Metering and tracing method for carbon emission of building material mine

Abstract

The invention discloses a building material mine carbon emission metering traceability method, which realizes full-chain accurate metering and credible traceability of carbon emission through multi-process division, multi-source sensing data acquisition, dynamic model correction and block chain traceability technologies. The method comprises the following steps: refining a building material mine production process into five main procedures of mining, raw material processing, calcining / sintering, finished product preparation and transportation, and deploying a multi-source sensor group to collect raw material quality, particle size distribution, energy consumption and waste gas emission data in real time; constructing a multi-layer carbon emission calculation model, and dynamically correcting emission factors in combination with a material balance method, life cycle evaluation (LCA) and a machine learning algorithm; determining a carbon emission distribution coefficient based on the product output proportion and the process contribution degree, and generating a block chain carbon footprint label; coupling a mine restoration carbon sink evaluation and deduction mechanism, and outputting a dynamic net carbon accounting report; and an abnormal emission early warning mechanism is integrated. According to the method, the accuracy of carbon emission metering is remarkably improved, and technical support is provided for building material mine carbon neutralization.

Description

Technical Field

[0001] The present invention relates to the field of mining production, and specifically to a method for measuring and tracing carbon emissions in building material mines. Background Art

[0002] As an important part of the high-carbon emission industry, the production process of building material mines covers multiple links such as mining, raw material processing, calcination / sintering, finished product preparation and transportation. The carbon emission sources are scattered and the data is complex. Traditional carbon emission measurement methods usually focus on a single high-energy-consuming process (such as calcination), while ignoring the carbon emission contributions of other sub-processes such as mining blasting, raw material pre-homogenization, and finished product packaging, resulting in unclear accounting boundaries and fragmented data. In addition, existing methods mostly rely on fixed emission factors to estimate indirect emissions (such as purchased electricity, upstream production of fuels), and it is difficult to dynamically reflect the actual working condition fluctuations (such as changes in ore moisture content, equipment energy efficiency decay), and cumulative errors are likely to occur in long-term use. At the same time, carbon emission allocation methods mostly adopt mass or economic value ratio allocation, lacking dynamic association with process contribution degrees, being highly subjective, and difficult to meet the transparency and accuracy requirements of international carbon footprint certification.

[0003] On the other hand, existing technologies rarely integrate a credible traceability mechanism, and carbon data is easily tampered with or lost, making it difficult to achieve full-process carbon emission tracking and verification. In addition, the carbon sink benefits generated by the ecological restoration of building material mines have not been effectively incorporated into the carbon emission accounting system, resulting in incomplete net carbon emission assessment. These problems seriously restrict the accuracy and credibility of carbon emission measurement in building material mines and are difficult to support the realization of the industry's carbon neutrality goal. Therefore, there is an urgent need for a carbon emission measurement method that covers all processes, integrates dynamic models and credible traceability technologies, and at the same time couples carbon sink assessment and deduction mechanisms to provide accurate and transparent carbon emission data support for building material mines. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a method for measuring and tracing carbon emissions in building material mines, which can effectively solve the above problems.

