents issued by the National Development and Reform Commission (NDRC): "Several Opinions on Strictly Enforcing Energy Efficiency Constraints and Promoting Energy Conservation and Carbon Reduction in Key Areas" (NDRC Industry

[2021] No. 1464) and "Energy Efficiency Benchmark Levels and Baselines for Key Areas of High-Energy-Consuming Industries (2021 Edition)" (NDRC Industry

[2021] No. 1609) have set new requirements for energy efficiency management in the iron and steel metallurgy industry. At the same time, the national and local governments have imposed dual controls on the total energy consumption and intensity of steel enterprises, requiring them to reduce production energy consumption. In the near term, without major changes in iron and steel metallurgy processes, energy conservation and energy efficiency improvement will remain the primary measures for enterprises to reduce energy consumption and emissions. In recent years, the vigorous development of new-generation information technologies such as the Industrial Internet, 5G, and artificial intelligence has brought opportunities for energy conservation in steel enterprises. Introducing these technologies into enterprise production and management scenarios, using big data analysis methods and system theory approaches, and managing the energy efficiency of energy-consuming equipment from the overall perspective of the energy-consuming equipment system, has become a development trend for improving the energy efficiency of energy-consuming equipment through digital means.

[0003] The ironmaking process in steel metallurgy is the most energy-intensive process per ton of steel, making energy conservation and consumption reduction a major concern. The introduction of energy efficiency benchmarks and standards has placed immense pressure on enterprises, as less than 10% of blast furnace processes in Chinese steel companies currently achieve benchmark energy efficiency. Significant energy conservation and consumption reduction measures are needed to fully tap the energy-saving potential of the process and reduce production energy consumption through energy efficiency management. In the blast furnace process, the blast furnace gas residual pressure turbine power generation unit (TRT) is a crucial waste energy recovery device, vital for improving process energy efficiency. However, current methods lack digital means to improve equipment energy efficiency. Energy efficiency management of TRT devices often relies on collecting online data for offline analysis and then making energy efficiency improvement operations based on expert experience. This approach lacks timely response and fails to provide optimal energy efficiency improvement suggestions, hindering refined and intelligent energy efficiency management. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a digital energy efficiency improvement method for blast furnace gas residual pressure turbine power generation devices, comprising the following steps:

[0005] S1: Obtain historical operating data for offline data analysis, filter the offline data to obtain a normal operating dataset for determining the value range of important parameters, the initial value of the energy efficiency benchmark value, and the initial value of the energy efficiency advanced value, and calculate the energy efficiency index value based on the normal operating dataset;

[0006] S2: Based on the value range of the important parameters, the initial value of the energy efficiency benchmark value and the initial value of the energy efficiency advanced value, determine whether the main parameters in the production process are abnormal in real time, and calculate in real time online the real-time energy efficiency index value and production status output process data used to determine whether the production efficiency of the model equipment is abnormal;

[0007] S3: Read the process data from the database, perform linear fitting of energy efficiency indicators based on the calculated process data to determine whether optimization is needed and propose optimization suggestions;

[0008] S4: Determine whether to replace the advanced energy efficiency value based on the calculated average energy efficiency, calculate the average energy efficiency and determine whether to replace the initial value of the energy efficiency benchmark value, output the energy efficiency benchmark value and the advanced energy efficiency value and issue an alarm;

[0009] S5: The computer acquires the historical operating data of the model device and determines the value range of the second important parameter online. Based on the second important parameter, it performs multiple linear regression to obtain the energy efficiency quantification value for analyzing the energy efficiency impact law, and calculates and outputs the average energy efficiency value as a new energy efficiency benchmark value.

[0010] Preferably, in step S1, the important parameters further include:

[0011] Based on the normal operation dataset, the important parameters affecting energy efficiency and their influence patterns are analyzed, and the important parameters are ranked according to their degree of influence on energy efficiency.

[0012] Preferably, in step S2, the step of determining whether the main parameters in the production process are abnormal based on the value range of the important parameters further includes:

[0013] First, based on the important parameters including power generation, operating current and operating speed, determine whether two or more of the main parameters of the model equipment are abnormal. If normal, determine the other main parameters. If not, terminate step S2, report an error and output the error parameters, and the calculated real-time energy efficiency index value and the output value of the production status are empty.

