Carbon emission accounting and evaluation method and system for coking industry

Abstract

The invention relates to the technical field of data processing, and discloses a carbon emission accounting and evaluation method and system for the coking industry. The method comprises the following steps: collecting coal coke data through analysis equipment to establish a parameter library; measuring the carbon content of the material to form a material table; analyzing the gas flow direction and drawing a carbon element flow diagram; generating an intensity distribution table in combination with the emission data; comparing actual and theoretical emission identification key points; and generating an emission reduction scheme based on a key point regulation and control process. According to the method, the key points of the carbon emission are accurately identified, and a targeted emission reduction technical scheme is provided based on correlation analysis of the process parameters and the carbon emission, so that fine management and effective emission reduction of the carbon emission of coking enterprises are realized.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a carbon emission accounting and evaluation method and system for the coking industry. Background Art

[0002] The coking industry is an important energy conversion and steel production support industry, occupying an important position in the national economy. With the increasingly prominent global climate change problem, 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 "Guidelines for Calculating and Reporting Greenhouse Gas Emissions from Independent Coking Enterprises in China (Trial)" and "IPCC Guidelines for National Greenhouse Gas Inventories". These methods are based on the principle of material balance and calculate from the perspectives 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 the coking production process, implicit carbon dioxide emissions during the enterprise's net purchased heat and electricity production process, and the amount of carbon dioxide recovered and utilized by the enterprise. In practice, coking enterprises usually calculate carbon emissions by means of statistical reports and empirical coefficients, and conduct accounting using fixed parameters and default emission factors.

[0003] However, the existing carbon emission accounting methods have many deficiencies. First, the traditional methods do not adequately consider the characteristics of the coking process and fail to fully reflect the carbon emission differences under different process conditions. In particular, the confusion between the concepts of elemental carbon and fixed carbon leads to calculation deviations. Second, using default emission factors for calculation often overestimates the actual emissions, which is not conducive to enterprises accurately understanding their own carbon emission status. Third, the existing methods lack an effective mechanism for identifying carbon emission abnormal points and cannot put forward targeted emission reduction optimization suggestions. Fourth, the correlation analysis between carbon emission data and process parameters is insufficient, making it difficult to support enterprises in carrying out refined carbon management. Finally, the traditional methods focus on total accounting and lack in-depth analysis of the spatio-temporal distribution characteristics of carbon emissions, and cannot provide clear guidance on the carbon emission reduction path for enterprises. These deficiencies seriously restrict the refinement and scientific level of carbon emission management in coking enterprises. Summary of the Invention

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

[0005] In a first aspect, the present application provides a carbon emission accounting and assessment method for the coking industry. The carbon emission accounting and assessment method for the coking industry includes: collecting data on the coal input amount of the coke oven, coke output, gas generation amount, and gas composition through an infrared carbon element on-line analyzer and a differential pressure type gas flowmeter to generate a coking production process parameter database; using a gas chromatography-mass spectrometry instrument to measure the elemental carbon content of the collected physical samples of gas, coal tar, and crude benzene, and inputting the measurement results into the coking production process parameter database to form a coking material carbon content table; determining the amount of coke oven gas recycled for combustion and the amount of externally supplied gas based on the coking material carbon content table and in combination with the real-time monitored gas flow data, and generating a coking process carbon element material flow diagram; forming a coking process carbon emission intensity distribution table based on the coking process carbon element material flow diagram and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment for each process; using the coking process carbon emission intensity distribution table to identify and generate a list of key points for coking process carbon emissions by analyzing the difference between the actual carbon emissions and the theoretical carbon emissions for each process; and based on the list of key points for coking process carbon emissions, optimizing and adjusting the coking production process through a process parameter control device to generate a coking process energy conservation and emission reduction technology implementation plan.

[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 includes: A collection module for collecting data on the coal input amount of the coke oven, coke output, gas generation amount, and gas composition through an infrared carbon element on-line analyzer and a differential pressure type gas flowmeter to generate a coking production process parameter database; A determination module for using a gas chromatography-mass spectrometry instrument to measure the elemental carbon content of the collected physical samples of gas, coal tar, and crude benzene, and inputting the measurement results into the coking production process parameter database to form a coking material carbon content table; A generation module for determining the amount of coke oven gas recycled for combustion and the amount of externally supplied gas based on the coking material carbon content table and in combination with the real-time monitored gas flow data, and generating a coking process carbon element material flow diagram; A monitoring module for forming a coking process carbon emission intensity distribution table based on the coking process carbon element material flow diagram and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment for each process; An identification module for using the coking process carbon emission intensity distribution table to identify and generate a list of key points for coking process carbon emissions by analyzing the difference between the actual carbon emissions and the theoretical carbon emissions for each process; An adjustment module for optimizing and adjusting the coking production process through a process parameter control device based on the list of key points for coking process carbon emissions to generate a coking process energy conservation and emission reduction technology implementation plan.

[0007] 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 assessment method for the coking industry.

[0008] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned carbon emission accounting and assessment method for the coking industry.

[0009] In the technical solution provided by the present application, the data on the amount of coal fed into the coke oven, the amount of coke produced, the amount of gas generated and the components are collected in real time by an infrared carbon element online analyzer and a differential pressure gas flowmeter, 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 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, forming a The intuitive carbon element material flow diagram of the coking process breaks through the technical bottleneck of inaccurate estimation of 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 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 anomalies; 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 implementation plan for energy-saving and emission reduction technology for 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 link of identifying carbon emission anomalies can intelligently identify the key factors affecting carbon emissions from massive process data; the adaptive response model used in the parameter optimization link can automatically learn the complex nonlinear relationship between process parameters and carbon emissions based on historical data, and give the optimal control strategy, which significantly improves the intelligence level and practical effect of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of an embodiment of the carbon emission accounting and evaluation method for the coking industry in the embodiments of the present application; Figure 2 It is a schematic diagram of an embodiment of the carbon emission accounting and evaluation system for the coking industry in the embodiments of the present application; Figure 3 It is a schematic block diagram of the structure of the computer device in the embodiments of the present invention. Detailed implementation manners

[0012] The embodiments of the present application provide a carbon emission accounting and evaluation method and system for the coking industry. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "comprising" or "having" and any of its variations are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0013] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 An embodiment of the carbon emission accounting and evaluation method for the coking industry in the embodiments of the present application includes: Step S101: Collect the data of the coal input amount, coke output, gas generation amount and gas components of the coke oven through an infrared carbon element on-line analyzer and a differential pressure type gas flowmeter, and generate a coking production process parameter database; Step S102: Use a gas chromatography-mass spectrometry instrument to measure the elemental carbon content of the collected physical samples of gas, coal tar and crude benzene, and input the measurement results into the coking production process parameter database to form a coking material carbon content table; Step S103: According to the coking material carbon content table, combined with the real-time monitored gas flow data, determine the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas, and generate a coking process carbon element material flow diagram; Step S104: Based on the carbon element material flow diagram of the coking process, combined with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment for each process, form a carbon emission intensity distribution table for the coking process; Step S105: Utilize the carbon emission intensity distribution table for the coking process. By analyzing the difference between the actual carbon emissions and the theoretical carbon emissions of each process, identify and generate a list of key points for carbon emissions in the coking process; Step S106: Based on the list of key points for carbon emissions in the coking process, optimize and adjust the coking production process through the process parameter control device to generate a technical implementation plan for energy conservation and emission reduction in the coking process.

[0014] It can be understood that the execution entity of this application can be a carbon emission accounting and assessment system for the coking industry, or a terminal or a server. Specifically, it is not limited here. This application example is described by taking the server as the execution entity as an example.

[0015] Specifically, basic data is obtained by installing professional monitoring equipment on the coking production line. The infrared carbon element on-line analyzer is a precision device that works based on the characteristic absorption principle of different substances for infrared spectra. By measuring the absorption degree of the sample for infrared light of a specific wavelength, the carbon element content is determined. This analyzer is installed at key positions in the coal feeding system and coke discharging system of the coke oven, and can continuously monitor the mass flow of coal entering the coke oven and the output of the coke produced for 24 hours without interruption. The differential pressure type gas flowmeter is a device that calculates the flow rate by measuring the pressure difference of the gas before and after the standard throttling element. The working principle of this type of flowmeter is based on Bernoulli's equation. When the fluid passes through the contraction section, the flow rate increases and the pressure decreases, and the flow rate can be calculated by measuring the pressure difference. The collected data of the coal feeding amount, coke output, gas generation amount and its components in the coke oven are stored according to a unified time tag to construct a database of coking production process parameters. This database adopts a relational data structure and contains multi-dimensional information such as time, process, material type, quantity, and composition.