[0005] A method for measuring and tracing carbon emissions in building material mines, the method comprising the following steps: S1. Divide the production process of the building material mine into five main processes: mining, raw material processing, calcination / sintering, finished product preparation and transportation, and further refine each main process into several sub-processes; S2. Deploy a multi-source sensor group at the key nodes of each sub-process to collect raw material quality parameters, particle size distribution, energy consumption data and waste gas emission concentration in real time. The multi-source sensor group includes an image sensor, a pressure sensor, an infrared thermal imager and a gas component analyzer; S3. Construct a multi-layer carbon emission calculation model based on process characteristics, and each layer of the carbon emission calculation model includes the following content: S3.1. Calculate the direct carbon emissions of each sub-process using the material balance method for the first layer; S3.2. Calculate the embodied carbon emissions from the upstream production of purchased electricity and fuels by associating indirect carbon emission factors through the Life Cycle Assessment (LCA) database for the second layer; S3.3. Integrate machine learning algorithms to dynamically correct the emission factors based on the mapping relationship between historical process parameters and carbon emissions for the third layer; S4. Determine the carbon emission allocation coefficients for each sub-process based on the product output ratio and process contribution degree. The specific formula is: ; where, is the quality of the j th building material product, is the process contribution weight of the i th sub-process to the j th product; S5. Establish a blockchain traceability platform, encrypt and upload the carbon emission data, sensor raw parameters, and allocation coefficients of each sub-process to the blockchain, generate an immutable blockchain carbon footprint label, and bind it to the unique batch code of the building material product; S6. Periodically evaluate the impact of mine restoration measures on net carbon emissions, offset the total emissions through the vegetation carbon sink and the CO2 sequestration volume in abandoned mine pits, and output a dynamic net carbon accounting report.

[0006] In the preferred solution, in the step S1: 2.1. Refine the mining process into blasting, loading, and ore transportation sub-processes; 2.2. Refine the raw material processing process into crushing, screening, washing, and pre-homogenization sub-processes; 2.3. For cement production, refine the calcination / sintering process into raw meal preparation, rotary kiln calcination, and clinker cooling sub-processes; 2.4. The finished product preparation process includes grinding, mixing, forming, and packaging sub-processes.

[0007] In the preferred solution, in the step S2, each component of the multi-source sensor group has the following functions: 3.1. The image sensor is used to capture the particle size distribution image after ore crushing and extract the particle size characteristic parameters through a Convolutional Neural Network (CNN); 3.2. The pressure sensor is installed on the conveyor belt support shaft to calculate the real-time material mass flow rate in combination with the belt speed; 3.3. The infrared thermal imager monitors the surface temperature field of the rotary kiln and infers the fuel combustion efficiency and CO2 generation intensity.

[0008] In the preferred solution, in the step S3: 4.1. In the machine learning algorithm of the third layer, a random forest regression model is adopted. The input variables include the moisture content of raw materials, the calcination temperature, and the grinding fineness, and the output variable is the carbon emission correction factor per unit product of the process. 4.2. The model is iteratively trained every quarter with the newly collected data in step S2, and the error threshold is set at ±5%.

[0009] In the preferred solution, the process contribution weight in step S4 is calculated as follows: 5.1. For cement products, the weight of the calcination process is 0.6, the raw material processing is 0.2, and the mining and transportation each account for 0.1. 5.2. For aggregate products, the weight of the crushing and screening process is 0.8, and the mining and transportation each account for 0.1. 5.3. The above weight values are comprehensively determined through the Delphi method in combination with the proportion of process energy consumption.

[0010] In the preferred solution, in step S5: 6.1. The blockchain platform adopts a consortium chain architecture, and the nodes include mining enterprises, regulatory departments, and third-party certification agencies. 6.2. The blockchain carbon footprint label contains a QR code, and after scanning, the carbon emission heat map and carbon reduction measure description of the whole process of this batch of products can be traced.

[0011] In the preferred solution, the calculation method of the vegetation carbon sink amount in step S6 is as follows: 7.1. Ecological quadrats are arranged in the restoration area to measure the carbon storage of the biomass of arbors and shrubs and the increment of soil organic carbon. 7.2. The improved CASA model is used to estimate the net primary productivity (NPP) of vegetation, and the formula is: NPP = ε * APAR * f(T) * f(W); In the formula, ε is the light energy utilization rate, APAR is the absorbed photosynthetically active radiation; f(T) and f(W) are the temperature and humidity stress factors.

[0012] In the preferred solution, the calculation of the CO2 sequestration amount in the abandoned mine pit in step S6 includes: 8.1. Industrial CO2 is injected into the mine pit space, and the sequestration amount is monitored in real time through the pressure - volume - temperature (PVT) equation. 8.2. The sequestration efficiency coefficient is determined according to the rock formation permeability and the type of sequestration technology, and the value range is 0.7 - 0.9.