[0014] Preferably, determining the other key parameters among the key parameters further includes:

[0015] The computer reads in other key parameters in real time and determines whether they are within the range of values ​​of the important parameters that affect energy efficiency. If not, it outputs the error parameter and reports an error. If so, it calculates the energy efficiency index value and the production status and outputs the calculation data of the calculation process.

[0016] Preferably, in step S2, the process data further includes:

[0017] The process data, including the main parameters of the production process, the output error parameters, and the calculation data, is stored in the database and displayed.

[0018] Preferably, in step S3, the linear fitting of the energy efficiency index further includes:

[0019] The process data over a certain period of time is filtered to obtain usable data. The energy efficiency index of the usable data is linearly fitted over time to show the trend of the energy efficiency of the model equipment over time.

[0020] Preferably, in step S3, the optimization further includes:

[0021] S31: Calculate the arithmetic mean of the adjustable parameters in the available data;

[0022] S32: Obtain the quantized value of the curve fitting of the adjustable parameter, calculate the difference between the initial value of the advanced energy efficiency value and the arithmetic mean of the adjustable parameter, multiply the difference by the quantized value, and select the adjustable parameter corresponding to the maximum absolute value of the product as the optimized adjustment parameter.

[0023] S33: Adjust the optimized adjustment parameters, obtain the expected energy efficiency value through the adjusted data, and obtain the expected energy efficiency improvement rate after adjustment;

[0024] S34: Output information including the difference, the adjusted data, the expected energy efficiency value, the energy efficiency improvement rate, and the optimization suggestions.

[0025] Preferably, in step S4, determining whether to change the advanced energy efficiency value further includes:

[0026] Set a maximum value for the count n and assign an initial value of zero to the count n. Compare the average energy efficiency with the advanced energy efficiency value. If the average energy efficiency is greater than the advanced energy efficiency value and the count n is less than the maximum value, then increment the count n by 1. When the count n reaches the maximum value, replace it with the advanced energy efficiency value. Otherwise, the advanced energy efficiency value remains the initial value of the advanced energy efficiency value.

[0027] Preferably, in step S5, the step of analyzing the laws affecting energy efficiency further includes:

[0028] The second important parameter is standardized, and the energy efficiency index value is subjected to multiple linear regression with the second important parameter. The dimensionless coefficient of the regression equation is used as the quantitative value of the influence of the second important parameter on the energy efficiency index.

[0029] Preferably, in step S5, the range of values ​​further includes:

[0030] The range of the fluctuation range covered by the maximum and minimum values ​​of the second important parameter is expanded to the value range of the second important parameter, centered on the average value of the fluctuation range covered by the maximum and minimum values ​​of the second important parameter.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] This invention obtains the real-time energy efficiency of the device through online energy efficiency mechanism analysis, and can propose energy-saving optimization operation suggestions based on big data analysis. Compared with the traditional offline method and expert experience, this invention can provide optimization operation suggestions more timely and accurately, and can also achieve more refined, intelligent and timely energy efficiency control of the device, which is more conducive to energy saving of the device and meeting the energy efficiency standards of the process. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0034] Figure 1 A flowchart for a digital energy efficiency improvement method for a blast furnace gas residual pressure turbine power generation device. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Obviously, the described embodiments are only some, not all, of the embodiments described in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application.

[0036] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a” and “an” used herein, and “the”, may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0037] Example

[0038] Please see Figure 1 As shown in the figure, this embodiment provides a digital energy efficiency improvement method for a blast furnace gas residual pressure turbine power generation device, which includes the following steps:

[0039] S1: Obtain historical operating data for offline data analysis. Based on the offline data analysis, filter to obtain a normal operating dataset for determining the value range of important parameters, the initial value of the energy efficiency benchmark, and the initial value of the advanced energy efficiency value. Calculate the energy efficiency index value based on the normal operating dataset. Specifically, in this embodiment, historical operating data is analyzed based on key parameters such as power generation patterns, operating current, and operating speed. The normal operating dataset is selected by utilizing on-site production conditions and operators' experience with the variation range of key parameters, or by using cluster analysis. Furthermore, after excluding obviously unreasonable data records, the selected normal operating dataset is determined based on the fluctuation range covered by the important parameters from minimum to maximum value. The value range of important parameters is determined by the range covered by 10% (adjustable) of the fluctuation range above and below the average value of each important parameter. Important parameters include gas flow rate, gas inlet pressure, gas outlet pressure, gas inlet temperature, gas outlet temperature, inlet and outlet butterfly valve opening, stationary vane opening, power generation, operating current, and operating speed. These parameters are important parameters related to the energy efficiency index value.

