Carbon emission reduction optimization method for preparing green methanol from mixed biomass raw materials

Abstract

The invention discloses a carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material. The carbon emission reduction optimization method comprises the following steps: step 1, intelligent evolution of a raw material mixing ratio and self-adaptive optimization of regional climate: constructing a calorific value-water content-regional climate three-dimensional dynamic coupling model; 2, green hydrogen-biomass synergistic preparation and hydrogen-carbon ratio dynamic balance are carried out; step 3, methanol derivative multi-stage path tree extension and recovery rate intelligent feedback: constructing a production path tree containing multiple methanol derivatives, presetting a full life cycle parameter packet for each path, dynamically calibrating a technical progress coefficient of the path through a tracing platform based on actual recovery rate data, and performing multi-stage path tree extension and recovery rate intelligent feedback; feeding back and updating an emission factor in the full-life-cycle parameter packet; 4, uncertainty quantification and risk assessment: generating a risk assessment result; 5, region parameter dynamic loading and region sensitivity compensation are carried out; and step 6, outputting a two-dimensional optimization scheme and supporting a decision.

Description

Technical Field

[0001] This invention relates to the field of carbon emission reduction technology, and in particular to an optimized method for carbon emission reduction in the preparation of green methanol from mixed biomass feedstocks. Background Technology

[0002] Given the urgent global need to address climate change, the high carbon emissions from traditional fossil fuel methanol production pose a significant challenge, highlighting the unique advantages of biomass as a renewable resource in the carbon cycle. A carbon reduction optimization method for producing green methanol from mixed biomass feedstocks is proposed. Based on mixed biomass feedstocks, this method systematically optimizes processes including feedstock pretreatment, efficient conversion technology, carbon capture and utilization (CCUS) technology integration, and full life-cycle carbon footprint tracking. This achieves improved carbon reduction efficiency across the entire chain from biomass collection to methanol production. By synergistically utilizing multiple biomass sources, it overcomes the limitations of single feedstock supply, improves energy conversion efficiency, and reduces the carbon intensity per unit product. Simultaneously, process optimization reduces energy consumption and indirect emissions during production. Thus, while ensuring methanol's function as a clean energy carrier, it significantly reduces greenhouse gas emissions throughout its production process. This not only directly promotes industrial decarbonization and contributes to achieving national "dual carbon" goals, but also builds a "negative carbon" production model through the maximization of biomass resources. This promotes the resource utilization of agricultural waste, the collaborative treatment of urban and rural organic waste, and the extension of the circular economy industrial chain, forming a closed-loop system of "waste-energy-carbon sink."

[0003] Existing carbon reduction models for green methanol typically categorize indirect methanol uses into three main types, failing to specify detailed product pathways. Furthermore, their lifecycle data largely relies on static assumptions rather than actual data. This results in an inability to accurately reflect the significant differences in carbon emissions among various high-value-added derivatives, potentially leading users to choose suboptimal carbon reduction solutions. Therefore, this paper proposes an optimization method for carbon reduction in the production of green methanol from mixed biomass feedstocks. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an optimized carbon emission reduction method for preparing green methanol from mixed biomass feedstocks.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An optimized method for carbon emission reduction in the preparation of green methanol from mixed biomass feedstocks includes the following steps: Step 1: Intelligent Evolution of Raw Material Mixing Ratio and Adaptive Optimization to Regional Climate: Construct a three-dimensional dynamic coupling model of calorific value, moisture content, and regional climate, collect calorific value and moisture content data of biomass raw materials in real time, and dynamically adjust the mixing ratio of different biomass raw materials in combination with climate data of the project location; Step 2: Green hydrogen-biomass synergistic preparation and dynamic balance of hydrogen-carbon ratio: Real-time monitoring of the electrical carbon intensity of the green electricity hydrogen production unit. When the electrical carbon intensity fluctuates beyond a preset threshold, the hydrogen flow rate is automatically adjusted by the controller to maintain the hydrogen-carbon ratio in the optimal range during the synthesis reaction. At the same time, the efficiency of the electrolyzer is predicted and warned by the predictive model. Step 3: Multi-level path tree expansion and intelligent feedback of recovery rate for methanol derivatives: Construct a production path tree containing multiple methanol derivatives. Each path has a preset full life cycle parameter package. Based on actual recovery rate data, the technology progress coefficient of the path is dynamically calibrated through the traceability platform, and the emission factor in the full life cycle parameter package is updated accordingly. Step 4: Uncertainty Quantification and Risk Assessment: Construct a probabilistic model that includes biomass yield per acre, electricity carbon intensity, transportation fuel prices, and policy subsidy coefficients. Output the confidence interval of total carbon emissions through Monte Carlo simulation, and identify key risk sources through sensitivity analysis to generate risk assessment results. Step 5: Dynamic loading of regional parameters and regional sensitivity compensation: Based on the input project coordinates, the corresponding regional parameter package is automatically loaded. When calculating transportation emissions, a correction factor based on real-time traffic data and regional new energy penetration rate is introduced to optimize the calculation accuracy of transportation emissions. Step Six: Dual-Dimensional Optimization Scheme Output and Decision Support: Integrate the risk assessment results and regional parameters, generate multiple carbon emission reduction optimization schemes through a cost-benefit analysis model, and output decision support information for each scheme, including expected carbon emissions, stability rating, and regional suitability score.