[0013] In the preferred solution, in step S2, according to the real-time raw material parameters collected by the multi-source sensor group, an abnormal emission warning mechanism needs to be established: 9.1 When the real-time carbon emissions of a certain sub-process exceed 20% of the historical average, a third-level warning is triggered and automatically pushed to the management terminal; The above real-time carbon emissions are calculated from the waste gas emission concentration data collected in real time by sensors such as gas composition analyzers deployed in step S2; The historical average is a reference value established based on the data collected by sensors in step S2 for a long time; 9.2 The system is associated with the equipment operation and maintenance database and recommends troubleshooting solutions, including dust collector blockage or abnormal fuel calorific value; The system-associated equipment operation and maintenance database analyzes the energy consumption data, raw material quality parameters, etc. collected by sensors in step S2, combined with historical data, and recommends troubleshooting solutions.

[0014] In the preferred solution, in S6, the finally output dynamic net carbon accounting report includes: 10.1 Carbon emission intensity per product (kgCO2 / t); 10.2 Ranking of emission reduction potential at the process level; 10.3 Carbon sink deduction ratio and prediction of carbon neutrality compliance progress.

[0015] A safety warning system method for an energy storage power station based on a battery substrate coupling model, and its beneficial effects include but are not limited to the following points: 1. In the present invention, the production process of a building materials mine is refined into five main processes: mining, raw material processing, calcination / sintering, finished product preparation and transportation, and further divided into several sub-processes, realizing comprehensive coverage of carbon emission source items; combining multi-source sensor groups to collect raw material quality parameters, particle size distribution, energy consumption data and waste gas emission concentration in real time, solving the problem of data fragmentation in traditional methods, and significantly improving the accuracy and integrity of carbon emission accounting; 2. By adopting a multi-layer carbon emission calculation model based on process characteristics, through the material balance method, life cycle assessment (LCA) database and machine learning algorithm, the emission factor is dynamically corrected, avoiding the cumulative error caused by the traditional fixed factor method due to working condition fluctuations; the introduction of the machine learning algorithm enables the emission factor to be dynamically adjusted according to the mapping relationship between historical process parameters and carbon emissions, further improving the reliability of the accounting results; 3. Determining the carbon emission distribution coefficient of each sub-process based on the product output ratio and process contribution degree, avoiding the problems of strong subjectivity and unscientific distribution in traditional methods. Through the dynamic calculation of the process contribution weight, it is ensured that the carbon emission distribution is closely related to key parameters such as process energy consumption and material conversion rate, meeting the transparency and accuracy requirements of international carbon footprint certification; 4. By establishing a blockchain traceability platform, encrypt the carbon emission data, sensor raw parameters and distribution coefficients of each sub-process and upload them to the blockchain to generate an immutable blockchain carbon footprint label, which is bound to the unique code of the building material product batch. This mechanism not only improves the credibility of the data, but also realizes the traceability of the entire carbon emission process, providing strong technical support for carbon footprint certification; 5. By periodically evaluating the impact of mine restoration measures on net carbon emissions, incorporate the vegetation carbon sink and the amount of CO2 sequestered in abandoned mine pits into the accounting system, offset the total emissions, and output a dynamic net carbon accounting report. This mechanism not only comprehensively reflects the net carbon benefits of building material mines, but also provides a scientific basis for the realization of the industry's carbon neutrality goal. Description of the Drawings

[0016] Figure 1 It is the architecture diagram of the carbon emission measurement and traceability system for building material mines of the present invention; Figure 2 It is the flow chart of the multi-layer carbon emission calculation model; Figure 3 It is the schematic diagram of the generation of blockchain carbon footprint labels. Detailed Embodiments

