Carbon emission accounting and assessment method and system for coking industry

Abstract

This application relates to the field of data processing technology and discloses a carbon emission accounting and assessment method and system for the coking industry. The method includes: collecting coal coke data through analytical equipment to establish a parameter library; determining the carbon content of the material to form a material table; analyzing the gas flow direction to draw a carbon element flow diagram; combining emission data to generate an intensity distribution table; comparing actual and theoretical emissions to identify key points; and generating an emission reduction plan based on key point control process. This application accurately identifies carbon emission key points and, based on the correlation analysis between process parameters and carbon emissions, provides targeted emission reduction technology solutions, thereby achieving refined management and effective emission reduction of carbon emissions in coking enterprises.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a carbon emission accounting and assessment method and system for the coking industry. Background Art

[0002] The coking industry is an important energy conversion and steel production support industry, and occupies an important position in the national economy. As the issue of global climate change becomes increasingly prominent, carbon emission management has become an important issue that coking enterprises must face. The existing carbon emission accounting methods for the coking industry mainly include the "China Independent Coking Enterprise Greenhouse Gas Emissions Accounting Methodology and Reporting Guidelines (Trial)" and the "IPCC National Greenhouse Gas Inventory Guidelines". These methods are based on the principle of material balance and are calculated from the perspective of carbon input and carbon output. Traditional carbon emission accounting mainly considers four aspects: carbon dioxide emissions from fossil fuel combustion, carbon dioxide emissions during coking production, carbon dioxide emissions implicit in the company's net purchase of heat and electricity production, and the company's carbon dioxide recycling and utilization. In practice, coking enterprises usually use statistical reports and empirical coefficients to calculate carbon emissions, and use fixed parameters and default emission factors for accounting.

[0003] However, existing carbon emission accounting methods suffer from numerous shortcomings. First, traditional methods fail to adequately account for the unique characteristics of the coking process and fail to fully reflect the differences in carbon emissions under different process conditions. In particular, confusion between elemental carbon and fixed carbon leads to calculation errors. Second, the use of default emission factors often overestimates actual emissions, hindering companies from accurately understanding their carbon emissions. Third, existing methods lack an effective mechanism for identifying outliers in carbon emissions, making it difficult to provide targeted emission reduction optimization recommendations. Fourth, insufficient analysis of the correlation between carbon emission data and process parameters makes it difficult to support companies in implementing refined carbon management. Finally, traditional methods focus on total volume accounting and lack in-depth analysis of the temporal and spatial distribution characteristics of carbon emissions, failing to provide companies with clear guidance on carbon emission reduction paths. These shortcomings severely restrict the refined and scientific level of carbon emission management in coking enterprises. Summary of the Invention

[0004] This application provides a carbon emission accounting and assessment method and system for the coking industry, which is used to accurately identify key points of carbon emissions and provide targeted emission reduction technical solutions based on the correlation analysis between process parameters and carbon emissions, thereby achieving refined management and effective emission reduction of carbon emissions in coking enterprises.

[0005] In the first aspect, the present application provides a carbon emission accounting and evaluation method for the coking industry, which comprises: collecting data on the amount of coal fed into the coke oven, coke output, gas generation and gas components through an infrared carbon element online analyzer and a differential pressure gas flow meter to generate a coking production process parameter database; using a gas chromatography-mass spectrometer to measure the elemental carbon content of the collected coal gas, coal tar and crude benzene physical samples, and entering the measurement results into the coking production process parameter database to form a coking material carbon content table; based on the coking material carbon content table, combined with real-time monitoring of the gas flow, The amount of coke oven gas recycled and burned and the amount of externally supplied gas are determined by the amount data, and a carbon element material flow diagram of the coking process is generated; based on the carbon element material flow diagram of the coking process and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment of each process, a carbon emission intensity distribution table of the coking process is formed; using the carbon emission intensity distribution table of the coking process, by analyzing the difference between the actual carbon emissions and the theoretical carbon emissions of each process, a list of key carbon emission points of the coking process is identified and generated; based on the list of key carbon emission points of the coking process, the coking production process is optimized and adjusted through the process parameter control device, and a technical implementation plan for energy conservation and emission reduction of the coking process is generated.

[0006] In a second aspect, the present application provides a carbon emission accounting and assessment system for the coking industry, the carbon emission accounting and assessment system for the coking industry comprising:

[0007] The acquisition module is used to collect data on the amount of coal fed into the coke oven, coke output, gas generation, and gas composition through an infrared carbon element online analyzer and a differential pressure gas flow meter, and generate a database of coking production process parameters;

[0008] a determination module for determining the elemental carbon content of the collected coal gas, coal tar, and crude benzene physical samples using a gas chromatography-mass spectrometer, and entering the determination results into the coking production process parameter database to form a coking material carbon content table;

[0009] A generation module is used to determine the amount of coke oven gas recycled and supplied based on the carbon content table of the coking materials and in combination with the real-time monitored gas flow data, and to generate a carbon element material flow diagram for the coking process;

[0010] A monitoring module is used to form a carbon emission intensity distribution table of the coking process based on the carbon element material flow diagram of the coking process and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment of each process;

[0011] an identification module for identifying and generating a list of key carbon emission points in the coking process by analyzing the difference between the actual carbon emission and the theoretical carbon emission of each process using the carbon emission intensity distribution table of the coking process;

[0012] The adjustment module is used to optimize and adjust the coking production process based on the coking process carbon emission key point list through the process parameter control device, and generate a coking process energy-saving and emission reduction technology implementation plan.

[0013] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned carbon emission accounting and evaluation method for the coking industry.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, enables the computer to execute the above-mentioned carbon emission accounting and assessment method for the coking industry.

[0015] In the technical solution provided by the present application, the data on the amount of coal fed into the coke oven, coke output, gas generation and components are collected in real time by an infrared carbon element online analyzer and a differential pressure gas flow meter, and a real-time dynamic database of coking production process parameters is established, which solves the problems of data lag and inaccuracy in traditional methods and realizes the continuity and real-time collection of carbon emission data; the elemental carbon content of physical samples of gas, coal tar and crude benzene is accurately determined by gas chromatography-mass spectrometry, and combined with advanced data processing algorithms, the calculation deviation caused by the confusion between the concepts of elemental carbon and fixed carbon in traditional methods is overcome, and the accuracy of carbon content determination is greatly improved; according to the carbon content table of coking materials combined with the real-time monitored gas flow data, an innovative gas pipe network diversion calculation method is adopted to realize the accurate distinction between the amount of coke oven gas recycled to the furnace and the amount of externally supplied gas, forming The intuitive carbon element material flow diagram of the coking process breaks through the technical bottleneck of inaccurate estimation of coal gas flow direction by traditional methods; based on the carbon element material flow diagram of the coking process combined with the flue gas monitoring data of each process, a carbon emission intensity distribution table was established, realizing the spatiotemporal and temporal refinement of carbon emissions, providing an intuitive and visual decision-making basis for emission reduction; through the standard deviation analysis of the difference between the actual carbon emissions and the theoretical carbon emissions of each process, a carbon emission anomaly identification algorithm with self-learning ability was constructed, which can intelligently screen out key carbon emission points, solving the problem that traditional methods cannot effectively identify carbon emission anomaly points; based on the in-depth analysis of the correlation between process parameters and carbon emissions, a parameter-emission response model was developed, and the process parameter control device was used to achieve precise optimization of the coking production process, forming a practical implementation plan for energy conservation and emission reduction technology for the coking process. The present invention is particularly outstanding in the application of artificial intelligence algorithms. The standard deviation analysis and multi-parameter correlation analysis algorithms introduced in the carbon emission anomaly identification link can intelligently identify the key factors affecting carbon emissions from massive process data; the adaptive response model applied in the parameter optimization link can automatically learn the complex nonlinear relationship between process parameters and carbon emissions based on historical data, and provide the optimal control strategy, which significantly improves the intelligence level and practical effect of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a method for calculating and evaluating carbon emissions in the coking industry according to an embodiment of the present application;

[0018] Figure 2This is a schematic diagram of an embodiment of a carbon emission accounting and evaluation system for the coking industry in an embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide a carbon emission accounting and assessment method and system for the coking industry. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the carbon emission accounting and assessment method for the coking industry includes:

[0022] Step S101: Collect data on the amount of coal fed into the coke oven, coke output, gas generation, and gas composition using an infrared carbon element online analyzer and a differential pressure gas flowmeter to generate a coking production process parameter database;

[0023] Step S102: Using a gas chromatography-mass spectrometer, the elemental carbon content of the collected coal gas, coal tar, and crude benzene samples is measured, and the measurement results are entered into a coking production process parameter database to form a coking material carbon content table;

[0024] Step S103: Determine the amount of coke oven gas recycled and supplied based on the carbon content table of the coking materials and the real-time monitored gas flow data, and generate a carbon element material flow diagram for the coking process;

[0025] Step S104: Based on the carbon element material flow diagram of the coking process and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment of each process, a carbon emission intensity distribution table of the coking process is formed;

[0026] Step S105: using the carbon emission intensity distribution table of the coking process, by analyzing the difference between the actual carbon emission and the theoretical carbon emission of each process, identify and generate a list of key carbon emission points in the coking process;

[0027] Step S106: Based on the list of key carbon emission points of the coking process, the coking production process is optimized and adjusted through the process parameter control device to generate a technical implementation plan for energy conservation and emission reduction of the coking process.

