Industrial park carbon asset dynamic optimization method
By constructing a virtual mapping system and dynamic optimization algorithms, the real-time and flexibility issues of carbon emission management in industrial parks have been solved, resulting in improved emission reduction effects and reduced costs, while ensuring normal production operations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- POWERCHINA HUADONG ENG CORP LTD
- Filing Date
- 2025-11-21
- Publication Date
- 2026-04-10
AI Technical Summary
Existing carbon emission management methods for industrial parks lack real-time and flexibility, failing to reflect the real-time carbon emission situation of industrial parks in a timely manner, resulting in delayed emission reduction measures and potentially affecting production efficiency.
A virtual mapping system is constructed using digital twin technology to simulate carbon emission scenarios in real time, dynamically simulate various emission reduction schemes, and select the optimal emission reduction scheme through a multi-objective optimization algorithm to achieve dynamic optimization of carbon assets.
It has improved emission reduction effectiveness, reduced emission reduction costs, ensured production efficiency, adapted to real-time changes in equipment and energy structure within the park, and achieved better real-time and flexible carbon emission management.
Smart Images
Figure CN121835973A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of carbon emission management technology, specifically to a method for dynamic optimization of carbon assets in industrial parks. Background Technology
[0002] Existing carbon emission management methods for industrial parks are primarily based on historical data and statistical models. Some industrial parks manage carbon emissions by installing sensors to collect energy consumption data from equipment and then calculating total carbon emissions. For example, some industrial parks use simple linear regression models to predict carbon emissions based on the linear relationship between energy consumption and carbon emissions. Other industrial parks focus on developing fixed emission reduction plans, such as setting power limits for certain equipment or requiring specific energy types. These plans lack consideration for the real-time status of the industrial park and often fail to adapt to changes in actual production processes, failing to reflect real-time carbon emissions and resulting in a lag in the implementation of emission reduction measures. Furthermore, fixed emission reduction plans cannot be adjusted according to real-time changes in equipment operating status and energy structure within the park, potentially leading to poor emission reduction results and even impacting production efficiency. Summary of the Invention
[0003] This application provides a method for dynamic optimization of carbon assets in industrial parks. This method has better real-time performance and flexibility in carbon emission management and can better reflect the real-time carbon emission situation of industrial parks.
[0004] The dynamic optimization method for carbon assets in industrial parks provided in this application includes the following steps: S1. Data Acquisition and Preprocessing: Collect data from various carbon emission sources within the industrial park and preprocess the collected data to eliminate noise and outliers. S2. Construct a virtual mapping system: Based on the preprocessed data, construct a virtual mapping system for the industrial park using digital twin technology; S3. Real-time simulation of carbon emission scenarios: In the virtual mapping system, carbon emission scenarios are simulated in real time based on the operating status of the equipment and energy consumption data. S4. Dynamic simulation of emission reduction schemes: Multiple emission reduction schemes are preset, and dynamic simulations of each scheme are performed in the virtual mapping system to evaluate the emission reduction effect. S5. Dynamic Optimization of Carbon Assets: Based on the simulation results of emission reduction schemes, the carbon assets of the industrial park are dynamically optimized. By comparing the emission reduction and cost of different emission reduction schemes, the optimal emission reduction scheme is selected to maximize carbon assets.
[0005] In one alternative approach, in step S1, the carbon emission sources for data collection include production equipment, transport vehicles, and energy consumption facilities within the industrial park; the types of data collected include equipment operating parameters, energy consumption data, and material input and output data during the production process.
[0006] In one alternative approach, in step S1, the mean filtering algorithm is used to smooth the continuous data during preprocessing of the collected data, as shown in the following formula: ; In the formula, The original data sequence, For the processed data, For the size of the filter window, This represents the total length of the data.
[0007] In one alternative approach, step S2, when constructing the virtual mapping system, includes the following steps:
[0008] Establish a real-time mapping relationship between physical entities and virtual models, enabling the virtual models to accurately reflect the real-time status of the physical park. For production equipment, establish a virtual model based on the physical model, with the following state equations: ; In the formula, This is the device state vector. For the input control vector, For device parameter vectors, This is the state transition function.
