Improvement potential quantification method for carbon emission in building material mining life cycle process

Abstract

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a method for quantifying the improvement potential of carbon emissions during the life cycle of building material mining. Background Art

[0002] Current carbon emission management in building materials mining processes primarily focuses on static accounting within a single process step, lacking dynamic analysis encompassing the entire lifecycle of mining, transportation, processing, and waste disposal. Traditional methods typically rely on historical empirical data or simplified models, making it difficult to reflect real-time fluctuations in equipment energy consumption, changes in material flow, and the impact of external environmental factors, resulting in inaccurate identification of carbon emission hotspots. Furthermore, existing optimization strategies often employ single-goal linear programming, which is unable to effectively coordinate emission reduction targets with multi-dimensional constraints such as production costs and process safety, hindering the feasibility of implementing emission reduction measures.

[0003] With the development of digital twin and Internet of Things technologies, some research has attempted to construct mine carbon emission models. However, their dynamic parameter update mechanisms are still underdeveloped, and they lack a quantitative link between sensitivity factors and optimization paths. Existing technologies suffer from lags in updating carbon emission factor libraries and insufficient adaptability of multi-objective optimization algorithms, making them unable to support closed-loop iterative optimization. Therefore, a full-lifecycle carbon emission management approach integrating dynamic modeling, sensitivity analysis, and intelligent optimization is urgently needed to accurately quantify emission reduction potential and guide decision-making and implementation of low-carbon technology paths. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining, which can effectively solve the above problems.

[0005] A method for quantifying the improvement potential of carbon emissions during the life cycle of building material mining, comprising the following steps: S1. Based on the process stage division of mining, a full life cycle carbon footprint model covering mining, transportation, processing, and waste disposal is established. Digital twin technology is integrated to build a dynamic model. Sensors are used to collect energy consumption, material flow, and carbon emission data at each stage in real time. This data is then combined with the BIM model to generate a multi-dimensional carbon footprint topology network. S2. Based on the full life cycle carbon footprint model described in step S1, calculate the carbon emissions of each process stage, use Monte Carlo simulation and partial least squares regression method to perform data sensitivity analysis, and identify key carbon emission influencing factors; S3. Based on the sensitivity analysis results of step S2, a multi-objective optimization model is established to generate an optimized combination of process parameters and a technical path improvement plan. The improvement potential of different plans on carbon emissions is calculated through an iterative optimization algorithm. S4: Input the optimized combination of process parameters after optimization in S3 into the digital model to verify the carbon emission improvement effect, and update the model parameters based on real-time data to form a closed-loop iterative optimization mechanism.

[0006] In a preferred solution, in step S1, the construction of the full life cycle carbon footprint model specifically includes: 2.1. Using BIM modeling software, establish geometric models and process logic models for each stage of mining, and define the coding rules for model component attributes, including energy type, equipment power, transportation distance, and material quality; 2.2. Dynamically associate the model attributes with the carbon emission factor library, which includes equipment energy consumption factor, fuel carbon intensity, and material embodied carbon emission factor. The association formula is: ; in, is the carbon emissions in stage i, is the energy consumption of category j, is the carbon emission factor corresponding to the energy source, is the usage of the kth type of material, is the material's implicit carbon factor; 2.3. Use IoT sensors to collect real-time data on mining equipment operation, transport vehicle GPS trajectories, and processing line energy consumption data, dynamically update the model, and input parameters.

[0007] In a preferred solution, in step S2, the data sensitivity analysis is specifically as follows: 3.1. Use Latin hypercube sampling to generate multiple sets of process parameter combinations, which are input into the digital model to simulate carbon emissions; 3.2. Establish the sensitivity equation of carbon emissions and parameters through partial least squares regression method: ; in, is the change in carbon emissions, is the sensitivity coefficient of the pth parameter, is the parameter change, is the error term; 3.3. Screen the influencing factors whose absolute values ​​of sensitivity coefficients are greater than the threshold as the key optimization targets.

[0008] In a preferred solution, in step S3, the establishment of the multi-objective optimization model includes: 4.1. Taking carbon emission minimization and production cost minimization as the dual objective function, the constraints are defined as equipment capacity, process safety threshold, and resource availability; 4.2. Use the NSGA-II algorithm to solve the Pareto optimal solution set and generate the optimized parameter combination; 4.3. The grey correlation analysis method is used to quantify the potential value of each optimization scheme to improve carbon emissions. The calculation formula is: ; in, is the improvement potential value of the k-th solution, is the weight of stage i, and are the carbon emissions in stage i under the baseline and optimization schemes, respectively.

