A building material production carbon emission dynamic prediction and optimization method, system, medium and electronic equipment

Abstract

The present disclosure provides a building material production carbon emission dynamic prediction and optimization method, system, device and storage medium, which comprises the following steps: obtaining basic parameters; constructing a material efficiency factor based on the basic parameters, establishing a correlation between material input and thermodynamic energy consumption, and obtaining coupled useful energy demand; constructing a dynamic evolution model using a segmented S-shaped curve, updating the carbon intensity of the power grid and the energy efficiency level of the equipment; calculating the carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model; and finally determining the emission reduction path and generating process control parameters. The present disclosure accurately captures the heat load reduction effect brought by the improvement of material efficiency, dynamically reflects the influence of technological progress and energy structure transformation on carbon emission, realizes the calculation of carbon emission intensity under multiple scenarios, solves the problems of material and energy calculation fragmentation, parameter static lag and macro and micro scale mismatch in traditional methods, and significantly improves the carbon emission prediction accuracy and the scientificity and practicality of process optimization decision.

Description

Technical Field

[0001] This disclosure relates to the technical field of carbon emissions, and in particular to a method, system, equipment and storage medium for dynamic prediction and optimization of carbon emissions from building material production. Background Technology

[0002] As the global carbon neutrality process accelerates, bio-based building materials, due to their renewable and carbon-sequestering properties, have become an important direction for green building materials. Their production process involves energy-intensive thermal processing steps such as drying and hot pressing, making accurate carbon emission accounting and process optimization crucial for the industry's low-carbon transformation. Currently, carbon emission prediction and optimization in bio-based building material production mainly employs Life Cycle Assessment (LCA) methods, scenario analysis, and technology diffusion models. However, these methods have significant shortcomings in practical applications and struggle to meet the needs of deep industrial decarbonization.

[0003] Traditional life cycle assessment methods generally treat material consumption and energy consumption as independent variables and calculate them separately, failing to establish the intrinsic relationship between material efficiency and thermodynamic load. This makes it impossible to quantify the synergistic multiplier effect of material and energy savings, leading to underestimated carbon emissions and emission reduction potential. Furthermore, existing methods often use static parameters and fixed emission factors, failing to dynamically reflect the time-varying characteristics of equipment energy efficiency improvements, technological upgrades, and grid carbon intensity decline. Long-term predictions often deviate significantly from reality, resulting in insufficient prediction accuracy.

[0004] Furthermore, existing scenario analyses only make simple assumptions about macro-level energy structure transformation and fail to integrate them with micro-level production processes, equipment upgrade cycles, and technology diffusion processes. There is a timescale mismatch between macro-level decarbonization pathways and micro-level process decisions, making it impossible to quantify and compare the emission reduction effects of different technology routes. This makes it difficult to support the need for refined decision-making regarding material efficiency improvement, equipment upgrade timing, and energy structure optimization.

[0005] Therefore, current carbon emission prediction and optimization technologies in building materials production suffer from technical problems such as material-energy decoupling, static parameter lag, and mismatch between macro and micro scales. These technologies cannot achieve dynamic and accurate prediction of carbon emissions and optimization of the entire process, making it difficult to meet the practical application needs of industrial low-carbon transformation. Summary of the Invention

[0006] This disclosure provides a method, system, equipment, and storage medium for dynamic prediction and optimization of carbon emissions in building materials production, aiming to at least solve the aforementioned technical problems existing in the prior art. This invention is not merely a mathematical prediction, but rather a computer-executable control logic formed by deeply coupling material input with thermodynamic energy consumption. This logic can provide precise process adjustment reference parameters for actual production management systems (such as MES / ERP systems) or underlying programmable logic controllers (PLCs), thereby guiding the production line to achieve energy conservation and emission reduction at the physical level, resulting in substantial industrial technological effects.

[0007] According to a first aspect of this disclosure, a method for dynamic prediction and optimization of carbon emissions from building materials production is provided, the method comprising: Obtain basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters; Based on the aforementioned basic parameters, a material efficiency factor is constructed, and the correlation between material input and thermodynamic energy consumption is established to obtain the coupled useful energy demand. A piecewise S-curve is used to construct a dynamic evolution model, and the grid carbon intensity and equipment energy efficiency level are updated synchronously based on the dynamic evolution model; Based on the coupled useful energy demand and the dynamic evolution model, the carbon emission intensity under multiple scenarios is calculated. Based on the carbon emission intensity comparison results under the multiple scenarios, the target emission reduction path is determined, and the corresponding process control parameters are generated. The process control parameters are then sent to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

[0008] In one possible implementation, the step of constructing a material efficiency factor based on the fundamental parameters, establishing the correlation between material input and thermodynamic energy consumption, and obtaining the coupled useful energy demand includes: The material efficiency factor is used as the multiplier coefficient for useful energy demand; According to the formula Calculate the useful energy requirement after coupling; in, The combined useful energy demand is given by α, where α is the material efficiency factor. Based on the energy demand.

[0009] In one possible implementation, the basic energy demand is based on the formula Sure; in As a benchmark for thermal energy demand, it characterizes the thermal energy that the equipment needs to provide; The baseline efficiency characterizes the proportion of input thermal energy that a device converts into usable energy.

[0010] In one embodiment, the dynamic evolution model includes a technology diffusion model and an equipment energy efficiency evolution model. The technology diffusion model is used to simulate the evolution of the penetration rate of new technologies over time, and the equipment energy efficiency evolution model is used to simulate the nonlinear evolution of the equipment energy efficiency ratio with technological progress. The functional form of the technology diffusion model is: in, For the year technology penetration rate This is the saturation value. The diffusion rate coefficient is... The year of the turning point; The functional form of the equipment energy efficiency evolution model is as follows: in, For the year The equipment energy efficiency ratio, As the benchmark energy efficiency value, The target energy efficiency value, k is the transition rate coefficient, and t0 is the inflection point year.

[0011] In one possible implementation, calculating carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model includes: The current technology share, equipment energy efficiency ratio, and grid carbon intensity are determined based on the dynamic evolution model. Based on the coupled useful energy demand and the energy efficiency ratio of the equipment, the operating energy consumption of each process is determined; Based on the aforementioned material efficiency factor, the raw material consumption is corrected to determine the physicochemical carbon emission intensity; The operating carbon emission intensity is determined based on the operating energy consumption of each process, the technology share, and the grid carbon intensity. Based on the physical carbon emission intensity and the operational carbon emission intensity, the total carbon emission intensity under multiple scenarios is obtained, according to the formula. Calculated; where Total carbon intensity, To measure carbon emission intensity, To determine the carbon emission intensity of operation.

[0012] In one possible implementation, the operational carbon emission intensity includes carbon emissions from the drying process, which are calculated based on a technology share weighting formula: ; Among them, S trad For traditional technology share, S trans For transitional technology share, Sadv The share of advanced technology, and the sum of the three is 1.

[0013] In one possible implementation, generating the corresponding process control parameters includes: By comparing the differences in total carbon emission intensity, physical carbon emission intensity, and operational carbon emission intensity under different scenarios, the emission reduction contribution of improved material efficiency, upgraded equipment energy efficiency, and optimized energy structure can be quantified. Identify the technological path that contributes the most to emission reduction as the target emission reduction path; Based on the target emission reduction path, process control parameters are extracted. These process control parameters include: a material efficiency threshold parameter for limiting the raw material feeding system, an energy efficiency ratio technology inflection point timestamp for triggering production line equipment replacement, and a grid carbon intensity matching instruction for scheduling the proportion of distributed renewable energy power supply.