[0040] Preferably, the initial value of the energy efficiency benchmark is the arithmetic mean of the energy efficiency index values ​​calculated from the normal operation dataset according to the energy efficiency mechanism model, and the initial value of the advanced energy efficiency is the maximum value of the energy efficiency index values ​​calculated from the normal operation dataset according to the energy efficiency mechanism model, wherein the energy efficiency mechanism model is as follows:

[0041]

[0042] Where η is the total power generation efficiency of the turbine, expressed as a percentage (%); W is the power generation capacity of the TRT, expressed in kW; and ρ is the density of blast furnace gas under standard conditions, expressed in kg / m³. 3 G represents the flow rate of blast furnace gas under standard conditions, expressed in meters (m). 3 / h indicates; C p t1 is the specific heat of the gas at constant pressure, expressed in kJ / (kg·℃); t1 is the gas temperature at the TRT inlet, expressed in ℃; P1 is the absolute pressure at the gas inlet, expressed in Pa; P2 is the absolute pressure at the gas outlet, expressed in Pa; P w The partial pressure of saturated water vapor under the temperature and pressure of coal gas, denoted by Pa, is obtained from a table for the wet form and is 0 for the dry form; k is the adiabatic index; η tTurbine efficiency is expressed as a percentage (%); η d Generator efficiency is expressed as a percentage (%). In the computer program, data such as the range of values, baseline values, advanced values, and index values ​​are set up so that operators can modify and adjust them. In this embodiment, adjustable parameters include: inlet and outlet butterfly valve opening degree, stationary vane opening degree, etc.

[0043] S2: Based on the value range of important parameters, the initial value of the energy efficiency benchmark, and the initial value of the advanced energy efficiency value, determine whether the main parameters in the production process are abnormal in real time, and calculate in real time online the real-time energy efficiency index value and production status output process data used to determine whether the production efficiency of the model equipment is abnormal. Specifically, in this embodiment, real-time online energy efficiency analysis includes the following steps: data reading within the energy efficiency calculation cycle, normal operation judgment, main parameter data validity judgment, real-time energy efficiency index value calculation, basic production energy efficiency judgment, and production energy efficiency excellence judgment. Data reading within the energy efficiency calculation cycle reads the production process data with unit conversion already done from the steel database in real time for real-time energy efficiency analysis. Real-time energy efficiency analysis can be calculated at a 5-minute granularity, and the time granularity can be adjusted according to the actual situation. Real-time energy efficiency index value calculation calculates the input and output items in the energy efficiency index according to the calculation method of the energy efficiency mechanism model, and calculates the energy efficiency index value of the model equipment in real time online according to the valid items defined by the energy efficiency index formula. The basic judgment of production energy efficiency is to determine whether the real-time energy efficiency index value is higher than the energy efficiency index benchmark value. If not, the production efficiency of the model equipment in the current calculation cycle is in an abnormally inefficient production state, outputs a production abnormality alarm, and saves the data in the database and displays it. If it is, it is in a normal production state.

[0044] More preferably, production energy efficiency superiority is the degree of advancement of the model equipment relative to the advanced value of energy efficiency index under the current production conditions, Δη. The formula for calculating Δη is as follows:

[0045]

[0046] Where f is the current energy efficiency value, f 基准值 f is the energy efficiency benchmark value. 先进值 The advanced value of the energy efficiency index Δη is determined to be higher than the set value of 0.8 in this embodiment. The set value can be adjusted and should preferably be above 0.7. If so, it is determined that the model equipment is in a high-efficiency production state in the current calculation cycle.

