CN117574690A - Biochar preparation analysis method based on negative carbon emission and related device - Google Patents

Biochar preparation analysis method based on negative carbon emission and related device Download PDF

Info

Publication number
CN117574690A
CN117574690A CN202410056680.2A CN202410056680A CN117574690A CN 117574690 A CN117574690 A CN 117574690A CN 202410056680 A CN202410056680 A CN 202410056680A CN 117574690 A CN117574690 A CN 117574690A
Authority
CN
China
Prior art keywords
preparation
biochar
mixed
data
target
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202410056680.2A
Other languages
Chinese (zh)
Other versions
CN117574690B (en
Inventor
张文斌
聂原宽
张家平
王建新
张金红
龙泽望
刘言甫
王玉云
赵羊羊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Carbonneutral Bio Gas Co ltd
Original Assignee
Shenzhen Carbonneutral Bio Gas Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Carbonneutral Bio Gas Co ltd filed Critical Shenzhen Carbonneutral Bio Gas Co ltd
Priority to CN202410056680.2A priority Critical patent/CN117574690B/en
Publication of CN117574690A publication Critical patent/CN117574690A/en
Application granted granted Critical
Publication of CN117574690B publication Critical patent/CN117574690B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Algebra (AREA)
  • Fluid Mechanics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application relates to the technical field of carbon emission and discloses a biochar preparation and analysis method based on carbon emission and a related device. The method comprises the following steps: acquiring first biochar preparation parameter data of a target preparation container; performing dynamics simulation to generate a mixed preparation uniformity distribution map and a mixed preparation efficiency distribution map; extracting features to generate a mixed preparation distribution feature matrix; performing preparation parameter adjustment analysis through a mixed integer linear programming model to generate second biochar preparation parameter data; generating target biochar, and performing negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis to obtain a first preparation parameter optimization strategy; and carrying out full life cycle assessment and preparation parameter optimization strategy analysis to obtain a second preparation parameter optimization strategy, and carrying out iterative analysis to obtain target biochar preparation parameter data.

Description

Biochar preparation analysis method based on negative carbon emission and related device
Technical Field
The application relates to the technical field of carbon emission, in particular to a biochar preparation and analysis method based on carbon emission and a related device.
Background
Biochar fertilizer has attracted extensive research interest as a potential carbon-negative technology due to its potential in soil improvement, carbon storage and reduction of greenhouse gas emissions.
However, the current biochar preparation process has the problems of insufficient parameter regulation and control, and difficult balance of preparation efficiency and preparation uniformity. The traditional preparation method often depends on experience or single parameter optimization, and multiple factors in the preparation process are difficult to comprehensively consider, so that the quality of the biochar and the carbon negative emission effect are difficult to reach the optimum.
Disclosure of Invention
The application provides a biochar preparation analysis method based on carbon negative emission and a related device.
In a first aspect, the present application provides a method for analyzing the preparation of biochar based on carbon negative emission, the method comprising:
acquiring historical biochar preparation parameter data of a target preparation container, and performing biochar preparation optimization on the historical biochar preparation parameter data based on a preset genetic algorithm to obtain first biochar preparation parameter data;
Performing dynamics simulation on the first biochar preparation parameter data to obtain mixed preparation uniformity data and mixed preparation efficiency data, generating a mixed preparation uniformity distribution map according to the mixed preparation uniformity data, and generating a mixed preparation efficiency distribution map according to the mixed preparation efficiency data;
respectively carrying out feature extraction on the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map to obtain mixed preparation uniformity distribution features and mixed preparation efficiency distribution features, and generating a mixed preparation distribution feature matrix according to the mixed preparation uniformity distribution features and the mixed preparation efficiency distribution features;
inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model for preparation parameter adjustment analysis, and generating second biochar preparation parameter data;
updating preparation parameters of the target preparation container through the second biochar preparation parameter data to generate target biochar, and performing negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy;
and carrying out full life cycle assessment and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and carrying out iterative analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
In a second aspect, the present application provides a carbon emission-based biochar preparation and analysis device including:
the acquisition module is used for acquiring historical biochar preparation parameter data of the target preparation container, and carrying out biochar preparation optimization on the historical biochar preparation parameter data based on a preset genetic algorithm to obtain first biochar preparation parameter data;
the processing module is used for carrying out dynamics simulation on the first biochar preparation parameter data to obtain mixed preparation uniformity data and mixed preparation efficiency data, generating a mixed preparation uniformity distribution map according to the mixed preparation uniformity data, and generating a mixed preparation efficiency distribution map according to the mixed preparation efficiency data;
the characteristic extraction module is used for respectively carrying out characteristic extraction on the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map to obtain mixed preparation uniformity distribution characteristics and mixed preparation efficiency distribution characteristics, and generating a mixed preparation distribution characteristic matrix according to the mixed preparation uniformity distribution characteristics and the mixed preparation efficiency distribution characteristics;
the analysis module is used for inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model to carry out preparation parameter adjustment analysis and generate second biochar preparation parameter data;
The updating module is used for updating the preparation parameters of the target preparation container through the second biochar preparation parameter data to generate target biochar, and carrying out negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy;
the iteration module is used for carrying out full life cycle assessment and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and carrying out iteration analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
A third aspect of the present application provides a computer device comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the computer device to perform the carbon emission-based biochar preparation analysis method described above.