[0014] According to a second aspect of this disclosure, a dynamic prediction and optimization system for carbon emissions from building materials production is provided, the system comprising: The data acquisition unit is used to acquire basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters. The material-energy coupling calculation unit is used to construct a material efficiency factor based on the aforementioned basic parameters, establish the correlation between material input and thermodynamic energy consumption, and obtain the coupled useful energy demand. The dynamic simulation unit is used to construct a dynamic evolution model using a piecewise S-curve, and to synchronously update the grid carbon intensity and equipment energy efficiency level based on the dynamic evolution model. The scenario calculation unit is used to calculate the carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model. The decision output unit is used to determine the target emission reduction path based on the carbon emission intensity comparison results under the multiple scenarios, and generate corresponding process control parameters; the process control parameters are sent to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described in this disclosure.

[0016] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the methods described in this disclosure.

[0017] This disclosure provides a method for dynamic prediction and optimization of carbon emissions in building materials production. By constructing a material efficiency factor, it establishes a coupled relationship between material input and thermodynamic energy consumption, creating a synergistic multiplier reduction effect between raw material reduction and heat load demand, thus overcoming the shortcomings of traditional methods where material and energy calculations are separated. Simultaneously, it employs a piecewise S-shaped curve function to construct a dynamic evolution model, synchronously updating equipment energy efficiency levels and grid carbon intensity. This overcomes the limitation that static parameters cannot reflect technological progress and energy structure transformation, achieving precise matching between macro-level energy decarbonization paths and micro-level production process evolution. Based on this, it combines coupled useful energy demand and the dynamic evolution model to perform multi-scenario carbon emission accounting, accurately quantifying the emission reduction contribution of each technological path and identifying the optimal emission reduction scheme. Ultimately, this significantly improves the accuracy and timeliness of carbon emission prediction, providing feasible, quantifiable, and dynamically iterative carbon emission reduction process optimization decision support for bio-based building materials production.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0020] Figure 1 A flowchart illustrating a method for dynamic prediction and optimization of carbon emissions from building material production, according to an embodiment of this disclosure, is shown. Figure 2 This diagram illustrates a dynamic prediction and optimization system for carbon emissions from building material production, according to an embodiment of the present disclosure. Figure 3 This illustration shows a schematic diagram of another embodiment of the building materials production carbon emission dynamic prediction and optimization system. Figure 4 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0021] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0022] In the current field of carbon emission prediction and optimization in building materials production, especially for bio-based building materials, traditional life cycle assessment methods generally suffer from a disconnect between material consumption and energy consumption calculations, failing to reflect the synergistic emission reduction effect of saving materials and energy. Furthermore, the use of static parameters makes it difficult to dynamically reflect the evolution trends of technological progress, equipment upgrades, and grid carbon intensity. Moreover, there is a time scale mismatch between macro-scenario analysis and micro-process evolution, hindering accurate carbon emission accounting and scientific emission reduction decisions. Therefore, this disclosure proposes a dynamic prediction and optimization method for carbon emissions in building materials production to achieve accurate dynamic prediction and refined optimization decisions for carbon emissions from bio-based building materials. Example 1

[0023] like Figure 1 This illustration shows a flowchart of a method for dynamic prediction and optimization of carbon emissions from building material production according to an embodiment of the present disclosure. The method includes: Step S1: Obtain basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters.

[0024] This embodiment takes bio-based building materials as an example. Bio-based building materials are green building materials made from renewable biological resources such as wood biomass and agricultural and forestry residues as the main raw materials, through processes such as crushing, drying, gluing, and hot pressing. They include engineered wood products such as oriented strand board (OSB), fiberboard, and plywood. They have the characteristics of being renewable, carbon-fixing, and environmentally friendly, and are widely used in building decoration, structural load-bearing and other fields.

[0025] First, we need to obtain the basic parameters for the production of bio-based building materials. These basic parameters include four categories: material parameters, equipment parameters, time parameters, and scenario parameters. Material parameters include the types of bio-based raw materials, wood moisture content, raw material utilization rate, type and unit dosage of adhesives, material consumption baseline, wood fixed carbon footprint, adhesive carbon footprint, transportation carbon footprint, and other parameters that characterize the efficiency of material input and utilization. Equipment parameters: These include rated energy consumption, operating efficiency, and energy efficiency benchmarks for drying equipment, hot pressing equipment, and auxiliary equipment. Time parameters include time-related parameters such as the prediction start year, prediction period, technology introduction time, technology maturity time, and equipment upgrade cycle. Scenario parameters include macroeconomic and industry development parameters such as the evolution path of grid carbon intensity under the baseline scenario, policy commitment scenario, net-zero emission scenario, technology diffusion rate, and policy constraints.

[0026] The aforementioned basic parameters are obtained through production management systems, energy management systems, on-site data collection, and publicly available industry databases to provide data support for subsequent calculations.

[0027] Step S2: Construct a material efficiency factor based on the basic parameters, establish the correlation between material input and thermodynamic energy consumption, and obtain the coupled useful energy demand.

[0028] The material efficiency factor is used to intrinsically map the changes in the physical properties of raw materials and the improvement in utilization efficiency during the production of bio-based building materials into the thermodynamic load changes in downstream thermal processing processes such as drying and hot pressing. This achieves a coupled correlation between material consumption and energy demand, quantifies the synergistic multiplier reduction effect of improved material efficiency on heat load, and overcomes the shortcomings of traditional methods that separate material and energy calculations. Based on the material efficiency factor, the basic useful energy demand is coupled and corrected to obtain a coupled useful energy demand suitable for dynamic carbon emission calculations.

[0029] Step S3: Construct a dynamic evolution model using a piecewise S-curve, and update the grid carbon intensity and equipment energy efficiency level synchronously based on the dynamic evolution model.

[0030] The dynamic evolution model can simultaneously simulate technology diffusion, equipment energy efficiency improvement, and energy structure transformation. Based on this dynamic evolution model, the grid carbon intensity and equipment energy efficiency levels are updated synchronously. On the one hand, it dynamically updates the background grid carbon intensity under different years and scenarios, reflecting the decarbonization process of the power system; on the other hand, it dynamically updates the energy efficiency level of production equipment, reflecting the energy efficiency improvement brought about by technological progress and equipment upgrades. This achieves collaborative simulation of macro-level energy transition paths and micro-level production process evolution, solving the problem that traditional static parameters cannot reflect the dynamic changes in technology and energy.

[0031] Step S4: Calculate the carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model.

[0032] Using the coupled useful energy demand as the basis for energy consumption calculation, and combining dynamic parameters such as real-time grid carbon intensity, equipment energy efficiency, and technology share output by the dynamic evolution model, carbon emission intensity under multiple scenarios is calculated. During the calculation, the total carbon emission intensity is decomposed into physical carbon emission intensity and operational carbon emission intensity, which are calculated separately and then aggregated to obtain the total carbon emission intensity, thus achieving accurate and dynamic prediction of carbon emission intensity under different policy scenarios and different technology paths.