[0017] Example 1: Carbon Emission Measurement and Traceability of Cement Production The first step: Divide the cement production line into five main processes, namely mining (blasting, ore transportation), raw material processing (crushing, pre-homogenization), calcination (raw material preparation, rotary kiln calcination, clinker cooling), finished product preparation (grinding, packaging) and transportation, with a total of 12 sub-processes; The second step: Arrange sensors: Install a pressure sensor (range 0 - 50 tons, accuracy ±0.5%) and an infrared thermal imager (monitor the temperature of the ore after blasting, resolution 640×480) on the ore transportation belt; Install a gas composition analyzer (measure the CO2 and NOx concentrations, sampling frequency 1 time / second) on the tail gas pipeline of the rotary kiln, and deploy an infrared thermal imager on the surface of the kiln body (monitor the temperature distribution); Deploy an image sensor (resolution 1080p, and analyze the fineness of cement in real time through the CNN algorithm) at the outlet of the grinding mill; The third step: Dynamic calculation and correction of carbon emissions, where: Direct emission calculation: Taking the rotary kiln calcination sub-process as an example, collect the CO2 concentration in real time through the gas composition analyzer (for example, measured 12.5%), and combine it with the flue gas flow rate (1000 m³ / h) to calculate the direct emission per hour: E 直接 = 12.5%×1000×1.96 kg / m³ = 245 kgCO2 / h; Indirect Emission Correction: The initial value of the carbon emission factor for purchased electricity is 0.85 kgCO2 / kWh, which is dynamically corrected through a random forest model (input variables: grinding fineness, raw material moisture content); for example, when the grinding fineness decreases from 45 μm to 38 μm, the correction factor is increased by 5% and updated to 0.89 kgCO2 / kWh; Step 4: Encrypt and upload the carbon emission data of each sub-process (such as 245 kgCO2 / h in the calcination process) and the original sensor data (such as the infrared thermal imaging temperature field image) to the consortium blockchain (Hyperledger Fabric architecture) to generate a unique blockchain carbon footprint label (example: CF-20231001-001); Users can scan the QR code on the cement packaging bag to view the full-process carbon emission heat map and carbon reduction measures of this batch of products (such as "The oxy-fuel combustion technology is adopted in the calcination process, with an 8% emission reduction"); Step 5: Arrange 10 ecological quadrats (each 10m * 10m) in the mine restoration area. Measure the carbon storage of tree biomass as 15tC / ha, the increment of soil organic carbon as 2tC / ha, and the total annual carbon sink is: Carbon sink = (15 + 2) × 100ha × 3.67 = 6239tCO2-eq / year; The net carbon emission report: After deduction, the annual net carbon emission of the cement production line is reduced from the original 150,000tCO2 to 143,761tCO2, and the deduction ratio is 4.1%; Step 6: When the real-time carbon emission of the crushing sub-process exceeds 20% of the historical average (50 kgCO2 / t) (i.e., 60kgCO2 / t), the system triggers a level 3 warning, pushes a message to the management terminal, and recommends a troubleshooting plan: "The abnormal pressure difference of the crusher dust collector is detected (>200 Pa), and it is recommended to clean the filter bag".