[0047] S3: Read the process data from the database, perform linear fitting of energy efficiency indicators based on the calculated process data to determine whether optimization is needed and propose optimization suggestions. Specifically, in this embodiment, the operation optimization suggestions can be calculated at a time granularity of 1 hour, and the time granularity can be adjusted.

[0048] S4: Determine whether to replace the advanced energy efficiency value based on the calculated average energy efficiency. Calculate the average energy efficiency and determine whether to replace the initial value of the energy efficiency benchmark. Output the energy efficiency benchmark value and the advanced energy efficiency value and issue an alarm. Specifically, in this embodiment, the replacement of the energy efficiency index benchmark value and advanced value should be calculated according to the time granularity of the blast furnace smelting cycle. The time granularity can be adjusted. The average output energy efficiency data within 24 hours can be read from the database. Calculate the arithmetic average energy efficiency and the arithmetic average expected energy efficiency improvement rate within the current time granularity. Replace and update the benchmark values ​​of the important parameters and energy efficiency index parameters used in the real-time energy efficiency analysis with the arithmetic average of the hourly average values ​​of the corresponding parameters within these 24 hours. Replace and update the advanced values ​​of the important parameters and energy efficiency index parameters used in the energy efficiency analysis with the hourly average values ​​of the corresponding parameters under the nth medium-hour average energy efficiency suboptimal working conditions. Output the benchmark value and advanced value replacement alarm, as well as the energy efficiency suboptimal efficiency value and its timestamp, to complete this cycle.

[0049] S5: The computer acquires historical operating data of the model equipment and determines the value range of the second important parameter online. Based on multiple linear regression of the second important parameter, it obtains a quantitative energy efficiency value for analyzing the energy efficiency impact pattern and calculates and outputs the average energy efficiency value as a new energy efficiency benchmark. Specifically, in this embodiment, the online quantitative analysis of energy efficiency influencing factors is run on a monthly time granularity, which can be adjusted.

[0050] Preferably, in step S1, the important parameters further include:

[0051] Based on the analysis of the normal operation dataset, the important parameters affecting energy efficiency and their influence patterns are analyzed, and the important parameters are ranked according to their degree of influence on energy efficiency. Specifically, in this embodiment, the ranking of the degree of influence on energy efficiency includes first standardizing the values ​​of the main influencing parameters. Standardization involves subtracting the minimum value of the parameter in the normal operation dataset from the value to be processed, dividing the difference by the difference in the range of the parameter's values, performing multiple linear regression between the energy efficiency index and each main influencing parameter, using the coefficient of the main influencing parameter as the quantified value of the parameter's influence on the energy efficiency index, and ranking the degree of influence based on the absolute value of each main influencing parameter's quantified value.

[0052] Preferably, in step S2, determining whether the main parameters read in real-time during the production process are abnormal based on the value range of the important parameters further includes:

[0053] First, based on key parameters including power generation, operating current, and operating speed, it is determined whether two or more of the model equipment's operating parameters are abnormal. If normal, other key parameters are checked; otherwise, step S2 terminates, an error is reported, and the calculated real-time energy efficiency index and production status output values ​​are empty. Specifically, in this embodiment, normal operation is determined based on whether two or more of the parameters, including power generation, operating current, and operating speed, are within their range to determine whether the model equipment is operating normally and in production.

[0054] Preferably, determining the main parameters affecting energy efficiency further includes:

[0055] The computer reads in the main parameters affecting energy efficiency during the real-time production process and determines whether they are within the range of values ​​for important parameters affecting energy efficiency. If not, it outputs incorrect parameters and reports an error to prevent some invalid data from causing a large deviation or meaninglessness in the final energy efficiency index result. If so, it calculates the energy efficiency index value and production status and outputs the calculation data of the calculation process. Specifically, in this embodiment, this content is the validity judgment of the main parameter data.

[0056] Preferably, in step S2, the process data further includes:

[0057] The process data, including the main parameters of the production process, the output error parameters, and the calculation data, is stored in the database and displayed.