[0028] It is understood that the execution subject of this application can be a carbon emission accounting and assessment system for the coking industry, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, basic data is obtained by installing specialized monitoring equipment in the coking production line. An infrared carbon online analyzer is a precision device that works based on the characteristic absorption of infrared light by different substances. It determines the carbon content of a sample by measuring its absorption of infrared light of a specific wavelength. Installed at key locations in the coke oven's coal inlet and coke outlet systems, this analyzer provides 24-hour, uninterrupted monitoring of the mass flow of coal entering the coke oven and the output of coke leaving the oven. A differential pressure gas flowmeter calculates flow by measuring the pressure difference between the gas before and after a standard throttling element. This type of flowmeter operates based on the Bernoulli equation: as a fluid passes through a constriction, the flow rate increases while the pressure decreases. The flow rate can be calculated by measuring the pressure difference. The collected data on the coke oven's coal inlet, coke output, gas generation, and its components are stored with unified time tags to construct a coking production process parameter database. This database utilizes a relational data structure and contains multi-dimensional information such as time, process, material type, quantity, and composition.

[0030] High-precision analytical instruments are used to accurately determine the elemental carbon content of key materials. Gas chromatography-mass spectrometry is one of the most powerful analytical tools in contemporary analytical chemistry, combining the efficient separation capabilities of gas chromatography with the precise identification capabilities of mass spectrometry. In practice, an automated sampling device extracts samples from gas pipelines, coal tar storage tanks, and crude benzene storage facilities at preset intervals (e.g., every four hours). Gas samples undergo vacuum filtration to remove moisture and impurities. Coal tar samples require solvent dilution and separation due to their high viscosity. Crude benzene samples undergo purification to remove interference from non-benzene substances. The processed samples are then fed into the gas chromatography-mass spectrometry instrument, where they undergo high-temperature pyrolysis to convert them into characteristic fragment ions. For example, the pyrolysis of methane in coal gas produces characteristic ion peaks. By measuring the relative abundance of these characteristic fragment ions and comparing them with a standard curve, the methane content in the coal gas is calculated to be 28.5%, and the ethane content is 3.2%. The elemental carbon content of the coal gas is then calculated based on these values. Similarly, coal tar and crude benzene samples are analyzed to determine their elemental carbon content. These precisely measured elemental carbon content values ​​are associated with sampling information and entered into a coking process parameter database, forming a systematic carbon content table for coking materials. Coal gas carbon content data is extracted from the coking material carbon content table to form a time-series carbon content feature dataset. This dataset contains the changes in carbon content in coal gas at different time points, such as the carbon content of coal gas during the morning, midday, and night shifts. This data is time-matched with real-time gas flow data to generate basic gas carbon flux data. Flow meters installed at various nodes in the gas pipeline network monitor the flow distribution of each branch, and the gas flow distribution is determined using pipeline network fluid dynamics calculation principles. In a real-world example at a coking plant, the proportion of recycled coal gas and the proportion of externally supplied coal gas are determined through pipeline network flow splitting calculations, allowing the specific recycled and externally supplied coal gas volumes to be calculated. Combined with the coal gas carbon content, the recycled and externally supplied carbon volumes are calculated. Similarly, the flow of carbon elements in materials such as coke, coal tar, and crude benzene is calculated, and finally integrated into a complete carbon element material flow diagram for the coking process. This diagram clearly shows the flow path and quantity distribution of carbon elements from raw coal to various products and emissions.

[0031] Carbon input and output data for each process unit (such as coke ovens, gas purification, and chemical product recovery) are extracted from the carbon material flow diagram to establish a basic data set for the process carbon balance. Flue gas monitoring equipment, deployed at key emission points in each process, then collects flue gas volume flow and CO2 concentration data. The collected data undergoes standard state conversion and dimensionality unification to calculate CO2 emissions at each emission point. The carbon emission intensity per unit of output for each process is calculated by calculating the ratio of the emissions to the material throughput of the corresponding process unit. For example, if the coke oven process processes several tons of coal per hour and measures several tons of CO2 emissions, the carbon emission intensity per unit of coal throughput can be calculated. The temporal and spatial distribution of carbon emission intensities for all processes is analyzed to form a spatiotemporal characteristic matrix of carbon emission intensity. This matrix is ​​then categorized and organized by process type and emission intensity level, ultimately generating a carbon emission intensity distribution table for the coking process. Anomalous emission points are identified by comparing actual carbon emissions with theoretical carbon emission values. Actual carbon emission intensity values ​​are first extracted from the coking process carbon emission intensity distribution table. Simultaneously, theoretical carbon emission values ​​expected under corresponding process conditions are obtained from the coking industry carbon emission benchmark database. The difference between measured and theoretical values ​​is calculated to determine the carbon emission deviation for each process. Standard deviation analysis is used to categorize the deviations into different levels, creating a carbon emission deviation rating table. Processes are ranked based on this table, and process points with deviations exceeding preset thresholds are identified. These points often indicate process anomalies or areas for optimization. These identified points are integrated with relevant process parameters to generate a list of key carbon emission points for the coking process. From this list, the abnormal processes and their corresponding process parameters are extracted to form a set of target parameters for optimization. The relationship between process parameters and carbon emissions in historical data is analyzed to determine the direction and magnitude of parameter adjustments. For example, if excessively high coke oven temperatures lead to increased carbon emissions, the temperature should be appropriately lowered; if low gas recovery efficiency increases carbon losses, the recovery system should be optimized. The determined parameter adjustment plan is converted into specific operational instructions and implemented using the process parameter control device. After adjustments, the monitoring system records changes in carbon emissions at each key point to verify the effectiveness of the adjustments. Effective parameter adjustment plans are integrated with the corresponding emission reduction results to form a technical implementation plan for energy conservation and emission reduction in the coking process.

[0032] In the embodiment of the present application, an infrared carbon element online analyzer and a differential pressure gas flow meter are used to collect data on the amount of coal fed into the coke oven, coke output, gas generation and components in real time, and a real-time dynamic database of coking production process parameters is established, which solves the problems of data lag and inaccuracy in traditional methods and realizes the continuity and real-time collection of carbon emission data; a gas chromatography-mass spectrometer is used to accurately determine the elemental carbon content of physical samples of gas, coal tar and crude benzene, and combined with advanced data processing algorithms, the calculation deviation caused by the confusion between the concepts of elemental carbon and fixed carbon in traditional methods is overcome, and the accuracy of carbon content determination is greatly improved; based on the carbon content table of coking materials combined with the real-time monitored gas flow data, an innovative gas pipe network diversion calculation method is adopted to realize the accurate distinction between the amount of coke oven gas recycled to the furnace and the amount of externally supplied gas, forming an intuitive The carbon element material flow diagram of the coking process has broken through the technical bottleneck of inaccurate estimation of coal gas flow direction by traditional methods; based on the carbon element material flow diagram of the coking process and combined with the flue gas monitoring data of each process, a carbon emission intensity distribution table was established, which realized the spatiotemporal and temporal refinement of carbon emissions and provided an intuitive and visual decision-making basis for emission reduction; through the standard deviation analysis of the difference between the actual carbon emissions and the theoretical carbon emissions of each process, a carbon emission anomaly recognition algorithm with self-learning ability was constructed, which can intelligently screen out key carbon emission points, solving the problem that traditional methods cannot effectively identify carbon emission anomaly points; based on the in-depth analysis of the correlation between process parameters and carbon emissions, a parameter-emission response model was developed, and the process parameter control device was used to achieve precise optimization of the coking production process, forming a coking process energy-saving and emission reduction technology implementation plan with practical value. The present invention is particularly outstanding in the application of artificial intelligence algorithms. The standard deviation analysis and multi-parameter correlation analysis algorithms introduced in the carbon emission anomaly identification link can intelligently identify the key factors affecting carbon emissions from massive process data; the adaptive response model applied in the parameter optimization link can automatically learn the complex nonlinear relationship between process parameters and carbon emissions based on historical data, and provide the optimal control strategy, which significantly improves the intelligence level and practical effect of the solution.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] (1) The infrared carbon element online analyzer installed at key positions of the coke oven coal feeding system and coke discharge system is used to monitor and collect the coke oven coal feeding amount and coke output in real time;

[0035] (2) Continuously measure and record the amount of gas generated by using a differential pressure gas flow meter arranged in the gas pipe network system;

[0036] (3) Sampling and analyzing coal gas component data through gas component analysis equipment to obtain hydrocarbon content data in coal gas;

[0037] (4) The data on the amount of coal fed into the coke oven, coke output, gas generation, and gas composition are stored in time series to form time-series associated data;

[0038] (5) Conduct data quality review on time series correlation data, remove outliers and interrupted data, and supplement estimated values;

[0039] (6) Import the time series correlation data into the database management system after quality review to generate a coking production process parameter database.