[0033] Step S5: Determine the target emission reduction path based on the carbon emission intensity comparison results under the multiple scenarios, and generate the corresponding process control parameters; send the process control parameters to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

[0034] By comparing and analyzing the carbon emission intensity calculated under different scenarios, the target emission reduction path with the optimal emission reduction effect is determined, and corresponding process control parameters are generated based on this target emission reduction path. These process control parameters include, but are not limited to: target value of material efficiency factor, target heat load of drying process, set value of heating power of drying equipment, valve opening coefficient of raw material ratio, equipment start-up and shutdown sequence, and threshold of green electricity usage ratio.

[0035] The process control parameters are then sent to the building materials production control system, which includes, but is not limited to, a Manufacturing Execution System (MES), an Enterprise Resource Planning (ERP) system, and a Programmable Logic Controller (PLC) at the underlying level, to achieve closed-loop control at the physical production level. Based on the process control parameters, the control system adjusts the heating power of the drying equipment or the valve opening of the raw material proportioning valve to achieve energy-saving and emission-reduction control in the production process; or, based on the determined target emission reduction path, it constructs a digital twin configuration model of the production line equipment. In the digital twin system, it simulates the carbon emission effects of different equipment update sequences, technological alternatives, and energy structure adjustment strategies, providing data support for the optimized configuration and dynamic control of the production system.

[0036] In the above scheme, a material efficiency factor is constructed to establish a coupled relationship between material input and thermodynamic energy consumption, forming a synergistic multiplier reduction effect between raw material reduction and heat load demand, thus overcoming the shortcomings of traditional methods that separate material and energy calculations. At the same time, a piecewise S-shaped curve function is used to construct a dynamic evolution model, which synchronously and dynamically updates the energy efficiency level of equipment and the carbon intensity of the power grid, overcoming the limitation that static parameters cannot reflect technological progress and energy structure transformation, and achieving a precise match between macro-level energy decarbonization paths and micro-level production process evolution. On this basis, combined with the coupling of useful energy demand and the dynamic evolution model, multi-scenario carbon emission accounting can be performed, which can accurately quantify the emission reduction contribution of each technology path and identify the optimal emission reduction scheme. Ultimately, this significantly improves the accuracy and timeliness of carbon emission prediction, providing feasible, quantifiable, and dynamically iterative carbon emission reduction process optimization decision support for bio-based building materials production.

[0037] In one example, a material efficiency factor is constructed based on the aforementioned fundamental parameters to establish the correlation between material input and thermodynamic energy consumption, thereby obtaining the coupled useful energy demand, including: The material efficiency factor is used as the multiplier of the useful energy demand, and the coupled useful energy demand is calculated according to the following formula (1).

[0038] First, a material efficiency factor α is constructed based on the basic parameters obtained in step S1. The material efficiency factor α is used to quantify the synergistic relationship between "improved material utilization efficiency" and "reduced thermodynamic energy consumption" in the production of bio-based building materials. The higher the material utilization rate and the lower the moisture content of the wood, the smaller the value of α, which indicates that the material efficiency improvement has a more significant effect on reducing heat load. The specific value of α can be dynamically calibrated according to the material parameters of actual production to ensure that it matches the actual production conditions.

[0039] Secondly, the material efficiency factor α is used as a multiplier coefficient for the energy demand of the core thermal processing steps (mainly drying and hot pressing) in the production of bio-based building materials. This multiplier coefficient directly maps changes in material input, such as increased raw material utilization and reduced material consumption, to the calculation of thermodynamic energy consumption, breaking down the traditional barrier between material calculation and energy calculation and achieving a coupling relationship between materials and energy. Subsequently, the coupled energy demand is calculated according to formula (1), which accurately reflects the corrective effect of the material efficiency factor on the energy demand: Formula (1) in, The coupled useful energy demand represents the actual useful energy that the equipment needs to provide after considering the improvement of material efficiency; α is the material efficiency factor. Basic useful energy demand characterizes the basic useful energy required for normal operation of equipment without considering the impact of material efficiency.

[0040] Furthermore, basic energy demand Determined according to formula (2), Formula (2) in As a benchmark for heat energy demand, it represents the total heat energy required to achieve the production process requirements, such as drying wood to the target moisture content or hot pressing wood to the target density, for the corresponding heat processing steps (drying, hot pressing). Its value can be determined based on production process standards, material parameters (such as initial moisture content of wood, target moisture content) combined with industry experience formulas or field test data. Based on baseline efficiency, due to heat losses in the equipment (such as heat carried away by flue gas and heat dissipation from the equipment), only a portion of the thermal energy is effectively utilized for moisture evaporation. This characterizes the proportion of total input thermal energy converted into usable energy by the equipment in this heat treatment process. The specific value is determined based on data such as the equipment's rated efficiency and operating losses in the equipment parameters. The better the equipment performance, the higher the efficiency. The higher the value, the higher the thermal energy conversion efficiency.

[0041] In one example, the dynamic evolution model includes a technology diffusion model and a device energy efficiency evolution model. The technology diffusion model is used to simulate the evolution of the penetration rate of new technologies over time, and the device energy efficiency evolution model is used to simulate the nonlinear evolution of the device energy efficiency ratio with technological progress.

[0042] Both the technology diffusion model and the equipment energy efficiency evolution model are constructed using piecewise S-shaped curve functions. They work together to dynamically simulate macroscopic technological development and microscopic equipment performance. The technology diffusion model is specifically used to simulate the evolution of the penetration rate of new technologies related to bio-based building material production, such as advanced drying technology, high-efficiency hot pressing technology, and low-carbon adhesive technology, on the production line over time. It clearly reflects the entire life cycle of new technologies from introduction and promotion to maturity, solving the problem that traditional static calculations cannot reflect the impact of technological iteration on carbon emissions. The equipment energy efficiency evolution model is used to simulate production equipment, mainly the nonlinear evolution of the energy efficiency ratio of drying and hot pressing equipment with technological progress and equipment upgrades. It accurately captures the energy consumption reduction effect brought about by improved equipment performance, ensuring that energy efficiency parameters are consistent with actual production evolution patterns.

[0043] The functional form of the technology diffusion model is: Formula (3) in, For the year The technology penetration rate, ranging from 0 to 1, represents the proportion of the new technology applied in the corresponding production process at year t. For example, P(t) = 0.6 means that 60% of the production equipment in that year adopts the new technology. The saturation value is 1, which indicates that after long-term promotion of the new technology, 100% equipment coverage can be achieved, meaning that the entire production line adopts the technology. The diffusion rate coefficient is the coefficient that indicates the faster the new technology is promoted. The specific value can be determined based on the scenario parameters obtained in step S1 (such as the level of policy support and investment in technology promotion) and industry experience in technology diffusion; t is the predicted year. The inflection point year represents the year when the penetration rate of a new technology reaches 50%. Before the inflection point, the penetration rate of new technologies increases slowly, and after the inflection point, the penetration rate grows rapidly. It can be determined based on time parameters such as the time of technology introduction and the maturity cycle.