[0016] 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 material analysis tools in contemporary analytical chemistry, combining the efficient separation capability of gas chromatography with the precise identification capability of mass spectrometry. In actual operation, an automatic sampling device is used to extract samples from gas pipelines, coal tar storage tanks, and crude benzene storage facilities at preset time intervals (such as every 4 hours). Gas samples need to be filtered under reduced pressure to remove moisture and impurities, coal tar samples need to be diluted and separated by solvent due to high viscosity, and crude benzene samples are purified to remove interference from non-benzene substances. The treated samples enter the gas chromatography-mass spectrometry and are converted into characteristic fragment ions by high-temperature cracking. For example, methane in coal gas produces characteristic ion peaks after cracking. By measuring the relative abundance of characteristic fragment ions and comparing them with the standard curve, the methane content in coal gas is calculated to be 28.5%, and the ethane content is 3.2%, and the elemental carbon content in coal gas is calculated based on this. Similarly, coal tar and crude benzene samples are analyzed to obtain their elemental carbon content values. These precisely measured element carbon content values ​​are associated with the sampling information and entered into the coking production process parameter database to form a systematic carbon content table for coking materials. The coal gas carbon content data is extracted from the coking material carbon content table to form a time series carbon content feature data set. This data set contains the changes in the carbon content of coal gas at different time points, such as the carbon content of coal gas in the morning shift, the carbon content of the middle shift, and the carbon content of the night shift. These data are time-matched with the real-time monitored coal gas flow data to form the basic data of coal gas carbon flux. Then, the flow distribution of each branch is monitored by the flow meter installed at each node of the coal gas pipeline network, and the direction of the coal gas is determined by applying the calculation principle of pipe network fluid mechanics. In an actual case of a coking plant, the proportion of recycled coal gas and the proportion of externally supplied coal gas are obtained through the pipe network diversion calculation, so as to calculate the specific recycled combustion amount and external supply amount. Combined with the coal gas carbon content, the recycled combustion carbon amount and external carbon amount are calculated. Similarly, the flow of carbon elements in materials such as coke, coal tar, and crude benzene is calculated and finally integrated to form a complete carbon element material flow diagram for the coking process, which clearly shows the flow path and quantity distribution of carbon elements from raw coal to various products and emissions.

[0017] Extract the carbon element input and output data of each process unit (such as coke oven, gas purification, chemical product recovery, etc.) from the carbon element material flow diagram to establish a basic data set for the carbon balance of the process. Then, through the flue gas monitoring equipment arranged at the key emission points of each process, collect the flue gas volume flow rate and carbon dioxide concentration data. Perform standard state conversion and dimension unification processing on the collected data, and calculate the carbon dioxide emissions at each emission point. Calculate the ratio of the emissions to the material processing volume of the corresponding process unit to obtain the carbon emission intensity value per unit output of each process. For example, if the amount of coal processed in the coke oven process is several tons per hour and the measured carbon dioxide emissions are several tons, calculate the carbon emission intensity per unit coal processing volume. Conduct time statistics and spatial distribution analysis on the carbon emission intensities of all processes to form a spatio-temporal characteristic matrix of carbon emission intensity, and classify and organize it according to process categories and emission intensity levels. Finally, generate a carbon emission intensity distribution table for the coking process. Identify abnormal emission points by comparing and analyzing the actual carbon emissions and the theoretical carbon emission values. First, extract the actual carbon emission intensity values from the carbon emission intensity distribution table of the coking process, and at the same time obtain the theoretical carbon emission values that should be under the corresponding process conditions from the carbon emission benchmark database of the coking industry. Calculate the difference between the measured value and the theoretical value to obtain the carbon emission deviation amount for each process. Through standard deviation analysis, divide the deviation amount into different levels to form a carbon emission deviation rating table. Sort the processes according to this table and screen out the process points where the deviation exceeds the preset threshold. These points often indicate that there are abnormalities or optimization spaces in the process operation. Integrate the selected points with the relevant process parameters to generate a list of key points for carbon emissions in the coking process. Extract the abnormal processes and their corresponding process parameters from the list to form an optimization target parameter set. Determine the direction and range of parameter adjustment by analyzing the relationship between process parameters and carbon emissions in historical data. For example, if it is found that too high a coke oven temperature will lead to an increase in carbon emissions, the temperature should be appropriately reduced; low gas recovery efficiency will increase carbon losses, so the recovery system should be optimized. Convert the determined parameter adjustment plan into specific operation instructions and implement them through the process parameter control device. After adjustment, record the changes in carbon emissions at each key point through the monitoring system to verify the adjustment effect. Integrate the effective parameter adjustment plan with the corresponding emission reduction effect to form an implementation plan for energy conservation and emission reduction technologies in the coking process.

[0018] In the embodiments of the present application, the coal input amount, coke output, gas generation amount and component data of the coke oven are collected in real time through an infrared carbon element on-line analyzer and a differential pressure type gas flowmeter, and a real-time dynamic database of coking production process parameters is established, solving the problems of data lag and inaccuracy in traditional methods and realizing the continuity and real-time of carbon emission data collection; the element carbon content of physical samples of gas, coal tar and crude benzene is accurately determined by a gas chromatography-mass spectrometry (GC-MS) instrument, and combined with advanced data processing algorithms, the calculation deviation caused by the confusion of 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 coking material carbon content table and the gas flow data monitored in real time, an innovative calculation method for gas pipeline network shunt is adopted to accurately distinguish the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas, forming an intuitive material flow diagram of carbon elements in the coking process, breaking through the technical bottleneck of inaccurate estimation of gas flow direction in traditional methods; based on the combination of the material flow diagram of carbon elements in the coking process and the flue gas monitoring data of each process, a carbon emission intensity distribution table is established, realizing the spatio-temporal fine characterization of carbon emissions and providing an intuitive and visual decision-making basis for emission reduction; through the standard deviation analysis of the difference between the actual carbon emission and the theoretical carbon emission of each process, a carbon emission anomaly recognition algorithm with self-learning ability is constructed, which can intelligently screen out key carbon emission points and solve 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 is developed, and the coking production process is precisely optimized through a process parameter regulation device, forming a practical coking process energy conservation and emission reduction technology implementation plan. The present invention is particularly prominent in the application of artificial intelligence algorithms. The standard deviation analysis and multi-parameter correlation analysis algorithms introduced in the carbon emission anomaly recognition link can intelligently identify the key factors affecting carbon emissions from a large amount of process data; the adaptive response model applied in the parameter optimization link can automatically learn the complex non-linear relationship between process parameters and carbon emissions according to historical data and give the optimal regulation strategy, significantly improving the intelligent level and practical effect of the solution.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Real-time monitoring and collection of the coal input amount and coke output of the coke oven are carried out through an infrared carbon element on-line analyzer installed at key positions of the coke oven coal input system and coke discharging system; (2) Continuous measurement and recording of the gas generation amount are carried out through a differential pressure type gas flowmeter arranged in the gas pipeline network system; (3) Sampling and analysis of gas component data are carried out through a gas component analysis device to obtain the content data of hydrocarbons in the gas; (4) The coal input amount, coke output, gas generation amount and gas component data of the coke oven are stored according to the time series to form time series associated data; (5) Conduct data quality audits on time-series correlated data, remove outliers and interrupted data, and supplement estimated values; (6) Import the time-series correlated data into the database management system after quality audit to generate a database of coking production process parameters.