A fourth aspect of the present application provides a computer-readable storage medium having instructions stored therein that, when run on a computer, cause the computer to perform the above-described carbon emission-based biochar preparation analysis method.
In the technical scheme provided by the application, the historical biochar preparation parameter data and the genetic algorithm are used for optimization, so that the optimal adjustment of the biochar preparation parameters is realized. This helps to improve the quality and performance of the biochar. And the uniformity and efficiency of the mixed preparation are analyzed in detail by utilizing a dynamics simulation technology. Generating a mixed preparation uniformity distribution map and a mixed preparation efficiency distribution map, and providing specific data support for subsequent feature extraction and preparation parameter adjustment. And generating a mixed preparation distribution characteristic matrix through characteristic extraction of the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map. This helps to systematically understand key features in the hybrid manufacturing process and provides targeted information for subsequent manufacturing parameter adjustments. And the mixed integer linear programming model is utilized to analyze the mixed preparation distribution characteristic matrix, so that the intelligent adjustment of the preparation parameters is realized. This helps to optimize the process of preparing the biochar and to improve the carbon negative effect of the biochar. By performing multi-scale analysis on the carbon negative emission of the target biochar, factors such as energy consumption, greenhouse gas emission, resource use and the like are systematically considered in combination with full life cycle assessment. This helps to optimize the overall preparation parameters and ensures that the carbon emission effect of the biochar is maximized. The preparation parameters are continuously optimized through iterative analysis, so that the fine adjustment of the preparation parameters of the target biochar is realized, the preparation efficiency of the biochar fertilizer is improved and the preparation parameters of the biochar fertilizer are optimized through combining carbon emission.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained based on these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of a method for analyzing the preparation of biochar based on carbon emissions in the examples of the present application;
FIG. 2 is a schematic view of an embodiment of a biochar preparation and analysis apparatus based on carbon negative emissions in the embodiment of the present application.
Detailed Description
The embodiment of the application provides a biochar preparation and analysis method based on carbon emission and a related device. The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, the following describes a specific flow of an embodiment of the present application, referring to fig. 1, an embodiment of a method for preparing and analyzing biochar based on carbon emission in the embodiment of the present application includes:
step 101, acquiring historical biochar preparation parameter data of a target preparation container, and performing biochar preparation optimization on the historical biochar preparation parameter data based on a preset genetic algorithm to obtain first biochar preparation parameter data;
it is understood that the execution subject of the present application may be a biochar preparation analysis device based on carbon emission, and may also be a terminal or a server, which is not limited herein. The embodiment of the present application will be described by taking a server as an execution body.
Specifically, historical biochar preparation parameter data of a target preparation container are obtained, wherein the historical biochar preparation parameter data comprise preparation temperature, preparation humidity, preparation time and the like. The historical biochar preparation parameter data are subjected to standardized processing, and are converted into standard biochar preparation parameter data. The normalization is a common data preprocessing means, and removes dimension and normalizes data to the same numerical range so as to eliminate dimension difference among different parameters and ensure the effectiveness and accuracy of subsequent genetic algorithms. Based on a preset genetic algorithm, carrying out population initialization on the standardized biochar preparation parameter data to generate a plurality of candidate biochar preparation parameter data, wherein each group of parameter data represents a preparation condition combination. Genetic algorithms are optimization algorithms that mimic natural evolution, gradually improving the quality of solutions through selection, crossover and mutation operations of populations. In this process, each candidate biochar production parameter data requires calculation of fitness data, which is typically a measure of the candidate parameter impact on biochar quality and yield according to some predetermined evaluation criteria. And selecting, crossing and mutating the candidate biochar preparation parameter data according to the calculated adaptation data. The selection operation is to decide which candidate parameters are to be retained and passed on to the next generation based on fitness data, which generally tends to select parameter data with high fitness; the crossover operation is then performed in combination in the selected parameter data, mimicking chromosomal crossover in biological genetics, to generate new candidate parameter combinations; the mutation operation is to randomly and slightly change the candidate parameters so as to increase the parameter diversity and avoid the algorithm from converging to the local optimal solution prematurely. Through the series of operations, the genetic algorithm can continuously optimize the charcoal preparation parameters in a plurality of iteration cycles, and finally the obtained first charcoal preparation parameter data are the results of multiple rounds of screening and improvement, and the parameters bring about high-quality and high-efficiency charcoal preparation.