[0040] Specifically, a database of coking production process parameters was constructed. The analyzer operates based on the principles of infrared spectroscopy. Different substances have different absorption rates for specific wavelengths of infrared light, and hydrocarbon molecules produce characteristic absorption of infrared light at specific wavelengths. Installed at the coal bunker outlet of the coke oven's coal inlet system and on the coke conveyor belt of the coke outlet system, this analyzer can measure the carbon content in coal and coke in real time. Simultaneously, the analyzer, in conjunction with a metering device, can continuously monitor and record coke oven coal intake and coke production data. In actual operation, an infrared carbon online analyzer typically performs sampling and analysis every 5-10 seconds, transmitting the data to a central control unit to form a data stream. A differential pressure gas flowmeter is a key instrument for measuring gas generation. It measures flow rate by utilizing the relationship that the pressure difference generated when a fluid passes through a throttling device (such as an orifice plate, venturi tube, or nozzle) is proportional to the square of the flow rate. In a coking plant, differential pressure gas flow meters are arranged on the main pipeline and branch pipelines of the gas network system, recording data every 1-2 seconds. Through gas density correction and standard state conversion, the gas generation data under standard conditions is obtained.

[0041] Quantitative analysis of coal gas components. A gas chromatograph separates different components by using differences in their distribution coefficients between the stationary and mobile phases. Quantitative detection is then performed using a thermal conductivity detector or a hydrogen flame ionization detector. Infrared spectrometers utilize the characteristic absorption of infrared light by different coal gas components for quantitative analysis. In coking plants, gas component analysis equipment is typically installed at the outlet of the coal gas purification system. Gas samples are automatically collected and analyzed every 30-60 minutes to obtain data on the content of hydrocarbons such as methane, ethane, propane, hydrogen, and carbon monoxide.

[0042] Collected data on coke oven coal intake, coke production, gas generation, and gas composition is uniformly stored and associated in time series to form time-series correlated data. Data collected by different devices is labeled with a unified timestamp. Considering the varying frequency of data collection (for example, coal intake may be recorded once an hour, while gas flow may be recorded multiple times a second), unified temporal resolution is required. The structure of time-series correlated data includes fields such as timestamp, data type, data value, equipment number, and process unit, enabling data from different sources to be linked across time.

[0043] Conduct a quality audit on time-series correlation data. Data quality audits include multiple sub-processes: performing a range check to determine whether the data is within a reasonable range. For example, if the coke oven gas flow exceeds 150% of the design value, it may be an outlier; then perform a continuity check to identify data interruption points; and then perform a consistency check to verify whether the logical relationship between related data is reasonable, such as the material balance relationship between coal input and coke output. For identified outliers, methods such as median replacement or averaging of adjacent values ​​are used for processing; for interrupted data, mathematical methods such as linear interpolation or spline interpolation are used for supplementary estimation. For example, when gas flow data is missing in a certain period of time, the estimated value can be calculated using the interpolation formula using the data from the previous and next periods to ensure data continuity.

[0044] Importing quality-audited, time-series, correlated data into a database management system generates a structured database of coking production process parameters. This process involves data format conversion, index creation, and storage optimization. The database typically adopts a relational database structure, creating multiple related tables, including equipment information tables, process parameter tables, and gas composition tables. During the data import process, primary and foreign key relationships are established, data type constraints are set, and multi-dimensional indexes, such as time, equipment, and parameter type, are created to optimize query performance. Once the database is constructed, a complete database of coking production process parameters is formed, providing the data foundation for subsequent carbon emissions accounting.

[0045] For example, a coking plant processes 3,000 tons of coking coal daily, produces 2,250 tons of coke, and generates approximately 160,000 cubic meters of gas per day. To implement the method described in the present invention, an online infrared carbon analyzer was installed on the plant's coal loading and coke discharge conveyors, differential pressure gas flow meters were installed on the gas main and branch pipes, and a gas chromatograph was installed at the outlet of the gas purification system. These devices continuously collected data for two weeks, generating approximately 330,000 raw data records. This data was then time-correlated and integrated to form time-series data. During a data quality review, anomalies or missing data were identified for approximately 2.3% of the data. For example, gas flow data from 2:00 PM to 4:00 PM on October 15th was missing due to equipment maintenance. This data was then estimated using linear interpolation of data from the preceding and following time periods. The processed data was imported into a MySQL database system, and a multi-table relational structure was established, encompassing material flows, energy flows, and process parameters. This ultimately resulted in a complete database of coking process parameters, encompassing comprehensive information on coal feed, coke output, gas generation, and its composition.

[0046] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0047] (1) Using an automatic sampling device, extract gas samples from the pipeline system and coal tar and crude benzene samples from the storage facilities at preset time intervals to form a material sample set;

[0048] (2) Pre-processing the material sample set, including vacuum filtration of gas samples, dilution and separation of coal tar samples, and purification of crude benzene samples, to obtain test-ready samples;

[0049] (3) The test-ready samples were subjected to high-temperature pyrolysis by gas chromatography-mass spectrometry to convert the hydrocarbons in each material into identifiable characteristic fragment ions;

[0050] (4) Calculate the carbon content in coal gas, coal tar, and crude benzene samples based on the relative peak intensity of the characteristic fragment ions in the mass spectrum and the carbon content standard curve;

[0051] (5) Correlate and match the elemental carbon content value with the sample collection time, material type, and production batch information to construct the original record of the carbon content of the coking material;

[0052] (6) The original records of carbon content of coking materials are integrated and classified according to material type, sampling location and time series, and entered into the coking production process parameter database to form a carbon content table of coking materials.

[0053] Specifically, material samples are collected at preset time intervals through an automatic sampling device. The automatic sampling device is a programmable mechanical device consisting of a sampling pump, a controller, a timer, a sampling pipeline, and a sample storage container. For gas samples, the sampling device is connected to the sampling point on the gas pipeline, and is opened at a timed interval through a controllable valve to draw the gas into a special gas bag or gas cylinder; for coal tar samples, the sampling device is equipped with a heating pipeline to prevent coal tar solidification and blockage, and representative samples are extracted from different liquid levels of the coal tar storage tank; for crude benzene samples, they are extracted from the crude benzene storage facility using a similar method. The sampling time interval is usually set according to the fluctuations in the production process. Gas samples are generally collected every 4 hours because their composition changes rapidly, while coal tar and crude benzene samples can be collected once per shift or once a day. All collected samples are sorted and classified by number, time, and material type to form a material sample set.

[0054] The material sample set is pre-treated. The pre-treatment of coal gas samples mainly involves vacuum filtration, which reduces the high-pressure coal gas to normal pressure through a pressure reducing valve. At the same time, it passes through a multi-stage filter to remove moisture, tar droplets, and particulate impurities to obtain a clean gas sample. Due to its high viscosity and complex composition, coal tar samples need to be diluted and separated. They are usually diluted with organic solvents such as toluene or dichloromethane at a ratio of 1:10 to 1:20, and then insoluble matter is removed by centrifugation or filtration. Crude benzene samples need to be purified, mainly to remove sulfides, ammonia, and other non-aromatic compounds. This can be achieved through washing, acid treatment, and distillation. The samples after pre-treatment are called test-ready samples. They have a physical state and chemical purity suitable for instrument analysis.