[0020] Specifically, a database of coking production process parameters is constructed. This analyzer operates based on the principle of infrared spectroscopy. Different substances have different absorption rates for infrared light of specific wavelengths, and hydrocarbon molecules will produce characteristic absorption of infrared light of specific wavelengths. Installing this analyzer at the coal bunker outlet of the coal feeding system of the coke oven and on the coke conveyor belt of the coke discharging system can detect the carbon element content in coal and coke in real time. At the same time, in cooperation with the metering device, the analyzer can continuously monitor and record the data of the coal feeding amount and coke production of the coke oven. In actual operation, the on-line infrared carbon element analyzer usually samples and analyzes once every 5 - 10 seconds, and transmits the data to the central control unit to form a data stream. The differential pressure type gas flowmeter is a key instrument for measuring the gas generation amount. The working principle of the differential pressure type gas flowmeter is to measure the flow rate based on the relationship that the pressure difference generated when the fluid passes through the throttling device (such as orifice plate, Venturi tube, nozzle, etc.) is proportional to the square of the flow rate. In the coking plant, the differential pressure type gas flowmeter is arranged on the main pipeline and each branch pipeline of the gas pipeline network system, records data once every 1 - 2 seconds, and obtains the gas generation amount data under standard conditions through gas density correction and standard state conversion.

[0021] Conduct quantitative analysis on the gas components. The gas chromatograph realizes separation through the difference in the distribution coefficients of different components between the stationary phase and the mobile phase, and then conducts quantitative detection through a thermal conductivity detector or a hydrogen flame ionization detector. The infrared spectroscopy analyzer conducts quantitative analysis by using the characteristic absorption of different gas components for infrared light. In the coking plant, the gas component analysis equipment is usually set at the outlet of the gas purification system, automatically samples and analyzes the gas samples once every 30 - 60 minutes to obtain the content data of hydrocarbon compounds such as methane, ethane, propane, hydrogen, and carbon monoxide in the gas.

[0022] Unify the storage and correlation of the time series of the collected data of the coal feeding amount, coke production, gas generation amount, and gas components of the coke oven to form time-series correlated data. Mark the data collected by different devices according to a unified time stamp. Considering the difference in the collection frequencies of different data (for example, the coal feeding amount may be recorded once an hour, while the gas flow rate is recorded multiple times per second), it is necessary to conduct unified processing of the time resolution. The structure of the time-series correlated data includes fields such as time stamp, data type, data value, equipment number, and process unit, so that the data from different sources are correlated in the time dimension.

[0023] Conduct quality audits on time-series related data. Data quality audits include multiple subprocesses: perform range checks 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 conduct continuity checks to identify data breakpoints. Next, conduct consistency checks to verify whether the logical relationships between related data are reasonable, such as the material balance relationship between coal input and coke output. For the 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 the gas flow data is missing during a certain period, the data from the previous and subsequent periods can be used to calculate the estimated value through an interpolation formula to ensure data continuity.

[0024] Import the time-series related data that has passed the quality audit into a database management system to generate a structured coking production process parameter database. This process involves data format conversion, index establishment, and storage optimization. The database usually adopts a relational database structure, creating multiple related tables including equipment information tables, process parameter tables, gas component tables, etc. Primary key and foreign key relationships are established during the data import process, data type constraints are set, and multi-dimensional indexes such as time, equipment, and parameter type are created to optimize query performance. After the database construction is completed, a complete coking production process parameter database is formed, providing a data basis for subsequent carbon emission accounting.

[0025] Taking a coking plant as an example, the plant processes 3,000 tons of coking coal per day, produces 2,250 tons of coke per day, and the gas generation is about 160,000 cubic meters per day. When implementing the method of the present invention, infrared carbon element on-line analyzers are installed on the coal charging conveyor and the coke discharging conveyor of the plant, differential pressure gas flow meters are installed on the main gas pipe and branch pipes, and gas chromatographs are installed at the outlet of the gas purification system. Through these devices, data is continuously collected for two weeks, obtaining about 330,000 original data records. These data are time-correlated and integrated to form time-series related data. During the data quality audit process, it is found that about 2.3% of the data is abnormal or missing. For example, the gas flow data is missing from 14:00 to 16:00 on October 15 due to equipment maintenance, and it is supplemented and estimated by the linear interpolation method of the data from the previous and subsequent periods. The processed data is imported into the MySQL database system, and a multi-table association structure including material flow, energy flow, and process parameters is established, and finally a complete coking production process parameter database is formed, which contains comprehensive information such as coal feeding, coke output, gas generation and components.

[0026] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Extract gas samples from the pipeline system and extract coal tar and crude benzene samples from storage facilities at preset time intervals through an automatic sampling device to form a set of material samples; (2) Pretreat the material sample set, including vacuum filtration of the gas sample, dilution and separation of the coal tar sample, and purification of the crude benzene sample, to obtain test-ready samples; (3) Pyrolyze the test-ready samples by gas chromatography-mass spectrometry (GC-MS) at high temperature to convert the hydrocarbons in each material into recognizable characteristic fragment ions; (4) Calculate the elemental carbon content values in the gas, coal tar, and crude benzene samples based on the relative peak intensities of the characteristic fragment ions in the mass spectrum and the standard curve of carbon element content; (5) Associate and match the elemental carbon content values with the sample collection time, material type, and production batch information to construct the original record of the carbon content of coking materials; (6) Integrate and classify the original record of the carbon content of coking materials according to material type, sampling location, and time series, and enter it into the coking production process parameter database to form the carbon content table of coking materials.

[0027] Specifically, collect material samples at preset time intervals through an automatic sampling device. The automatic sampling device is a programmable mechanical device composed 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 periodically opened through a controllable valve to pump 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 from solidifying and blocking, 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 in a similar manner. The sampling time interval is usually set according to the fluctuations of the production process. Since the composition of gas samples changes relatively quickly, they are generally collected once every 4 hours, while coal tar and crude benzene samples can be collected once per shift or per day. All collected samples are sorted and classified according to number, time, and material type to form a material sample set.

[0028] Pretreat the material sample set. The pretreatment of gas samples is mainly vacuum filtration, which reduces the high-pressure gas to atmospheric pressure through a pressure reducing valve and removes moisture, tar droplets, and particulate impurities through multiple filters to obtain a clean gas sample; due to the high viscosity and complex composition of coal tar samples, dilution and separation treatment are required. Usually, organic solvents such as toluene or dichloromethane are used for dilution in a ratio of 1:10 to 1:20, and then insoluble substances are removed by centrifugation or filtration; crude benzene samples need to be purified, mainly to remove sulfides, ammonia water, and other non-aromatic compounds, which can be achieved through washing, acid treatment, and distillation. The samples after pretreatment are called test-ready samples, which have the physical state and chemical purity suitable for instrumental analysis.

[0029] The pyrolysis analysis of the test-ready sample is carried out using a gas chromatography-mass spectrometry (GC-MS) instrument. The GC-MS instrument is a high-precision analytical instrument that combines gas chromatography (GC) and mass spectrometry (MS). The GC part is used to separate the components in the mixture, and the MS part is used to identify the molecular structures of these components. The sample first enters the injection port of the gas chromatography, vaporizes at a high temperature (usually 250 - 350 °C), and then enters the chromatographic column with the carrier gas (usually helium) for separation. Different hydrocarbons have different retention times in the chromatographic column due to differences in their physical and chemical properties such as boiling point and polarity. The separated components enter the mass spectrometer in sequence, undergo pyrolysis at high temperature under the action of the ionization source (usually a 70 eV electron impact source), forming charged characteristic fragment ions. These fragment ions are separated by the mass analyzer (such as quadrupole, time-of-flight, ion trap, etc.) according to the mass-to-charge ratio (m / z), and finally are detected by the detector and recorded as a mass spectrum.

[0030] The quantitative calculation of the elemental carbon content is carried out based on the mass spectrum, which involves a relatively complex data processing process. In the mass spectrum, the relative peak intensity of the characteristic fragment ions is proportional to the content of the corresponding substance. The calculation of the elemental carbon content is based on the identification and quantification of the characteristic fragment ions, and the calculation formula is as follows: ; where, represents the elemental carbon content (mass percentage) in the sample, represents the number of identified hydrocarbon species, represents the th relative content of the hydrocarbon (determined by its characteristic peak area), represents the th number of carbon atoms in the hydrocarbon molecule, represents the molar mass of carbon element (12 g / mol), represents the th molar mass of the hydrocarbon (g / mol), represents the The correction factors of hydrocarbons are used to correct the response differences of different compounds. A mass spectrometry database of hydrocarbon standards is established, which includes the characteristic fragment ions and their relative peak intensities of various hydrocarbons with known concentrations. Then, the mass spectrum of the sample is compared with the standard spectrum to identify the various hydrocarbons in the sample. A calibration curve is constructed by the internal standard method or the external standard method, that is, the relationship curve between the characteristic peak area and the concentration of the standard substance with known concentration, so as to calculate the content of each hydrocarbon in the sample. Finally, according to the above formula, considering the number and proportion of carbon atoms in each hydrocarbon, the total elemental carbon content in the sample is calculated. The calculated elemental carbon content value is associated and matched with the sample collection information. The elemental carbon content value of each sample needs to establish an association relationship with information such as its collection time, material type, production batch, etc. to form a complete data record. This process is realized through a database management system, which matches the analysis result with the unique identifier (such as sample number) in the sampling record form to establish an association table. The association table contains fields: sample number, collection time, material type, sampling location, production batch number, elemental carbon content value, etc. This kind of association and matching ensures the traceability of the data, and any elemental carbon content value can be traced back to its corresponding sample source and collection conditions.