[0009] In a preferred solution, the specific implementation of step S4 includes the following steps: 5.1. Input the optimized process parameters into the digital twin model, simulate the operation and predict carbon emissions; 5.2. Calculate the model confidence by analyzing the deviation between the actual collected data and the predicted value. If the confidence is lower than the set threshold, the model parameter calibration is triggered; 5.3. Use the Kalman filter algorithm to fuse real-time data with model output and update the carbon emission factor library and process logic parameters.

[0010] In a preferred solution, in step S1, a multi-dimensional carbon footprint topological network is generated, including visualizing the spatiotemporal distribution of carbon emissions. The specific operation steps are as follows: S5.1. Map carbon emission data at each stage to the mine's geographic spatial model based on the GIS platform to generate heat maps and spatiotemporal evolution animations; S5.2. Identify high-emission hotspots and prioritize them by comparing carbon emission distribution differences under different technology pathways.

[0011] In a preferred solution, in step 2.3, the dynamic update model is specifically as follows: 7.1. Access external carbon market data and policy databases to obtain the latest carbon emission factors and carbon tax prices in real time; 7.2. Dynamically adjust the calculation of electricity-related carbon emissions based on the emission factor of the power grid in the area where the mine is located. The formula is: ; in, These are the real-time proportions of coal-fired power, natural gas and renewable energy in the power grid.

[0012] In a preferred solution, in step S3, the iterative optimization algorithm adopts a reinforcement learning framework: 8.1. Define the state space as the combination of process parameters, the action as the parameter adjustment direction, and the reward function as the weighted sum of carbon emission reduction and cost change; 8.2. Train the agent using the Q-learning algorithm to explore the optimal strategy in a simulated environment; 8.3. Deploy the trained strategy to the actual mining system to achieve adaptive optimization.

[0013] In a preferred solution, in step S1, establishing a full life cycle carbon footprint model also includes calculating carbon sink offset potential, and the specific steps are as follows: 9.1. Assess the carbon sequestration capacity of vegetation during the mine restoration phase and calculate carbon sequestration using the InVEST model; 9.2. Incorporate carbon sequestration into the calculation of net carbon emissions over the entire life cycle using the following formula: ; in, is the net carbon emissions, is the total emissions, The carbon sink offset.

[0014] In a preferred solution, in step S3, generating the process parameter optimization combination and technical path improvement plan includes generating a carbon emission optimization report: 10.1. Automatically output technical and economic analysis of each optimization scheme, including investment cost, emission reduction and investment payback period; 10.2. Carbon emission data, optimization process and results are stored on the chain based on blockchain technology to ensure data immutability and traceability.

[0015] A method for quantifying the potential for improving carbon emissions during the life cycle of building material mining, with beneficial effects including but not limited to the following: 1. This invention integrates digital twin technology with BIM models to construct a multi-dimensional carbon footprint topology network covering the mining, transportation, processing, and remediation stages, enabling refined modeling of carbon emissions throughout the mine's life cycle. Compared to traditional static models, this solution uses IoT sensors to collect real-time equipment energy consumption and material flow data, and combines this with a dynamically updated carbon emission factor library (such as power grid emission factors and fuel carbon intensity). This significantly improves the spatiotemporal accuracy of carbon emission calculations, addressing the data lag and localized measurement shortcomings of existing technologies. 2. This method uses Monte Carlo simulation and partial least squares regression (PLSR) to perform data sensitivity analysis, overcoming the limitations of traditional single-parameter sensitivity analysis. Latin hypercube sampling is used to generate multiple sets of process parameter combinations. The regression equation is then used to quantify the weight of each parameter's impact on carbon emissions. This allows for precise identification of key optimization targets (such as transport distance and crusher energy consumption), providing a scientific basis for targeted improvements. This method improves efficiency by over 60% compared to manual screening based on experience in comparative documents. 3. The innovative introduction of the NSGA-II algorithm and reinforcement learning framework generates a Pareto-optimal solution set with the dual objectives of minimizing carbon emissions and controlling production costs. Grey correlation analysis is used to quantify the improvement potential of different technical paths (such as new energy equipment replacement and process timing adjustments). The combined use of a GIS platform visualizes the spatiotemporal emission reduction effects, helping decision makers quickly locate high-emission hotspots. Compared to the single-target optimization approach in the comparative documents, this solution achieves a 15%-30% increase in carbon emission reduction potential while maintaining economic benefits. 4. Based on the Kalman filter algorithm, the fusion calibration of real-time data and model output is achieved, solving the problem of prediction bias caused by environmental changes in traditional models. By defining a confidence threshold to trigger the automatic calibration mechanism, the robustness of the model is ensured in complex scenarios such as fluctuations in mining geological conditions and policy adjustments. In addition, the reinforcement learning agent autonomously explores the optimal strategy in the simulated environment, realizes adaptive adjustment of process parameters, and reduces the cost of manual intervention. 5. Blockchain technology is used to store carbon emissions data, optimization plans, and implementation results on-chain. Hashing algorithms and timestamps are used to ensure data cannot be tampered with. Compared to traditional centralized databases, this solution enhances data audit transparency, meets international carbon verification standards, and provides trusted credentials for companies participating in the carbon trading market. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is the overall flow chart of the present invention; Figure 2 Construct a flow chart for the full life cycle carbon footprint model of the present invention; Figure 3 This is a sensitivity analysis flow chart of the present invention; Figure 4 This is a flowchart of the specific implementation steps of S4 of the present invention; Figure 5 This is a flow chart of the spatiotemporal distribution visualization steps of the present invention. DETAILED DESCRIPTION