[0009] In one alternative approach, in step S3, when simulating carbon emission scenarios in real time, different carbon emission calculation models need to be used for different carbon emission sources; for carbon emissions caused by electricity consumption, the formula is as follows: ; In the formula, For total carbon emissions from electricity, Let be the power of the i-th device. For equipment uptime, It is a carbon emission factor for electricity.
[0010] In one optional scheme, in step S4, the preset multiple emission reduction schemes include at least one of equipment upgrading and transformation, energy structure adjustment, and production process optimization; when evaluating the emission reduction effect of energy structure adjustment, it is assumed that new energy sources are introduced to replace part of the traditional energy, and the emission reduction formula is as follows: ; In the formula, This represents the total carbon emissions under the original energy structure. This refers to the total carbon emissions under the adjusted energy structure.
[0011] In one alternative approach, in step S5, a multi-objective optimization algorithm is used during dynamic optimization, as shown in the following formula: ; In the formula, To reduce emissions, To reduce emissions costs, and These are weighting coefficients, which are adjusted according to the actual needs of the industrial park. After outputting the optimization results, return to step S3 for dynamic optimization and comparison, and finally obtain the optimal carbon asset solution for the industrial park.
[0012] The beneficial effects of this application are as follows: The dynamic optimization method for carbon assets in industrial parks presented in this application, through real-time simulation and dynamic optimization, enables timely adjustments to emission reduction strategies. This provides better real-time performance and flexibility in carbon emission management, better reflecting the real-time carbon emission situation of the industrial park. Therefore, emission reduction plans can be adjusted according to the park's real-time status, improving emission reduction effectiveness and carbon asset value while reducing emission reduction costs. By rationally selecting emission reduction plans, unnecessary equipment upgrades and energy replacements can be avoided, further reducing emission reduction costs. Furthermore, it can adapt to real-time changes in production equipment and energy structure within the park, ensuring that emission reduction measures do not affect production efficiency and guaranteeing the normal production and operation of the park.
[0013] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description
[0014] Figure 1 A flowchart illustrating the dynamic optimization method for carbon assets in industrial parks.
[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. Detailed Implementation
[0016] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0017] It should be understood that the described embodiments are merely some embodiments of this application, and not all embodiments. All other technical solutions obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0018] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0019] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0020] It should be noted that the directional terms such as "upper," "lower," "left," and "right" described in the embodiments of this application are used to describe the angles shown in the accompanying drawings and should not be construed as limiting the embodiments of this application. Furthermore, in the context, it should be understood that when it is mentioned that an element is connected "upper" or "lower" to another element, it can be directly connected to the other element "upper" or "lower," or indirectly connected to the other element "upper" or "lower" through an intermediate element.
[0021] like Figure 1 As shown in the figure, this application provides a method for dynamic optimization of carbon assets in industrial parks. This method introduces a virtual mapping system to link the physical park with a virtual model in real time, thereby realizing real-time simulation of carbon emission scenarios and dynamic optimization of emission reduction plans. Specifically, it includes the following steps: S1. Data Acquisition and Preprocessing: Data collection is conducted on various carbon emission sources within the industrial park, including but not limited to production equipment, transport vehicles, and energy-consuming facilities. The types of data collected include equipment operating parameters (such as power and speed), energy consumption data (such as electricity, water, and natural gas usage), and material input and output data during the production process.
[0022] The collected data is preprocessed to remove noise and outliers. A mean filtering algorithm is used to smooth the continuous data, as shown in the following formula: ; In the formula, The original data sequence, For the processed data, For the size of the filter window, This represents the total length of the data.
[0023] S2. Construct a virtual mapping system: Based on the preprocessed data, a virtual mapping system for the industrial park is constructed using digital twin technology. By establishing a real-time mapping relationship between physical entities and the virtual model, the virtual model can accurately reflect the real-time status of the physical park. For production equipment, a virtual model based on the physical model is established, and its state equation is as follows: ; In the formula, This is the device state vector. For the input control vector, For device parameter vectors, This is the state transition function.