[0044] The functional form of the equipment energy efficiency evolution model is: Formula (4) in, For the year The energy efficiency ratio (EER) of a device characterizes the efficiency with which a device converts input electrical energy into useful thermal energy. The higher the EER, the lower the energy consumption of the device. The baseline energy efficiency value represents the initial energy efficiency level of the equipment in the predicted starting year and is directly taken from the rated energy efficiency value of the equipment in the equipment parameters obtained in step S1. The target energy efficiency value represents the highest energy efficiency level that the equipment can achieve after the technology matures, and is determined based on the scenario parameters (such as technology development goals and industry energy efficiency standards) obtained in step S1; k is the transition rate coefficient, and the larger the coefficient, the faster the equipment's energy efficiency improves; t0 is the inflection point year, which represents the year in which the equipment's energy efficiency ratio reaches the average of the benchmark energy efficiency value and the target energy efficiency value.

[0045] In one example, calculating carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model includes: (1) Determine the current technology share, equipment energy efficiency ratio and grid carbon intensity based on the dynamic evolution model; Specifically, based on the dynamic evolution model constructed in step S3, the time parameters (current predicted year t) and scenario parameters (policy constraints and technology promotion conditions corresponding to the scenario) obtained in step S1 are input, and the share of various technologies (traditional technology, transitional technology, and advanced technology) in the current year t is calculated using formula (3) of the technology diffusion model (i.e., S). trad S trans S adv The sum of the three is 1), and the equipment energy efficiency ratio y(t) for the current year t is calculated by formula (4) of the equipment energy efficiency evolution model. At the same time, the grid carbon intensity for the current year t is determined by combining the grid carbon intensity evolution path of the corresponding scenario in the scenario parameters. The above three parameters are all dynamically updated values ​​and are adjusted with the predicted year and scenario changes to ensure that the accounting parameters are consistent with the actual technological evolution and energy structure transformation trend.

[0046] (2) Based on the coupled useful energy demand and the energy efficiency ratio of the equipment, determine the operating energy consumption of each process; Operating energy consumption primarily targets the core thermal processing steps (drying and hot pressing) and auxiliary processes in the production of bio-based building materials. The energy consumption calculation for the core steps is directly linked to the coupled useful energy demand. This coupled useful energy demand represents the effective thermodynamic energy required by the production process. The equipment energy efficiency ratio (EER) characterizes the useful energy output per unit of electrical energy consumed by the equipment. According to the definition of energy efficiency, operating energy consumption (electricity consumption) = coupled useful energy demand / equipment EER, thus yielding the actual electrical energy consumption of core processes such as drying and hot pressing. The operating energy consumption of auxiliary processes is determined using industry-standard calculation methods, combining the equipment parameters (rated energy consumption of public equipment) and operating time obtained in step S1. The total operating energy consumption for each process is then summed, ensuring that the operating energy consumption calculation is directly linked to material efficiency and equipment energy efficiency, overcoming the problem of traditional energy consumption calculations being disconnected from actual operating conditions.

[0047] (3) Based on the material efficiency factor, the raw material consumption is corrected to determine the physical carbon emission intensity; Physical carbon emission intensity C embodied The main sources are the consumption of raw materials (wood raw materials, adhesives) and the raw material transportation process in the production of bio-based building materials. The core of its calculation is to correct for raw material consumption using a material efficiency factor α, reflecting the reduction effect of improved material efficiency on physicochemical carbon emissions. Specifically, the basic physicochemical carbon emission intensity without considering material efficiency improvement is first calculated based on the material parameters obtained in step S1; then, it is corrected using the material efficiency factor α constructed in step S2 (correction formula: C). embodied =Basic physical and chemical carbon emission intensity × α), where the smaller the value of α, the higher the material efficiency and the lower the physical and chemical carbon emission intensity.

[0048] (4) Determine the operating carbon emission intensity based on the operating energy consumption of each process, the technology share, and the grid carbon intensity; Operating carbon emission intensity C operational The carbon emissions mainly originate from energy consumption during the operation of each process. Their calculation needs to incorporate the weighted effect of technology share to ensure alignment with the dynamic technology diffusion process. Specifically, for each core process (such as the drying process), carbon emissions are first calculated based on different technology shares (S...). trad S trans S adv The carbon emissions of the corresponding technologies are weighted and calculated to obtain the carbon emissions of a single process. Then, the carbon emissions of all processes are summed to obtain the total carbon emission intensity. Among them, the calculation of carbon emissions of core processes (such as drying process) needs to be combined with the energy consumption of each process and the carbon intensity of the power grid to reflect the correlation between energy consumption and the decarbonization process of the power grid, and to ensure that the calculation of carbon emission intensity and the parameters output by the dynamic evolution model and the coupled energy consumption output by step S2 form a closed loop.

[0049] (5) Based on the physical carbon emission intensity and the operational carbon emission intensity, the total carbon emission intensity under multiple scenarios is obtained and calculated according to formula (5); Formula (5) in Total carbon intensity is a measure of the total carbon emissions generated throughout the entire production process of a unit of bio-based building materials product. Carbon emission intensity is a materialized measure that reflects carbon emissions from raw material consumption and transportation. Carbon emission intensity reflects the carbon emissions from energy consumption during the operation of production equipment.

[0050] In one example, the operational carbon emission intensity includes carbon emissions from the drying process, which are calculated based on a technology share weighting, using the following formula: Formula (6) Among them, S trad For traditional technology share, S trans For transitional technology share, S adv The share of advanced technology, and the sum of the three is 1.

[0051] C dry_trad C dry_trans C dry_adv These represent the carbon emission intensity per unit product for traditional, transitional, and advanced drying technologies, respectively.

[0052] For traditional drying technologies, carbon emissions originate from both biomass heat energy consumption and auxiliary electrical energy consumption, specifically calculated according to formula (7). For transitional drying technologies, carbon emissions mainly originate from biomass heat energy consumption, specifically calculated according to formula (8). For advanced drying technologies, electric power-driven high-efficiency heat pumps or waste heat recovery drying methods are used, specifically calculated according to formula (9).

[0053] Formula (7) Formula (8) Formula (9) Where, q thermal This serves as a benchmark for the thermal energy requirements of the drying process. The carbon emission factor corresponding to biomass fuel; This is an auxiliary power consumption for traditional drying technology; The corresponding year and scenario represent the carbon intensity of the power grid; Based on the basic energy demand; This refers to the energy efficiency ratio of MVR devices.

[0054] In one example, generating the corresponding process control parameters includes: By comparing the differences in total carbon emission intensity, physical carbon emission intensity, and operational carbon emission intensity under different scenarios, the emission reduction contribution of improved material efficiency, upgraded equipment energy efficiency, and optimized energy structure can be quantified. Identify the technological path that contributes the most to emission reduction as the target emission reduction path; Based on the target emission reduction path, process control parameters are extracted. These process control parameters include: a material efficiency threshold parameter for limiting the raw material feeding system, an energy efficiency ratio technology inflection point timestamp for triggering production line equipment replacement, and a grid carbon intensity matching instruction for scheduling the proportion of distributed renewable energy power supply.

[0055] By comparing the differences in total carbon emission intensity, physical carbon emission intensity, and operational carbon emission intensity under baseline, policy commitment, and net-zero emission scenarios, this study quantifies the emission reduction contributions of three types of technologies: material efficiency improvement, equipment energy efficiency upgrade, and energy structure optimization. The emission reduction contribution represents the emission reduction potential and implementation value of each measure. The path with the largest emission reduction contribution is identified as the target emission reduction path. Finally, based on the regulation requirements of this target emission reduction path, process control parameters that can be directly used for production execution and system scheduling are extracted. These parameters include material efficiency threshold parameters to limit the operating upper limit of the raw material feeding system and ensure efficient utilization of raw materials; energy efficiency ratio technology inflection point timestamps determined based on dynamic evolution models to accurately trigger the replacement of aging equipment and the commissioning of high-efficiency equipment on the production line; and grid carbon intensity matching instructions generated based on real-time changes in grid carbon intensity to rationally schedule the proportion of distributed renewable energy power supply to reduce electricity emissions.