[0031] Integrate and classify the original records of the carbon content of coking materials to form a table of the carbon content of coking materials. This step is realized through database query and processing techniques. First, the original records are grouped according to the material types (gas, coal tar, crude benzene), and then within each material type, they are sorted and classified according to the sampling location and time series. Statistical quantities such as the average carbon content, maximum value, minimum value, and standard deviation of various materials under different time periods and different production conditions are calculated through database aggregation functions (such as AVG, MAX, MIN, etc.). The integrated data forms a table of the carbon content of coking materials, which is a multi-dimensional data structure. The horizontal dimension is the material type and sampling location, the vertical dimension is the time series, and the cell value is the elemental carbon content value under the corresponding conditions.

[0032] Taking a coking plant as an example, when implementing the method of the present invention in this plant, an automatic sampling system is first set up. Gas samples are collected every 4 hours at three key nodes of the gas pipeline network (raw gas, purified gas, and gas sent to the coke oven), and liquid samples are collected every shift (8 hours) at the coal tar and crude benzene storage tanks. The gas samples are pretreated through a condensate separator and an activated carbon filter. The coal tar samples are diluted with dichloromethane at a ratio of 1:15 and then filtered. The crude benzene samples are purified by distillation. These treated samples are sent to an Agilent 7890B-5977A gas chromatography-mass spectrometry instrument for analysis. It is found that the gas samples mainly consist of methane, ethane, propane, hydrogen, and carbon monoxide. By comparing with the standard spectra of each component, the content of each component is calculated, and then combined with the carbon atom ratio in each hydrocarbon, the elemental carbon content of the gas samples is calculated. Similarly, the coal tar and crude benzene samples are analyzed. The elemental carbon content values are associated with the sampling information to form a comprehensive carbon content data table containing three materials, different time points, and different sampling positions. This table is entered into the coking production process parameter database as the basic data for subsequent carbon emission accounting.

[0033] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Extract the elemental carbon content values in the gas from the coking material carbon content table to establish a gas carbon content characteristic data set; (2) Perform time-correlation matching between the gas carbon content characteristic data set and the real-time monitored gas flow data to form the basic data of gas carbon flux; (3) Analyze and process the basic data of gas carbon flux through the gas pipeline network shunt calculation method to distinguish the flow distribution ratio between the gas return-to-coke-oven branch and the external supply branch; (4) Multiply the flow distribution ratio by the total gas flow to calculate the specific values of the gas combustion amount for coke oven return and the external supply gas amount; (5) Combine the specific values of the gas combustion amount for coke oven return and the external supply gas amount with the elemental carbon content data in the coking material carbon content table to calculate the elemental carbon input and output amounts at each process node; (6) Based on the elemental carbon input and output amount data at each process node, draw a coking process elemental carbon material flow diagram showing the flow relationship of elemental carbon among various coking processes.

[0034] Specifically, extract the carbon element content value in the gas from the coking material carbon content table to establish a gas carbon content characteristic data set. The coking material carbon content table contains the elemental carbon content data of materials such as gas, coal tar, and crude benzene. The extraction process is realized through database query statements. Specifically, filter out the records related to gas from the table, including the gas carbon content values at different time points and different sampling positions. These extracted data constitute the gas carbon content characteristic data set, which is a time series data structure containing timestamps and corresponding carbon content values.

[0035] Perform time-correlation matching on the gas carbon content characteristic data set and the real-time monitored gas flow data to form the basic gas carbon flux data. This step involves data integration and time synchronization processing. Gas flow data is usually collected in real time by differential pressure gas flow meters with a relatively high collection frequency, perhaps one record per minute or even per second, while the collection frequency of carbon content data is relatively low, perhaps one record per hour or per shift. Therefore, it is necessary to perform time scale matching processing to aggregate the high-frequency flow data according to the time points of the carbon content data, usually using the average value method within a time window. After time matching, multiply the gas carbon content value at each time point by the corresponding gas flow 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: ; Where, represents the gas carbon flux at time point (unit: kg / h), represents the gas volume flow at time point (unit: m³ / h), represents the density of gas under standard conditions (unit: kg / m³), represents the mass fraction of carbon element in the gas at time point (dimensionless).

[0036] Analyze and process the basic gas carbon flux data through the gas pipeline network shunt calculation method to distinguish the flow distribution ratio between the gas return furnace branch and the external supply branch. The gas pipeline network shunt calculation is a calculation method based on the principle of fluid mechanics, considering factors such as pipeline network structure, pipe diameter, valve opening, and pressure distribution to calculate the flow distribution of each branch. In a coking plant, after the gas is generated, it is usually divided into two parts: one part is returned to the furnace for heating the coke oven, and the other part is supplied externally to other users or facilities. The core formula for shunt calculation is: ; Where, represents the volume flow of the return furnace branch (unit: m³ / h), represents the volume flow of the external supply branch (unit: m³ / h), Represents the pressure of the main gas pipe (unit: kPa), Represents the pressure of the return furnace branch (unit: kPa), Represents the pressure of the externally supplied branch (unit: kPa), Represents the pipe diameter of the return furnace branch (unit: mm), Represents the pipe diameter of the externally supplied branch (unit: mm), Represents the flow coefficient of the return furnace branch (dimensionless), Represents the flow coefficient of the externally supplied branch (dimensionless).

[0037] Through the above formula, combined with the real-time measured pipe network pressure and the known pipe network parameters, the flow ratio relationship between the return furnace branch and the externally supplied branch is calculated. In practical applications, the flow balance constraint conditions also need to be considered, that is, the total flow is equal to the sum of the flows of each branch: ; Among them, Represents the total gas flow (unit: m³ / h).

[0038] Multiply the flow distribution ratio by the total gas flow to calculate the specific values of the coke oven gas return furnace combustion amount and the externally supplied gas amount. The specific formula is: ; Among them, Represents the flow distribution ratio of the return furnace branch (dimensionless), usually between 0.4 - 0.6, and the specific value is determined by the process requirements and the pipe network design.

[0039] The fifth step is to combine the specific values of the coke oven gas return furnace combustion amount and the externally supplied gas amount with the carbon element content data in the coking material carbon content table to calculate the carbon element input and output amounts at each process node. This calculation takes into account the transfer and transformation of carbon elements between process units, and the core formula is as follows: ; Among them, Represents the carbon element input amount of process unit u (unit: kg), Represents process unit 's carbon element output amount (unit: kg), Represents the material set flowing into process unit ; Represents the material set flowing out of process unit ; Represents the carbon flux of material (unit: kg / h), Represents the calculation time interval (unit: h).

[0040] For the coke oven unit, the carbon input includes the carbon in coal and the carbon in the recirculating gas, and the outputs include the carbon in coke, the carbon in raw gas, and the carbon in the carbon dioxide emitted by combustion. For the gas purification unit, the carbon input is the carbon in the raw gas, and the outputs include the carbon in the purified gas and the carbon in chemical products (such as coal tar and crude benzene). Based on the carbon input and output data of each process node, a carbon element material flow diagram of the coking process showing the flow relationship of carbon elements between various coking processes is drawn. This diagram is a visual representation, usually in the form of a Sankey Diagram, clearly showing the flow direction and quantity distribution of carbon elements from raw materials to various products and emissions. In the diagram, the nodes represent process units or materials, the connecting lines represent the carbon element flow paths, and the line widths are proportional to the carbon element flow rates. The diagram is usually drawn using professional visualization tools or programming languages, and the input data is the carbon input and output quantities of each node calculated previously.

