Coal gas data management and dynamic distribution method and system for process-level carbon emission accounting in iron and steel industry

Abstract

The invention relates to a coal gas data management and dynamic distribution method and system for process-level carbon emission accounting in the iron and steel industry. The method comprises the following steps: collecting flow and component parameters of coal gas in coal gas channels corresponding to a blast furnace, a coke oven and a converter; performing real-time data quality control processing and statistical calculation processing on the acquired data, and screening to obtain effective data; calculating the low calorific value and the carbon content per unit calorific value of the coal gas based on the effective data, and carrying out uncertainty analysis; and constructing a mixed gas carbon emission distribution model based on a mass energy conservation equation, solving the proportion of each source gas in the mixed gas based on the low calorific value and the unit calorific value carbon content, and verifying and correcting a distribution result in combination with an uncertainty analysis result to complete splitting and distribution of carbon emission. Compared with the prior art, the method has the advantages that the carbon data quality reliability is improved, the labor cost and the efficiency loss are reduced, and the data are traceable.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology in the steel industry, and in particular to a method and system for the management and dynamic allocation of coal gas data for process-level carbon emission accounting in the steel industry. Background Technology

[0002] In accordance with the requirements for accurate carbon emission data in achieving carbon peaking and carbon neutrality, carbon emission accounting in the steel industry is developing towards process-level and baseline methods. As the main secondary energy sources flowing between processes, the accurate carbon footprint allocation of blast furnace gas (BFG), coke oven gas (COG), and converter gas (LDG) is a prerequisite for achieving precise process-level accounting and also a current technical challenge for the industry.

[0003] Existing technologies typically employ fixed-factor methods and periodic manual detection methods. Fixed-factor methods use industry averages or default emission factors for calculation. The problem with this approach is that it cannot reflect the actual fluctuations in the composition and calorific value of the enterprise's own gas, leading to a systematic deviation between the calculated results and actual emissions. Periodic manual detection methods rely on periodic (e.g., monthly) manual sampling and offline analysis data to represent the entire accounting cycle. The problems with this approach are low data density, poor timeliness, inability to capture real-time changes in gas parameters, and susceptibility to human error during data acquisition, making it difficult to meet the high requirements for data representativeness and accuracy in process-level accounting. Furthermore, for mixed-use gas, existing methods lack accurate and reliable models to reasonably break down carbon emissions and trace them back to each generating process, resulting in a distorted carbon footprint for each process. Patent CN117635170A proposes a carbon emission management method for steel production enterprises in the steel manufacturing process, including: obtaining a carbon flow map of the entire manufacturing process and generating a carbon flow model based on the carbon flow map. The system calculates the carbon emissions of each manufacturing stage within the current emission cycle using carbon flow models, environmental data, and loss data. Based on the current stage carbon emissions and current carbon sequestration data, the carbon flow model predicts the current category carbon emissions for each emission category within the current emission cycle. The system then compares the predicted total carbon emissions with preset carbon emission thresholds for the current emission cycle to determine if the company's carbon emissions exceed the limits. If they do, a preset first quota rule is applied to limit the current emission sources for each manufacturing stage. However, this system has certain shortcomings in data quality, work efficiency and automation, auditing and standardization, and handling mixed gas scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art by providing a method and system for coal gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry. This method replaces manual accounting with automated data flow, achieving high-frequency near real-time carbon accounting and providing timely and reliable data for the refined management of carbon emissions in enterprises.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for the governance and dynamic allocation of coal gas data for process-level carbon emission accounting in the steel industry includes the following steps: Collect the flow rate and composition parameters of the gas in the gas passages corresponding to the blast furnace, coke oven, and converter; The collected data undergoes real-time data quality control and statistical calculation to filter and obtain valid data. The lower heating value and carbon content per unit calorific value of coal gas were calculated based on valid data, and uncertainty analysis was performed. A carbon emission allocation model for mixed coal gas is constructed based on the mass-energy conservation equation. The proportion of each source of coal gas in the mixed coal gas is calculated based on the lower heating value and carbon content per unit calorific value. The allocation results are verified and corrected by combining the results of uncertainty analysis, and the carbon emission splitting and allocation are completed.

[0006] Furthermore, the flow rate and composition parameters are obtained based on online monitoring instruments deployed at key gas generation nodes.