[0056] If improving material efficiency contributes the most to emission reduction, then optimizing material efficiency should be prioritized as the target emission reduction path. If upgrading equipment energy efficiency contributes the most to emission reduction, then promoting equipment upgrades and technological iterations should be the key target emission reduction path. If optimizing the energy structure contributes the most to emission reduction, then adjusting the energy consumption structure to match the decarbonization pace of the power grid should be the target emission reduction path. Based on the target emission reduction path, process control parameters are extracted, including material efficiency threshold parameters for limiting the raw material feeding system, energy efficiency ratio technology inflection point timestamps for triggering production line equipment replacement, and grid carbon intensity matching instructions for scheduling the proportion of distributed renewable energy power supply. This provides bio-based building material production enterprises with accurate and executable low-carbon transformation decision-making basis.

[0057] For example, the material efficiency threshold parameter corresponding to a 4.6% saving in raw materials is set to 0.954 to limit the upper limit of losses in the raw material feeding system; the energy efficiency ratio technology inflection point time stamp corresponding to the large-scale replacement node of MVR drying equipment is set to January 1, 2030 to trigger the upgrade and replacement of production line equipment; and the renewable energy power supply ratio is increased to above 80% when the grid carbon intensity is below 0.35 kgCO2e / kWh to schedule the proportion of distributed green electricity input, thereby providing a directly executable quantitative control basis for the production control system. Example 2

[0058] This embodiment takes an OSB (Oriented Strand Board) production line as an example and uses the dynamic prediction and optimization method for carbon emissions from bio-based building materials production described in this disclosure to perform multi-scenario accounting and emission reduction path analysis for its entire production process from 2023 to 2050. The specific implementation steps are as follows: Step 1: Obtain basic parameters for OSB (Oriented Strand Board) production, including material parameters, equipment parameters, time parameters, and scenario parameters.

[0059] (1) The material parameters are shown in Table 1 below.

[0060] Table 1. Examples of Material Parameters (2) The equipment parameters are shown in Table 2 below.

[0061] Table 2 Examples of Equipment Parameters (3) Time parameters Predicted starting year: 2023 Predicted end year: 2050 Year of technology introduction: 2023 Technology maturity year: 2030 (4) Scenario parameters The baseline scenario (Business As Usual, BAU) is a baseline scenario that does not introduce new climate policies but simply follows the natural development of existing policies and technological trends.

[0062] The Announced Pledges Scenario (APS) is a scenario that assumes all climate commitments announced by countries / companies will be fulfilled on time and in full.

[0063] Net Zero Emissions (NZE) scenarios are emission reduction scenarios aimed at achieving net zero emissions globally or in industries by 2050.

[0064] Table 3. Grid carbon intensity data (unit: g CO2 / kWh): Table 4 Technology Diffusion Parameters Table 5 Equipment Energy Efficiency Evolution Parameters Step 2: Constructing the material efficiency factor Thus, the useful energy requirement after coupling is obtained.

[0065] (1) Constructing the material efficiency factor The material efficiency factor α characterizes the degree of improvement in material utilization efficiency, and its value ranges from 0.954 to 1.0. When α = 1.0, it indicates that the material utilization efficiency is at the baseline level; when α = 0.954, it indicates that the material utilization efficiency has increased by 4.6%. The target material saving ratio δ target It is determined based on the goal of improving the efficiency of raw material utilization. In this embodiment, the target material saving ratio is 4.6%.

[0066] The specific formula for calculating the material efficiency factor α is as follows: Formula (10) in, The material efficiency factor for year t; This is the technology maturity progress coefficient, with a value range of [0,1]. To achieve the target material saving ratio, this embodiment uses 4.6%.

[0067] Technology Maturity Progress Coefficient The calculation uses the following smooth evolution formula: Formula (10-1) in, Normalized timeline of technological progress: Formula (10-2) in, The year the technology was introduced is 2023 in this embodiment; In this embodiment, 2030 is chosen as the year when the technology matures. exist When the time is 0, in Take 1 at the time.

[0068] The dynamic evolution rule of the material efficiency factor is as follows: That year When the year of technology introduction is less than 2023, ; That year When the technology matures by a date greater than or equal to 2030, ; When the year t is between the year of technology introduction and the year of technology maturity, it is calculated according to formulas (10), (10-1), and (10-2).

[0069] Taking 2026 as an example: α(2026) = 1.0 - 0.393×0.046 = 1.0 - 0.018 = 0.982 (2) Calculate the basic useful energy demand and the coupled useful energy demand.

[0070] In this example, the thermal energy demand baseline for the drying process is... = 1184.85 MJ / m, baseline efficiency .

[0071] therefore, Furthermore, based on Substitute the corresponding year and This allows us to calculate the useful energy demand after coupling in each year.

[0072] Taking 2030 as an example, =0.954 =734.68 Heat load reduction = (770.15 - 734.68) / 770.15 = 0.046 It is evident that when material efficiency is improved by 4.6%, the reduction in useful energy demand is also 4.6%, demonstrating the linear coupling relationship between materials and energy.

[0073] Step 3: Construct a dynamic evolution model and update the grid carbon intensity and equipment energy efficiency level synchronously.

[0074] (a) Technology Diffusion Model The methods for determining the parameters in the model are as follows: (1) Method for determining the diffusion rate coefficient k: Historical data fitting method: When historical diffusion data of similar technologies exist, the least squares method is used to fit the logistic function to obtain the k value; Analogical estimation method: Referencing the diffusion rate of similar technologies, and adjusting for technological maturity and market acceptance; Scenario hypothesis method: Determined based on policy scenario assumptions, with aggressive scenarios (such as NZE) employing a higher diffusion rate.

[0075] In this embodiment, the diffusion rate coefficient of the MVR drying technology is determined based on the following criteria: APS Scenario: Referring to the average diffusion rate of industrial energy-saving technologies under the IEA commitment scenario, and considering the replacement cycle of industrial equipment in China (approximately 8-10 years), we take k=0.184; NZE Scenario: Assuming accelerated policy implementation, the technology diffusion rate increases by approximately 2.3 times, and k=0.421.

[0076] (2) Method for determining the inflection point year t0: Technology maturity assessment method: Based on the assessment of the technology development stage, the inflection point usually occurs in the year when the technology maturity reaches 50%; Policy target anchoring method: Calculate the inflection point by working backward from the policy planning target year; Market penetration rate target method: determined based on the target year when the market penetration rate reaches 50%.

[0077] In this embodiment, the inflection point year is determined based on the following criteria: APS Scenario: Referring to the industrial electrification technology popularization timeline under the IEA commitment scenario, and combined with the equipment replacement cycle, t0 = 2034.6; NZE Scenario: Assuming accelerated policy implementation and technology adoption approximately 4 years ahead of schedule, take t0 = 2030.7.

[0078] The technology penetration rate was calculated using normalization to ensure that the penetration rate was zero in the initial year and reached the preset value in the target year. in, The penetration rate in the initial year. The target year's penetration rate.