[0055] Test-ready samples were subjected to high-temperature pyrolysis analysis using a gas chromatography-mass spectrometer. A gas chromatography-mass spectrometer is a high-precision analytical instrument that combines gas chromatography (GC) with mass spectrometry (MS). The GC portion separates the components in a mixture, while the MS portion identifies their molecular structures. The sample first enters the GC inlet, where it vaporizes at high temperatures (typically 250-350°C). It then enters the chromatographic column along with a carrier gas (usually helium) for separation. Different hydrocarbons exhibit different retention times in the chromatographic column due to differences in physicochemical properties such as boiling points and polarity. The separated components then enter the mass spectrometer sequentially, where they undergo high-temperature pyrolysis under the influence of an ionization source (typically a 70 eV electron bombardment source), forming characteristic charged fragment ions. These fragment ions are separated by mass-to-charge ratio (m / z) in a mass analyzer (such as a quadrupole, time-of-flight, or ion trap). The fragment ions are detected by a detector and recorded as a mass spectrum.

[0056] Quantitative calculation of elemental carbon content based on mass spectra involves a relatively complex data processing process. In the mass spectrum, the relative peak intensity of characteristic fragment ions is directly proportional to the content of the corresponding substance. The calculation of elemental carbon content is based on the identification and quantification of characteristic fragment ions. The calculation formula is as follows:

[0057] ;

[0058] in, Indicates the elemental carbon content in the sample (mass percentage), represents the number of hydrocarbon species identified, Indicates the The relative content of the hydrocarbons (determined by their characteristic peak areas), Indicates the The number of carbon atoms in a hydrocarbon molecule, represents the molar mass of carbon (12 g / mol), Indicates the The molar mass of the hydrocarbon (g / mol), Indicates the A calibration factor for each hydrocarbon is used to correct for response differences between different compounds. A database of hydrocarbon standard mass spectra is established, containing the characteristic fragment ions and relative peak intensities of various hydrocarbons at known concentrations. The sample's mass spectrum is then compared with the standard spectra to identify the various hydrocarbons in the sample. Using either internal or external standard methods, a calibration curve is constructed—a plot of the characteristic peak area versus concentration of a standard substance at known concentrations—to calculate the content of each hydrocarbon in the sample. Finally, the total elemental carbon content of the sample is calculated using the aforementioned formula, taking into account the number and ratio of carbon atoms in each hydrocarbon. The calculated elemental carbon content is then correlated with sample collection information. Each sample's elemental carbon content value must be correlated with its collection time, material type, production batch, and other information to form a complete data record. This process is implemented using a database management system, which matches the analysis results with a unique identifier (such as the sample number) in the sampling record table to create a correlation table. This correlation table contains fields such as sample number, collection time, material type, sampling location, production batch number, and elemental carbon content value. This correlation matching ensures the traceability of the data, and any element carbon content value can be traced back to its corresponding sample source and collection conditions.

[0059] The original records of carbon content of coking materials are integrated and classified to form a carbon content table of coking materials. This step is achieved through database query and processing technology. First, the original records are grouped according to material type (coal gas, coal tar, crude benzene), and then within each material type, they are sorted and classified according to sampling location and time series. The average carbon content, maximum value, minimum value and standard deviation and other statistics of various materials in different time periods and under different production conditions are calculated through the database aggregation functions (such as AVG, MAX, MIN, etc.). The integrated data forms a carbon content table of coking materials. The table is a multidimensional data structure with the horizontal dimension being the material type and sampling location, the vertical dimension being the time series, and the cell value being the element carbon content value under the corresponding conditions.

[0060] Taking a coking plant as an example, when implementing the method of the present invention, the plant first installed an automatic sampling system to collect gas samples every four hours at three key nodes in the gas pipeline network (raw gas, purified gas, and gas sent to the recycle furnace). Liquid samples were collected once every eight hours from the coal tar and crude benzene storage tanks. The gas samples were pre-treated through a condensate separator and activated carbon filter. The coal tar samples were diluted with dichloromethane at a ratio of 1:15 and then filtered. The crude benzene samples were purified by distillation. These treated samples were analyzed on an Agilent 7890B-5977A gas chromatograph-mass spectrometer. The gas samples were found to be primarily composed of methane, ethane, propane, hydrogen, and carbon monoxide. The content of each component was calculated by comparing it with a standard spectrum. The elemental carbon content of the gas sample was then calculated based on the carbon atomic ratio of each hydrocarbon. The coal tar and crude benzene samples were analyzed in a similar manner. The carbon content values ​​of these elements are associated with the sampling information to form a comprehensive data table of carbon content including three materials, different time points, and different sampling locations. This table is entered into the coking production process parameter database as the basic data for subsequent carbon emission accounting.

[0061] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0062] (1) Extract the carbon content value in coal gas from the carbon content table of coking materials and establish a coal gas carbon content characteristic data set;

[0063] (2) Temporally correlate and match the coal gas carbon content characteristic dataset with the real-time monitored coal gas flow data to form coal gas carbon flux basic data;

[0064] (3) Analyze and process the basic data of coal gas carbon flux through the coal gas network diversion calculation method, and distinguish the flow distribution ratio of the coal gas return branch and the external supply branch;

[0065] (4) Calculate the specific values ​​of the coke oven gas return combustion volume and external gas supply volume by multiplying the flow distribution ratio by the total gas flow;

[0066] (5) Combine the specific values ​​of the coke oven gas recycled combustion volume and the external gas supply volume with the carbon content data in the coking material carbon content table to calculate the carbon input and output of each process node;

[0067] (6) Based on the carbon element input and output data of each process node, a carbon element material flow diagram of the coking process is drawn to show the flow relationship of carbon element between the coking processes.

[0068] Specifically, we extract the carbon content of coal gas from the coking material carbon content table to create a coal gas carbon content feature dataset. The coking material carbon content table contains elemental carbon content data for materials such as coal gas, coal tar, and crude benzene. The extraction process is implemented using database query statements. Specifically, we filter out coal gas-related records from the table, including coal gas carbon content values ​​at different time points and sampling locations. This extracted data constitutes the coal gas carbon content feature dataset, a time series data structure consisting of timestamps and corresponding carbon content values.

[0069] The gas carbon content characteristic data set is temporally correlated with the real-time monitored gas flow data to form the gas carbon flux basic data. This step involves data integration and time synchronization processing. Gas flow data is usually collected in real time by a differential pressure gas flow meter, and the collection frequency is relatively high, which may be one record per minute or even per second, while the collection frequency of carbon content data is relatively low, which may be one record per hour or per shift. Therefore, time scale matching processing is required to aggregate the high-frequency flow data according to the time point of the carbon content data, usually using the average value method within the time window. After time matching, the gas carbon content value at each time point is multiplied by the corresponding gas flow rate to obtain the gas carbon flux, that is, the mass of carbon element flowing through the pipeline per unit time. The calculation formula is as follows:

[0070] ;

[0071] in, Indicates a time point Gas carbon flux (unit: kg / h), Indicates a time point Gas volume flow rate (unit: m³ / h), Indicates the density of gas under standard conditions (unit: kg / m³), Indicates a time point The mass fraction of carbon in the coal gas (dimensionless).

[0072] The gas network diversion calculation method is used to analyze and process the basic data of coal gas carbon flux, and the flow distribution ratio of the coal gas return branch and the external supply branch is distinguished. The coal gas network diversion calculation is a calculation method based on the principles of fluid mechanics. It takes into account factors such as pipe network structure, pipe diameter, valve opening, pressure distribution, etc. to calculate the flow distribution of each branch. In a coking plant, coal gas is usually divided into two parts after it is generated: one part is returned to the furnace for heating the coke oven, and the other part is supplied to other users or facilities. The core formula of the diversion calculation is:

[0073] ;

[0074] in, Indicates the volume flow rate of the recycle branch (unit: m³ / h), Indicates the volume flow rate of the external supply branch (unit: m³ / h), Indicates the gas main pressure (unit: kPa), Indicates the recycle branch pressure (unit: kPa), Indicates the external supply branch pressure (unit: kPa), Indicates the diameter of the return branch pipe (unit: mm), Indicates the diameter of the external supply branch pipe (unit: mm), represents the flow coefficient of the recycle branch (dimensionless), Represents the flow coefficient of the external supply branch (dimensionless).

[0075] The above formula, combined with the real-time measured network pressure and known network parameters, can be used to calculate the flow ratio between the recycle branch and the external supply branch. In practical applications, the flow balance constraint must also be considered, i.e., the total flow is equal to the sum of the flow rates of each branch:

[0076] ;

[0077] in, Indicates the total gas flow rate (unit: m³ / h).