[0007] Furthermore, the real-time data quality control processing includes flow threshold verification, component concentration threshold verification, and jump verification.

[0008] Furthermore, the statistical calculation includes dynamic confidence interval calculation, where boundary values ​​are used instead of real-time data when the data exceeds the interval, thus participating in subsequent calculations.

[0009] Furthermore, the calculation of the dynamic confidence interval is specifically as follows: Based on historical data within a set time period, the mean and standard deviation of key parameters are calculated, and dynamic confidence intervals are generated.

[0010] Furthermore, the calculation method for the lower heating value is as follows: , in, The percentage of each component by volume. This represents the lower heating value coefficient of the corresponding component.

[0011] Furthermore, the carbon content per unit calorific value is calculated as follows: , in, The percentage of each component by volume. The molar mass of the component, It is the lower heating value of coal gas.

[0012] Furthermore, the uncertainty analysis is implemented using Monte Carlo simulation.

[0013] Furthermore, when the mixed gas contains three or more sources, the constructed splitting model is an overdetermined system of equations, and the least squares method is used for optimal fitting and solution.

[0014] This invention also provides a gas data management and dynamic allocation system for process-level carbon emission accounting in the steel industry, comprising: The online data acquisition module for gas channels is used to collect the flow rate and composition parameters of gas in the gas channels corresponding to blast furnaces, coke ovens, and converters. Data processing module: performs real-time data quality control and statistical calculation on the collected data, filters out valid data; calculates the lower heating value and carbon content per unit calorific value of coal gas based on the valid data, and performs uncertainty analysis; Model building and solution module: Based on the mass-energy conservation equation, a carbon emission distribution model for mixed coal gas is built. Based on the lower heating value and carbon content per unit calorific value, the proportion of each source of coal gas in the mixed coal gas is solved. Combined with the results of uncertainty analysis, the distribution results are verified and corrected to obtain carbon emission split data and complete the splitting and distribution of carbon emissions. Process-level accounting application module: Converts the carbon emission breakdown data obtained from the model building and solution module into process-level carbon emission accounting results.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve the reliability of carbon data quality: This invention organically combines high-precision online monitoring, data governance process, dynamic statistical model and physical conservation law model to form a complete solution for process-level accounting. At the same time, dynamic confidence interval control and Monte Carlo simulation are applied to carbon emission data governance and uncertainty analysis, which effectively improves the reliability of data quality, so that the final data quality and accounting accuracy can meet the accounting requirements of the process-level baseline method.

[0016] 2. Reduce labor costs and efficiency losses: This invention replaces traditional manual accounting operations with automated data flow, enabling high-frequency, near real-time carbon accounting, and providing timely and reliable data for enterprises' refined carbon emission management.

[0017] 3. Traceability: This invention constructs a splitting model based on the conservation law, which solves the problem of tracing carbon emissions from mixed coal gas. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the overall system architecture and data flow of the present invention. Figure 2 The flowchart for dynamic confidence interval control; Figure 3 This is a schematic diagram of the carbon emission allocation model for mixed coal gas. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0020] Example 1 This embodiment provides a method for coal gas data governance and dynamic allocation for process-level carbon emission accounting in the steel industry. The core of this method lies in constructing a complete technical system of "data acquisition-governance-modeling-allocation," including the following steps: First, online monitoring instruments are deployed in the gas channels corresponding to blast furnaces, coke ovens, and converters to collect the flow rate and composition parameters of the gas in each channel. Then, real-time data quality control and statistical calculation processing are performed on the collected parameters to screen valid data. Based on the valid data, the lower heating value and carbon content per unit calorific value of the gas are calculated, and uncertainty analysis is conducted. A model is constructed based on the mass and energy conservation equations, and the lower heating value and carbon content per unit calorific value of the gas are substituted into the model as basic parameters to solve for the proportion of each source of gas in the mixed gas. At the same time, the confidence interval of the results is verified and corrected by combining the uncertainty analysis results to complete the splitting and allocation of carbon emissions. Finally, the carbon emission splitting data obtained by the model solving module is transformed into process-level carbon emission accounting results that meet the requirements of the "Technical Guidelines for Enterprise Greenhouse Gas Emission Verification in the Iron and Steel Industry" through the process-level carbon accounting and application platform.