[0006] The above further includes: Furthermore, in step one, the three-dimensional dynamic coupling model of calorific value-moisture content-regional climate is constructed by an LSTM neural network and integrates a seasonal decay rate correction factor based on climate zoning data. The goal of dynamically adjusting the mixing ratio is to reduce the standard deviation of the calorific value of the mixed raw materials to within ±3%.

[0007] Furthermore, in step two, the controller is a PID controller, and the optimal range for the hydrogen-to-carbon ratio is 1.2-1.5; the prediction model is a deep learning algorithm model, used to provide early warning of the equipment aging risk of the electrolyzer.

[0008] Furthermore, in step three, the methanol derivatives include polyoxymethylene, formaldehyde, MTG gasoline, bio-based polyurethane, and carbon fiber precursors; the traceability platform is a blockchain-based recycling data traceability platform, used to collect recycling rate data in real time and feed it back into the function that calibrates the technological progress coefficient.

[0009] Furthermore, in step four, when constructing the probability model, a Copula function is introduced to model the correlation between the parameters. The sensitivity analysis is a Sobol sensitivity analysis, which is used to output the Sobol index of each parameter. The risk assessment results include a carbon emission reduction resilience index and a risk radar chart.

[0010] Furthermore, in step five, the regional parameter package includes climate zoning, power grid carbon intensity, and transportation corridor data; the correction factor is... Factors, the Factor calculation integrates real-time traffic congestion index and new energy truck penetration rate.

[0011] Furthermore, in step six, the formula for calculating the stability rating S is as follows: ,in, The standard deviation of total carbon emissions. The expected value of total carbon emissions; the regional suitability score is generated in real time based on the GIS system.

[0012] Furthermore, the data interaction in steps one through six is ​​achieved through a unified data interface, forming a closed loop for optimizing carbon emission reduction across the entire chain, from raw material ratio to scheme decision-making.