[0024] S3, Real-time simulation of carbon emission scenarios: In the virtual mapping system, carbon emission scenarios are simulated in real time based on the operating status and energy consumption data of the equipment. Different carbon emission calculation models are required for different carbon emission sources. The formula for carbon emissions caused by electricity consumption is as follows: ; In the formula, For total carbon emissions from electricity, Let be the power of the i-th device. For equipment uptime, It is a carbon emission factor for electricity.
[0025] S4. Dynamic simulation of emission reduction schemes: Multiple emission reduction schemes are preset, such as equipment upgrades, energy structure adjustments, and production process optimizations. These schemes are then dynamically simulated in a virtual mapping system to evaluate their effectiveness. Taking the energy structure adjustment scheme as an example, assuming the introduction of new energy sources to replace some traditional energy sources, the emission reduction formula is as follows: ; In the formula, This represents the total carbon emissions under the original energy structure. This refers to the total carbon emissions under the adjusted energy structure.
[0026] S5. Dynamic optimization of carbon assets: Based on simulation results of emission reduction schemes, the carbon assets of the industrial park are dynamically optimized. By comparing the emission reduction amounts and costs of different schemes, the optimal scheme is selected to maximize carbon assets. A multi-objective optimization algorithm is used for dynamic optimization, as shown in the following formula: ; In the formula, To reduce emissions, To reduce emissions costs, and These are weighting coefficients, which are adjusted according to the actual needs of the industrial park. After outputting the optimization results, return to step S3 for dynamic optimization and comparison, and finally obtain the optimal carbon asset solution for the industrial park.
[0027] The dynamic optimization method for carbon assets in industrial parks presented in this embodiment, through real-time simulation and dynamic optimization, enables timely adjustments to emission reduction strategies. This provides better real-time performance and flexibility in carbon emission management, better reflecting the real-time carbon emission situation of the industrial park. Therefore, emission reduction plans can be adjusted according to the park's real-time status, improving emission reduction effectiveness and carbon asset value while reducing emission reduction costs. By rationally selecting emission reduction plans, unnecessary equipment upgrades and energy replacements can be avoided, further reducing emission reduction costs. Furthermore, it can adapt to real-time changes in production equipment and energy structure within the park, ensuring that emission reduction measures do not affect production efficiency and guaranteeing the normal production and operation of the park.
[0028] Specifically, the advantages of the industrial park's dynamic carbon asset optimization method, after being verified through use, are reflected in the following aspects:
[0029] Improving emission reduction effectiveness: Through real-time simulation and dynamic optimization, emission reduction strategies can be adjusted in a timely manner, resulting in a significant increase in emission reductions. For example, in a pilot application at a large chemical industrial park, the total carbon emissions were reduced by 22.3% compared to traditional methods after adopting this model. By using digital twin technology to simulate the operating status of production equipment in real time, high-carbon emission links (such as insufficient boiler combustion efficiency and redundant transportation routes) can be accurately identified, and production scheduling can be dynamically optimized. For example, by adjusting equipment start-up and shutdown strategies, the daily carbon emissions of a key piece of equipment in the park decreased from 12.5 tons to 9.8 tons, a reduction of 21.6%.
[0030] Reducing Emissions Reduction Costs: By rationally selecting emission reduction schemes, unnecessary equipment upgrades and energy substitutions are avoided, thus reducing emission reduction costs. For example, in a case study of energy structure adjustment in a steel industrial park, this model dynamically simulated and compared five new energy alternatives (such as photovoltaic power generation and hydrogen-powered hybrid power supply), ultimately selecting the hybrid power supply strategy with the optimal cost. Compared with traditional fixed schemes, under the same emission reduction target, equipment modification costs are reduced by 18%, annual operation and maintenance costs are reduced by 14.7%, and statistics show that the overall carbon asset management cost of the park has decreased from approximately 3.2 million yuan / year to approximately 2.6 million yuan / year.