[0021] like Figure 1 The diagram illustrates the overall architecture and data flow of this invention. Firstly, online monitoring instruments conforming to national metrological standards are deployed at key nodes such as gas generation (blast furnace, coke oven, converter outlet), storage (gas holder), and consumption (entry points of each process).

[0022] Blast furnace gas (BFG): High dust content and relatively high temperature (typically 100-300℃). Dust-proof ultrasonic flow meters are usually selected, along with dust filtration and cooling pretreatment systems to ensure that the dust content before the gas enters the analyzer is <10 mg / Nm³. 3 Online gas analyzers should preferably be a combination of non-dispersive infrared (NDIR) and thermal conductivity (TCD) spectrometers, equipped with automatic backflushing capabilities, and suitable for high-dust environments.

[0023] Coke oven gas (COG): Contains impurities such as tar and naphthalene, and is easily condensed. Ultrasonic or turbine flow meters with heating functions should be selected to prevent coking. The gas analyzer needs to be equipped with multi-component chromatography (GC) or laser spectrometry, and have tar collection and dehumidification units.

[0024] Converter gas (LDG): Its composition fluctuates drastically (CO concentration ranges from extremely low to over 70%), and its temperature is high (reaching over 1000℃). Flow meters and analyzers are typically installed at the outlet of the converter gas recovery system after cooling and dust removal. High-temperature resistant ultrasonic flow meters should be selected, and the analyzer should have a fast response (T90 < 5s) and a high linear dynamic range to adapt to drastic changes in CO concentration.

[0025] To accommodate different pipe diameters and flow rates, ultrasonic flow meters with a range of 0.5–30 m / s, an accuracy class of ±1.0% of the reading, and a protection rating of IP65 or higher should be selected. The probe material should be 316L stainless steel or Hastelloy, suitable for corrosive gases such as H2S and NH3. For high-dust environments, ultrasonic flow meters with dust covers and purging devices should be installed, and on-site comparison or laboratory calibration should be performed every 6 months to ensure consistent accuracy. Simultaneously, online gas analyzers with an accuracy class of ±1%FS at full scale and a response time ≤10s should be selected. To adapt to the environment, the analysis cabinet should be equipped with constant temperature control and positive pressure explosion-proof design, and the sampling pipeline should be heated to prevent condensation. Calibration should be performed every 3 months using standard gases, and the instrument should be returned to the factory for verification every 12 months to ensure accuracy.

[0026] The aforementioned instruments enable continuous and automatic acquisition of key parameters (flow rate, composition, calorific value), with an acquisition frequency of no less than once per minute and a data storage time of no less than 5 years, supporting data traceability and auditing.

[0027] To ensure data quality, the company has established an internal "Gas Data Acquisition and Quality Management Standard" and set up automatic data verification rules. Flow rate thresholds are set to 0.5%–100% of the range; exceeding these limits triggers an alarm. Component concentration thresholds are also established: CO: 0-80%, H2: 0-60%; exceeding these ranges is considered abnormal. A jump alarm is set, triggered when the rate of change between two consecutive collected values ​​exceeds 20% (configurable). A third-party periodic calibration mechanism is established to ensure data integrity, accuracy, and traceability. This step aims to meet the stringent data quality control requirements of the "Technical Guidelines for Enterprise Greenhouse Gas Emission Verification in the Steel Industry."

[0028] After automatic verification is completed, the lower heating value and carbon content per unit calorific value of the gas are dynamically calculated based on the real-time collected effective data (the calculation method follows thermodynamics and stoichiometry).

[0029] Low heat output ( The formula for calculating ) is: ,in, The percentage of each component by volume. The lower heating coefficient of the corresponding component (e.g., CO: 12.64 MJ / Nm³) 3 H2: 10.79 MJ / Nm 3CH4: 35.88 MJ / Nm 3 (etc.). The formula for calculating the carbon content (C) per unit of calorific value is: in, The percentage of each component by volume. The molar mass (g / mol) of the component, and the number of carbon atoms, such as 1 for CO2 and 1 for CH4.

[0030] like Figure 2 As shown, a dynamic confidence interval and uncertainty quantification method are introduced simultaneously. Specifically, based on recent (e.g., the past 30 days) historical data, the mean and standard deviation of key parameters (such as carbon content) are automatically calculated, and a dynamic confidence interval (e.g., 95% confidence level) is generated. When real-time data exceeds this interval, a conservative substitution is automatically applied using the interval boundary value to smooth out abnormal fluctuations and improve the stability of the calculation. Monte Carlo simulation (a recognized probabilistic statistical method) is used to perform propagation analysis on the uncertainty of the input parameters, quantifying the possible range of the final emission results and enhancing the scientific rigor and reliability of the results.