102, performing dynamics simulation on first biochar preparation parameter data to obtain mixed preparation uniformity data and mixed preparation efficiency data, generating a mixed preparation uniformity distribution map according to the mixed preparation uniformity data, and generating a mixed preparation efficiency distribution map according to the mixed preparation efficiency data;
specifically, a preliminary Computational Fluid Dynamics (CFD) simulation is performed on the target preparation vessel through the first biochar preparation parameter data, and an initial CFD model is established. Computational fluid dynamics simulation is a method of analyzing and solving fluid flow problems using numerical analysis and data structures that can simulate the flow and mixing of gases and liquids under specific conditions. On the basis of establishing an initial CFD model, initial conditions and boundary conditions of the model, such as temperature, pressure, speed and the like, are further set so as to obtain a target CFD model which is more in line with actual conditions. And performing grid division and dynamic parameter setting on the target CFD model, and ensuring the accuracy and efficiency of simulation. Meshing is the division of the entire simulation area into many small cells in order to solve the hydrodynamic equations on these small cells, while the dynamic parameter settings are the adjustment of the simulation parameters according to the physical and chemical characteristics of the actual manufacturing process. After these settings are completed, simulations are run by which flow characteristics and mixing effects during the biochar preparation process, as well as changes in these characteristics over time, can be observed. And after simulation is completed, carrying out mixed preparation uniformity analysis and mixed preparation efficiency analysis to respectively obtain mixed preparation uniformity data and mixed preparation efficiency data. The uniformity analysis of the mixed preparation is concerned with the uniformity and uniformity of the material mixing in the preparation process of the biochar, and the efficiency analysis of the mixed preparation is concerned with the speed and quality of the biochar preparation under specific conditions. Both of these analyses provide quantitative descriptions of key properties in the manufacturing process. Extracting a plurality of first data points from the mixed preparation uniformity data, and mapping the data points into a distribution map to generate a mixed preparation uniformity distribution map; similarly, a plurality of second data points are extracted from the mixed preparation efficiency data, and a profile map is performed to generate a mixed preparation efficiency profile. These profiles not only intuitively demonstrate the uniformity and efficiency characteristics of the preparation process, but also provide basis for subsequent optimization.
Step 103, respectively carrying out feature extraction on the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map to obtain mixed preparation uniformity distribution features and mixed preparation efficiency distribution features, and generating a mixed preparation distribution feature matrix according to the mixed preparation uniformity distribution features and the mixed preparation efficiency distribution features;
specifically, extremum analysis is performed on the mixture preparation uniformity distribution map, and a first maximum value point and a first minimum value point in the map are found, wherein the two points reflect the highest and lowest levels of the mixture uniformity. Similarly, similar extremum analysis is performed on the hybrid preparation efficiency profile to determine a second maximum point and a second minimum point, which mark the highest and lowest efficiency. Determining a plurality of first characteristic point positions of the mixed preparation uniformity distribution map according to the first maximum value point and the first minimum value point, and determining a plurality of second characteristic point positions of the mixed preparation efficiency distribution map according to the second maximum value point and the second minimum value point. The feature point positions are selected according to specific rules and trends in the graph, and represent important changes and characteristics in the graph. And carrying out numerical extraction on the characteristic points so as to obtain a uniformity distribution characteristic of the mixed preparation and a efficiency distribution characteristic of the mixed preparation. These distribution features are essentially key numerical information extracted from the graph, reflecting the core characteristics and rules of the hybrid manufacturing process. After the distribution characteristics are obtained, the corresponding relation between the uniformity distribution characteristics of the mixed preparation and the efficiency distribution characteristics of the mixed preparation is constructed. Taking the mixed preparation uniformity distribution characteristic as a first matrix element, generating a first row matrix according to the elements, and simultaneously taking the mixed preparation efficiency distribution characteristic as a second matrix element, and generating a second row matrix according to the elements. Thus, two rows of matrixes respectively representing two distribution characteristics are obtained, and comprehensive information of two different characteristics is reflected. And fusing the first row matrix and the second row matrix to generate a final mixed preparation distribution characteristic matrix. This matrix fuses all key features of mixing uniformity and efficiency, providing a comprehensive, multi-dimensional data structure for further analysis and optimization of the mixing preparation process.
104, inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model to carry out preparation parameter adjustment analysis, and generating second biochar preparation parameter data;
specifically, the mixed preparation distribution characteristic matrix is input into a preset mixed integer linear programming model. Mixed integer linear programming is an optimization algorithm for finding an optimized integer solution while satisfying a series of linear constraints. In this model, an objective function is constructed, which is a guideline for the algorithm to find the optimal solution, reflecting the optimization objective for the adjustment of the hybrid preparation parameters, such as maximizing efficiency or minimizing cost. And carrying out feasibility solution of preparation parameters on the mixed preparation distribution characteristic matrix through an objective function, and evaluating various parameter combinations by an algorithm to find a plurality of feasible solutions meeting all constraint conditions. Each feasible solution represents a potential biochar preparation parameter configuration that meets the model optimization objectives to some extent. And respectively inputting the plurality of feasible solutions into a decision variable layer to predict decision variable indexes. Each feasible solution is evaluated for corresponding decision variable evaluation indexes, wherein the indexes comprise key factors such as quality, yield, energy consumption and the like of the biochar. Thus providing insight into the specific and potential value of each feasible solution. And judging whether the decision variable evaluation indexes accord with preset index reference values or not. These reference values represent the desired properties or minimum requirements in the preparation process, and only the feasible solutions satisfying these reference values can be considered for further analysis. For those feasible solutions that meet the conditions, an optimization analysis is performed to select the optimal solution from among them. This optimal solution represents the configuration of the charcoal production parameters that best meet the requirements of the objective function under all conditions considered. A preparation parameter adjustment range of the optimal solution is defined, and the range defines upper and lower limits of parameters which can be adjusted in actual operation. Then, a plurality of parameter subsets are divided according to this range, each subset representing a specific set of parameter adjustment options. And adjusting the technological parameters of the target preparation container according to the plurality of parameter subsets to generate final second biochar preparation parameter data.
Step 105, updating preparation parameters of a target preparation container through second biochar preparation parameter data to generate target biochar, and carrying out negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy;
specifically, the existing parameters of the target preparation container are updated and adjusted through the second biochar preparation parameter data. By precisely controlling key parameters such as temperature, time, humidity and the like, target biochar with expected characteristics is generated, and the biochar has better carbon emission performance in theory and practical application. And carrying out negative carbon emission index analysis on the target biochar. The negative carbon emission index is an important index for evaluating the carbon fixing capability of the biochar and reducing the greenhouse gas emission effect, and comprehensively considers a plurality of influencing factors of the biochar, including chemical components, physical structures, stability and the like. By analysis of these data, a quantitative description of the carbon negative emission performance of the biochar can be obtained. And obtaining the multi-scale characteristic weight by carrying out multi-scale analysis on the carbon emission index data. The multi-scale analysis is a method capable of revealing the change rule of the data on different scales, and key factors affecting the performance of the negative carbon emission and the action intensity of the key factors on different scales can be identified through the analysis. These multi-scale feature weights provide a quantitative way to understand and evaluate the impact of different factors on biochar carbon negative emission performance. And carrying out preparation parameter optimization strategy analysis on the negative carbon emission index data according to the obtained multi-scale characteristic weight. By comprehensively considering the influence and weight of different factors, the optimal preparation parameter combination capable of improving the negative carbon emission performance of the biochar is determined. This involves precise adjustments of various manufacturing parameters such as temperature, time, pressure, etc., to maximize the carbon negative emission index of the biochar. Through analysis and optimization, a first preparation parameter optimization strategy is finally obtained, and the strategy is based on deep data analysis, gives consideration to the influence of multi-scale characteristics, and is a comprehensive optimization scheme comprehensively considering factors in various aspects.
And 106, carrying out full life cycle assessment and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and carrying out iterative analysis on second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain the target biochar preparation parameter data.
Specifically, a Life Cycle Assessment (LCA) model of the target biochar is constructed. Based on the LCA model, the target biochar is comprehensively evaluated, and key environmental impact indexes such as energy consumption, greenhouse gas emission, resource use and the like in the whole life cycle can be obtained. And creating a corresponding second preparation parameter optimization strategy according to the key environmental impact indexes of the target biochar in each stage of the life cycle. This strategy focuses on adjusting and optimizing the parameters of biochar production to reduce energy consumption, greenhouse gas emissions and resource usage, thereby improving the overall environmental performance of the biochar. The strategy will take into account the various links in the biochar lifecycle, ensuring that each step from raw material acquisition to final disposal is as environmentally friendly and efficient as possible. The two sets of strategies are applied to iterative analysis of the second biochar preparation parameter data in obtaining a first preparation parameter optimization strategy and a second preparation parameter optimization strategy. Iterative analysis is an iterative process that gradually approaches optimal biochar production conditions by continually adjusting and optimizing parameters. In this process, the first preparation parameter optimization strategy and the second preparation parameter optimization strategy will complement each other, together guiding the adjustment of the parameters. The first strategy is more focused on improving the quality and yield of the biochar, while the second strategy is more focused on improving the environmental performance of the biochar. Finally, a group of target biochar preparation parameter data comprehensively considering various factors such as biochar quality, yield, environmental influence and the like is obtained through an iterative analysis process.