[0013] The present invention has the following beneficial effects: This invention introduces a multi-level path tree for methanol derivatives, including polyoxymethylene, formaldehyde, MTG gasoline, bio-based polyurethane, and carbon fiber precursors, through multi-level path tree expansion and intelligent feedback of recovery rates. A dynamically adjustable full lifecycle parameter package is preset for each path. By deploying a blockchain-based recycling data traceability platform, actual recovery rate data is collected in real time and used to feed back into the calibration of the technology progress coefficient, forming an intelligent closed loop of preset-measurement-feedback-optimization. This achieves refined and personalized calculation of the carbon footprint of different methanol derivatives, avoiding decision-making biases caused by a one-size-fits-all simplified classification. It upgrades the full lifecycle data from static assumptions to dynamic measurements, significantly improving the accuracy and reliability of carbon emission reduction calculations. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the steps of an optimized carbon emission reduction method for preparing green methanol from mixed biomass feedstocks proposed in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 As shown, this invention provides an optimized method for carbon emission reduction in the preparation of green methanol from mixed biomass feedstocks, comprising the following steps: Step 1: Intelligent Evolution of Raw Material Mixing Ratio and Adaptive Optimization to Regional Climate: Construct a three-dimensional dynamic coupling model of calorific value, moisture content, and regional climate, collect calorific value and moisture content data of biomass raw materials in real time, and combine them with climate data of the project location to dynamically adjust the mixing ratio of different biomass raw materials in order to improve energy efficiency and reduce carbon emissions during the storage stage. Step 2: Green hydrogen-biomass synergistic preparation and dynamic balance of hydrogen-carbon ratio: Real-time monitoring of the electrical carbon intensity of the green electricity hydrogen production unit. When the electrical carbon intensity fluctuates beyond a preset threshold, the hydrogen flow rate is automatically adjusted by the controller to maintain the hydrogen-carbon ratio in the optimal range during the synthesis reaction. At the same time, the efficiency of the electrolyzer is predicted and warned by the predictive model. Step 3: Multi-level path tree expansion and intelligent feedback of recovery rate for methanol derivatives: Construct a production path tree containing multiple methanol derivatives. Each path has a preset full life cycle parameter package. Based on actual recovery rate data, the technology progress coefficient of the path is dynamically calibrated through the traceability platform, and the emission factor in the full life cycle parameter package is updated accordingly. Step 4: Uncertainty Quantification and Risk Assessment: Construct a probabilistic model that includes biomass yield per acre, electricity carbon intensity, transportation fuel prices, and policy subsidy coefficients. Output the confidence interval of total carbon emissions through Monte Carlo simulation, and identify key risk sources through sensitivity analysis to generate risk assessment results. Step 5: Dynamic loading of regional parameters and regional sensitivity compensation: Based on the input project coordinates, the corresponding regional parameter package is automatically loaded. When calculating transportation emissions, a correction factor based on real-time traffic data and regional new energy penetration rate is introduced to optimize the calculation accuracy of transportation emissions. Step Six: Dual-Dimensional Optimization Scheme Output and Decision Support: Integrate the risk assessment results and regional parameters, generate multiple carbon emission reduction optimization schemes through a cost-benefit analysis model, and output decision support information for each scheme, including expected carbon emissions, stability rating, and regional suitability score.

[0017] In one embodiment, in step one, the three-dimensional dynamic coupling model of calorific value-moisture content-regional climate is constructed by an LSTM neural network and integrates a seasonal decay rate correction factor based on climate zoning data. The goal of dynamically adjusting the mixing ratio is to reduce the standard deviation of the calorific value of the mixed raw materials to within ±3%.

[0018] In this embodiment: Step 1: Data Acquisition and Preprocessing Input variable definition: Real-time calorific value (unit: MJ / kg) and moisture content (unit: %) of biomass raw materials (such as straw and forestry waste), as well as regional climate data (temperature ℃, humidity %, and rainfall mm), are collected and a time series dataset is constructed according to the season (spring / summer / autumn / winter).

[0019] Data standardization: Z-score standardization is used to process calorific value, moisture content and climate data to eliminate dimensional differences.

[0020] Step 2: LSTM Model Architecture Design

[0021] Model structure: A three-layer LSTM network is constructed, with 64 hidden units in each layer. The input layer receives the standardized five-dimensional time series of calorific value, moisture content, temperature, humidity and rainfall. The output layer predicts the optimized ratio for the next period (e.g., straw:forestry waste = 6:4).

[0022] Gating mechanism: Dynamically regulates information flow through forget gate, input gate, and output gate.

[0023] The Forgotten Gate: Deciding which historical information to discard. ; in, For the Sigmoid function, This is the weight matrix. This is the hidden state from the previous moment. For the current input, For the bias term of the forget gate; Input gate: Determine which new information to update: ; ; in, The output of the input gate, Here is the weight matrix of the input gate. For the bias term of the input gate, The candidate cell states are generated using the tanh function, representing new information, and their range is between -1 and 1. To generate the weight matrix for candidate cell states, Bias terms for generating candidate cell states; Cell state update: fusing historical and new information: ; in, This represents the current cell state, and ⊙ represents element-wise multiplication. This represents the cell state at the previous moment; Output gate: Generates the current hidden state: ; ; in, For the output of the output gate, This is the weight matrix of the output gate. This is the bias term for the output gate. This represents the current hidden state.

[0024] Step 3: Integration of Seasonal Decay Rate Correction Factors

[0025] Climate zone mapping: Based on RegionProfile, four climate zones are defined (tropical humid / temperate monsoon / arctic arid / plateau mountainous), and a basic decay rate is preset for each zone. (such as tropical humid regions) =0.25 / month).

[0026] Dynamic correction formula: Introducing a seasonal correction coefficient Combined with temperature ,humidity Adjust the decay rate: .

[0027] Step 4: Model Training and Optimization

[0028] Loss function: The mean squared error (MSE) is used to quantify the deviation between the predicted proportion and the standard deviation (±3%) of the target calorific value.

