Ceramic industry electrical carbon model and carbon emission prediction method and system thereof

Abstract

The invention provides a ceramic industry electrical carbon model and a carbon emission prediction method and system thereof, and the construction process of the ceramic industry electrical carbon model comprises the steps: determining the emission boundary of the ceramic industry electrical carbon model, and the emission boundary comprises the determination of the region and time span covered by the model; determining an emission source, wherein the emission source comprises natural gas and electric power data in the production process of the ceramic industry; metering and monitoring: metering and monitoring the activity data of the emission source in real time, and regularly maintaining and calibrating monitoring equipment; evaluating the data quality, and correcting the data when the evaluation is not passed; and constructing the following multi-source heterogeneous electrical carbon model. According to the method, the carbon emission is predicted by constructing the multi-source heterogeneous electric carbon model, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission prediction, and in particular to a carbon emission prediction method and system for the ceramic industry. Background Art

[0002] Excessive greenhouse gas emissions are the main cause of global warming and the frequent occurrence of extreme climate disasters, which have an adverse impact on human survival and sustainable social and economic development. Therefore, since the 21st century, reducing greenhouse gas emissions and developing a low-carbon economy with "low pollution, low energy consumption and low emissions" has become a common choice for countries around the world.

[0003] Electricity is an energy-based industry that is related to the national economy, people's livelihood and national security. Electricity consumption, as the main energy consumption type for national economic development, is also an indicator for measuring carbon dioxide emissions. Electricity accounts for the largest proportion of energy consumption. The advantages of power big data in terms of real-time, accuracy and universal coverage have irreplaceable value in carbon emission accounting, providing scientific data reference for intuitively understanding carbon emission trends and implementing carbon reduction actions.

[0004] As a key emission-controlled industry, the ceramic industry in my country emits 140 million to 200 million tons of carbon dioxide, accounting for 1.5% to 2% of the country's total emissions. Among them, carbon emissions from building ceramics account for about 96% of the industry, and carbon emissions from sanitary ceramics account for about 4% of the industry. At present, carbon emissions from building sanitary ceramics are mainly caused by the burning of coal gas and natural gas in the drying and firing process of ceramic product manufacturing. The use of electricity by major energy-consuming equipment such as ball mills used for raw material powders will also indirectly produce carbon emissions. Under the goals of carbon peak and carbon neutrality, green production, energy conservation and emission reduction have become important trends in the development of the ceramic industry.

[0005] The ceramic industry has been included in the local carbon market. At present, the carbon emission data of enterprises are obtained through carbon verification work, which has a time lag of one natural year, which is not conducive to supervision. Constructing the "electricity carbon model" of the ceramic industry, using the characteristics of precise electricity measurement, real-time acquisition, and unified management, to accurately predict carbon dioxide emissions, can effectively and effectively survey the carbon assets and carbon emissions of the province in real time, which is not only conducive to enriching carbon supervision methods, but also conducive to providing a reference basis for carbon price forecasting, thereby helping key emission units to better participate in national carbon market transactions.

[0006] The accuracy of the electric carbon model depends not only on the relationship model of the historical emission data of the emission unit, but also on the actual production situation of the emission unit, such as process changes, capacity changes, and changes in production management level. Therefore, the carbon emission prediction model needs to have multi-source data to analyze, correct, and verify the model to ensure the prediction accuracy of the carbon emission prediction model. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for predicting carbon emissions in the ceramic industry, and to achieve carbon emissions prediction and improve prediction accuracy by constructing a multi-source heterogeneous electric-carbon model.