In the embodiment of the application, the optimization is performed by using the historical biochar preparation parameter data and the genetic algorithm, so that the optimal adjustment of the biochar preparation parameters is realized. This helps to improve the quality and performance of the biochar. And the uniformity and efficiency of the mixed preparation are analyzed in detail by utilizing a dynamics simulation technology. Generating a mixed preparation uniformity distribution map and a mixed preparation efficiency distribution map, and providing specific data support for subsequent feature extraction and preparation parameter adjustment. And generating a mixed preparation distribution characteristic matrix through characteristic extraction of the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map. This helps to systematically understand key features in the hybrid manufacturing process and provides targeted information for subsequent manufacturing parameter adjustments. And the mixed integer linear programming model is utilized to analyze the mixed preparation distribution characteristic matrix, so that the intelligent adjustment of the preparation parameters is realized. This helps to optimize the process of preparing the biochar and to improve the carbon negative effect of the biochar. By performing multi-scale analysis on the carbon negative emission of the target biochar, factors such as energy consumption, greenhouse gas emission, resource use and the like are systematically considered in combination with full life cycle assessment. This helps to optimize the overall preparation parameters and ensures that the carbon emission effect of the biochar is maximized. The preparation parameters are continuously optimized through iterative analysis, so that the fine adjustment of the preparation parameters of the target biochar is realized, the preparation efficiency of the biochar fertilizer is improved and the preparation parameters of the biochar fertilizer are optimized through combining carbon emission.
In a specific embodiment, the process of executing step 101 may specifically include the following steps:
(1) Obtaining historical biochar preparation parameter data of a target preparation container, wherein the historical biochar preparation parameter data comprises: preparation temperature, preparation humidity and preparation time;
(2) Carrying out standardized treatment on the historical biochar preparation parameter data to obtain standard biochar preparation parameter data;
(3) Carrying out population initialization on the standard biochar preparation parameter data based on a preset genetic algorithm to obtain a plurality of candidate biochar preparation parameter data;
(4) And respectively calculating the adaptability data of each candidate biochar preparation parameter data, and selecting, crossing and mutating the plurality of candidate biochar preparation parameter data according to the adaptability data to obtain first biochar preparation parameter data.
Specifically, historical biochar preparation parameter data are collected from a target preparation vessel, and include key factors such as preparation temperature, preparation humidity, preparation time and the like. These parameters are important factors affecting the quality and yield of biochar. For example, one particular preparation temperature results in more carbon fixation, while a particular humidity and time affects the pore structure and stability of the biochar. And then, carrying out standardized processing on the historical biochar preparation parameter data, and converting the data from different scales to a uniform scale to ensure the comparability and the effectiveness of the data in subsequent analysis. Standard biochar production parameter data were obtained by subtracting the mean value from each parameter and dividing by the standard deviation. And initializing the population of the standardized biochar preparation parameter data based on a preset genetic algorithm. Genetic algorithm is a search algorithm simulating natural evolution mechanism, which evolves the optimal solution of the problem through population propagation, mutation, crossover and selection. During the population initialization phase, the algorithm will randomly generate a plurality of candidate biochar production parameter data based on historical data, the candidate data representing different combinations of production parameters. The algorithm calculates fitness data for each candidate biochar production parameter data separately. Fitness functions are criteria in genetic algorithms to evaluate candidate solutions for quality, and are typically designed according to the specific needs of the problem. The fitness function may be based on factors such as quality, yield, energy consumption, etc. of the biochar. For example, one candidate data is given a higher fitness value because of the high quality or high yield of biochar produced. The algorithm selects, crosses and mutates the candidate biochar preparation parameter data according to the fitness data. The selection operation is to select those candidate data to be retained and propagated to the next generation according to the fitness. For example, a candidate data with a high fitness will have a higher chance to be selected. The interleaving operation refers to combining partial parameters of two candidate data together to form new candidate data. For example, the temperature of one candidate data may be combined with the humidity of the other. The mutation operation is to randomly change certain parameters in candidate data so as to introduce new genetic diversity. For example, the preparation time of one candidate data may be randomly increased or decreased. After a series of selection, crossover and mutation operations, the genetic algorithm will generate a new set of candidate biochar production parameter data representing improvements to the original population. After multiple iterations, the process will gradually optimize the biochar preparation parameters, and the final first biochar preparation parameter data will be the optimal combination of parameters that will yield high quality and high efficiency biochar under the given preparation conditions.