[0031] Enhanced Production Flexibility: The model of this invention can adapt to real-time changes in production equipment and energy structure within the industrial park, ensuring that production efficiency is not affected by emission reduction measures and guaranteeing the normal production and operation of the park. For example, in a certain automobile manufacturing park, the model monitors the operating status of more than 2,000 pieces of equipment in real time through a virtual mapping system, dynamically adjusting emission reduction plans. During peak order periods, the system automatically balances production load and carbon emission limits, ensuring a 15% increase in production capacity while ensuring that total carbon emissions do not exceed the threshold. Compared with traditional production restriction strategies, this solution avoids a loss of approximately 6 million yuan per month in output value.
[0032] Overall benefit comparison:
[0033] Simulation tests showed that total carbon emissions were reduced by 22%, carbon asset management costs were reduced by 19%, and production interruption losses were reduced by 100%.
[0034] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for dynamic optimization of carbon assets in industrial parks, characterized in that, Includes the following steps: S1. Data Acquisition and Preprocessing: Collect data from various carbon emission sources within the industrial park and preprocess the collected data to eliminate noise and outliers. S2. Construct a virtual mapping system: Based on the preprocessed data, construct a virtual mapping system for the industrial park using digital twin technology; S3. Real-time simulation of carbon emission scenarios: In the virtual mapping system, carbon emission scenarios are simulated in real time based on the operating status of the equipment and energy consumption data. S4. Dynamic simulation of emission reduction schemes: Multiple emission reduction schemes are preset, and dynamic simulations of each scheme are performed in the virtual mapping system to evaluate the emission reduction effect. S5. Dynamic Optimization of Carbon Assets: Based on the simulation results of emission reduction schemes, the carbon assets of the industrial park are dynamically optimized. By comparing the emission reduction and cost of different emission reduction schemes, the optimal emission reduction scheme is selected to maximize carbon assets.
2. The method for dynamic optimization of carbon assets in industrial parks according to claim 1, characterized in that, In step S1, the carbon emission sources for data collection include production equipment, transport vehicles, and energy consumption facilities within the industrial park; the types of data collected include equipment operating parameters, energy consumption data, and material input and output data during the production process.
3. The method for dynamic optimization of carbon assets in industrial parks according to claim 2, characterized in that, In step S1, the mean filtering algorithm is used to smooth the continuous data during the preprocessing of the collected data. The formula is as follows: ； In the formula, The original data sequence, For the processed data, For the size of the filter window, This represents the total length of the data.
4. The method for dynamic optimization of carbon assets in industrial parks according to claim 2 or 3, characterized in that, Step S2, in constructing the virtual mapping system, includes the following steps: Establish a real-time mapping relationship between physical entities and virtual models, enabling the virtual models to accurately reflect the real-time status of the physical park. For production equipment, establish a virtual model based on the physical model, with the following state equations: ； In the formula, This is the device state vector. For the input control vector, For device parameter vectors, This is the state transition function.
5. The method for dynamic optimization of carbon assets in industrial parks according to any one of claims 1-3, characterized in that, In step S3, when simulating carbon emission scenarios in real time, different carbon emission calculation models need to be used for different carbon emission sources; for carbon emissions caused by electricity consumption, the formula is as follows: ； In the formula, For total carbon emissions from electricity, Let be the power of the i-th device. For equipment uptime, It is a carbon emission factor for electricity.
6. The method for dynamic optimization of carbon assets in industrial parks according to claim 5, characterized in that, In step S4, the preset multiple emission reduction schemes include at least one of equipment upgrading and transformation, energy structure adjustment, and production process optimization; when evaluating the emission reduction effect of energy structure adjustment, it is assumed that new energy sources are introduced to replace part of the traditional energy, and the emission reduction formula is as follows: ; In the formula, This represents the total carbon emissions under the original energy structure. This refers to the total carbon emissions under the adjusted energy structure.
7. The method for dynamic optimization of carbon assets in industrial parks according to any one of claims 1-3 or 6, characterized in that, In step S5, a multi-objective optimization algorithm is used for dynamic optimization, as shown in the following formula: ； In the formula, To reduce emissions, To reduce emissions costs, and These are weighting coefficients, which are adjusted according to the actual needs of the industrial park. After outputting the optimization results, return to step S3 for dynamic optimization and comparison, and finally obtain the optimal carbon asset solution for the industrial park.