[0008] In a first aspect, the present invention provides an electric carbon model for the ceramic industry, which is constructed by the following process:

[0009] S11. Define the emission boundaries of the electric carbon model for the ceramic industry. The emission boundaries include determining the region and time span covered by the model. The region is the geographical boundary where the ceramic enterprises are located; the time span is determined based on data availability and forecasting needs;

[0010] S12. Determine emission sources, wherein the emission sources include natural gas and electricity data in the production process of the ceramic industry;

[0011] S13. Measurement and monitoring: real-time measurement and monitoring of emission source activity data, and regular maintenance and calibration of monitoring equipment;

[0012] S14, data quality evaluation, including data quality evaluation of the completeness, accuracy, verifiability, validity and timeliness of real-time monitoring data. If the evaluation is passed, proceed to the next step. If the evaluation is not passed, correct the data before proceeding to the next step.

[0013] S15. Construct the following multi-source heterogeneous electric carbon model:

[0014] f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b;

[0015] Where: X1 is the independent variable parameter representing natural gas emission data;

[0016] X2 is the independent variable parameter representing the electricity emission data;

[0017] f(X1,X2) is the total emissions of natural gas and electricity;

[0018] EF 电 is the emission factor for the latest electricity in the region;

[0019] AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used.

[0020] EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and

[0021] EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12;

[0022] CC 天然气 ——Carbon content per unit calorific value of natural gas used by ceramic enterprises;

[0023] α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises;

[0024] a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the equation is decoupled to obtain the specific values.

[0025] In a second aspect, the present invention provides a method for predicting carbon emissions using an electric carbon model in the ceramic industry, comprising:

[0026] S21. Obtain the natural gas emission source metering data and electricity emission source metering data of the latest cycle of the ceramic industry, use the natural gas emission source metering data as the independent variable parameter X1 of the natural gas emission data, and use the electricity emission source metering data as the independent variable parameter X2 of the electricity emission data to input into the following multi-source heterogeneous electric carbon model to calculate and output the total emissions f(X1,X2):

[0027] f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b;

[0028] Where:

[0029] EF 电 is the emission factor for the latest electricity in the region;

[0030] AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used.

[0031] EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and

[0032] EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12;

[0033] CC 天然气 ——Carbon content per unit calorific value of natural gas used by ceramic enterprises;

[0034] α天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises;

[0035] a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the decoupling equation is used to obtain specific values;

[0036] S22. Compare the deviation between the calculated output total emissions f(X1, X2) and the accounted total emissions. If the deviation is not greater than the preset value, it indicates that the model has good predictive ability. If the deviation is greater than the preset value, historical data is added for remodeling.

[0037] In a third aspect, the present invention provides a system for predicting carbon emissions using an electric carbon model in the ceramic industry, including a data acquisition subsystem and a data analysis subsystem;

[0038] The data acquisition subsystem is connected to the natural gas emission source metering data table and the electric power emission source metering data table of the ceramic industry, obtains the latest cycle of natural gas emission source metering data and electric power emission source metering data, and then sends them to the data analysis subsystem;

[0039] After preprocessing the received natural gas emission source metering data and electricity emission source metering data, the data analysis subsystem calls the multi-source heterogeneous electric carbon model of the ceramic industry for calculation to obtain the output total emissions f(X1, X2), and compares the deviation between the total emissions f(X1, X2) and the calculated total emissions. If the deviation value is not greater than the preset value, the total emissions f(X1, X2) are output; if the deviation is greater than the preset value, historical data is added for remodeling;

[0040] The multi-source heterogeneous electric carbon model is as follows:

[0041] f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b;

[0042] Where:

[0043] EF 电 is the emission factor for the latest electricity in the region;

[0044] AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used.

[0045] EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and

[0046] EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12;

[0047] CC 天然气 ——Carbon content per unit calorific value of natural gas used by ceramic enterprises;

[0048] α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises;

[0049] a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the equation is decoupled to obtain the specific values.

[0050] One or more technical solutions provided by the present invention have at least the following technical effects or advantages: the present invention constructs a multi-source heterogeneous electric-carbon model for the ceramic industry through Yan Jin's method, predicts the main carbon emissions of the ceramic industry through the multi-source heterogeneous electric-carbon model, and corrects or adjusts the prediction results to make subsequent predictions more accurate.