[0017] Example 1: Carbon emission optimization of a medium-sized limestone mine; Parameter settings: Mine scale: Annual mining capacity of 2 million tons, including 1 mining area, 2 crushing lines, and 5 transport trucks; Time interval: T=1 year; Optimization goal: Reduce carbon emissions throughout the life cycle by 15%, and increase production costs by no more than 5%; Key equipment parameters: Crusher power 250kW, transport vehicle fuel consumption 30L / 100km, grid emission factor EF grid =0.8 kgCO2 / kWh (initial value); Implementation steps: The first step is parameter setting: 1.1. Build a mine BIM model based on Revit and define component attribute codes for the mining, crushing, and transportation stages (e.g., crusher ID-PJ001, transportation route RT01-RT05); 1.2. Deploy IoT sensors: Install smart meters (sampling frequency 1Hz) on the crusher motors, and add GPS and OBD fuel consumption monitoring terminals to transport vehicles; 1.3. Associated dynamic carbon emission factor database, where the electricity carbon emission factor is updated hourly, the formula is: ; (Where: EF coal =1.2 kgCO2 / kWh, EF gas =0.5 kgCO2 / kWh, EF solar =0.05 kgCO2 / kWh); Step 2: Parameter setting: 2.1. Latin hypercube sampling was used to generate 500 parameter combinations (variables included crusher load rate, transport distance, and fuel efficiency), which were input into the model to calculate carbon emissions. 2.2. The sensitivity equation is obtained by partial least squares regression (PLSR): ; The transport distance (sensitivity coefficient 0.38) and crusher load rate (0.25) were selected as key factors; The third step is multi-objective optimization and solution generation: 3.1. Establish dual objective function: ; (λ=0.7 is the cost weight factor, constraints: crusher load rate ≤ 85%, single-trip transport distance ≤ 50 km); 3.2. Apply the NSGA-II algorithm to solve the Pareto frontier and obtain three sets of optimization solutions: Option A: Adjusting transportation routes (average distance shortened by 12%), reducing carbon emissions by 10.2% and increasing costs by 1.5%; Option B: Installing a frequency converter on the crusher (increasing the load factor to 80%) reduces carbon emissions by 8.7% and increases costs by 3.1%. Option C: Replace 20% of diesel trucks with electric trucks, reducing carbon emissions by 14.5% and increasing costs by 4.8%; Step 4: Dynamic feedback and model calibration: 4.1. After implementing Scenario C, real-time monitoring of electric truck energy consumption data (average power consumption 1.2 kWh / km) revealed that actual carbon emissions were 2.3% lower than predicted; 4.2. Kalman filtering was used to update model parameters and modify the grid emission factor weight (the proportion of solar energy increased to 15%), increasing the model confidence from 89% to 94%; Step 5: Carbon offset and report generation: 5.1. Use the InVEST model to assess the carbon sequestration capacity of vegetation in the reclaimed area and calculate the annual carbon sequestration amount ; 5.2. Calculation of Net Carbon Emissions: ; 5.3. Generate blockchain report: The optimization plan, monitoring data and carbon sequestration results are uploaded to the blockchain through Hyperledger Fabric, generating an unalterable hash value (such as 0x3a7b...e9c2).