[0031] like Figure 3 As shown in the figure, it is a schematic diagram of the carbon emission allocation model of mixed gas. The carbon emission splitting and tracing model of mixed gas is constructed based on the mass and energy conservation equations.

[0032] Specifically, when the mixed gas comes from only two sources, such as a mixture of BFG and COG, we have: Measured calorific value = (BFG percentage × BFG calorific value + COG percentage × COG calorific value), where BFG percentage + COG percentage = 1. By solving the above equations, the proportion of each gas source can be accurately calculated, thus fairly and accurately allocating the total carbon emissions of the mixed gas back to its production process.

[0033] When the mixed gas consists of three sources (such as BFG, COG, and LDG), the proportions of the three gases cannot be uniquely determined solely by equations that balance the calorific value and sum the proportions to one (the number of equations is less than the number of unknowns). Therefore, it is necessary to introduce gas component concentration data as an auxiliary constraint, construct an overdetermined set of equations, and use the least squares method for optimal fitting and solution, thereby achieving accurate separation of the mixed gas scenario.

[0034] Suppose the following data is monitored at a certain hybrid node: Measured average lower heating value of mixed gas (kJ / Nm) 3 ) Volume concentration of a key component (such as CO or H2) in a mixed gas (%) Known typical or real-time parameters for various gas sources: BFG: Calorific Value CO concentration H2 concentration COG: Calories CO concentration H2 concentration LDG: Calorific Value CO concentration H2 concentration Let the volume percentages of the three types of gas in the mixture be respectively , , (All are decimals between 0 and 1, and) ).

[0035] The following system of equations can be established: Heat balance equation: CO concentration equilibrium equation: H2 concentration equilibrium equation (optional, for improved accuracy): Proportion Normalization Equation: This system of equations is overdetermined (number of equations ≥ 4, number of unknowns 3). It can be solved using the least squares method to minimize the sum of squares of the fitting residuals of each equation, thus obtaining the optimal solution. The proportion of coal gas from various sources was determined, thus completing the breakdown and allocation of carbon emissions.

[0036] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0037] Example 2 Ultrasonic flow meters and online gas analyzers conforming to GB 17167 standards were installed at the blast furnace gas outlet, coke oven gas outlet, and the main mixed gas pipeline supplying gas to the rolling mill process in a large steel enterprise. The instruments continuously collect the volumetric flow rate (Nm³) of the gas at a frequency of 1 time per minute. 3 The data includes the volume concentrations (%) of components such as CO, H2, CH4, and CO2. Real-time data is then uploaded to a central data processing platform, which dynamically calculates the lower heating value (Q_net) and carbon content per unit calorific value (C) of the gas based on the real-time component concentrations and thermodynamic formulas.

[0038] At 10:00 AM on a certain day, the system monitoring and calculation showed that the real-time carbon content per unit calorific value of blast furnace gas was 25.1 g / MJ. However, the dynamic confidence interval (95% confidence level) calculated based on historical data from the past 30 days was (27.5 ± 1.5) g / MJ. Since the real-time value (25.1) was lower than the lower limit of the interval (26.0), the lower limit of the interval, 26.0 g / MJ, was used in the calculation of carbon emissions at that moment.

[0039] At a certain time period, the total flow rate of mixed gas consumed in the steel rolling process was 250,000 Nm³. 3 / h, the measured average lower heating value is 3800 kJ / Nm 3 The online monitoring system simultaneously measured the calorific value of the blast furnace gas to be 3200 kJ / Nm³. 3 The calorific value of coke oven gas is 17000 kJ / Nm³. 3 .

[0040] Model solution: Substitute the above data into the system of equations described in the technical solution: 3800 = (BFG percentage × 3200 + COG percentage × 17000) BFG percentage + COG percentage = 1 Solving the system of equations, it is found that under this operating condition, the proportion of blast furnace gas in the mixed gas is approximately 95.7%, and the proportion of coke oven gas is approximately 4.3%.