[0041] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Extract the carbon 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 for the carbon balance of each process; (2) Collect the flue gas volume flow rate and carbon dioxide concentration data through the flue gas monitoring equipment arranged at the emission points of the coke oven, dry quenching coke device, gas purification system, and chemical product recovery system; (3) Perform standard state conversion and dimension unification processing on the collected flue gas volume flow rate and carbon dioxide concentration data to calculate the carbon dioxide emission per unit time at each emission point; (4) Calculate the ratio of the carbon dioxide emission 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; (5) Conduct time cumulative statistics and spatial distribution mapping on the carbon emission intensity values per unit output of each process to form a spatio-temporal characteristic matrix of carbon emission intensity; (6) Classify and organize the spatio-temporal characteristic matrix of carbon emission intensity according to process categories and emission intensity levels to generate a carbon emission intensity distribution table for coking processes.

[0042] Specifically, extract the carbon element input and output data of each process unit from the carbon element material flow diagram of the coking process. Convert the chart information into a structured data table. The specific operation is to use a data parsing tool to identify the nodes and connection lines in the diagram and convert them into a data table containing information such as process unit identification, carbon element input quantity, and carbon element output quantity. For example, for the coke oven unit, the extracted data includes the carbon quantity in the coal input, the carbon quantity in the coke output, the carbon quantity in the raw gas, and the carbon quantity in the carbon dioxide emitted by combustion; for the gas purification unit, the extracted data includes the carbon quantity in the raw gas input, the carbon quantity in the purified gas output, and the carbon quantity in the chemical products. The extracted data is arranged in the order of the process flow to form a complete basic data set of the process carbon balance.

[0043] Collect actual emission data through flue gas monitoring equipment. In the coking industry, the main emission points include the coke oven chimney, the exhaust port of the dry coke quenching device, the release point of the gas purification system, and the exhaust port of the chemical product recovery system. Install professional flue gas monitoring equipment at these locations, including a flue gas flow meter and a gas analyzer. The flue gas flow meter is used to measure the volume of flue gas passing through the emission port 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 velocity by measuring the difference between the dynamic pressure and the static pressure in the flue; the ultrasonic flow meter calculates the flow velocity by measuring the time difference of sound wave propagation in the flue gas; the thermal mass flow meter calculates the mass flow by measuring the cooling rate of the heated probe. The gas analyzer is used to measure the concentration of carbon dioxide in the flue gas. Common technologies include non-dispersive infrared method, electrochemical sensor method, and Fourier transform infrared spectroscopy method. The non-dispersive infrared method measures the concentration by using the absorption characteristics of carbon dioxide for infrared light of a specific wavelength; the electrochemical sensor method calculates the concentration by using the current signal generated when carbon dioxide contacts the electrolyte; the Fourier transform infrared spectroscopy method determines the component concentration by analyzing the interference pattern formed after the flue gas absorbs infrared light. These devices usually record data every 10 - 60 seconds to form a continuous time series of flue gas volume flow and carbon dioxide concentration.

[0044] Perform standard state conversion and dimension unification processing on the collected raw data. Standard state conversion refers to converting the gas data measured under different temperature and pressure conditions into values under standard conditions. The standard conditions are usually defined as 0°C, 101.325 kPa, and dry basis. The gas state equation is required in the conversion process, considering the effects of temperature, pressure, and moisture content, to convert the measured flue gas volume flow rate into the flow rate under standard conditions. Dimension unification processing is to convert data with different units into a unified unit, such as converting the carbon dioxide concentration from volume percentage to mass concentration, or converting the hourly flow rate to the daily flow rate. After processing, calculate the carbon dioxide emissions per unit time at each emission point, that is, the product of the flue gas volume flow rate under standard conditions and the carbon dioxide concentration, considering the conversion relationship between the carbon dioxide molecular weight and the molar volume under standard conditions. Calculate the carbon emission intensity value per unit output of each process. Calculate the ratio of the carbon dioxide emissions at each emission point to the material processing volume of the corresponding process unit. The material processing volume data comes from the production record system, including the coking coal processing volume of the coke oven, the coke output, the raw coke oven gas processing volume of the gas purification system, the chemical product output of the chemical production and recovery system, etc. Through division operation, obtain the carbon emission intensity value per unit output of each process, such as the carbon dioxide emissions of the coke oven process during the production of each ton of coke, the carbon dioxide emissions during the purification of every thousand cubic meters of gas, etc. These intensity values are important indicators to measure the carbon emission efficiency of each process, reflecting the carbon emission level during the production of unit products.

[0045] Perform time accumulation statistics and spatial distribution mapping on the carbon emission intensity values per unit output of each process to form a spatio-temporal characteristic matrix of carbon emission intensity. Time accumulation statistics refers to statistically analyzing the carbon emission intensity values in different time periods (hours, shifts, days, months, quarters), calculating statistical quantities such as the average value, maximum value, minimum value, and standard deviation, and identifying the time variation law of carbon emissions. Spatial distribution mapping refers to visually displaying 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 spatio-temporal 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 corresponding to the processes and times. This matrix visually displays the spatio-temporal distribution characteristics of carbon emissions through the depth of color or the size of the values.

[0046] It is to classify and organize the spatio-temporal characteristic matrix of carbon emission intensity according to the process categories and emission intensity levels to generate a distribution table of carbon emission intensity for the coking process. The process categories include coal preparation process, coke oven process, dry coke quenching process, gas purification process, chemical product recovery process, etc. The emission intensity levels are classified into different levels such as low, medium, and high according to the industry benchmark value or historical data. The classification and organization process uses the pivot table technology to summarize and reorganize the data in the spatio-temporal characteristic matrix according to the process categories and emission intensity levels, forming a more structured and easy-to-understand distribution table of carbon emission intensity for the coking process. This table not only shows the carbon emission intensity values of each process, but also demonstrates the carbon emission change trends of different processes at different times, providing an important basis for subsequent carbon emission analysis and emission reduction decision-making.

[0047] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Extract the actual carbon emission intensity values of each process from the distribution table of carbon emission intensity for the coking process to form a measured carbon emission data set; (2) Obtain the theoretical carbon emission reference values under the corresponding process conditions through the carbon emission benchmark database of the coking industry to establish a theoretical carbon emission benchmark set; (3) Compare and calculate the measured carbon emission data set with the theoretical carbon emission benchmark set to obtain the quantified carbon emission difference values of each process; (4) Conduct standard deviation analysis on the quantified carbon emission difference values, divide the carbon emission anomaly levels, and form a carbon emission deviation rating table; (5) Sort the carbon emission differences of each process through the carbon emission deviation rating table, and screen out the process points where the deviation exceeds the preset threshold; (6) Integrate the screened process points with the corresponding quantified carbon emission difference values and process parameter association records to generate a list of key points for carbon emission in the coking process.

[0048] Specifically, extract the actual carbon emission intensity values of each process from the carbon emission intensity distribution table of the coking process to form an actual carbon emission data set. The carbon emission intensity distribution table of the coking process is a structured data table that records the carbon emission intensity values per unit output of each process under different times and different operating conditions. The extraction process is achieved through data screening technology. Select the data for the latest period (usually the recent 7 - 30 days) from the table and group and organize it by process category. The actual carbon emission data set is a multi - dimensional data set that contains information such as process identification, time identification, carbon emission intensity values, and process condition descriptions, comprehensively reflecting the carbon emission situation under the actual operating state of the coking plant. Obtain the theoretical carbon emission reference value through the carbon emission benchmark database of the coking industry. The carbon emission benchmark database of the coking industry is a professional database that collects the carbon emission data of coking enterprises of different scales and different process types at home and abroad and forms a reference system after standardized processing. This database usually contains three levels of benchmark values: international advanced level (the average value of the top 10% coking enterprises globally), domestic advanced level (the average value of the top 20% coking enterprises in China), and industry average level. When obtaining the theoretical reference value, it is necessary to consider the matching of the process conditions of the coking plant to be evaluated. The main matching factors include coke oven type (top - charging type, stamping type, heat - recovery type, etc.), coking coal type (primary coking coal, gas coal, fat coal, etc.), equipment scale (single - oven volume, total enterprise production capacity, etc.), and supporting facilities (dry coke quenching, wet coke quenching, etc.). Through multi - condition query and matching, extract the carbon emission data of enterprises with process conditions similar to those of the current coking plant to be evaluated from the database to form a theoretical carbon emission benchmark set.