[0018] Example 2: Collaborative optimization of multiple mining areas in a large granite mine; Parameter settings: Mine scale: 3 mining areas, annual mining capacity of 5 million tons, equipped with 10 excavators, 8 crushing lines, and 15 hydrogen fuel cell trucks; Optimization goals: Reduce carbon emissions throughout the entire life cycle by 25% and increase cross-mining resource scheduling efficiency by 20%; Advanced parameters: Dynamic carbon price = 50 yuan / tCO2, Inter-mine Transportation Network Complexity Index = 0.75; Implementation steps: Implementation steps: The first step is to build digital twins of multiple mining areas: 1.1. Build a 3D geological model based on the Bentley platform, integrate BIM data of each mining area (accuracy ±0.1m), and define the topological relationship between shared transportation routes and transfer stations; 1.2. Deploy edge computing nodes: Embed an AI chip (computing power 4 TOPS) in each crushing line controller to calculate local carbon emissions in real time (the formula is the same as in Example 1); 1.3. Establish a shared library of carbon emission factors across mining areas and update the energy efficiency parameters of crushers in each mining area through federated learning; The second step is spatiotemporal sensitivity analysis and reinforcement learning optimization: 2.1. Using spatiotemporal Monte Carlo simulations to analyze the sensitivity of carbon emissions during different periods (peak and valley), we found that carbon emissions during the peak transport period during the morning shift accounted for 40% of total emissions. 2.2. Define the reinforcement learning state space: S = {mine area production demand, equipment status, energy price}, action space A = {number of dispatched vehicles, crusher start and stop sequence}; 2.3. Training Q-learning agent: The reward function is: ; (I 安全 is a safety constraint indicator function, with a penalty of -100 when violated); 2.4. After 10,000 iterations, the agent generated the optimal scheduling strategy: 40% of the morning shift traffic was shifted to the afternoon shift, reducing carbon emissions by 18.7%; The third step is blockchain-enhanced trusted management: 3.1. Build a private chain network. Each mining area acts as a node to upload daily carbon emission data (in JSON-LD format). The smart contract automatically verifies the logical consistency of the data: 3.2. Implement cross-mining carbon trading: Mining Area A reduces emissions by 500tCO2 in excess of its quota and sells it to Mining Area B at 45 yuan / tCO2 through a smart contract. The transaction record hash is stored in a block (e.g., 0x8d3f...a1b4); Step 4: Visualization and decision support: 4.1. Rendering a carbon emission heat map on the GIS platform shows that the crushing section of mining area C is a hotspot (carbon emission intensity exceeds the average by 2.3 times); 4.2. Optimization suggestion: Replace the two old crushers in Mine C with an estimated investment of RMB 1.2 million, reduce annual CO2 emissions by 1,800 t, and have a payback period of 2.3 years.

[0019] Implementation effect: 1. Example 1 achieved a net emission reduction of 14.5%, with a confidence level of 94% after model calibration, and a data on-chain storage response time of less than 2 seconds; 2. Example 2 reduces cross-mining area transportation carbon emissions by 22% through spatiotemporal scheduling and smart contracts, and increases carbon trading revenue by 180,000 yuan per year; The above examples confirm that the present invention can achieve accurate quantification, intelligent optimization and reliable management of carbon emissions in complex mining scenarios, providing a feasible technical path for carbon neutrality in the building materials industry.

[0020] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description. It is intended that all variations within the meaning and range of equivalents of the claims be embraced therein; and any reference signs in the claims should not be construed as limiting the claim to which they relate.

Claims

1. A method for quantifying the improvement potential of carbon emissions during the life cycle of building material mining, characterized by: The method comprises the following steps: S1. Based on the process stage division of mining, a full life cycle carbon footprint model covering mining, transportation, processing, and waste disposal is established. Digital twin technology is integrated to build a dynamic model. Sensors are used to collect energy consumption, material flow, and carbon emission data at each stage in real time. This data is then combined with the BIM model to generate a multi-dimensional carbon footprint topology network. S2. Based on the full life cycle carbon footprint model described in step S1, calculate the carbon emissions of each process stage, use Monte Carlo simulation and partial least squares regression method to perform data sensitivity analysis, and identify key carbon emission influencing factors; S3. Based on the sensitivity analysis results of step S2, a multi-objective optimization model is established to generate an optimized combination of process parameters and a technical path improvement plan. The improvement potential of different plans on carbon emissions is calculated through an iterative optimization algorithm. S4: Input the optimized combination of process parameters after optimization in S3 into the digital model to verify the carbon emission improvement effect, and update the model parameters based on real-time data to form a closed-loop iterative optimization mechanism.

2. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S1, the construction of the full life cycle carbon footprint model specifically includes: 2.