[0051] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0053] Figure 1 This is a flow chart of the process of constructing an electric carbon model for the ceramic industry according to the first embodiment of the present invention;

[0054] Figure 2 This is a flow chart of a method for predicting carbon emissions using an electric carbon model in the ceramic industry according to the second embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the structure of a system for predicting carbon emissions in the ceramic industry according to Embodiment 3 of the present invention;

[0056] Figure 4 This is a schematic diagram of the structure of the data acquisition subsystem of Embodiment 3 of the present invention;

[0057] Figure 5 This is a schematic diagram of the structure of the data analysis subsystem of the third embodiment of the present invention. DETAILED DESCRIPTION

[0058] The embodiment of the present application provides a method and system for predicting carbon emissions in the ceramic industry, and realizes carbon emissions prediction by constructing a multi-source heterogeneous electric carbon model to improve prediction accuracy.

[0059] The overall idea of ​​the technical solution in the embodiments of the present application is as follows: based on the carbon emission characteristics of the ceramic industry, the emission boundaries and emission sources are determined, and a multi-source heterogeneous electric-carbon model for the ceramic industry is constructed through Yan Jin's method. The main carbon emissions of the ceramic industry are predicted through the multi-source heterogeneous electric-carbon model, and the prediction results are corrected or adjusted to make subsequent predictions more accurate.

[0060] Embodiment 1

[0061] like Figure 1 As shown, this embodiment provides an electric carbon model for the ceramic industry, which is constructed by the following process:

[0062] S11. Define the emission boundaries of the electric carbon model for the ceramic industry. The emission boundaries include determining the region and time span covered by the model. The region is the geographical boundary where the ceramic enterprises are located. The time span is determined based on data availability and forecasting needs, which can be annual, quarterly or monthly.

[0063] S12. Determine the emission sources, which include the natural gas and electricity data in the production process of the ceramic industry. Natural gas and electricity have the characteristics of good real-time performance and stable data transmission, and can be used as model independent variable parameters. It includes direct emission sources and indirect emission sources. Direct emission sources include emissions generated by the combustion of fossil fuels such as raw coal and natural gas, and emissions from the production process of carbonate decomposition; indirect emission sources include emissions indirectly generated by electricity;

[0064] S13. Measurement and monitoring: real-time measurement and monitoring of emission source activity data, and regular maintenance and calibration of monitoring equipment; the measurement is to use accurate and reliable measuring instruments that can trace the value of the emission source to measure and monitor the activity data of the emission source in real time;

[0065] S14, data quality evaluation, including data quality evaluation of the completeness, accuracy, verifiability, validity and timeliness of real-time monitoring data. If the evaluation is passed, proceed to the next step. If the evaluation is not passed, correct the data before proceeding to the next step.

[0066] S15. Construct the following multi-source heterogeneous electric carbon model:

[0067] f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b;

[0068] Where: X1 is the independent variable parameter representing natural gas emission data;

[0069] X2 is the independent variable parameter representing the electricity emission data;

[0070] f(X1,X2) is the total emissions of natural gas and electricity;

[0071] EF 电 is the emission factor for the latest electricity in the region;

[0072] AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used.

[0073] EF 天然气 is the emission factor of natural gas, which is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate as follows; if not measured, the default value provided in the carbon emission accounting guide for the ceramic industry (e.g., "Guidelines for the accounting method and reporting of greenhouse gas emissions from Chinese ceramic production enterprises (Trial)") shall be used; and

[0074] EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12;

[0075] CC 天然气 ——Carbon content per unit calorific value of natural gas in ceramic enterprises, unit: tc / GJ;

[0076] α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises, unit: %wt;

[0077] a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the equation is decoupled to obtain the specific values.

[0078] Compared with other energy data, natural gas and electricity data have the characteristics of accurate measurement, real-time acquisition, and unified management. Therefore, the present invention adds both to the model, which greatly improves the accuracy of the ceramic industry electric carbon model in predicting the carbon emissions of the ceramic industry. And after data analysis of multiple companies, this prediction model that adds two energy data of natural gas and electricity has better prediction effect than the model that only adds any one energy data.