[0018] Example 2: Carbon Emission Measurement and Traceability of Aggregate Production Step 1: For the aggregate production line, it is divided into three main processes: mining (blasting, transportation), raw material processing (primary crushing, screening, washing), and finished product transportation, with a total of 8 sub-processes; Step 2: Arrange sensors: Install an image sensor at the outlet of the jaw crusher (analyze the aggregate particle size distribution, and set the qualified particle size to 5 - 40mm), and a pressure sensor monitors the hourly processing volume (for example, 120 t / h); Deploy a flow meter (accuracy ±1%) and a turbidity sensor (detect the suspended solid content of the washing water) in the water circulation system; Step 3: Optimize the dynamic model and distribution coefficient, where: Calculation of process contribution weight: According to the Delphi method, the process contribution weight of the coarse crushing and screening process to the aggregate product is determined to be 0.8, and mining and transportation each account for 0.1; Carbon emission allocation: If the output of a batch of aggregates is 5000 tons and the total carbon emission of the coarse crushing process is 400 tCO2, the carbon emission allocated to the aggregates is: E 分配 = 400 * 0.8 = 320 tCO2; Step 4: Upload the energy consumption data of the washing process (such as power consumption of 2000 kWh and water consumption of 50 m³) to the blockchain in real time to generate a blockchain carbon footprint label (CF-20231002-002); When the carbon emission of the screening process suddenly increases by 30% (from 10 kgCO2 / t to 13 kgCO2 / t), the system pushes an alarm and prompts: "The screen is damaged, resulting in an increase in the return rate. It is recommended to replace the screen (model XS-305)"; Step 5: Use abandoned mine pits to sequester CO2, and the injection volume is calculated by the PVT equation: ; The sequestration efficiency coefficient is taken as 0.85, and the actual deduction amount is 224.6 * 0.85 = 190.9 tCO2.

[0019] The comparative analysis of Example 1 and Example 2 is as follows:

[0020] As can be seen from the above examples, the present invention can be flexibly adapted to the production processes of different building materials products, realizing accurate measurement, dynamic correction, and reliable traceability of carbon emissions, and providing a universal solution for carbon neutrality in building materials mines.

[0021] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention; any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A method for measuring and tracing carbon emissions from building materials mines, characterized in that: The method comprises the following steps: S1. Divide the production process of building materials mines into five main processes: mining, raw material processing, calcination / sintering, finished product preparation and transportation, and further refine each main process into several sub-processes; S2. Deploy a multi-source sensor group at the key nodes of each sub-process to collect raw material quality parameters, particle size distribution, energy consumption data and exhaust gas emission concentration in real time. The multi-source sensor group includes an image sensor, a pressure sensor, an infrared thermal imager and a gas composition analyzer; S3. Construct a multi-layer carbon emission calculation model based on process characteristics, where each layer of the carbon emission calculation model includes the following contents: S3.1, the first level uses the material balance method to calculate the direct carbon emissions of each sub-process; S3.2, the second layer calculates the implicit carbon emissions from the upstream production of purchased electricity and fuel by associating indirect carbon emission factors with the life cycle assessment (LCA) database; S3.3, the third layer integrates machine learning algorithms to dynamically correct emission factors based on the mapping relationship between historical process parameters and carbon emissions; S4. Based on the product output ratio and process contribution, determine the carbon emission allocation coefficient of each sub-process. The specific formula is: ； in, For the j The quality of building materials products, For the i Sub-process for j The process contribution weight of each product; S5. Establish a blockchain traceability platform to encrypt the carbon emission data, sensor original parameters and distribution coefficients of each sub-process on the chain, generate an unalterable blockchain carbon footprint label, and bind it to the unique batch code of the building materials product; S6. Periodically evaluate the impact of mine restoration measures on net carbon emissions, offset total emissions through vegetation carbon sinks and CO2 stored in abandoned mines, and output a dynamic net carbon accounting report.

2. The carbon emission measurement and traceability method for building materials mines according to claim 1 is characterized in that: In step S1: 2.

1. Divide the mining process into blasting, loading and ore transportation sub-processes; 2.

2. The raw material processing procedures are divided into crushing, screening, washing and pre-homogenization sub-procedures; 2.

3. The calcination / sintering process is divided into raw material preparation, rotary kiln calcination and clinker cooling sub-processes for cement production; 2.

4. The finished product preparation process includes grinding, mixing, molding and packaging sub-processes.

3. The carbon emission measurement and traceability method for building materials mines according to claim 1 is characterized in that: In step S2, each component of the multi-source sensor group includes the following functions: 3.

1. The image sensor is used to capture the particle size distribution image after ore crushing, and the particle size characteristic parameters are extracted through the convolutional neural network (CNN); 3.