[0078] By multiplying the flow distribution ratio by the total gas flow, the specific values ​​of the coke oven gas recycled combustion volume and the external gas supply volume are calculated. The specific formula is:

[0079] ;

[0080] in, It represents the flow distribution ratio of the recycle branch (dimensionless), usually between 0.4-0.6, and the specific value is determined by the process requirements and pipeline network design.

[0081] The fifth step is to combine the specific values ​​of the coke oven gas recycled combustion volume and the external gas supply volume with the carbon content data in the coking material carbon content table to calculate the carbon input and output of each process node. This calculation takes into account the transfer and conversion of carbon between each process unit. The core formula is as follows:

[0082] ;

[0083] in, Indicates the carbon input of process unit u (unit: kg), Indicates process unit The output of carbon element (unit: kg), Indicates the flow into the process unit A collection of materials, Indicates outflow process unit A collection of materials, Indicates material Carbon flux (unit: kg / h), Indicates the calculation time interval (unit: h).

[0084] For the coke oven unit, carbon input includes carbon from coal and recycled gas, while output includes carbon from coke, raw gas, and carbon from combustion carbon dioxide emissions. For the gas purification unit, carbon input is carbon from raw gas, while output includes carbon from purified gas and carbon from chemical products (coal tar, crude benzene, etc.). Based on the carbon input and output data for each process node, a carbon material flow diagram for the coking process is drawn, representing the flow relationship between the various coking processes. This diagram is a visual representation, usually in the form of a Sankey diagram, which clearly shows the flow direction and quantity distribution of carbon from raw materials to various products and emissions. In the diagram, nodes represent process units or materials, and connecting lines represent the carbon flow path. The line width is proportional to the carbon flow rate. The diagram is usually drawn using professional visualization tools or programming languages, and the input data is the carbon input and output of each node calculated previously.

[0085] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0086] (1) Extract the carbon element input and output data of each process unit from the carbon element material flow diagram of the coking process and establish a basic data set for process carbon balance;

[0087] (2) Collect flue gas volume flow and carbon dioxide concentration data through flue gas monitoring equipment arranged at the emission points of coke ovens, dry coke quenching devices, gas purification systems, and chemical product recovery systems;

[0088] (3) Perform standard state conversion and dimension unification on the collected flue gas volume flow rate and carbon dioxide concentration data to calculate the carbon dioxide emissions per unit time at each emission point;

[0089] (4) Calculate the ratio of the carbon dioxide emissions per unit time at each emission point to the material processing volume of the corresponding process unit to obtain the carbon emission intensity value per unit output of each process;

[0090] (5) Perform temporal cumulative statistics and spatial distribution mapping on the carbon emission intensity values ​​per unit output of each process to form a spatiotemporal characteristic matrix of carbon emission intensity;

[0091] (6) The spatiotemporal characteristic matrix of carbon emission intensity is classified and sorted according to process category and emission intensity level to generate a carbon emission intensity distribution table for the coking process.

[0092] Specifically, the carbon input and output data for each process unit are extracted from the carbon material flow diagram of the coking process. The chart information is converted into a structured data table. The specific operation is to use data parsing tools to identify the nodes and connecting lines in the diagram and convert it into a data table containing information such as the process unit identification, carbon input, and carbon output. For example, for the coke oven unit, the extracted data includes the carbon content of coal input, the carbon content of coke output, the carbon content in raw gas, and the carbon content in carbon dioxide emitted by combustion; for the gas purification unit, the data such as the carbon content of raw gas input, the carbon content of purified gas output, and the carbon content in chemical products are extracted. These extracted data are arranged in the order of the process flow to form a complete process carbon balance basic data set.

[0093] Actual emission data is collected through flue gas monitoring equipment. In the coking industry, the main emission points include coke oven chimneys, exhaust outlets of dry coke quenching devices, release points of coal gas purification systems, and exhaust outlets of chemical product recovery systems. Professional flue gas monitoring equipment, including flue gas flow meters and gas analyzers, are installed at these locations. Flue gas flow meters are used to measure the volume of flue gas passing through the exhaust outlet per unit time. Common types include pitot tube flow meters, ultrasonic flow meters, and thermal mass flow meters. The pitot tube flow meter calculates the flow rate by measuring the difference between the dynamic pressure and the static pressure in the flue; the ultrasonic flow meter calculates the flow rate by measuring the difference in the propagation time of the sound wave in the flue gas; and the thermal mass flow meter calculates the mass flow by measuring the cooling rate of the heated probe. Gas analyzers are used to measure the concentration of carbon dioxide in flue gas. Common technologies include non-dispersive infrared method, electrochemical sensor method, and Fourier transform infrared spectroscopy. The non-dispersive infrared method uses CO2's absorption characteristics of specific wavelengths of infrared light to measure concentration. The electrochemical sensor method calculates concentration using the current signal generated by the contact between CO2 and an electrolyte. Fourier transform infrared spectroscopy determines component concentrations by analyzing the interference pattern formed when flue gas absorbs infrared light. These devices typically record data every 10-60 seconds, forming a continuous time series of flue gas volume flow and CO2 concentration.

[0094] The collected raw data undergoes standard state conversion and dimensionality unification. Standard state conversion involves converting gas data measured under different temperature and pressure conditions to values ​​under standard conditions, typically defined as 0°C, 101.325 kPa, and dry basis. This conversion requires the use of the gas state equation, accounting for the effects of temperature, pressure, and moisture content, to convert the measured flue gas volume flow rate to the flow rate under standard conditions. Dimensional unification involves converting data in different units to a unified unit, such as converting CO2 concentration from volume percentage to mass concentration or hourly flow rate to daily flow rate. After this processing, the CO2 emissions per unit time at each emission point are calculated as the product of the flue gas volume flow rate under standard conditions and the CO2 concentration, taking into account the conversion relationship between CO2 molecular weight and molar volume under standard conditions. The carbon emission intensity per unit output of each process is calculated. The CO2 emissions at each emission point are then compared to the material throughput of the corresponding process unit. Material throughput data is sourced from the production record system and includes data on coking coal throughput, coke production, raw gas processing in the gas purification system, and chemical product output in the chemical product recovery system. Through division, we obtain the carbon emission intensity per unit of output for each process, such as the CO2 emissions from the coke oven process per ton of coke production and the CO2 emissions from the coal gas purification process per thousand cubic meters. These intensity values ​​are important indicators for measuring the carbon emission efficiency of each process, reflecting the carbon emission level per unit of product output.

[0095] The carbon emission intensity values ​​per unit output of each process are statistically accumulated over time and spatially mapped to form a spatiotemporal characteristic matrix of carbon emission intensity. Time-accumulated statistics refer to statistical analysis of carbon emission intensity values ​​in different time periods (hours, shifts, days, months, and seasons), calculation of statistical quantities such as average, maximum, minimum, and standard deviation, and identification of temporal variations in carbon emissions. Spatial distribution mapping refers to visualizing the carbon emission intensity values ​​according to the spatial layout of the process flow, clearly presenting the contribution ratio of each process to carbon emissions. The spatiotemporal characteristic matrix is ​​a two-dimensional data structure, with the horizontal axis representing different processes and the vertical axis representing different times. The matrix element values ​​are the carbon emission intensities of the corresponding processes and times. This matrix intuitively displays the spatiotemporal distribution characteristics of carbon emissions through color depth or numerical value size.

[0096] The carbon emission intensity distribution table for the coking process is generated by categorizing and organizing the spatiotemporal characteristic matrix of carbon emission intensity according to process categories and emission intensity levels. Process categories include coal preparation, coke oven, dry coke quenching, gas purification, and chemical product recovery. Emission intensity levels categorize carbon emission intensity into low, medium, and high levels based on industry benchmarks or historical data. The categorization and organization process utilizes pivot table technology to aggregate and reorganize the data in the spatiotemporal characteristic matrix according to process categories and emission intensity levels, creating a more structured and understandable carbon emission intensity distribution table for the coking process. This table displays both the carbon emission intensity values ​​for each process and the changing trends in carbon emissions over time and across different processes, providing an important basis for subsequent carbon emission analysis and emission reduction decisions.

[0097] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0098] (1) Extract the actual carbon emission intensity values ​​of each process from the carbon emission intensity distribution table of the coking process to form a measured carbon emission data set;

[0099] (2) Obtain theoretical carbon emission reference values ​​under corresponding process conditions through the coking industry carbon emission benchmark database and establish a theoretical carbon emission benchmark set;

[0100] (3) Compare and calculate the measured carbon emission data set with the theoretical carbon emission benchmark set to obtain the quantitative value of the carbon emission difference of each process;

[0101] (4) Conduct standard deviation analysis on the quantitative values ​​of carbon emission differences, classify carbon emission anomaly levels, and form a carbon emission deviation rating table;

[0102] (5) Sort the carbon emission differences of each process using the carbon emission deviation rating table, and select the process points where the deviation exceeds the preset threshold;

[0103] (6) Integrate the selected process points with the corresponding carbon emission difference quantitative values ​​and process parameter association records to generate a list of key carbon emission points in the coking process.