[0058] Preferably, in step S3, the linear fitting of the energy efficiency index further includes:

[0059] Process data over a certain time period is filtered to obtain usable data. Linear fitting of energy efficiency indicators on the usable data is used to display the trend of energy efficiency changes of the model equipment over time. Specifically, in this embodiment, the certain time period can be determined in conjunction with the blast furnace smelting cycle, such as all real-time online energy efficiency analysis output data within a 6-hour time range. By default, all real-time data within the time range is read as usable data. In special cases, usable data can be filtered based on parameter validity judgment, real-time basic energy efficiency judgment, and energy efficiency excellence judgment results. The hourly average values ​​of the main parameters and energy efficiency indicator parameters are calculated based on the usable data (only the values ​​within the current hour need to be calculated), and relevant real-time energy efficiency statistics data within the hour (such as basic production energy efficiency judgment and production energy efficiency excellence judgment) are output.

[0060] Preferably, in step S3, the optimization further includes:

[0061] S31: Calculate the arithmetic mean of the adjustable parameters in the available data;

[0062] S32: Obtain the quantized value of the curve fitting of the adjustable parameter, calculate the difference between the initial value of the advanced energy efficiency value and the arithmetic mean of the adjustable parameter, multiply the difference with the quantized value, and select the adjustable parameter corresponding to the largest absolute value of the product as the optimized adjustment parameter.

[0063] S33: Adjust and optimize the adjustment parameters, obtain the expected energy efficiency value through the adjusted data, and obtain the expected energy efficiency improvement rate after adjustment;

[0064] S34: Output information including the difference, adjusted data, expected energy efficiency value, energy efficiency improvement rate, and optimization suggestions. Specifically, in this embodiment, determining whether optimization is needed involves judging the sign of the linear fitting slope. When the slope ε > 0, it indicates that the model equipment's energy efficiency is developing towards higher energy efficiency. Then, the arithmetic mean of the energy efficiency index within the current time range is calculated and compared with the energy efficiency benchmark value. If it is lower than the energy efficiency benchmark value, it indicates that the model equipment is developing towards higher energy efficiency from a low-energy-efficiency state, and the next step of the efficiency improvement operation optimization suggestion process is entered. If it is higher than the energy efficiency benchmark value, it indicates that the current state needs to be maintained and production should continue. The prompt statement "Continue to maintain the current state of production." is output, and the current loop is completed. When the slope ε ≤ 0, it indicates that the model equipment's energy efficiency is developing towards lower energy efficiency, and operation optimization is needed, and the efficiency improvement optimization operation suggestion step is entered. The operational optimization suggestion first involves calculating the arithmetic mean of all operable adjustable parameters in the available data to obtain the quantified values ​​of the influencing factors of each operable adjustable parameter. These coefficients are initially determined from the historical normal operating condition dataset, i.e., the quantified values ​​of the parameter curve fitting, and later obtained from online quantitative analysis of energy efficiency influencing factors. Next, the difference between the value of each operable adjustable parameter and its arithmetic mean under the state corresponding to the advanced energy efficiency value is calculated (i.e., the quantified value of the parameter's distance from the advanced level). Then, each quantified value of the energy efficiency influencing factor is multiplied by this difference, and the operable adjustable parameter with the largest absolute value of the corresponding product is selected as the optimized adjustment parameter. Simulations are then performed on this optimized adjustment parameter. The parameter is adjusted to approach the advanced level (e.g., the average value of the operable adjustable parameter is taken as the average value of the parameter under the corresponding advanced energy efficiency value, or a certain intermediate value). By inputting the adjusted data (expected parameter value) into the energy efficiency mechanism model for simulation, the expected energy efficiency value after parameter adjustment is obtained. The difference between the expected energy efficiency value and the average energy efficiency value of the current hour is obtained by subtracting the difference from the average energy efficiency value of the current hour, and then dividing by the average energy efficiency value of the current hour, the expected energy efficiency improvement rate after adjustment is obtained. The output includes the quantitative value of the parameter's distance from the advanced level, the expected parameter value and the advanced energy efficiency value, the expected energy efficiency value, the expected energy efficiency improvement rate, and the corresponding energy-saving optimization operation suggestions, thus completing this cycle.