[0049] Compare and calculate the actual carbon emission data set with the theoretical carbon emission benchmark set to obtain a quantified carbon emission difference value. The comparison and calculation adopt two methods: difference method and ratio method. The difference method calculates the absolute difference between the actual value and the theoretical value, that is, the actual carbon emission intensity minus the theoretical carbon emission intensity. A positive value obtained indicates excessive emissions, and a negative value indicates being better than the standard; the ratio method calculates the relative difference between the actual value and the theoretical value, that is, the actual carbon emission intensity divided by the theoretical carbon emission intensity. A value greater than 1 indicates excessive emissions, and a value less than 1 indicates being better than the standard. The comparison and calculation generate a corresponding difference quantification value for the actual value of each process and each time point, forming a complete difference data set.

[0050] Perform standard deviation analysis on the quantified carbon emission differences to classify the carbon emission anomaly levels. Standard deviation analysis is a statistical method used to measure the degree of data dispersion. First, calculate the mean and standard deviation of the quantified differences, and then classify them into different anomaly levels according to the number of standard deviations between each difference value and the mean. Usually, the difference values are divided into four levels according to the 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 (more than ±3 standard deviations). After classification, a carbon emission deviation rating table is formed. This table contains fields such as process identification, time identification, quantified difference value, and anomaly level, visually showing the severity of carbon emission anomalies in each process. Sort the carbon emission differences of each process through the carbon emission deviation rating table, and screen out the process points where the deviation exceeds the preset threshold. The sorting is usually carried out according to the anomaly level and the absolute value of the quantified difference value, with the most severe anomaly points ranked first. The preset threshold is a screening criterion set according to the enterprise management requirements and technical feasibility. Common threshold settings include: exceeding moderately abnormal (i.e., the difference value exceeds ±2 standard deviations), exceeding the theoretical value by more than 20%, maintaining at a slightly abnormal level or above for more than 3 consecutive days, etc. By applying these threshold conditions, screen out the process points that need to be focused on from the carbon emission deviation rating table. These points represent the weak links or problem areas in carbon emission management. Integrate the screened process points with the corresponding process parameter association records to generate a list of key points for coking process carbon emissions. Process parameter association records refer to the production parameter data corresponding to the screened abnormal points, usually sourced from the distributed control system (DCS) or manufacturing execution system (MES) of the coking plant. Key process parameters include factors affecting carbon emissions such as coke oven temperature, coal charging density, coke oven gas recovery rate, dry coke quenching steam output, and gas purification efficiency. The integration process uses the timestamp and process identification as the association keys to match and merge the abnormal point data with the process parameter data, forming a complete record including anomaly descriptions and possible reasons. The finally generated list of key points for coking process carbon emissions is a structured document, which details the process points to be optimized, the degree of anomaly, the corresponding process parameter status, and the preliminary cause analysis, providing a clear goal for subsequent emission reduction optimization.

[0051] Taking a certain coking plant as an example, the plant implements the identification of abnormal carbon emission points through the above method. Extract the data of the past 15 days from the carbon emission intensity distribution table of the coking process, including the actual carbon emission intensity values of four processes: coke oven, dry quenching of coke, gas purification, and chemical product recovery, to form an actual carbon emission data set. Then query the theoretical carbon emission reference values under similar conditions (6.25-meter top-loading coke oven, supporting dry quenching of coke, and an annual coke production scale of 2.4 million tons) of this plant from the carbon emission benchmark database of the coking industry to establish a theoretical carbon emission benchmark set. Comparative calculations show that the actual carbon emission intensity of the coke oven process is 0.152 tons of carbon dioxide per ton of coal, while the theoretical reference value is 0.126 tons of carbon dioxide per ton of coal, with a difference value of +0.026 tons of carbon dioxide per ton of coal and a relative deviation of +20.6%. Through standard deviation analysis, this deviation is classified as a moderate abnormal level. According to the preset threshold condition of "more than 20% above the theoretical value", this coke oven process is screened as a process point that needs to be focused on. Further associating with the process parameter records, it is found that the average temperature of the coke oven during the abnormal period is 25°C higher than the normal working condition, and the leakage phenomenon is obvious due to the poor sealing of the oven door. Finally, integrate this coke oven process point with the carbon emission difference quantification value, temperature abnormality, poor sealing and other process parameter problems, and list them in the key points list of coking process carbon emissions.

[0052] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Extract the carbon emission abnormal processes and their corresponding process parameters from the key points list of coking process carbon emissions to form an optimization target parameter set; (2) Find out the correlation characteristics between the optimization target parameter set and the carbon emissions through the analysis of historical process parameter data, and establish a parameter-emission response relationship diagram; (3) Determine the adjustment direction and amplitude of the process parameters according to the parameter-emission response relationship diagram, and generate a parameter optimization adjustment instruction sequence; (4) Input the parameter optimization adjustment instruction sequence into the process parameter control device to accurately control the coke oven temperature, gas flow rate, coal charging amount, and coke quenching method; (5) Record the carbon emission changes of each key point after the control through the carbon emission real-time monitoring system to form emission reduction effect verification data; (6) Based on the emission reduction effect verification data, integrate the successful process parameter adjustment schemes and the corresponding emission reduction effects, and compile an implementation plan for energy conservation and emission reduction technology of the coking process.

[0053] Specifically, carbon emission abnormal processes and their corresponding process parameters are extracted from the carbon emission key point list of the coking process to form an optimized target parameter set. The carbon emission key point list of the coking process is the list generated in the previous step, which records the process points with serious carbon emission abnormalities and their related parameters. The extraction process is realized through data screening technology. The records in the key point list are sorted according to the degree of abnormality, and several records with the top rankings are selected. At the same time, the process parameter information associated with these abnormal points is extracted. The optimized target parameter set is a structured data table, which includes fields such as process identification, parameter name, current parameter value, and normal parameter range, and clarifies the specific objects and targets that need to be optimized and adjusted. The correlation characteristics between the optimized target parameter set and the carbon emission amount are found through the analysis of historical process parameter data. The historical process parameter data refers to the production operation records of coking enterprises in the past period (usually 3-6 months), which includes various process parameter values and corresponding carbon emission monitoring data. Three main methods are used for correlation analysis: scatter plot analysis, correlation coefficient calculation, and multivariate regression analysis. Scatter plot analysis is to plot a scatter plot with the process parameter value as the abscissa and the carbon emission amount as the ordinate to visually observe the relationship trend between the two; correlation coefficient calculation is to quantitatively measure the linear correlation degree between the parameter and the emission amount through the Pearson correlation coefficient or the Spearman rank correlation coefficient; multivariate regression analysis is to consider the comprehensive influence of multiple process parameters and establish a mathematical relationship model between the parameters and the emission amount. Through these analysis methods, the key parameters that have a significant impact on carbon emission are identified, and the influence degree of parameter changes on the emission amount is quantitatively characterized. Finally, a parameter-emission response relationship diagram is formed. The parameter-emission response relationship diagram is a visual expression method, with the process parameter value on the horizontal axis and the carbon emission amount on the vertical axis, and the curve shows the law of how parameter changes affect the emission amount.