In a specific embodiment, the process of executing step 102 may specifically include the following steps:
(1) Performing kinetic simulation on the target preparation container through the first biochar preparation parameter data to obtain an initial CFD model;
(2) Setting initial conditions and boundary conditions of an initial CFD model to obtain a target CFD model;
(3) Performing grid division and dynamic parameter setting on the target CFD model, and performing operation simulation on the target CFD model;
(4) Performing mixed preparation uniformity analysis on the target CFD model to obtain mixed preparation uniformity data, and performing mixed preparation efficiency analysis on the target CFD model to obtain mixed preparation efficiency data;
(5) Extracting a plurality of first data points in the mixed preparation uniformity data, and mapping a distribution map of the plurality of first data points to obtain a mixed preparation uniformity distribution map;
(6) And extracting a plurality of second data points in the mixed preparation efficiency data, and performing distribution map mapping on the plurality of second data points to obtain a mixed preparation efficiency distribution map.
Specifically, a Computational Fluid Dynamics (CFD) model is initialized with first biochar preparation parameter data. The initial CFD model is a starting point of dynamic simulation and simulates physical phenomena such as fluid flow, heat transfer and the like in the preparation process of the biochar. Initial conditions and boundary conditions of the initial CFD model are set. The initial conditions include an initial temperature distribution, an initial pressure distribution, etc., while the boundary conditions include a heat transfer coefficient of the container wall, a temperature of the external environment, etc. For example, the heat transfer coefficient of the vessel wall is set to simulate the loss of heat, or the temperature of the external environment is set to simulate the effect of external conditions on the manufacturing process. And performing grid division and dynamic parameter setting of the target CFD model. Meshing is the process of dividing the entire analog region into many small cells. The dynamic parameter setting includes setting the physical properties of the fluid, such as density, viscosity, etc. These parameters have a direct impact on the accuracy of the simulation. For example, the mesh division is performed according to the specific size and shape of the target preparation vessel, and the corresponding hydrodynamic parameters are set according to the characteristics of the biochar material. And performing operation simulation on the target CFD model. And (3) operating according to set initial conditions, boundary conditions, grid division and dynamic parameters, and simulating physical phenomena such as fluid flow, heat transfer and the like in the preparation process of the biochar. The results of the run simulation will provide detailed information about the hybrid preparation process, such as temperature profile, flow rate profile, etc. And carrying out mixed preparation uniformity analysis and mixed preparation efficiency analysis on the target CFD model. The uniformity analysis of the mixed preparation refers to the analysis of the uniformity of the mixing of materials during the preparation process, and the efficiency analysis of the mixed preparation refers to the analysis of the efficiency of the preparation process, such as time, energy consumption, etc. For example, a mixed preparation uniformity analysis may take into account the uniformity of the temperature or concentration distribution during preparation, while a mixed preparation efficiency analysis may take into account the time or energy consumption required to reach a particular temperature. And extracting a plurality of first data points in the mixed preparation uniformity data, and mapping the data points into a distribution map to obtain a mixed preparation uniformity distribution map. This profile will intuitively show the distribution of the uniformity of mixing of the materials during the preparation process, helping to identify non-uniform areas. Likewise, a plurality of second data points in the mixed preparation efficiency data are extracted, and the data points are subjected to profile mapping to obtain a mixed preparation efficiency profile. This profile will show the efficiency profile of the manufacturing process, helping to identify areas of inefficiency.
In a specific embodiment, the process of executing step 103 may specifically include the following steps:
(1) Performing extremum analysis on the mixed preparation uniformity distribution map to obtain a first maximum value point and a first minimum value point, and performing extremum analysis on the mixed preparation efficiency distribution map to obtain a second maximum value point and a second minimum value point;
(2) Determining a plurality of first characteristic point positions of the mixed preparation uniformity distribution map according to the first maximum value point and the first minimum value point, and determining a plurality of second characteristic point positions of the mixed preparation efficiency distribution map according to the second maximum value point and the second minimum value point;
(3) Extracting the characteristic point values of the first characteristic point positions to obtain a mixed preparation uniformity distribution characteristic, and extracting the characteristic point values of the second characteristic point positions to obtain a mixed preparation efficiency distribution characteristic;
(4) Constructing a corresponding relation between the uniformity distribution characteristic of the mixed preparation and the efficiency distribution characteristic of the mixed preparation;
(5) Taking the mixed preparation uniformity distribution characteristic as a first matrix element according to the corresponding relation, generating a first row of matrix according to the first matrix element, taking the mixed preparation efficiency distribution characteristic as a second matrix element, and generating a second row of matrix according to the second matrix element;
(6) And performing matrix fusion on the first row matrix and the second row matrix to generate a mixed preparation distribution characteristic matrix.