[0065] Preferably, in step S4, determining whether to change the advanced energy efficiency value further includes:

[0066] Set the maximum value of count n and assign an initial value of zero to count n. Compare the average energy efficiency and the advanced energy efficiency value. If the average energy efficiency is greater than the advanced energy efficiency value and count n is less than the maximum value, then count n is incremented by 1. If count n reaches the maximum value within 24 hours, then the advanced energy efficiency value is replaced. Otherwise, the advanced energy efficiency value remains the initial value of the advanced energy efficiency value. Specifically, in this embodiment, the maximum value of count n is 20, and count n can be adjusted.

[0067] Preferably, in step S5, the analysis of the factors affecting energy efficiency further includes:

[0068] The second important parameter is standardized, and a multiple linear regression is performed between the energy efficiency index value and the second important parameter. The dimensionless coefficient of the regression equation is used as the quantification value of the influence of the second important parameter on the energy efficiency index value. Specifically, in this embodiment, real-time data under normal operating conditions of the model equipment is obtained, and data analysis is performed on important parameters such as gas flow rate, gas inlet pressure, gas outlet pressure, gas inlet temperature, gas outlet temperature, inlet and outlet butterfly valve opening, inlet and outlet gate valve opening, stationary vane opening, power generation, operating current, and operating speed. Data sets with two or more of the three key parameters (power generation, operating current, and operating speed) within the normal operating range are extracted from historical data. The normal operating data set is selected, and the fluctuation range and value range of the normal operating data set are reset to determine the value range of the second important parameter.

[0069] More ideally, the influence patterns of the sample data are analyzed based on the selected second most important parameters. These second most important parameters include: gas flow rate, gas inlet pressure, gas outlet pressure, inlet and outlet butterfly valve opening degree, and stationary vane opening degree. The second most important parameters are standardized (i.e., the difference between the parameter value and the minimum value of the parameter in the sample data, divided by the difference in the parameter's fluctuation range). Multiple linear regression is then performed between the energy efficiency index and each major influencing parameter. The coefficient of the major influencing parameter is used as the quantitative value of its impact on the energy efficiency index to determine the influence pattern.

[0070] Preferably, in step S5, the range of values ​​further includes:

[0071] The range of fluctuation intervals is expanded to include the average of the fluctuation intervals covered by the maximum and minimum values ​​of the second most important parameter. The fluctuation interval is the range covered by each parameter from its minimum to its maximum value. The value range is centered on the average value of each parameter, extending outwards by 5% (default, adjustable) of the fluctuation interval above and below the average value of the second most important parameter. Furthermore, it is required that the upper and lower limits of the value ranges of each parameter can be manually modified at any time during the entire operation of the model.

[0072] In this embodiment, step S1 is performed offline and is not calculated repeatedly, while steps S2, S3, S4 and S5 are performed online and are calculated continuously in a loop, but the frequency of calculation is different. The frequency decreases from step S2 to step S5. For example, step S2 is calculated once every 5 minutes, step S3 can be calculated once every hour, and step S4 can be calculated once every 6 hours, etc.

[0073] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the present invention.