[0104] Specifically, the actual carbon emission intensity values ​​for each process are extracted from the carbon emission intensity distribution table of the coking process to form a measured carbon emission dataset. The carbon emission intensity distribution table for the coking process is a structured data table that records the carbon emission intensity values ​​per unit of output for each process at different times and under different operating conditions. The extraction process is achieved through data screening technology, selecting data from the most recent period (usually the last 7-30 days) from the table and grouping and organizing them by process category. The measured carbon emission dataset is a multidimensional data set that includes information such as process identifier, time identifier, carbon emission intensity value, and process condition description, comprehensively reflecting the carbon emissions of the coking plant under actual operating conditions. Theoretical carbon emission reference values ​​are obtained from the coking industry carbon emission benchmark database. The coking industry carbon emission benchmark database is a professional database that collects carbon emission data from coking enterprises of different sizes and process types at home and abroad, and forms a reference system through standardized processing. This database generally contains three levels of benchmark values: international advanced level (the average value of the top 10% of coking enterprises globally), domestic advanced level (the average value of the top 20% of coking enterprises domestically), and industry average level. When obtaining theoretical reference values, it is necessary to consider compatibility with the process conditions of the coking plant being evaluated. Key matching factors include coke oven type (top-loading, tamping, heat recovery, etc.), coking coal type (prime coking coal, gas coal, fat coal, etc.), equipment scale (single furnace volume, total enterprise production capacity, etc.), and supporting facilities (dry quenching, wet quenching, etc.). Through multi-condition query matching, carbon emission data from enterprises with process conditions similar to the current coking plant being evaluated are extracted from the database to form a theoretical carbon emission benchmark set.

[0105] The measured carbon emission dataset is compared with the theoretical carbon emission benchmark set to obtain a quantitative value of the carbon emission difference. The comparative calculation uses two methods: difference and ratio. The difference method calculates the absolute difference between the measured value and the theoretical value, that is, the measured carbon emission intensity minus the theoretical carbon emission intensity. A positive value indicates that the emission exceeds the standard, and a negative value indicates that it is better than the standard. The ratio method calculates the relative difference between the measured value and the theoretical value, that is, the measured carbon emission intensity is divided by the theoretical carbon emission intensity. A value greater than 1 indicates that the emission exceeds the standard, and a value less than 1 indicates that it is better than the standard. The comparative calculation generates a corresponding quantitative difference value for the measured value of each process and each time point, forming a complete difference dataset.

[0106] Standard deviation analysis is performed on the quantified carbon emission variance values ​​to categorize carbon emission anomaly levels. Standard deviation analysis is a statistical method used to measure the degree of data dispersion. The mean and standard deviation of the quantified variance values ​​are first calculated. Each variance value is then classified into different anomaly levels based on the number of standard deviations from the mean. Typically, variance values ​​are categorized into four levels based on their distance from the mean: normal range (within ±1 standard deviation), slightly abnormal (±1 to ±2 standard deviations), moderately abnormal (±2 to ±3 standard deviations), and severely abnormal (exceeding ±3 standard deviations). This categorization creates a carbon emission deviation rating table, which includes fields such as process ID, time stamp, quantified variance value, and anomaly level, visually displaying the severity of carbon emission anomalies for each process. The carbon emission deviation rating table is used to rank the carbon emission variances of each process, identifying process points with deviations exceeding a preset threshold. Sorting is typically performed based on the anomaly level and the absolute value of the quantified variance value, with the most severe anomalies ranked first. Preset thresholds are screening criteria set based on enterprise management needs and technical feasibility. Common threshold settings include exceeding moderate anomalies (i.e., deviations exceeding ±2 standard deviations), exceeding the theoretical value by more than 20%, and maintaining a level above the mild anomaly level for more than three consecutive days. By applying these threshold conditions, process points requiring special attention are screened from the carbon emission deviation rating table. These points represent weak links or problem areas in carbon emission management. The screened process points are integrated with the corresponding process parameter association records to generate a list of key carbon emission points in the coking process. These process parameter association records refer to the production parameter data corresponding to the screened anomalies, typically sourced from the coking plant's distributed control system (DCS) or manufacturing execution system (MES). Key process parameters that influence carbon emissions include coke oven temperature, coal charge density, coke oven gas recovery rate, dry quenching steam production, and gas purification efficiency. The integration process uses timestamps and process IDs as association keys to match and merge the anomaly point data with the process parameter data, creating a complete record that includes anomaly descriptions and possible causes. The final generated list of key carbon emission points for the coking process is a structured document that lists in detail the process points that need to be optimized, the degree of abnormality, the corresponding process parameter status, and a preliminary cause analysis, providing a clear goal for subsequent emission reduction optimization.

[0107] For example, a coking plant used the aforementioned method to identify outliers in carbon emissions. Data from the past 15 days were extracted from the carbon emission intensity distribution table for the coking process, including actual carbon emission intensity values ​​for four processes: coke oven, dry coke quenching, gas purification, and chemical product recovery. This data set was then used to construct a measured carbon emission dataset. The coking industry carbon emission benchmark database was then used to query theoretical carbon emission reference values ​​for a plant with similar conditions (6.25-meter top-loading coke ovens, equipped with dry coke quenching, and an annual coke production capacity of 2.4 million tons) to establish a theoretical carbon emission benchmark set. Comparative calculations revealed that the actual carbon emission intensity of the coke oven process was 0.152 tons of CO2 per ton of coal, while the theoretical reference value was 0.126 tons of CO2 per ton of coal, a difference of +0.026 tons of CO2 per ton of coal and a relative deviation of +20.6%. Standard deviation analysis classified this deviation as moderately outlier. Based on the pre-set threshold of "exceeding the theoretical value by more than 20%," the coke oven process was identified as a process requiring special attention. Further correlation with process parameter records revealed that the average coke oven temperature during the abnormal period was 25°C higher than normal operating conditions, and poor door sealing caused significant air leakage. Ultimately, this coke oven process point was integrated with the quantitative carbon emission difference, temperature anomalies, poor sealing, and other process parameter issues, and included in the list of key carbon emission points in the coking process.

[0108] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0109] (1) Extracting abnormal carbon emission processes and their corresponding process parameters from the list of key carbon emission points in the coking process to form an optimization target parameter set;

[0110] (2) By analyzing the historical data of process parameters, the correlation characteristics between the optimization target parameter set and carbon emissions are found, and a parameter-emission response relationship diagram is established;

[0111] (3) Determine the direction and amplitude of process parameter adjustment based on the parameter-emission response diagram, and generate a parameter optimization adjustment instruction sequence;

[0112] (4) Input the parameter optimization adjustment instruction sequence into the process parameter control device to accurately control the coke oven temperature, gas flow, coal loading, and coke quenching method;

[0113] (5) Record the changes in carbon emissions at key points after regulation through the real-time carbon emissions monitoring system to form emission reduction effect verification data;

[0114] (6) Based on the emission reduction effect verification data, integrate the successful process parameter adjustment plan and the corresponding emission reduction effect, and formulate the coking process energy conservation and emission reduction technology implementation plan.

[0115] Specifically, the carbon emission abnormal processes and their corresponding process parameters are extracted from the coking process carbon emission key point list to form an optimization target parameter set. The coking process carbon emission key point list is a list generated in the previous step, which records the process points with serious carbon emission abnormalities and their related parameters. The extraction process is achieved through data screening technology. The records in the key point list are sorted according to the degree of abnormality, and several records with the highest ranking are selected. At the same time, the process parameter information associated with these abnormal points is extracted. The optimization target parameter set is a structured data table that contains fields such as process identification, parameter name, current parameter value, and normal parameter range, which clearly define the specific objects and targets that need to be optimized and adjusted. The correlation characteristics between the optimization target parameter set and carbon emissions are found through the analysis of historical process parameter data. Historical process parameter data refers to the production and operation records of coking enterprises in the past period (usually 3-6 months), which contain the values ​​of each process parameter and the corresponding carbon emission monitoring data. Correlation analysis mainly uses three methods: scatter plot analysis, correlation coefficient calculation, and multivariate regression analysis. Scatter plot analysis is to draw a scatter plot with the process parameter value as the horizontal axis and the carbon emissions as the vertical axis to visually observe the relationship trend between the two; the correlation coefficient calculation is to quantitatively measure the degree of linear correlation between the parameters and emissions through the Pearson correlation coefficient or the Spearman rank correlation coefficient; the multivariate regression analysis considers the combined influence of multiple process parameters and establishes a mathematical relationship model between the parameters and emissions. Through these analysis methods, the key parameters that have a significant impact on carbon emissions are identified, and the degree of influence of parameter changes on emissions is quantitatively characterized, and finally a parameter-emission response relationship diagram is formed. The parameter-emission response relationship diagram is a visual expression method. The horizontal axis is the process parameter value and the vertical axis is the carbon emissions. The curve shows the law of how parameter changes affect emissions.