[0079] Embodiment 2

[0080] like Figure 2 As shown, based on the same inventive concept, this embodiment provides a method for predicting carbon emissions of an electric carbon model in the ceramic industry, including:

[0081] S21. Obtain the natural gas emission source metering data and electricity emission source metering data of the latest cycle of the ceramic industry, use the natural gas emission source metering data as the independent variable parameter X1 of the natural gas emission data, and use the electricity emission source metering data as the independent variable parameter X2 of the electricity emission data to input into the following multi-source heterogeneous electric carbon model to calculate and output the total emissions f(X1,X2):

[0082] f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b;

[0083] Where:

[0084] EF 电 is the emission factor for the latest electricity in the region;

[0085] AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used.

[0086] EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and

[0087] EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12;

[0088] CC 天然气 ——Carbon content per unit calorific value of natural gas in ceramic enterprises, unit: tc / GJ;

[0089] α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises, unit: %wt;

[0090] a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the decoupling equation is used to obtain specific values;

[0091] S22. Compare the deviation between the calculated output total emissions f(X1, X2) and the calculated total emissions. If the deviation is not greater than the preset value, it indicates that the model has good prediction ability; if the deviation is greater than the preset value, add historical data to remodel. The total emissions are calculated based on the data of coal, electricity, gas and production process emissions, and are verified by a third party, so the data is considered to be accurate.

[0092] The process of adding historical data for remodeling is: adding the data predicted this time to the original historical data, and inputting them into the multi-source heterogeneous electric carbon model for remodeling.

[0093] For example: the original modeling output parameters are based on 5 years of data, and the emissions in the 6th year are predicted. If the prediction deviation is greater than the preset value, the data in the 6th year will be added to the model as historical data, and the modeling will be re-performed using the 6-year data.

[0094] S23. Regularly backtest the multi-source heterogeneous electric carbon model and evaluate the prediction performance of the multi-source heterogeneous electric carbon model. If the prediction performance is lower than the expected value, adjust and optimize the multi-source heterogeneous electric carbon model. The adjustment and optimization includes re-modeling, re-prediction, and re-evaluation.

[0095] Embodiment 3

[0096] Based on the same inventive concept, Figure 3 As shown, this embodiment provides a system for predicting carbon emissions using an electric carbon model in the ceramic industry, including a data acquisition subsystem and a data analysis subsystem; it also includes a data output subsystem and a power supply module, the data output subsystem includes a function for displaying or printing the total emissions f(X1, X2); the power supply module is used to power the data acquisition subsystem, the data analysis subsystem and the data output subsystem.

[0097] The data acquisition subsystem is connected to the natural gas emission source metering data table and the electricity emission source metering data table of the ceramic industry, obtains the latest cycle of natural gas emission source metering data and electricity emission source metering data and sends them to the data analysis subsystem.

[0098] After preprocessing the received natural gas emission source metering data and electricity emission source metering data, the data analysis subsystem calls the multi-source heterogeneous electric carbon model of the ceramic industry for calculation to obtain the output total emissions f(X1, X2), and compares the deviation between the total emissions f(X1, X2) and the calculated total emissions. If the deviation value is not greater than the preset value, the total emissions f(X1, X2) are output; if the deviation is greater than the preset value, historical data is added for remodeling.

[0099] Specifically, Figure 4 As shown, the data acquisition subsystem includes a data acquisition instrument and a data transmission device connected in sequence. The data acquisition instrument is used to collect natural gas emission source metering data and electricity emission source metering data in the ceramic industry, and transmits them to the data analysis subsystem through the data transmission device.