[0079] (II) Equipment Energy Efficiency Evolution Model Taking mechanical vapor recompression (MVR) equipment as an example, its energy efficiency ratio has evolved as follows: For the year The energy efficiency ratio of the MVR equipment is initially set at 3.0, with a target value of 10.0 and an inflection point year of 2040.

[0080] Taking high-temperature heat pump (HP) equipment as an example, its energy efficiency ratio has evolved as follows: in, Let t be the energy efficiency ratio of the heat pump equipment, with an initial value of 2.2, a target value of 3.1, and an inflection point year of 2042.

[0081] (III) Power Grid Carbon Intensity Update Based on predetermined values ​​for three scenarios—BAU, APS, and NZE—the grid carbon intensity for the corresponding year is updated synchronously to achieve collaborative simulation of macro-level energy decarbonization and micro-level technological evolution.

[0082] Step 4: Calculate carbon emission intensity under multiple scenarios based on the coupled useful energy demand and dynamic evolution model.

[0083] (1) Calculation of physical carbon emissions Physical carbon emissions include the carbon footprint of wood, the carbon footprint of adhesives, and the carbon footprint of transportation. in, It is an emission reduction factor for adhesives, which changes dynamically with different scenarios and time.

[0084] Adhesive emission reduction factor The dynamic evolution formula is as follows: in, The baseline emission reduction factor is 1.0 (indicating no emission reduction). The target emission reduction factor is set at 0.30 for the APS scenario and 0.10 for the NZE scenario; This is the transition rate coefficient, with a value of 0.12; The inflection point year is 2035.

[0085] Take 2040 as an example: APS scenario: =0.615 NZE scenario: =0.505 (2) Calculation of carbon emissions during operation Carbon emissions from operation are weighted by technology share: The carbon emissions from the drying process are shown below: in, The share of traditional technologies refers to the proportion of drying equipment that uses gas or coal for heating; The share of transitional technologies refers to the proportion of drying equipment that uses biomass heating. The share of advanced technology refers to the percentage of MVR electrically driven drying equipment. + + = 1.

[0086] The carbon emission calculation formulas for each technical route are as follows: Taking the 2023 BAU scenario as an example, assuming technology share... =0.6、 =0.4、 =0.

[0087] (3) Calculate carbon emission intensity under multiple scenarios A. 2023 Baseline Emissions Calculation 2023 is the base year before the technology is introduced, α(2023) = 1.0, which means that traditional drying technology is used.

[0088] Physical carbon emissions: C embodied (2023) = 270 kg CO2e / m 3 ; Carbon emissions from transportation: C transport (2023) = 12 kg CO2e / m 3 Carbon emissions from operation: (2023) == 82 kg CO2e / m 3 Total carbon emission intensity Ctotal = 270 + 12 + 82 = 364 kg CO2e / m³ 3 B. Carbon emission calculations for various scenarios in 2030 2030 is the year when the technology matures, the material efficiency factor α (2030) = 0.954 (material utilization efficiency increases by 4.6%), the penetration rate of advanced drying technology reaches the inflection point level, the grid carbon intensity and technology share are different under different scenarios, and the specific technology share is shown in Table 6 below. After calculation, the prediction results of each scenario are shown in Table 7 below.

[0089] Table 6. Proportion of Each Technology in 2030 Table 7. 2030 Scenario Predictions C. Carbon emission calculations for various scenarios in 2050 2050 is the long-term forecast year. The material efficiency factor remains at α=0.954, with comprehensive technological upgrades and deep decarbonization of the energy structure. The carbon intensity and technology share of the power grid differ under different scenarios, as shown in Table 8 below. The calculation results for each scenario are shown in Table 9 below.

[0090] Table 8. Proportion of each technology in 2050 Table 9. Scenario Predictions for 2050 Step 5: Determine emission reduction paths based on carbon emission intensity comparison results and output process optimization strategies.

[0091] By comparing the baseline emissions for 2023 with the carbon emission projections for various scenarios in 2030 and 2050, the emission reduction contributions of different technologies are quantified, and the following optimization strategies are output: (1) Improved material efficiency: By comparing the difference in physicochemical carbon emissions before and after the correction of the material efficiency factor α, it was found that when α=0.954 in 2030, the physicochemical carbon emissions will decrease by 4.6% compared with 2023 (α=1.0). This emission reduction effect is entirely due to the optimization of raw material ratio and process parameters. Therefore, optimizing the raw material ratio and process parameters to achieve a 4.6% material saving and drive a proportional decrease in heat load is the key emission reduction path. (2) Equipment upgrade: By comparing the differences in operating carbon emissions under different technology shares, it was found that in the 2030 APS and NZE scenarios, the application of MVR drying equipment and high-temperature hot pressing equipment can improve the energy efficiency ratio of the equipment to the target level, and the operating carbon emissions are significantly reduced compared with the BAU scenario. Moreover, the introduction of equipment before 2030 can maximize the emission reduction benefits. Therefore, it is determined that the key emission reduction path is to introduce MVR drying equipment and high-temperature hot pressing equipment before 2030 and improve the energy efficiency ratio to the target level. (3) Energy structure optimization: By comparing the correlation data of grid carbon intensity and operation carbon emissions under three scenarios, it was found that the grid decarbonization process and the increase in the application share of electric drive equipment can further reduce operation carbon emissions, and the emission reduction contribution gradually increases over time. Therefore, it was determined that cooperating with the grid decarbonization process and gradually increasing the market share of electric drive equipment is the key emission reduction path.

[0092] To further verify the prediction accuracy of the method disclosed herein, a historical data backtesting method was used. Using historical data from 2018 to 2022 as input, the carbon emission intensity for 2023 was predicted and compared with the actual production data for 2023.

[0093] Verification data source: Raw material consumption data: Enterprise Production Management System (ERP); Energy consumption data: Enterprise Energy Management System (EMS); Carbon emission accounting: based on ISO 14064 standard and industry emission factors.

[0094] The prediction error is less than a preset threshold of 5%, and the prediction accuracy meets the requirements of industrial carbon emission management. Example 3 This embodiment verifies the applicability of the disclosed method to OSB production lines of different sizes.

[0095] Three OSB production lines with annual capacities of 100,000 cubic meters, 200,000 cubic meters, and 300,000 cubic meters were selected, and carbon emission prediction was performed using the method disclosed in this paper.

[0096] (a) Basic parameter settings (II) Detailed Calculation of APS Scenario for Medium-Sized Production Line (200,000 cubic meters) in 2030 Step 1: Calculation of Material Efficiency Factor Year of technology introduction t intro = 2023, the year the technology matures t mature = 2030; For 2030: = (2030 - 2023) / (2030 - 2023) = 1.0 (2030) = 3×1 2 - 2×1 3 = 1.0 α(2030) = 1.0 - 1.0×0.046 = 0.954 Step 2: Calculation of Coupled Energy Requirements The drying process requires energy: q req_dry = 1184.85 ×0.65 = 770.15 MJ / m 3 Useful energy requirement after coupling: q req_coupling = 0.954 ×770.15 = 734.68 MJ / m 3 Heat load reduction: (770.15 - 734.68) / 770.15 = 4.6% Step 3: Calculation of Technology Penetration Rate MVR technology penetration rate in the APS scenario (2030): That is, the share of advanced technology S adv = 0.429, Traditional Technology Share S trad = 0.571.