[0116] The direction and magnitude of process parameter adjustments are determined based on the parameter-emission response graph. By analyzing the slope and inflection points of the curve in the parameter-emission response graph, the optimal operating range and adjustment direction for each parameter are determined. For parameters with a positive correlation (increasing the parameter value leads to increased emissions), the adjustment direction is to decrease the parameter value; for parameters with a negative correlation (increasing the parameter value leads to decreased emissions), the adjustment direction is to increase the parameter value; and for parameters with a "U"-shaped relationship (where an optimal point exists), the adjustment direction is to move closer to the optimal point. The adjustment magnitude is determined based on a comprehensive consideration of the parameter's sensitivity (the degree to which emissions respond to parameter changes), the parameter's adjustable range (the upper and lower limits of the process allowable range), and safety and stability (to avoid process fluctuations caused by excessive adjustments). Once the adjustment direction and magnitude are determined, they are arranged in chronological order according to the process flow to generate a parameter optimization instruction sequence. An instruction sequence is a structured set of operational instructions, including the parameter name, initial value, target value, adjustment step size, and execution time. The parameter optimization instruction sequence is input into the process parameter control device to implement process adjustments. The process parameter control system is the automated control system of a coking enterprise, primarily comprising a distributed control system (DCS), a programmable logic controller (PLC), and a fieldbus control system. The adjustment implementation process utilizes an incremental adjustment strategy, breaking down large adjustments into multiple smaller ones. After each adjustment, the system response is observed, and the next adjustment is made only after stability is confirmed. The main parameters to be adjusted include four categories: coke oven temperature (furnace wall temperature, top temperature, bottom temperature, etc.), gas flow (return gas volume, external supply gas volume, etc.), coal charge (coal charge density, coal charge height, etc.), and quenching method (dry quenching parameters, wet quenching parameters, etc.). Each parameter adjustment requires specific operating procedures and technical points. For example, coke oven temperature adjustment requires consideration of temperature gradient and uniformity; gas flow adjustment requires balancing heat supply and gas recovery efficiency; coal charge adjustment requires balancing production capacity and coking quality; and quenching method adjustment requires ensuring that the physical properties of the coke meet standards.

[0117] A real-time carbon emissions monitoring system records changes in carbon emissions at key points after adjustments. This system is a network of equipment that continuously monitors flue gas emission parameters, including flow meters, gas analyzers, and data acquisition and processing units installed at each emission point. The system collects emission data at key points after adjustments at a high frequency (typically every 1-5 minutes), including flue gas flow rate, carbon dioxide concentration, oxygen concentration, and other parameters, while also recording the corresponding process parameter states. The collected data undergoes standard state conversion, validity verification, and statistical processing to generate emission reduction verification data. This verification data is a multidimensional time series dataset containing information such as pre- and post-adjustment emission comparisons, process parameter change trajectories, and changes in key performance indicators, comprehensively reflecting the impact of process parameter adjustments on carbon emissions. Based on this verification data, successful process parameter adjustments are integrated with their corresponding emission reduction results to develop a technical implementation plan for energy conservation and emission reduction in the coking process. This integration process first evaluates the effectiveness of each parameter adjustment, identifying adjustments that demonstrate significant emission reductions without compromising product quality or production safety. These successful adjustments are then categorized and organized by process unit and implementation difficulty. Finally, a systematic technical implementation plan document is developed. The coking process energy conservation and emission reduction technology implementation plan is a structured technical document, typically consisting of six sections: Background Overview (carbon emission status and issues), Technical Principles (emission reduction mechanism and theoretical basis), Specific Measures (detailed parameter adjustment plan), Implementation Steps (operational procedures and precautions), Results Evaluation (expected emission reductions and economic benefits), and Continuous Improvement (long-term monitoring and optimization recommendations). This plan provides both theoretical guidance and clear operational implications, providing a technical path for coking enterprises to achieve low-carbon production.

[0118] The carbon emission accounting and evaluation method for the coking industry in the embodiment of the present application is described above. The carbon emission accounting and evaluation system for the coking industry in the embodiment of the present application is described below. Figure 2 In one embodiment of the present application, a carbon emission accounting and evaluation system for the coking industry includes:

[0119] The acquisition module is used to collect data on the amount of coal fed into the coke oven, coke output, gas generation, and gas composition through an infrared carbon element online analyzer and a differential pressure gas flow meter, and generate a database of coking production process parameters;

[0120] a determination module for determining the elemental carbon content of the collected coal gas, coal tar, and crude benzene physical samples using a gas chromatography-mass spectrometer, and entering the determination results into the coking production process parameter database to form a coking material carbon content table;

[0121] A generation module is used to determine the amount of coke oven gas recycled and supplied based on the carbon content table of the coking materials and in combination with the real-time monitored gas flow data, and to generate a carbon element material flow diagram for the coking process;

[0122] A monitoring module is used to form a carbon emission intensity distribution table of the coking process based on the carbon element material flow diagram of the coking process and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment of each process;

[0123] an identification module for identifying and generating a list of key carbon emission points in the coking process by analyzing the difference between the actual carbon emission and the theoretical carbon emission of each process using the carbon emission intensity distribution table of the coking process;

[0124] The adjustment module is used to optimize and adjust the coking production process based on the coking process carbon emission key point list through the process parameter control device, and generate a coking process energy-saving and emission reduction technology implementation plan.

[0125] Through the coordinated cooperation of the above components, the infrared carbon element online analyzer and the differential pressure gas flow meter are used to collect the data of the coke oven coal input, coke output, gas generation and components in real time, and a real-time dynamic database of coking production process parameters is established, which solves the problems of data lag and inaccuracy in traditional methods and realizes the continuity and real-time collection of carbon emission data; the gas chromatography-mass spectrometry is used to accurately determine the elemental carbon content of physical samples of gas, coal tar and crude benzene. Combined with advanced data processing algorithms, the calculation deviation caused by the confusion between the concepts of elemental carbon and fixed carbon in traditional methods is overcome, and the accuracy of carbon content determination is greatly improved; according to the carbon content table of coking materials combined with the real-time monitored gas flow data, an innovative gas pipeline network diversion calculation method is adopted to realize the accurate distinction between the amount of coke oven gas recycled to the furnace and the amount of externally supplied gas. An intuitive carbon element material flow diagram for the coking process was formed, breaking through the technical bottleneck of inaccurate estimation of coal gas flow direction by traditional methods; based on the carbon element material flow diagram for the coking process and combined with the flue gas monitoring data of each process, a carbon emission intensity distribution table was established, realizing a refined spatiotemporal characterization of carbon emissions, providing an intuitive and visual decision-making basis for emission reduction; through standard deviation analysis of the difference between actual carbon emissions and theoretical carbon emissions of each process, a carbon emission anomaly identification algorithm with self-learning ability was constructed, which can intelligently screen out key carbon emission points, solving the problem that traditional methods cannot effectively identify carbon emission anomaly points; based on in-depth analysis of the correlation between process parameters and carbon emissions, a parameter-emission response model was developed, and the precise optimization of the coking production process was achieved through the process parameter control device, forming a practical coking process energy-saving and emission reduction technology implementation plan. The present invention is particularly outstanding in the application of artificial intelligence algorithms. The standard deviation analysis and multi-parameter correlation analysis algorithms introduced in the carbon emission anomaly identification link can intelligently identify the key factors affecting carbon emissions from massive process data; the adaptive response model applied in the parameter optimization link can automatically learn the complex nonlinear relationship between process parameters and carbon emissions based on historical data, and provide the optimal control strategy, which significantly improves the intelligence level and practical effect of the solution.