[0100] like Figure 5As shown, the data analysis subsystem includes a data receiving module, a data storage module and a data processing module. The data receiving module receives the natural gas emission source metering data and the electricity emission source metering data transmitted by the data transmission device, and the data storage module stores them. The data processing module pre-processes the natural gas emission source metering data and the electricity emission source metering data, and then calls the multi-source heterogeneous electric carbon model of the ceramic industry for calculation to obtain the output total emissions f(X1, X2). The total emissions f(X1, X2) are compared with the calculated total emissions for deviation. If the deviation value is not greater than the preset value, the total emissions f(X1, X2) are output. If the deviation is greater than the preset value, historical data is added for re-modeling.

[0101] The multi-source heterogeneous electric carbon model is as follows:

[0102] f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b;

[0103] Where:

[0104] EF 电 is the emission factor for the latest electricity in the region;

[0105] AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used.

[0106] EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and

[0107] EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12;

[0108] CC 天然气 ——Carbon content per unit calorific value of natural gas in ceramic enterprises, unit: tc / GJ;

[0109] α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises, unit: %wt;

[0110] a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the equation is decoupled to obtain the specific values.

[0111] The adding of historical data to remodel is specifically: adding the data predicted this time to the original historical data, and inputting them into the multi-source heterogeneous electric carbon model for remodeling.

[0112] The data analysis subsystem also regularly back-tests the multi-source heterogeneous electric carbon model and evaluates the predictive performance of the multi-source heterogeneous electric carbon model. If the predictive performance is lower than the expected value, the multi-source heterogeneous electric carbon model is adjusted and optimized, and the adjustment and optimization includes re-modeling, re-prediction, and re-evaluation.

[0113] The technical solution provided in the embodiments of the present application has at least the following technical effects or advantages: the present invention constructs a multi-source heterogeneous electric-carbon model for the ceramic industry through Yan Jin's method, predicts the main carbon emissions of the ceramic industry through the multi-source heterogeneous electric-carbon model, and corrects or adjusts the prediction results to make subsequent predictions more accurate.

[0114] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. An electric carbon model for the ceramic industry, characterized by: Constructed by the following process: S11. Define the emission boundaries of the electric carbon model for the ceramic industry. The emission boundaries include determining the region and time span covered by the model. The region is the geographical boundary where the ceramic enterprises are located; the time span is determined based on data availability and forecasting needs; S12. Determine emission sources, wherein the emission sources include natural gas and electricity data in the production process of the ceramic industry; S13. Measurement and monitoring: real-time measurement and monitoring of emission source activity data, and regular maintenance and calibration of monitoring equipment; S14, data quality evaluation, including data quality evaluation of the completeness, accuracy, verifiability, validity and timeliness of real-time monitoring data. If the evaluation is passed, proceed to the next step. If the evaluation is not passed, correct the data before proceeding to the next step. S15. Construct the following multi-source heterogeneous electric carbon model: f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b; Where: X1 is the independent variable parameter representing natural gas emission data; X2 is the independent variable parameter representing the electricity emission data; f(X1,X2) is the total emissions of natural gas and electricity; EF 电 is the emission factor for the latest electricity in the region; AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used. EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12; CC 天然气 ——Carbon content per unit calorific value of natural gas used by ceramic enterprises; α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises; a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the equation is decoupled to obtain the specific values.

2. The electric carbon model for ceramic industry according to claim 1, characterized in that: The measurement in S13 is to use accurate, reliable and traceable measuring instruments to measure and monitor the activity data of emission sources in real time.

3. A method for predicting carbon emissions using an electric carbon model in the ceramic industry, characterized in that: include: S21. Obtain the natural gas emission source metering data and electricity emission source metering data of the latest cycle of the ceramic industry, use the natural gas emission source metering data as the independent variable parameter X1 of the natural gas emission data, and use the electricity emission source metering data as the independent variable parameter X2 of the electricity emission data to input into the following multi-source heterogeneous electric carbon model to calculate and output the total emissions f(X1,X2): f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b; Where: EF 电 is the emission factor for the latest electricity in the region; AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used. EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12; CC 天然气 ——Carbon content per unit calorific value of natural gas used by ceramic enterprises; α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises; a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the decoupling equation is used to obtain specific values; S22. Compare the deviation between the calculated output total emissions f(X1, X2) and the accounted total emissions. If the deviation is not greater than the preset value, it indicates that the model has good predictive ability. If the deviation is greater than the preset value, historical data is added for remodeling.