[0041] Example 3 This embodiment provides a gas data management and dynamic allocation device for process-level carbon emission accounting in the steel industry, including: Gas channel data acquisition module: used to continuously collect basic data such as gas flow rate and composition in the gas channels corresponding to blast furnace, coke oven and converter; Data processing module: Performs real-time quality control and statistical calculations on the basic data collected online, filters out valid data, and further calculates the lower heating value and carbon content per unit calorific value of the gas, while also completing uncertainty analysis; Model building and solution module: Based on the mass and energy conservation equations, a mixed gas splitting model is built. The low heating value and carbon content per unit calorific value output by the data processing module are substituted into the model to solve for the proportion of each source of gas in the mixed gas. At the same time, the results of uncertainty analysis are combined to verify and correct the credibility interval of the allocation results. Process-level accounting application module: It transforms the carbon emission breakdown data obtained from the model building and solving module into process-level carbon emission accounting results that meet the requirements of the "Technical Guidelines for Enterprise Greenhouse Gas Emission Verification in the Iron and Steel Industry".

[0042] The rest is the same as in Example 1.

[0043] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for coal gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry, characterized in that, Includes the following steps: Collect the flow rate and composition parameters of the gas in the gas channels corresponding to the blast furnace, coke oven, and converter; The collected data undergoes real-time data quality control and statistical calculation to filter and obtain valid data. The lower heating value and carbon content per unit calorific value of coal gas were calculated based on valid data, and uncertainty analysis was performed. A carbon emission allocation model for mixed coal gas is constructed based on the mass-energy conservation equation. The proportion of each source of coal gas in the mixed coal gas is calculated based on the lower heating value and carbon content per unit calorific value. The allocation results are verified and corrected by combining the results of uncertainty analysis, and the carbon emission splitting and allocation are completed.

2. The method for gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry according to claim 1, characterized in that, The flow rate and composition parameters are obtained based on online monitoring instruments deployed at key gas generation nodes.

3. The method for gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry according to claim 1, characterized in that, The real-time data quality control processing includes flow threshold verification, component concentration threshold verification, and jump verification.

4. The method for gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry according to claim 1, characterized in that, The statistical calculations include dynamic confidence interval calculations, where boundary values ​​are used to replace real-time data when the data exceeds the interval in subsequent calculations.

5. A method for gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry, as described in claim 4, is characterized in that... The dynamic confidence interval calculation is specifically as follows: Based on historical data within a set time period, the mean and standard deviation of key parameters are calculated, and dynamic confidence intervals are generated.

6. The method for gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry according to claim 1, characterized in that, The calculation method for the lower heating value is as follows: ， in, The percentage of each component by volume. This represents the lower heating value coefficient of the corresponding component.

7. A method for gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry according to claim 1, characterized in that, The method for calculating the carbon content per unit calorific value is as follows: ， in, The percentage of each component by volume. The molar mass of the component, It is the lower heating value of coal gas.

8. A method for coal gas data management and dynamic allocation for process-level carbon emission accounting in the steel industry according to claim 1, characterized in that, The uncertainty analysis was performed using Monte Carlo simulation.

9. The method for coal gas data management and dynamic allocation of h in the steel industry process-level carbon emission accounting according to claim 1, characterized in that, When the mixed gas contains three or more sources, the constructed splitting model is an overdetermined set of equations, and the least squares method is used for optimal fitting and solution.

10. A gas data management and dynamic allocation system for process-level carbon emission accounting in the steel industry, characterized in that, include: The online data acquisition module for gas channels is used to collect the flow rate and composition parameters of gas in the gas channels corresponding to blast furnaces, coke ovens, and converters. Data processing module: performs real-time data quality control and statistical calculation on the collected data, filters out valid data; calculates the lower heating value and carbon content per unit calorific value of coal gas based on the valid data, and performs uncertainty analysis; Model building and solution module: Based on the mass-energy conservation equation, a carbon emission distribution model for mixed coal gas is built. Based on the lower heating value and carbon content per unit calorific value, the proportion of each source of coal gas in the mixed coal gas is solved. Combined with the results of uncertainty analysis, the distribution results are verified and corrected to obtain carbon emission split data and complete the splitting and distribution of carbon emissions. Process-level accounting application module: Converts the carbon emission breakdown data obtained from the model building and solution module into process-level carbon emission accounting results.

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