[0126] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0127] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0128] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0129] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0130] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A carbon emission accounting and assessment method for the coking industry, characterized in that: include: The infrared carbon element online analyzer and differential pressure gas flow meter are used to collect data on the amount of coal fed into the coke oven, coke output, gas generation, and gas composition, and generate a database of coking production process parameters. The elemental carbon content of collected coal gas, coal tar, and crude benzene samples was measured using a gas chromatography-mass spectrometer. The measurement results were entered into the coking production process parameter database to form a coking material carbon content table. Based on the coking material carbon content table and combined with real-time monitored coal gas flow data, the amount of coke oven gas recycled and supplied was determined to generate a carbon element material flow diagram for the coking process. Based on the coking process carbon element material flow diagram and combined with carbon dioxide concentration data collected by flue gas emission monitoring equipment for each process, a carbon emission intensity distribution table for the coking process was generated. Using the carbon emission intensity distribution table of the coking process, by analyzing the difference between the actual carbon emissions of each process and the theoretical carbon emissions, a list of key carbon emission points in the coking process is identified and generated. This includes extracting the actual carbon emission intensity values ​​of each process from the carbon emission intensity distribution table to form a measured carbon emission data set. The theoretical carbon emission reference values ​​under corresponding process conditions are obtained from the carbon emission benchmark database of the coking industry to establish a theoretical carbon emission benchmark set. The measured carbon emission data set is compared and calculated with the theoretical carbon emission benchmark set to obtain the quantitative value of the carbon emission difference of each process. The carbon emission difference quantitative values ​​are processed by standard deviation analysis to classify the carbon emission anomaly levels and form a carbon emission deviation rating table. The carbon emission differences of each process are ranked using the carbon emission deviation rating table to screen out process points with deviations exceeding the preset threshold. The screened process points are integrated with the corresponding carbon emission difference quantitative values ​​and process parameter association records to generate a list of key carbon emission points in the coking process, and key carbon emission points are intelligently screened out. Based on the coking process carbon emission key point list, the coking production process is optimized and adjusted through process parameter control devices to generate a coking process energy conservation and emission reduction technical implementation plan. This includes extracting abnormal carbon emission processes and their corresponding process parameters from the coking process carbon emission key point list to form an optimization target parameter set. The correlation characteristics between the optimization target parameter set and carbon emissions are identified through historical process parameter data analysis, and a parameter-emission response relationship diagram is established. Determine the direction and magnitude of process parameter adjustment based on the parameter-emission response diagram, and generate a parameter optimization adjustment instruction sequence. This parameter optimization adjustment instruction sequence is input into the process parameter control device to precisely control the coke oven temperature, gas flow, coal charge, and quenching method. The real-time carbon emission monitoring system records the changes in carbon emissions at key points after regulation and control, forming emission reduction effect verification data; Based on the emission reduction effect verification data, the successful process parameter adjustment plan and the corresponding emission reduction effect are integrated to formulate the coking process energy conservation and emission reduction technology implementation plan.

2. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that: The data on the amount of coal fed into the coke oven, coke production, gas generation and gas composition are collected through an infrared carbon element online analyzer and a differential pressure gas flow meter to generate a coking production process parameter database, including: real-time monitoring and collection of the amount of coal fed into the coke oven and coke production through an infrared carbon element online analyzer installed at key positions of the coke oven coal feeding system and coke discharge system; continuous measurement and recording of gas generation through a differential pressure gas flow meter arranged in the gas pipe network system; sampling and analysis of gas composition data through gas composition analysis equipment to obtain data on the hydrocarbon content in the gas; storage of the data on the amount of coal fed into the coke oven, coke production, gas generation and gas composition in time series to form time series associated data; data quality review of the time series associated data, elimination of abnormal values ​​and interrupted data, and supplementation of estimated values; importing the time series associated data into the database management system after quality review to generate a coking production process parameter database.

3. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that: The elemental carbon content of the collected coal gas, coal tar and crude benzene samples is determined by gas chromatography-mass spectrometry, and the determination results are entered into the coking production process parameter database to form a coking material carbon content table, including: extracting coal gas samples from the pipeline system and extracting coal tar and crude benzene samples from the storage facility at preset time intervals through an automatic sampling device to form a material sample set; pre-processing the material sample set, including vacuum filtration of the coal gas sample, dilution and separation of the coal tar sample, and purification of the crude benzene sample, to obtain a test-ready sample; and performing gas chromatography-mass spectrometry on the test sample. The prepared samples are subjected to high-temperature cracking to convert the hydrocarbons in each material into identifiable characteristic fragment ions; the elemental carbon content values ​​in the coal gas, coal tar and crude benzene samples are calculated based on the relative peak intensity of the characteristic fragment ions in the mass spectrum and the standard curve of carbon element content; the elemental carbon content values ​​are correlated and matched with the sample collection time, material type, and production batch information to construct the original records of the carbon content of the coking materials; the original records of the carbon content of the coking materials are integrated and classified according to the material type, sampling location and time series, and entered into the coking production process parameter database to form a coking material carbon content table.

4. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that: According to the carbon content table of coking materials and combined with the real-time monitored coal gas flow data, the amount of coke oven gas recycled and burned and the amount of externally supplied coal gas are determined, and a carbon element material flow diagram for the coking process is generated, including: extracting the carbon element content value in the coal gas from the coking material carbon content table to establish a coal gas carbon content characteristic data set; performing time correlation matching on the coal gas carbon content characteristic data set and the real-time monitored coal gas flow data to form coal gas carbon flux basic data; analyzing and processing the coal gas carbon flux basic data through the coal gas pipeline diversion calculation method to distinguish the flow distribution ratio of the coal gas recycled branch and the external supply branch; calculating the specific values ​​of the coke oven gas recycled and burned and the amount of externally supplied coal gas by multiplying the flow distribution ratio with the total coal gas flow; combining the specific values ​​of the coke oven gas recycled and burned and the amount of externally supplied coal gas with the carbon element content data in the coking material carbon content table to calculate the carbon element input and output of each process node; based on the carbon element input and output data of each process node, drawing a carbon element material flow diagram for the coking process that represents the flow relationship between the carbon element in each coking process.

5. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that: Based on the carbon element material flow diagram of the coking process and combined with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment of each process, a carbon emission intensity distribution table of the coking process is formed, including: extracting the carbon element input and output data of each process unit from the carbon element material flow diagram of the coking process to establish a basic data set of carbon balance of the process; collecting flue gas volume flow and carbon dioxide concentration data through flue gas monitoring equipment arranged at the emission points of the coke oven, dry quenching device, gas purification system, and chemical product recovery system; performing standard state conversion and dimensional unification processing on the collected flue gas volume flow and carbon dioxide concentration data to calculate the carbon dioxide emissions per unit time of each emission point; calculating the ratio of the carbon dioxide emissions per unit time of each emission point to the material handling volume of the corresponding process unit to obtain the carbon emission intensity value per unit output of each process; performing time accumulation statistics and spatial distribution mapping on the carbon emission intensity value per unit output of each process to form a carbon emission intensity spatiotemporal feature matrix; classifying and organizing the carbon emission intensity spatiotemporal feature matrix according to process category and emission intensity level to generate a carbon emission intensity distribution table for the coking process.

6. A carbon emission accounting and evaluation system for the coking industry, used to implement the carbon emission accounting and evaluation method for the coking industry as described in any one of claims 1 to 5, characterized in that: Carbon emission accounting and assessment systems for the coking industry include: The acquisition module is used to collect data on the amount of coal fed into the coke oven, coke output, gas generation, and gas composition through an infrared carbon element online analyzer and a differential pressure gas flow meter, and generate a database of coking production process parameters; The determination module is used to determine the elemental carbon content of collected coal gas, coal tar, and crude benzene samples using a gas chromatography-mass spectrometer, and to enter the determination results into the coking production process parameter database to form a coking material carbon content table; A generation module is used to determine the amount of coke oven gas recycled and supplied based on the carbon content table of the coking materials and the real-time monitored gas flow data, and to generate a carbon element material flow diagram for the coking process; The monitoring module is used to generate a carbon emission intensity distribution table for the coking process based on the carbon element material flow diagram of the coking process and the carbon dioxide concentration data collected by the flue gas emission monitoring equipment of each process; An identification module is used to identify and generate a list of key carbon emission points in the coking process by using the carbon emission intensity distribution table of the coking process and analyzing the difference between the actual carbon emissions and theoretical carbon emissions of each process; The adjustment module is used to optimize and adjust the coking production process based on the key point list of carbon emissions in the coking process through the process parameter control device, and generate a technical implementation plan for energy conservation and emission reduction in the coking process.

7. A computer device, characterized in that: The system comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the carbon emission accounting and evaluation method for the coking industry according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is caused to execute the carbon emission accounting and assessment method for the coking industry according to any one of claims 1 to 5.

Images