[0097] Step 4: Carbon Emission Calculation Grid carbon intensity (2030 APS scenario): EF grid = 0.4776 kg CO2 / kWh MVR device energy efficiency ratio (2030): Carbon emissions from the drying process: Physical carbon emissions: Total carbon emissions: Step 5: Verify Results The results show that the method of the present invention can accurately predict the trend of carbon emission changes in production lines of different scales, with prediction errors of less than 5%, which verifies the versatility and reliability of the method. Example 4

[0098] This embodiment uses non-biological building material logs / sawn timber as an example, assuming a timber processing enterprise processes 50,000 m³ of logs annually. 3 This study focuses on a drying production line for Pinus sylvestris sawn timber, and the basic thermodynamic data used are derived from wood science and engineering practice. The specific methods are as follows: I. Obtaining the basic parameters of building materials 1. Material parameters Basic density of wood (ρ): (Oven-dry density); Reference initial moisture content (MC) base ): (Dry basis moisture content, freshly cut condition); Optimized initial moisture content (MC) opt ): (The state of the kiln after being stacked and air-dried for one month). Target final moisture content (MC) f ): (Moisture content of standard commercial timber); Latent heat of vaporization of wood moisture (L w ): (Including the latent heat of vaporization of free water and the adsorption energy of bound water); Electricity consumption benchmark (e elec ): (The power consumption of the reference fan cycle is positively correlated with the amount of water evaporated.)

[0099] 2. Equipment and Scenario Parameters Traditional technology (gas-fired steam kiln): Thermal efficiency η = 85%, natural gas carbon emission factor: ; Advanced technology (high-temperature heat pump kiln): ; Grid carbon intensity (2030 APS scenario): .

[0100] II. Constructing the material efficiency factor (α) The material efficiency factor α for wood drying reflects the nonlinear reduction in heat load due to the decrease in moisture content entering the kiln.

[0101] Step 1: Calculation of baseline evaporation Thermodynamic evaporation rate formula based on dry basis moisture content : Step 2: Calculation of Optimized Evaporation Step 3: Generation of material efficiency factor α The physical meaning of the material efficiency factor α is that by reducing the absolute moisture content by 25% through pre-drying, a core heat load reduction of up to 52.1% can be achieved, which is the core manifestation of the synergistic multiplier reduction effect.

[0102] III. Dynamic Carbon Emission Calculation for the 2030 APS Scenario 1. Coupled useful energy demand calculation Baseline useful energy requirements: Useful energy requirements after coupling: Power consumption of the fan after coupling: 2. Set the technology penetration rate for 2030. Based on the S-shaped curve, assuming the market penetration rate of high-temperature heat pump kilns in 2030... (Right now ).

[0103] 3. Carbon emission accounting for each technological pathway Traditional gas-fired kilns (C dry_trad ): Thermal carbon emissions: Carbon emissions from electricity operation: Subtotal of traditional gas-fired kilns: Advanced heat pump kiln (C dry_adv ): Heat pump compressor power consumption: Total power consumption: Subtotal of Advanced Heat Pump Kilns: 4. Overall operating carbon emission intensity in 2030 IV. Comparative Verification (Differences from Static LCA) If the traditional static LCA method is used, it does not consider the synergistic coupling between the physical optimization of water content and the thermodynamic load (i.e., it assumes that the initial water content is constant at 60%): Carbon emissions corresponding to heat load of conventional methods: This publicly predicted value It can accurately capture 57.9% of the carbon reduction potential of processes that traditional methods miss.

[0104] This disclosure also provides a system for dynamic prediction and optimization of carbon emissions from building material production, such as... Figure 2 The diagram shown illustrates the system, which includes: Data acquisition unit 10 is used to acquire basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters; The material-energy coupling calculation unit 20 is used to construct a material efficiency factor based on the basic parameters, establish the correlation between material input and thermodynamic energy consumption, and obtain the coupled useful energy demand. The dynamic simulation unit 30 is used to construct a dynamic evolution model using a piecewise S-curve, and to synchronously update the grid carbon intensity and equipment energy efficiency level based on the dynamic evolution model. The scenario calculation unit 40 is used to calculate the carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model. The decision output unit 50 is used to determine the target emission reduction path based on the carbon emission intensity comparison results under the multiple scenarios, generate the corresponding process control parameters, and send the process control parameters to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

[0105] In one example, the physical-energy coupling computing unit 20 is also used for: The material efficiency factor is used as the multiplier coefficient for useful energy demand; According to the formula Calculate the useful energy requirement after coupling; in, The combined useful energy demand is given by α, where α is the material efficiency factor. Based on the energy demand.

[0106] In one example, the dynamic evolution model includes a technology diffusion model and an equipment energy efficiency evolution model. The technology diffusion model is used to simulate the evolution of the penetration rate of new technologies over time, and the equipment energy efficiency evolution model is used to simulate the nonlinear evolution of the equipment energy efficiency ratio with technological progress. The functional form of the technology diffusion model is: in, For the year technology penetration rate This is the saturation value. The diffusion rate coefficient is... The year of the turning point; The functional form of the equipment energy efficiency evolution model is as follows: in, For the year The equipment energy efficiency ratio, As the benchmark energy efficiency value, The target energy efficiency value, k is the transition rate coefficient, and t0 is the inflection point year.

[0107] In one example, scenario calculation unit 40 is also used for: The current technology share, equipment energy efficiency ratio, and grid carbon intensity are determined based on the dynamic evolution model. Based on the coupled useful energy demand and the energy efficiency ratio of the equipment, the operating energy consumption of each process is determined; Based on the aforementioned material efficiency factor, the raw material consumption is corrected to determine the physicochemical carbon emission intensity; The operating carbon emission intensity is determined based on the operating energy consumption of each process, the technology share, and the grid carbon intensity. Based on the physical carbon emission intensity and the operational carbon emission intensity, the total carbon emission intensity under multiple scenarios is obtained, according to the formula. Calculated; where Total carbon intensity, To measure carbon emission intensity, To determine the carbon emission intensity of operation.

[0108] In one example, the operational carbon emission intensity includes carbon emissions from the drying process, which are calculated based on a technology share weighting, using the following formula: ; Among them, S trad For traditional technology share, S trans For transitional technology share, S adv The share of advanced technology, and the sum of the three is 1.

[0109] In one example, the decision output unit 50 is also used for: By comparing the differences in total carbon emission intensity, physical carbon emission intensity, and operational carbon emission intensity under different scenarios, the emission reduction contribution of improved material efficiency, upgraded equipment energy efficiency, and optimized energy structure can be quantified. Identify the technological path that contributes the most to emission reduction as the target emission reduction path; Based on the target emission reduction path, process control parameters are extracted. These process control parameters include: a material efficiency threshold parameter for limiting the raw material feeding system, an energy efficiency ratio technology inflection point timestamp for triggering production line equipment replacement, and a grid carbon intensity matching instruction for scheduling the proportion of distributed renewable energy power supply.

[0110] In one example, such as Figure 3 The system also includes a display unit 60, which is used to generate charts or documents.

[0111] Specifically, the display unit 60 can generate a carbon emission prediction curve to show the changing trend of carbon emission intensity from 2023 to 2050.

[0112] It can also generate emission reduction contribution breakdown charts: displaying the contribution ratio of each emission reduction measure in a pie chart or bar chart.