2. The pressure sensor is placed on the conveyor belt support shaft to calculate the real-time material mass flow rate in combination with the belt speed; 3.

3. Use infrared thermal imager to monitor the surface temperature field of rotary kiln and infer the fuel combustion efficiency and CO2 generation intensity.

4. The carbon emission measurement and traceability method for building materials mines according to claim 1 is characterized in that: In step S3: 4.

1. The machine learning algorithm in the third layer adopts the random forest regression model. The input variables include the moisture content of raw materials, calcination temperature, and grinding fineness. The output variable is the carbon emission correction factor of the unit product of the process. 4.

2. The model is iteratively trained every quarter using the newly collected data in step S2, and the error threshold is set to ±5%.

5. The carbon emission measurement and traceability method for building materials mines according to claim 1 is characterized in that: The process contribution weight in step S4 The calculation method is: 5.

1. For cement products, the weight of calcination process is 0.6, raw material processing is 0.2, mining and transportation each account for 0.1; 5.

2. For aggregate products, the weight of crushing and screening process is 0.8, and mining and transportation each account for 0.1; 5.

3. The above weight values ​​are determined by combining the Delphi method with the energy consumption ratio of the process.

6. The carbon emission measurement and traceability method for building materials mines according to claim 1 is characterized in that: In step S5: 6.

1. The blockchain platform adopts a consortium chain architecture, and the nodes include mining enterprises, regulatory authorities and third-party certification agencies; 6.

2. The blockchain carbon footprint label contains a QR code, which can be scanned to trace the carbon emission heat map and carbon reduction measures of the entire process of the batch of products.

7. The method for measuring and tracing carbon emissions from building materials mines according to claim 1 is characterized in that: The calculation method of vegetation carbon sink in step S6 is: 7.

1. Set up ecological plots in the restoration area to measure the carbon storage of tree and shrub biomass and the increment of soil organic carbon; 7.

2. The improved CASA model is used to estimate the vegetation net primary productivity (NPP). The formula is: NPP = ε*APAR*f(T) *f(W); Where ε is the light energy utilization rate, APAR is the absorbed photosynthetically active radiation; f(T) and f(W) are temperature and humidity stress factors.

8. The method for measuring and tracing carbon emissions from building materials mines according to claim 7 is characterized in that: The calculation of the amount of CO2 stored in the abandoned mine in step S6 includes: 8.

1. Use the space in the mine to inject industrial CO2 and monitor the storage volume in real time through the pressure-volume-temperature (PVT) equation; 8.

2. The storage efficiency coefficient is determined based on the rock permeability and the type of storage technology, and the value range is 0.7~0.

9.

9. The method for measuring and tracing carbon emissions from building materials mines according to claim 1 is characterized in that: In step S2, based on the real-time raw material parameters collected by the multi-source sensor group, an abnormal emission warning mechanism needs to be established: 9.

1. When the real-time carbon emissions of a sub-process exceed the historical average by 20%, a level 3 warning is triggered and automatically pushed to the management terminal; The above-mentioned real-time carbon emissions are calculated by using the exhaust gas emission concentration data collected in real time by sensors such as the gas composition analyzer deployed in step S2; The historical mean is a benchmark value established based on the data collected by the sensor over a long period of time in step S2; 9.

2. The system-related equipment operation and maintenance database recommends troubleshooting solutions, including dust collector blockage or abnormal fuel calorific value; The system-related equipment operation and maintenance database is based on the energy consumption data, raw material quality parameters and other information collected by the sensors in step S2, combined with historical data for analysis, and recommends troubleshooting solutions.

10. The carbon emission measurement and traceability method for building materials mines according to claim 1 is characterized by: In S6, the final output dynamic net carbon accounting report contains: 10.

1. Carbon emission intensity by product (kgCO2 / t); 10.

2. Ranking of emission reduction potential at process level; 10.

3. Carbon sink offset ratio and carbon neutrality achievement progress forecast.

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