Specifically, extremum analysis is performed on the mixture preparation uniformity distribution map, and a first maximum value point and a first minimum value point are identified. Extremum analysis is a mathematical method for determining the maximum and minimum values of a function within a given interval. The mixing preparation uniformity distribution diagram is a diagram showing the spatial variation of the mixing uniformity of materials in the preparation process of the biochar, and the highest and lowest areas of the mixing uniformity can be known by identifying the maximum value and the minimum value points in the diagram. For example, the first maximum point represents an area where the materials are mixed particularly uniformly, while the first minimum point represents an area where the mixing is most non-uniform. Similarly, extremum analysis is performed on the hybrid preparation efficiency profile to identify a second maximum point and a second minimum point. The mixed preparation efficiency distribution diagram shows the space distribution condition of the efficiency in the preparation process of the biochar, and the regions with the highest efficiency and the lowest efficiency can be identified through extremum analysis. For example, the second maximum point is located where the efficiency is highest during the manufacturing process, and the second minimum point represents the area where the efficiency is lowest. Determining a plurality of first characteristic point positions of the mixed preparation uniformity distribution map according to the first maximum value point and the first minimum value point, and determining a plurality of second characteristic point positions of the mixed preparation efficiency distribution map according to the second maximum value point and the second minimum value point. These feature point locations are further refinements to the mixing uniformity and efficiency characteristics, which represent important areas for understanding and optimizing the mixing preparation process. For example, the first feature point is a position in the mixture uniformity profile where the rate of change is particularly large or there is a particular pattern, except for the extreme point. And then, extracting the feature point value of the first feature point position to obtain a mixed preparation uniformity distribution feature, and extracting the feature point value of the second feature point position to obtain a mixed preparation efficiency distribution feature. The identified feature points are quantitatively described and converted into numerical information that can be used for analysis and comparison. For example, the hybrid preparation uniformity profile includes a specific uniformity value at a first characteristic point location, while the hybrid preparation efficiency profile includes a specific efficiency value at a second characteristic point location. The corresponding relation between the uniformity distribution characteristics and the efficiency distribution characteristics of the mixed preparation is constructed, the interaction and the influence between the uniformity and the efficiency of the mixed preparation are explored, and how the interaction and the influence affect the preparation process of the biochar together is known. For example, by analyzing the correspondence, it is found that there is a significant positive or negative correlation between uniformity and efficiency in certain regions. And taking the mixed preparation uniformity distribution characteristic as a first matrix element according to the corresponding relation, generating a first row matrix according to the first matrix element, taking the mixed preparation efficiency distribution characteristic as a second matrix element, and generating a second row matrix according to the second matrix element. The extracted features are converted into a matrix form, so that further mathematical analysis and processing are facilitated. For example, a first row matrix contains mixing uniformity values at different first feature point locations, and a second row matrix contains mixing efficiency values at different second feature point locations. And performing matrix fusion on the first row matrix and the second row matrix to generate a mixed preparation distribution characteristic matrix. This feature matrix is a comprehensive description of the overall hybrid preparation process, which fuses all key features of uniformity and efficiency.
In a specific embodiment, the process of executing step 104 may specifically include the following steps:
(1) Inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model, and constructing an objective function of the mixed integer linear programming model;
(2) Carrying out preparation parameter feasibility solving on the mixed preparation distribution feature matrix through an objective function to obtain a plurality of feasible solutions;
(3) Inputting a plurality of feasible solutions into a decision variable layer to conduct decision variable index prediction, so as to obtain a decision variable evaluation index of each feasible solution;
(4) Judging whether the decision variable evaluation index accords with a preset index reference value or not to obtain a target judgment result, and carrying out optimization analysis on a plurality of feasible solutions according to the target judgment result to obtain an optimal solution;
(5) Defining a preparation parameter adjustment range of the optimal solution, and dividing a plurality of parameter subsets according to the preparation parameter adjustment range;
(6) And adjusting the technological parameters of the target preparation container according to the plurality of parameter subsets to generate second biochar preparation parameter data.