Claims

1. A method for improving the digital energy efficiency of a blast furnace gas residual pressure turbine power generation device, characterized in that, Includes the following steps: S1: Obtain historical operating data for offline data analysis, and filter the offline data to obtain a normal operating dataset for determining the value range of important parameters, the initial value of the energy efficiency benchmark value, and the initial value of the energy efficiency advanced value. Calculate the energy efficiency index value based on the normal operating dataset. Important parameters include gas flow rate, gas inlet pressure, and gas outlet pressure. S2: Based on the value range of the important parameters, the initial value of the energy efficiency benchmark value and the initial value of the energy efficiency advanced value, determine whether the main parameters in the production process are abnormal in real time, and calculate in real time online the real-time energy efficiency index value and production status output process data used to determine whether the production efficiency of the model equipment is abnormal; S3: Read the process data from the database, perform linear fitting of energy efficiency indicators based on the calculated process data to determine whether optimization is needed and propose optimization suggestions; S4: Determine whether to replace the advanced energy efficiency value based on the calculated average energy efficiency, calculate the average energy efficiency and determine whether to replace the initial value of the energy efficiency benchmark value, output the energy efficiency benchmark value and the advanced energy efficiency value and issue an alarm; S5: The computer acquires the historical operating data of the model device and determines the value range of the second important parameter online. Based on the second important parameter, it performs multiple linear regression to obtain the energy efficiency quantification value for analyzing the energy efficiency impact law, and calculates and outputs the average energy efficiency value as the energy efficiency benchmark value. Preferably, in step S3, the linear fitting of the energy efficiency index further includes: The process data over a certain period of time is filtered to obtain usable data. The energy efficiency index of the usable data is linearly fitted over time to show the trend of the energy efficiency of the model equipment over time. Preferably, in step S3, the optimization further includes: S31: Calculate the arithmetic mean of the adjustable parameters in the available data; S32: Obtain the quantized value of the curve fitting of the adjustable parameter, calculate the difference between the initial value of the advanced energy efficiency value and the arithmetic mean of the adjustable parameter, multiply the difference by the quantized value, and select the adjustable parameter corresponding to the maximum absolute value of the product as the optimized adjustment parameter. S33: Adjust the optimized adjustment parameters, obtain the expected energy efficiency value through the adjusted data, and obtain the expected energy efficiency improvement rate after adjustment; S34: Output information including the difference, the adjusted data, the expected energy efficiency value, the energy efficiency improvement rate, and the optimization suggestions; Preferably, in step S4, determining whether to change the advanced energy efficiency value further includes: Set a maximum value for the count n and assign an initial value of zero to the count n. Compare the average energy efficiency with the advanced energy efficiency value. If the average energy efficiency is greater than the advanced energy efficiency value and the count n is less than the maximum value, then increment the count n by 1. When the count n reaches the maximum value, replace it with the advanced energy efficiency value. Otherwise, the advanced energy efficiency value remains the initial value of the advanced energy efficiency value.

2. The method for digitally improving the energy efficiency of a blast furnace gas residual pressure turbine power generation device according to claim 1, characterized in that, In step S1, the important parameters further include: Based on the normal operation dataset, the important parameters affecting energy efficiency and their influence patterns are analyzed, and the important parameters are ranked according to their degree of influence on energy efficiency.

3. The method for digitally improving the energy efficiency of a blast furnace gas residual pressure turbine power generation device according to claim 2, characterized in that, In step S2, the step of determining whether the main parameters in the production process are abnormal based on the value range of the important parameters further includes: First, based on the important parameters including power generation, operating current and operating speed, determine whether two or more of the main parameters of the model equipment are abnormal. If normal, determine the other main parameters. If not, terminate step S2, report an error and output the error parameters, and the calculated real-time energy efficiency index value and the output value of the production status are empty.

4. The method for digitally improving the energy efficiency of a blast furnace gas residual pressure turbine power generation device according to claim 3, characterized in that, The determination of other key parameters among the key parameters further includes: The computer reads in other key parameters in real time and determines whether they are within the range of values ​​of the important parameters that affect energy efficiency. If not, it outputs the error parameter and reports an error. If so, it calculates the real-time energy efficiency index value and the production status and outputs the calculation data of the calculation process.

5. The method for digitally improving the energy efficiency of a blast furnace gas residual pressure turbine power generation device according to claim 4, characterized in that, In step S2, the process data further includes: The process data, including the main parameters of the production process, the output error parameters, and the calculated data, is stored in the database and displayed.

6. The method for digitally improving the energy efficiency of a blast furnace gas residual pressure turbine power generation device according to claim 1, characterized in that, In step S5, the step of analyzing the laws affecting energy efficiency further includes: The second important parameter is standardized, and the energy efficiency index value is subjected to multiple linear regression with the second important parameter. The dimensionless coefficient of the regression equation is used as the quantitative value of the influence of the second important parameter on the energy efficiency index.

7. The method for digitally improving the energy efficiency of a blast furnace gas residual pressure turbine power generation device according to claim 1, characterized in that, In step S5, the range of values ​​further includes: The range of the fluctuation range covered by the maximum and minimum values ​​of the second important parameter is expanded to the value range of the second important parameter, centered on the average value of the fluctuation range covered by the maximum and minimum values ​​of the second important parameter.

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