[0029] Hyperparameter tuning: Optimize the learning rate (0.001-0.01) and batch size (32-128) using grid search, and iterate 200 times using the Adam optimizer to ensure model convergence.

[0030] Step 5: Real-time forecasting and ratio adjustment

[0031] Online deployment: Deploy the trained LSTM model to the edge computing platform to receive real-time data streams of biomass feedstock calorific value, moisture content, and climate data.

[0032] Dynamic feedback: The model outputs an optimized ratio scheme every 2 hours, and adjusts the storage cycle by combining the seasonal decay rate correction factor.

[0033] Step 6: Verification and Iteration

[0034] Verification indicators: The accuracy of the model prediction is verified through actual production data. The target achievement rate of calorific value standard deviation control is required to be ≥95%, and the prediction error of decay rate is ≤10%.

[0035] Iterative updates: Climate data and biomass characteristic parameters are updated quarterly, and the weights of the LSTM model are fine-tuned through an online learning mechanism to adapt to long-term environmental changes.

[0036] In one embodiment, in step two, the controller is a PID controller, the optimal range of the hydrogen-to-carbon ratio is 1.2-1.5, and the prediction model is a deep learning algorithm model used to provide early warning of the equipment aging risk of the electrolyzer.

[0037] In one embodiment, in step three, the methanol derivatives include polyoxymethylene, formaldehyde, MTG gasoline, bio-based polyurethane, and carbon fiber precursors; the traceability platform is a blockchain-based recycling data traceability platform, used to collect recycling rate data in real time and feed it back into the function calibrating the technological progress coefficient.

[0038] In one embodiment, in step four, when constructing the probability model, a Copula function is introduced to model the correlation between the parameters. The sensitivity analysis is a Sobol sensitivity analysis, used to output the Sobol index of each parameter. The risk assessment results include a carbon emission reduction resilience index and a risk radar chart.

[0039] In one embodiment, in step five, the project coordinates are input and the regional parameter package is loaded using a GIS-LBS fusion positioning system integrating Gaode or Baidu APIs. The regional parameter package includes climate zoning, power grid carbon intensity, and transportation corridor data; the correction factor is... Factors, the Factor calculation integrates real-time traffic congestion index and new energy truck penetration rate.

[0040] In this embodiment: Step 1: Input coordinates and match them with the region template User input: Users input project coordinates through the system interface (e.g., latitude and longitude "31.23°N, 121.47°E" corresponds to Shanghai Pudong).

[0041] API call: The system calls the "reverse geocoding" interface of Gaode / Baidu API to obtain the administrative division corresponding to the coordinates (such as Pudong New Area) and the transportation corridor data within the nearest 10km (such as Yangshan Port-Railway Hub).

[0042] Regional template loading: Match the "Yangtze River Delta - Temperate Monsoon Region" template from the RegionProfile database and load the basic parameters: grid carbon intensity 0.45 kgC. / kWh, transport corridor efficiency correction factor 0.92.

[0043] Step 2: Real-time traffic data acquisition and congestion index calculation

[0044] API Real-time Data Capture: Obtain the average vehicle speed (e.g., 30km / h) and the proportion of congested road sections (e.g., 20%) within 5km of the project via the Gaode "Real-time Traffic Status" API.

[0045] Congestion Index Formula: ,in, Design speed for the road (e.g., 60 km / h). This represents the real-time average vehicle speed.

[0046] Step 3: Dynamic Adjustment of New Energy Truck Penetration Rate

[0047] Data source: Percentage of new energy truck registrations obtained from public platforms of local governments.

[0048] Permeability correction formula: .

[0049] Step 4: Regional Adjustment Factor calculate

[0050] Comprehensive correction formula: ,in, The basic transportation efficiency correction factor (e.g., 0.92). The congestion sensitivity coefficient (e.g., 0.01).

[0051] Step 5: Dynamic Calculation of Transportation Emissions

[0052] Basic emission formula: ,in, For transportation distance (e.g., 200km). This refers to the fuel consumption rate (e.g., 0.1 L / km). For fuel emission factors (e.g., 2.8 kgC) / L).

[0053] Revised emissions: .

[0054] Step 6: System Integration and Real-time Feedback

[0055] API Integration Architecture: A RESTful API is used to integrate with Gaode / Baidu services, and a scheduled task is set to refresh traffic data every 10 minutes to ensure... Dynamically updated.