1. Using BIM modeling software, establish geometric models and process logic models for each stage of mining, and define the coding rules for model component attributes, including energy type, equipment power, transportation distance, and material quality; 2.

2. Dynamically associate the model attributes with the carbon emission factor library, which includes equipment energy consumption factor, fuel carbon intensity, and material embodied carbon emission factor. The association formula is: ； in, is the carbon emissions in stage i, is the energy consumption of category j, is the carbon emission factor corresponding to the energy source, is the usage of the kth type of material, is the material's implicit carbon factor; 2.

3. Use IoT sensors to collect real-time data on mining equipment operation, transport vehicle GPS trajectories, and processing line energy consumption data, dynamically update the model, and input parameters.

3. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S2, the data sensitivity analysis is specifically as follows: 3.

1. Use Latin hypercube sampling to generate multiple sets of process parameter combinations, which are input into the digital model to simulate carbon emissions; 3.

2. Establish the sensitivity equation of carbon emissions and parameters through partial least squares regression method: ； in, is the change in carbon emissions, is the sensitivity coefficient of the pth parameter, is the parameter change, is the error term; 3.

3. Screen the influencing factors whose absolute values ​​of sensitivity coefficients are greater than the threshold as the key optimization targets.

4. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S3, the establishment of the multi-objective optimization model includes: 4.

1. Taking carbon emission minimization and production cost minimization as the dual objective function, the constraints are defined as equipment capacity, process safety threshold, and resource availability; 4.

2. Use the NSGA-II algorithm to solve the Pareto optimal solution set and generate the optimized parameter combination; 4.

3. The grey correlation analysis method is used to quantify the potential value of each optimization scheme to improve carbon emissions. The calculation formula is: ； in, is the improvement potential value of the k-th solution, is the weight of stage i, and are the carbon emissions in stage i under the baseline and optimization schemes, respectively.

5. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S4, its specific implementation includes the following steps: 5.

1. Input the optimized process parameters into the digital twin model, simulate the operation and predict carbon emissions; 5.

2. Calculate the model confidence by analyzing the deviation between the actual collected data and the predicted value. If the confidence is lower than the set threshold, the model parameter calibration is triggered; 5.

3. Use the Kalman filter algorithm to fuse real-time data with model output and update the carbon emission factor library and process logic parameters.

6. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S1, a multi-dimensional carbon footprint topological network is generated, including visualizing the spatiotemporal distribution of carbon emissions. The specific steps are as follows: S5.

1. Map carbon emission data at each stage to the mine's geographic spatial model based on the GIS platform to generate heat maps and spatiotemporal evolution animations; S5.

2. Identify high-emission hotspots and prioritize them by comparing carbon emission distribution differences under different technology pathways.

7. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 2 is characterized in that: In step 2.3, the dynamic update model is specifically as follows: 7.

1. Access external carbon market data and policy databases to obtain the latest carbon emission factors and carbon tax prices in real time; 7.

2. Dynamically adjust the calculation of electricity-related carbon emissions based on the emission factor of the power grid in the area where the mine is located. The formula is: ； in, These are the real-time proportions of coal-fired power, natural gas and renewable energy in the power grid.

8. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S3, the iterative optimization algorithm adopts a reinforcement learning framework: 8.

1. Define the state space as the combination of process parameters, the action as the parameter adjustment direction, and the reward function as the weighted sum of carbon emission reduction and cost change; 8.

2. Train the agent using the Q-learning algorithm to explore the optimal strategy in a simulated environment; 8.

3. Deploy the trained strategy to the actual mining system to achieve adaptive optimization.

9. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S1, the full life cycle carbon footprint model is established, which also includes calculating the carbon sink offset potential. The specific steps are as follows: 9.

1. Assess the carbon sequestration capacity of vegetation during the mine restoration phase and calculate carbon sequestration using the InVEST model; 9.

2. Incorporate carbon sequestration into the calculation of net carbon emissions over the entire life cycle using the following formula: ； in, is the net carbon emissions, is the total emissions, The carbon sink offset.

10. The method for quantifying the improvement potential of carbon emissions during the life cycle of building materials mining according to claim 1 is characterized in that: In step S3, generating the process parameter optimization combination and technical path improvement plan includes generating a carbon emission optimization report: 10.

1. Automatically output technical and economic analysis of each optimization scheme, including investment cost, emission reduction and investment payback period; 10.

2. Carbon emission data, optimization process and results are stored on the chain based on blockchain technology to ensure data immutability and traceability.

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