4. The method for predicting carbon emissions using an electric carbon model for the ceramic industry according to claim 3 is characterized by: The process of adding historical data for remodeling in S22 is: adding the data predicted this time to the original historical data, and inputting them into the multi-source heterogeneous electric carbon model for remodeling.

5. The method for predicting carbon emissions using an electric carbon model for the ceramic industry according to claim 3 is characterized by: It also includes S23, regularly backtesting the multi-source heterogeneous electric carbon model and evaluating the prediction performance of the multi-source heterogeneous electric carbon model. If the prediction performance is lower than the expected value, adjusting and optimizing the multi-source heterogeneous electric carbon model, and the adjustment and optimization includes re-modeling, re-prediction, and re-evaluation.

6. A system for predicting carbon emissions using an electric carbon model in the ceramic industry, characterized by: Including data acquisition subsystem and data analysis subsystem; The data acquisition subsystem is connected to the natural gas emission source metering data table and the electric power emission source metering data table of the ceramic industry, obtains the latest cycle of natural gas emission source metering data and electric power emission source metering data, and then sends them to the data analysis subsystem; After preprocessing the received natural gas emission source metering data and electricity emission source metering data, the data analysis subsystem calls the multi-source heterogeneous electric carbon model of the ceramic industry for calculation to obtain the output total emissions f(X1, X2), and compares the deviation between the total emissions f(X1, X2) and the calculated total emissions. If the deviation value is not greater than the preset value, the total emissions f(X1, X2) are output; if the deviation is greater than the preset value, historical data is added for remodeling; The multi-source heterogeneous electric carbon model is as follows: f(X1,X2)=a(EF 电 ×X1+EF 天然气 ×AD 天然气 ×X2)+b; Where: EF 电 is the emission factor for the latest electricity in the region; AD 天然气 The low calorific value of natural gas is determined by the measured value of natural gas. If it is not measured, the default value provided by the ceramic industry carbon emission accounting guide is used. EF 天然气 The emission factor of natural gas is calculated using the measured values ​​of carbon content per unit calorific value and carbon oxidation rate according to the following formula. If it is not measured, the default value provided in the carbon emission accounting guide for the ceramic industry is used; and EF 天然气 =CC 天然气 ×α 天然气 ×44 / 12; CC 天然气 ——Carbon content per unit calorific value of natural gas used by ceramic enterprises; α 天然气 ——Carbon oxidation rate of natural gas in ceramic enterprises; a and b are model parameters. The monitored data are substituted into the multi-source heterogeneous electric carbon model and then the equation is decoupled to obtain the specific values.

7. The system for predicting carbon emissions using an electric carbon model for the ceramic industry according to claim 6 is characterized by: The adding of historical data to remodel is specifically: adding the data predicted this time to the original historical data, and inputting them into the multi-source heterogeneous electric carbon model for remodeling.

8. The system for predicting carbon emissions using an electric carbon model for the ceramic industry according to claim 6 is characterized by: The data analysis subsystem also regularly back-tests the multi-source heterogeneous electric carbon model and evaluates the predictive performance of the multi-source heterogeneous electric carbon model. If the predictive performance is lower than the expected value, the multi-source heterogeneous electric carbon model is adjusted and optimized, and the adjustment and optimization includes re-modeling, re-prediction, and re-evaluation.

9. The system for predicting carbon emissions using an electric carbon model for the ceramic industry according to claim 6, characterized in that: It also includes a data output subsystem and a power module. The data output subsystem is used to display or print the total emission f(X1, X2); the power module is used to supply power to the data acquisition subsystem, the data analysis subsystem and the data output subsystem.

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