[0056] User interface display: The system interface visually displays the real-time congestion index, the penetration rate of new energy trucks, and the corrected transportation emissions, supporting users to manually adjust parameters (such as...). (Observe the changes in emissions as the percentage increases from 35% to 50%).

[0057] In one embodiment, in step six, the formula for calculating the stability rating S is: ,in, The standard deviation of total carbon emissions. The expected value of total carbon emissions; the regional suitability score is generated in real time based on the GIS system.

[0058] In one embodiment, the data interaction in steps one through six is ​​achieved through a unified data interface, forming a closed loop for optimizing carbon emission reduction across the entire chain, from raw material ratio to scheme decision-making.

[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A carbon emission reduction optimization method for preparing green methanol from a mixed biomass feedstock, characterized in that, The method comprises the following steps: Step one: Intelligent evolution of raw material mixing ratio and regional climate self-adaptive optimization: a three-dimensional dynamic coupling model of heat value-moisture content-regional climate is constructed, real-time data of heat value and moisture content of biomass raw materials are collected, and the mixing ratio of different biomass raw materials is dynamically adjusted in combination with the climate data of the project site; Step two: Green hydrogen-biomass collaborative preparation and dynamic balance of hydrogen-carbon ratio: real-time monitoring of the power carbon intensity of the green electricity hydrogen production unit, automatic adjustment of hydrogen flow by the controller when the power carbon intensity fluctuates beyond the preset threshold, maintaining the hydrogen-carbon ratio in the synthesis reaction process within the optimal range, and predicting and warning the efficiency of the electrolytic cell through a prediction model; Step three: Multi-level path tree expansion of methanol derivatives and intelligent feedback of recovery rate: a production path tree containing multiple methanol derivatives is constructed, each path is preset with a full life cycle parameter package, the technology progress coefficient of the path is dynamically calibrated based on actual recovery rate data through a traceability platform, and the emission factor in the full life cycle parameter package is updated; Step four: Uncertainty quantification and risk assessment: a probability model containing biomass yield per mu, power carbon intensity, transportation fuel price and policy subsidy coefficient is constructed, the confidence interval of total carbon emissions is output through Monte Carlo simulation, and the key risk sources are identified through sensitivity analysis to generate risk assessment results; Step five: Dynamic loading of regional parameters and regional sensitivity compensation: according to the input project coordinates, the corresponding regional parameter package is automatically loaded, and in the calculation of transportation emissions, a correction factor based on real-time traffic data and regional new energy penetration rate is introduced to optimize the calculation accuracy of transportation emissions; Step six: Output of double-dimensional optimization scheme and decision support: integrate the risk assessment results and regional parameters, generate multiple carbon emission reduction optimization schemes through a cost-benefit analysis model, and output decision support information including expected carbon emissions, stability rating and regional adaptation score for each scheme.

2. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, In step one, the three-dimensional dynamic coupling model of heat value-moisture content-regional climate is constructed by LSTM neural network, and has a seasonal decay rate correction factor based on climate zoning data, and the target of dynamically adjusting the mixing ratio is to reduce the standard deviation of the heat value of the mixed raw materials to within ±3%.

3. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, In step two, the controller is a PID controller, and the optimal range of the hydrogen-carbon ratio is 1.2-1.5; the prediction model is a deep learning algorithm model for early warning of the equipment aging risk of the electrolytic cell.

4. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, In step three, the methanol derivatives include polyformaldehyde, formaldehyde, MTG gasoline, bio-based polyurethane and carbon fiber precursor; the traceability platform is a blockchain recycling data traceability platform for real-time collection of recovery rate data and feedback to the function of calibrating the technology progress coefficient.

5. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, In step four, when constructing the probability model, the Copula function is introduced to model the correlation between parameters, the sensitivity analysis is Sobol sensitivity analysis for outputting Sobol indexes of parameters, and the risk assessment results include carbon emission reduction resilience index and risk radar chart.

6. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, In the fifth step, the regional parameter package includes climate zoning, grid carbon intensity, and transportation corridor data; the correction factor is a factor, the factor calculates the fusion of real-time traffic congestion index and new energy truck penetration rate.

7. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, In the step six, the calculation formula of the stability rating S is wherein, is the standard deviation of the total carbon emissions, is the expected value of the total carbon emissions; and the regional fitness score is generated based on real-time calculation of the GIS system.

8. The carbon emission reduction optimization method for preparing green methanol from a mixed biomass raw material according to claim 1, characterized in that, The data interaction of steps one to six is realized through a unified data interface.

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