[0054] Determine the adjustment direction and amplitude of process parameters according to the parameter-emission response diagram. By analyzing the curve slope and inflection points in the parameter-emission response diagram, determine the optimal operating range and adjustment direction for each parameter. For parameters with a positive correlation (an increase in parameter value leads to an increase in emissions), the adjustment direction is to decrease the parameter value; for parameters with a negative correlation (an increase in parameter value leads to a decrease in emissions), the adjustment direction is to increase the parameter value; for parameters with a "U"-shaped relationship (there is an optimal point), the adjustment direction is to approach the optimal point. The adjustment amplitude is comprehensively determined based on the sensitivity of the parameter (the response degree of emissions to parameter changes), the adjustable range of the parameter (the upper and lower limits permitted by the process), and safety and stability (to avoid process fluctuations caused by excessive adjustment). After determining the adjustment direction and amplitude, arrange them in the order of the process flow and time sequence to generate a parameter optimization adjustment instruction sequence. The instruction sequence is a set of structured operation instruction sets, including elements such as parameter name, pre-adjustment value, target value, adjustment step size, and execution time. Input the parameter optimization adjustment instruction sequence into the process parameter control device to implement the process adjustment. The process parameter control device is the automation control system of a coking enterprise, mainly including a distributed control system (DCS), a programmable logic controller (PLC), and a fieldbus control system. The adjustment implementation process adopts a progressive adjustment strategy, that is, decompose large-scale adjustments into multiple small-scale adjustments, observe the system response after each adjustment, and confirm stability before proceeding with the next adjustment. The main parameters to be adjusted include four categories: coke oven temperature (such as furnace wall temperature, top temperature, bottom temperature, etc.), gas flow rate (such as return gas volume, external gas supply volume, etc.), coal charging amount (such as coal charging density, coal charging height, etc.), and coke quenching method (such as dry coke quenching parameters, wet coke quenching parameters, etc.). The adjustment of each category of parameters has specific operating procedures and technical key points. For example, the adjustment of coke oven temperature needs to consider temperature gradient and uniformity, the adjustment of gas flow rate needs to balance heat supply and gas recovery efficiency, the adjustment of coal charging amount needs to take into account production capacity and coking quality, and the adjustment of coke quenching method needs to ensure that the physical properties of coke meet the standards.

[0055] Record the carbon emission changes at each key point after regulation through the real-time carbon emission monitoring system. The real-time carbon emission monitoring system is a network of equipment for continuously monitoring flue gas emission parameters, including flow meters, gas analyzers, and data acquisition and processing units installed at each emission point. The system collects the emission data of each key point after regulation at a high frequency (usually once every 1 - 5 minutes), including parameters such as flue gas flow rate, carbon dioxide concentration, and oxygen concentration, and simultaneously records the corresponding process parameter status. The collected data undergoes standard state conversion, validity verification, and statistical processing to form verification data for emission reduction effects. The verification data for emission reduction effects is a multi-dimensional time series data set, containing information such as the comparison of emissions before and after regulation, the change trajectory of process parameters, and the change of key performance indicators, comprehensively reflecting the impact of process parameter adjustment on carbon emissions. Based on the verification data for emission reduction effects, integrate the successful process parameter adjustment schemes and the corresponding emission reduction effects, and compile an implementation plan for energy conservation and emission reduction technologies in the coking process. This integration process first evaluates the effectiveness of each parameter adjustment, screens out the adjustment measures that actually produce obvious emission reduction effects and do not affect product quality and production safety; then classifies and organizes these successful measures according to process units and implementation difficulties; finally, forms a systematic technical implementation plan document. The implementation plan for energy conservation and emission reduction technologies in the coking process is a structured technical document, usually including six parts: background overview (current situation and problems of carbon emissions), technical principle (emission reduction mechanism and theoretical basis), specific measures (detailed parameter adjustment plan), implementation steps (operation procedures and precautions), effect evaluation (expected emission reduction and economic benefits), and continuous improvement (long-term monitoring and optimization suggestions). This plan has both theoretical guiding significance and clear operability, providing a technical path for coking enterprises to achieve low-carbon production.

[0056] The above describes the carbon emission accounting and assessment method for the coking industry in the embodiments of the present application. Next, the carbon emission accounting and assessment system for the coking industry in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the carbon emission accounting and assessment system for the coking industry in the embodiments of the present application includes: A collection module, configured to collect data on the coal input amount, coke output, gas generation amount, and gas components of the coke oven through an infrared carbon element on-line analyzer and a differential pressure type gas flow meter, and generate a coking production process parameter database; A determination module, configured to use a gas chromatography - mass spectrometry instrument to measure the elemental carbon content of the collected physical samples of gas, coal tar, and crude benzene, and input the measurement results into the coking production process parameter database to form a coking material carbon content table; A generation module, configured to determine the amount of coke oven gas recycled for combustion and the amount of externally supplied gas according to the coking material carbon content table and in combination with the real-time monitored gas flow data, and generate a coking process carbon element material flow diagram; A monitoring module, which is used to form a carbon emission intensity distribution table for the coking process according to 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 for each process; An identification module, which is used to utilize the carbon emission intensity distribution table for the coking process and identify and generate a list of key points for carbon emissions in the coking process by analyzing the difference between the actual carbon emissions and the theoretical carbon emissions for each process; An adjustment module, which is used to optimize and adjust the coking production process through a process parameter control device based on the list of key points for carbon emissions in the coking process, and generate a technical implementation plan for energy conservation and emission reduction in the coking process.

[0057] Through the collaborative cooperation of the above-mentioned various components, the amount of coal input into the coke oven, coke production, gas generation volume and component data are collected in real time through an infrared carbon element on-line analyzer and a differential pressure type gas flowmeter, and a real-time dynamic database of coking production process parameters is established, solving the problems of data lag and inaccuracy in the traditional method, and realizing the continuity and real-time of carbon emission data collection; the elemental carbon content of physical samples of gas, coal tar and crude benzene is accurately determined by a gas chromatography-mass spectrometry, and combined with an advanced data processing algorithm, overcoming the calculation deviation caused by the confusion of the concepts of elemental carbon and fixed carbon in the traditional method, and greatly improving the accuracy of carbon content determination; according to the carbon content table of coking materials and the gas flow data monitored in real time, an innovative calculation method for the diversion of the coal gas pipeline network is adopted to accurately distinguish the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas, forming an intuitive carbon element material flow diagram of the coking process, breaking through the technical bottleneck of inaccurate estimation of the coal gas flow direction in the traditional method; based on the combination of the carbon element material flow diagram of the coking process and the flue gas monitoring data of each process, a carbon emission intensity distribution table is established, realizing the spatio-temporal fine characterization of carbon emissions, and 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 for each process, an abnormal carbon emission identification algorithm with self-learning ability is constructed, which can intelligently screen out key carbon emission points, solving the problem that the traditional method cannot effectively identify abnormal carbon emission points; based on the in-depth analysis of the correlation between process parameters and carbon emissions, a parameter-emission response model is developed, and the coking production process is accurately optimized through a process parameter control device, forming a technical implementation plan for energy conservation and emission reduction in the coking process with practical value. The present invention is particularly prominent in the application of artificial intelligence algorithms. The standard deviation analysis and multi-parameter correlation analysis algorithms introduced in the abnormal carbon emission identification link can intelligently identify the key factors affecting carbon emissions from a large amount of process data; the adaptive response model applied in the parameter optimization link can automatically learn the complex non-linear relationship between process parameters and carbon emissions according to historical data and give the optimal control strategy, significantly improving the intelligent level and practical effect of the solution.

[0058] Refer to Figure 3, embodiments of the present invention also provide a computer device, which may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, 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 the 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 through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0059] Those skilled in the art can understand that Figure 3 the structure shown in

[0060] is only a block diagram of a part 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.

[0061] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can 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 data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0062] Those skilled in the art can 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 foregoing method embodiments and will not be described herein again.

[0063] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc., which can store program codes.

[0064] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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, The carbon emission accounting and assessment method for the coking industry includes: Collecting data on the coal input amount, coke output, gas generation amount, and gas components of the coke oven through an infrared carbon element on-line analyzer and a differential pressure gas flowmeter, and generating a coking production process parameter database; Using a gas chromatography-mass spectrometry (GC-MS) instrument to measure the elemental carbon content of the collected physical samples of gas, coal tar, and crude benzene, and entering the measurement results into the coking production process parameter database to form a coking material carbon content table; According to the coking material carbon content table and in combination with the real-time monitored gas flow data, determining the amount of coke oven gas recycled for combustion and the amount of externally supplied gas, and generating a coking process carbon element material flow diagram; Based on the coking process carbon element material flow diagram and in combination with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment for each process, forming a coking process carbon emission intensity distribution table; Using the coking process carbon emission intensity distribution table, by analyzing the difference between the actual carbon emissions and the theoretical carbon emissions of each process, identifying and generating a list of key points for coking process carbon emissions; Based on the list of key points for coking process carbon emissions, optimizing and adjusting the coking production process through a process parameter control device, and generating a technical implementation plan for energy conservation and emission reduction in the coking process.

2. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that The step of collecting data on the coal input amount, coke output, gas generation amount, and gas components of the coke oven through an infrared carbon element on-line analyzer and a differential pressure gas flowmeter, and generating a coking production process parameter database includes: Real-time monitoring and collecting the coal input amount and coke output of the coke oven through an infrared carbon element on-line analyzer installed at key positions in the coke oven coal input system and coke discharging system; Continuously measuring and recording the gas generation amount through a differential pressure gas flowmeter arranged in the gas pipeline network system; Sampling and analyzing the gas component data through a gas component analysis device to obtain the data on the hydrocarbon content in the gas; Storing the data on the coal input amount, coke output, gas generation amount, and gas components of the coke oven in a time series to form time-series associated data; Conducting data quality audits on the time-series associated data, removing outliers and interrupted data, and supplementing estimated values; After quality auditing the time-series associated data, importing it into a database management system to generate the coking production process parameter database.

3. The carbon emission accounting and assessment method for the coking industry according to claim 1, wherein The step of using a gas chromatography-mass spectrometry (GC-MS) instrument to measure the elemental carbon content of the collected physical samples of gas, coal tar, and crude benzene, and entering the measurement results into the coking production process parameter database to form a coking material carbon content table includes: Extracting gas samples from the pipeline system and coal tar and crude benzene samples from storage facilities at preset time intervals through an automatic sampling device to form a set of material samples; Performing pretreatment on the set of material samples, including vacuum filtration of gas samples, dilution and separation of coal tar samples, and purification treatment of crude benzene samples, to obtain test-ready samples; Performing high-temperature pyrolysis on the test-ready samples through a gas chromatography-mass spectrometry (GC-MS) instrument to convert the hydrocarbons in each material into identifiable characteristic fragment ions; Calculating the elemental carbon content values in the gas, coal tar, and crude benzene samples according to the relative peak intensities of the characteristic fragment ions in the mass spectrometry spectrum and the carbon element content standard curve. Associate the numerical value of the elemental carbon content with the sample collection time, material type, and production batch information to construct the original record of the carbon content of coking materials; Integrate and classify the original record of the carbon content of coking materials according to the material type, sampling location, and time series, and enter it into the coking production process parameter database to form a table of the carbon content of coking materials.

4. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that According to the table of the carbon content of coking materials, combined with the real-time monitored gas flow data, determine the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas, and generate a material flow diagram of carbon elements in the coking process, including: Extract the numerical value of the carbon element content in the gas from the table of the carbon content of coking materials to establish a characteristic data set of the carbon content of the gas; Perform time correlation matching on the characteristic data set of the carbon content of the gas and the real-time monitored gas flow data to form the basic data of the gas carbon flux; Analyze and process the basic data of the gas carbon flux through the gas pipeline network shunt calculation method to distinguish the flow distribution ratio of the gas return to the furnace branch and the externally supplied branch; Multiply the flow distribution ratio by the total gas flow to calculate the specific values of the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas; Combine the specific values of the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas with the carbon element content data in the table of the carbon content of coking materials to calculate the input and output amounts of carbon elements at each process node; Based on the data of the input and output amounts of carbon elements at each process node, draw a material flow diagram of carbon elements in the coking process showing the flow relationship of carbon elements between various coking processes.

5. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that According to the material flow diagram of carbon elements in the coking process, combined with the carbon dioxide concentration data collected by the flue gas emission monitoring equipment for each process, form a distribution table of the carbon emission intensity of the coking process, including: Extract the input and output data of carbon elements for each process unit from the material flow diagram of carbon elements in the coking process to establish a basic data set for the carbon balance of the process; Collect the flue gas volume flow and carbon dioxide concentration data through the flue gas monitoring equipment arranged at the emission points of the coke oven, dry coke quenching device, gas purification system, and chemical product recovery system; Perform standard state conversion and dimension unification processing on the collected flue gas volume flow and carbon dioxide concentration data to calculate the carbon dioxide emission amount per unit time at each emission point; Calculate the ratio of the carbon dioxide emission amount per unit time at each emission point to the material processing amount of the corresponding process unit to obtain the carbon emission intensity value per unit output of each process; Perform time cumulative statistics and spatial distribution mapping on the carbon emission intensity values per unit output of each process to form a spatio-temporal characteristic matrix of the carbon emission intensity; Classify and organize the spatio-temporal characteristic matrix of the carbon emission intensity according to the process category and emission intensity level to generate a distribution table of the carbon emission intensity of the coking process.

6. The carbon emission accounting and assessment method for the coking industry according to claim 1, characterized in that, Use the distribution table of the carbon emission intensity of the coking process to identify and generate a list of key points for carbon emissions in the coking process by analyzing the difference between the actual carbon emissions and the theoretical carbon emissions of each process, including: Extract the actual carbon emission intensity numerical values of each process from the distribution table of the carbon emission intensity of the coking process to form a measured carbon emission data set; Obtain the theoretical carbon emission reference values under the corresponding process conditions through the carbon emission benchmark database of the coking industry to establish a theoretical carbon emission benchmark set; Compare and calculate the measured carbon emission data set with the theoretical carbon emission benchmark set to obtain the quantified values of carbon emission differences for each process; Conduct standard deviation analysis on the quantified values of carbon emission differences, divide the carbon emission anomaly levels, and form a carbon emission deviation rating table; Sort the carbon emission differences of each process through the carbon emission deviation rating table, and screen out the process points where the deviation exceeds the preset threshold; Integrate the screened process points with the corresponding quantified values of carbon emission differences and process parameter correlation records to generate a list of key points for carbon emission in the coking process; 7. The carbon emission accounting and assessment method for the coking industry according to claim 1, wherein Based on the list of key points for carbon emission in the coking process, optimize and adjust the coking production process through the process parameter control device to generate an implementation plan for energy conservation and emission reduction technologies in the coking process, including: Extract the carbon emission abnormal processes and their corresponding process parameters from the list of key points for carbon emission in the coking process to form an optimized target parameter set; Find the correlation characteristics between the optimized target parameter set and the carbon emission through the analysis of historical process parameter data, and establish a parameter-emission response relationship diagram; Determine the adjustment direction and amplitude of process parameters according to the parameter-emission response relationship diagram, and generate a parameter optimization adjustment instruction sequence; Input the parameter optimization adjustment instruction sequence into the process parameter control device to accurately control the coke oven temperature, gas flow rate, coal charging amount, and coke quenching method; Record the changes in carbon emissions at each key point after the control through the carbon emission real-time monitoring system to form verification data for emission reduction effects; Based on the verification data for emission reduction effects, integrate the successful process parameter adjustment plans and the corresponding emission reduction effects to compile an implementation plan for energy conservation and emission reduction technologies in the coking process.

8. A carbon emission accounting and assessment system for the coking industry, which is used to implement the carbon emission accounting and assessment method for the coking industry described in any one of claims 1-7, characterized in that, The carbon emission accounting and evaluation system for the coking industry includes: A collection module for collecting the coal input amount, coke output, gas generation amount, and gas component data of the coke oven through an infrared carbon element on-line analyzer and a differential pressure type gas flowmeter, and generating a database of coking production process parameters; A determination module for measuring the elemental carbon content of the collected gas, coal tar, and crude benzene physical samples using a gas chromatography-mass spectrometry instrument, and entering the measurement results into the coking production process parameter database to form a table of carbon content in coking materials; A generation module for determining the amount of coke oven gas returned to the furnace for combustion and the amount of externally supplied gas according to the table of carbon content in coking materials and the real-time monitored gas flow data, and generating a material flow diagram of carbon elements in the coking process; A monitoring module for forming a distribution table of carbon emission intensity in the coking process based on the material flow diagram of carbon elements in the coking process and the carbon dioxide concentration data collected by the flue gas emission monitoring equipment for each process; An identification module for using the distribution table of carbon emission intensity in the coking process to identify and generate a list of key points for carbon emission in the coking process by analyzing the difference between the actual carbon emission and the theoretical carbon emission of each process; An adjustment module for optimizing and adjusting the coking production process through the process parameter control device based on the list of key points for carbon emission in the coking process to generate an implementation plan for energy conservation and emission reduction technologies in the coking process.

9. A computer device, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. It is characterized in that when the processor executes the computer program, it implements the carbon emission accounting and evaluation method for the coking industry described in any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the processor is caused to execute the carbon emission accounting and evaluation method for the coking industry described in any one of claims 1 to 7.

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