[0113] Equipment upgrade timing suggestion diagram: Marks technology inflection points and provides suggestions on when to upgrade equipment.

[0114] Process optimization plan report: Generates a text report that includes material efficiency targets, equipment upgrade plans, and energy optimization plans.

[0115] In one example, the system also includes an adjustment unit 70 for continuously monitoring the deviation between actual data and predicted results and dynamically adjusting model parameters. When the deviation rate exceeds a preset threshold (e.g., 10%), the material efficiency factor and equipment energy efficiency evolution parameters are automatically adjusted.

[0116] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0117] Figure 4 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0118] like Figure 4 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0119] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0120] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for dynamic prediction and optimization of carbon emissions from building materials production. For example, in some embodiments, the method for dynamic prediction and optimization of carbon emissions from building materials production can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the method for dynamic prediction and optimization of carbon emissions from building materials production described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured, by any other suitable means (e.g., by means of firmware), to perform a method for dynamic prediction and optimization of carbon emissions from building material production.

[0121] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0125] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0126] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0127] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0129] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for dynamic prediction and optimization of carbon emissions from building materials production, characterized in that, The method includes: Step S1: Obtain basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters; Step S2: Construct a material efficiency factor based on the aforementioned basic parameters, establish the correlation between material input and thermodynamic energy consumption, and obtain the coupled useful energy demand; Step S3: Construct a dynamic evolution model using a piecewise S-curve, and update the grid carbon intensity and equipment energy efficiency level synchronously based on the dynamic evolution model. Step S4: Based on the coupled useful energy demand and the dynamic evolution model, calculate the carbon emission intensity under multiple scenarios; Step S5: Determine the target emission reduction path based on the carbon emission intensity comparison results under the multiple scenarios, and generate the corresponding process control parameters; send the process control parameters to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

2. The method according to claim 1, characterized in that, The process of constructing a material efficiency factor based on the aforementioned fundamental parameters, establishing the correlation between material input and thermodynamic energy consumption, and obtaining the coupled useful energy demand includes: The material efficiency factor is used as the multiplier coefficient for useful energy demand; According to the formula Calculate the useful energy requirement after coupling; in, The combined useful energy demand is given by α, where α is the material efficiency factor. Based on the energy demand.

3. The method according to claim 2, characterized in that, The basic useful energy demand is based on the formula Sure; in As a benchmark for thermal energy demand, it characterizes the thermal energy that the equipment needs to provide; The baseline efficiency characterizes the proportion of input thermal energy that a device converts into usable energy.

4. The method according to claim 1, characterized in that, The dynamic evolution model includes a technology diffusion model and an equipment energy efficiency evolution model. The technology diffusion model is used to simulate the evolution of the penetration rate of new technologies over time, and the equipment energy efficiency evolution model is used to simulate the nonlinear evolution of the equipment energy efficiency ratio with technological progress. The functional form of the technology diffusion model is: in, For the year technology penetration rate This is the saturation value. The diffusion rate coefficient is... The year of the turning point; The functional form of the equipment energy efficiency evolution model is as follows: in, For the year The equipment energy efficiency ratio, As the benchmark energy efficiency value, The target energy efficiency value, k is the transition rate coefficient, and t0 is the inflection point year.

5. The method according to claim 1, characterized in that, The calculation of carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model includes: The current technology share, equipment energy efficiency ratio, and grid carbon intensity are determined based on the dynamic evolution model. Based on the coupled useful energy demand and the energy efficiency ratio of the equipment, the operating energy consumption of each process is determined; Based on the aforementioned material efficiency factor, the raw material consumption is corrected to determine the physicochemical carbon emission intensity; The operating carbon emission intensity is determined based on the operating energy consumption of each process, the technology share, and the grid carbon intensity. Based on the physical carbon emission intensity and the operational carbon emission intensity, the total carbon emission intensity under multiple scenarios is obtained, according to the formula. Calculated; where Total carbon intensity, To measure carbon emission intensity, To determine the carbon emission intensity of operation.

6. The method according to claim 5, characterized in that, The operational carbon emission intensity includes carbon emissions from the drying process, which are calculated based on a technology share weighting formula: ; Among them, S trad For traditional technology share, S trans For transitional technology share, S adv The share of advanced technology, and the sum of the three is 1.

7. The method according to claim 1, characterized in that, The generation of the corresponding process control parameters includes: By comparing the differences in total carbon emission intensity, physical carbon emission intensity, and operational carbon emission intensity under different scenarios, the emission reduction contribution of improved material efficiency, upgraded equipment energy efficiency, and optimized energy structure can be quantified. Identify the technological path that contributes the most to emission reduction as the target emission reduction path; Based on the target emission reduction path, process control parameters are extracted. These process control parameters include: a material efficiency threshold parameter for limiting the raw material feeding system, an energy efficiency ratio technology inflection point timestamp for triggering production line equipment replacement, and a grid carbon intensity matching instruction for scheduling the proportion of distributed renewable energy power supply.

8. A dynamic prediction and optimization system for carbon emissions from building materials production, characterized in that, The system includes: The data acquisition unit is used to acquire basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters. The material-energy coupling calculation unit is used to construct a material efficiency factor based on the aforementioned basic parameters, establish the correlation between material input and thermodynamic energy consumption, and obtain the coupled useful energy demand. The dynamic simulation unit is used to construct a dynamic evolution model using a piecewise S-curve, and to synchronously update the grid carbon intensity and equipment energy efficiency level based on the dynamic evolution model. The scenario calculation unit is used to calculate the carbon emission intensity under multiple scenarios based on the coupled useful energy demand and the dynamic evolution model. The decision output unit is used to determine the target emission reduction path based on the carbon emission intensity comparison results under the multiple scenarios, and generate corresponding process control parameters; the process control parameters are sent to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform: Obtain basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters; Based on the aforementioned basic parameters, a material efficiency factor is constructed, and the correlation between material input and thermodynamic energy consumption is established to obtain the coupled useful energy demand. A piecewise S-curve is used to construct a dynamic evolution model, and the grid carbon intensity and equipment energy efficiency level are updated synchronously based on the dynamic evolution model; Based on the coupled useful energy demand and the dynamic evolution model, the carbon emission intensity under multiple scenarios is calculated. Based on the carbon emission intensity comparison results under the multiple scenarios, the target emission reduction path is determined, and the corresponding process control parameters are generated. The process control parameters are then sent to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment based on the target emission reduction path.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute: Obtain basic production parameters, which include material parameters, equipment parameters, time parameters, and scenario parameters; Based on the aforementioned basic parameters, a material efficiency factor is constructed, and the correlation between material input and thermodynamic energy consumption is established to obtain the coupled useful energy demand. A piecewise S-curve is used to construct a dynamic evolution model, and the grid carbon intensity and equipment energy efficiency level are updated synchronously based on the dynamic evolution model; Based on the coupled useful energy demand and the dynamic evolution model, the carbon emission intensity under multiple scenarios is calculated. Based on the comparison results of carbon emission intensity under the multiple scenarios, the target emission reduction path is determined and the corresponding process control parameters are generated. The process control parameters are sent to the building materials production control system so that the control system can adjust the heating power of the current drying equipment or the opening of the raw material ratio valve, or generate a digital twin configuration model of the production line equipment according to the target emission reduction path.

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