Specifically, the mixed preparation distribution characteristic matrix is input into a preset mixed integer linear programming model. Mixed integer linear programming is a mathematical optimization technique for finding a set of integer solutions that optimize the objective function while satisfying a series of linear constraints. The objective function will define the objective of optimization, either to maximize the quality, yield or efficiency of the biochar, or to minimize cost, energy consumption, etc. And carrying out feasibility solving of the preparation parameters on the mixed preparation distribution characteristic matrix through an objective function. A mixed integer linear programming model is used to search through all parameter combinations to find a number of feasible solutions that satisfy all constraints. Each feasible solution is a specific set of preparation parameters that theoretically can meet the requirements of biochar preparation. For example, one possible solution is a specific set of temperature, time and humidity combinations that together produce a certain quality and yield of biochar. And respectively inputting the plurality of feasible solutions into a decision variable layer to perform decision variable index prediction, evaluating the potential performance of each feasible solution, and predicting decision variable evaluation indexes such as quality, yield, cost, energy consumption and the like of the feasible solutions. These evaluation metrics are quantitative estimates of the performance of viable solutions during actual biochar preparation, which help understand the advantages and disadvantages of each viable solution. For example, one feasible solution's evaluation index shows that it can produce higher quality biochar with lower energy consumption, while another shows that it is advantageous in terms of yield. And judging whether the decision variable evaluation indexes accord with preset index reference values or not. These reference values represent the desired properties or minimum requirements in the preparation process, and only the feasible solutions satisfying these reference values can be considered for further analysis. For those feasible solutions that meet the conditions, an optimization analysis is performed to select the optimal solution from among them. The optimal solution is the preparation parameter configuration which can most meet the requirement of the objective function under all the considered conditions. And defining the preparation parameter adjustment range of the optimal solution. This range defines upper and lower limits for parameters that can be adjusted in practice, which provides flexibility for further optimization. A plurality of parameter subsets are partitioned according to this range. Each subset of parameters represents a specific set of parameter tuning options that vary slightly based on the optimal solution. And adjusting the technological parameters of the target preparation container according to the parameter subset to generate second biochar preparation parameter data.
In a specific embodiment, the process of executing step 105 may specifically include the following steps:
(1) Updating preparation parameters of the target preparation container through the second biochar preparation parameter data to generate target biochar;
(2) Carrying out negative carbon emission index analysis on target biochar to obtain negative carbon emission index data, and carrying out multi-scale analysis on the negative carbon emission index data to obtain multi-scale characteristic weights;
(3) And carrying out preparation parameter optimization strategy analysis on the negative carbon emission index data according to the multi-scale feature weights to obtain a first preparation parameter optimization strategy.
Specifically, the preparation parameters of the target preparation container are updated through the second biochar preparation parameter data. These parameters include preparation temperature, humidity, time, type and ratio of raw materials, etc. And actually preparing the target biochar. Various parameters were monitored to ensure that they remained consistent with the second biochar production parameter data. And carrying out negative carbon emission index analysis on the target biochar. The carbon negative emission index is a comprehensive index for evaluating the ability of biochar to reduce the concentration of carbon dioxide in the atmosphere. Such analysis typically involves evaluating various aspects of the stability, durability, and adsorption capacity of the biochar. And carrying out multi-scale analysis through the carbon emission index data to obtain multi-scale characteristic weights. The multi-scale analysis is an analysis method considering the influence of different time and space scales, and can reveal the change rule of the negative carbon emission index under different conditions. The multi-scale feature weight is an index for quantifying the importance of different scale features based on such analysis. For example, it was found that the stability of biochar has a more pronounced effect on the carbon emission index than on the short time scale, and thus stability characteristics on the long time scale will be given a higher weight. And carrying out preparation parameter optimization strategy analysis on the negative carbon emission index data according to the multi-scale feature weights to obtain a first preparation parameter optimization strategy. The strategy is a series of suggestions for how to adjust the preparation parameters to optimize the carbon negative effect of the target biochar on the basis of comprehensively considering the multi-scale characteristics of the carbon negative emission index and the weight thereof. For example, if analysis shows that increasing the preparation time can significantly increase the stability of the biochar and thus its long-term negative carbon effects, then increasing the preparation time would be one suggestion in the first preparation parameter optimization strategy.
In a specific embodiment, the process of executing step 106 may specifically include the following steps:
(1) Constructing an LCA model of the target biochar, and carrying out full life cycle assessment on the target biochar based on the LCA model to obtain energy consumption, greenhouse gas emission and resource use influence indexes of each stage of the life cycle of the target biochar;
(2) Creating a corresponding second preparation parameter optimization strategy according to the energy consumption, greenhouse gas emission and resource use influence indexes of each stage of the target biochar life cycle;
(3) And carrying out iterative analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
Specifically, an LCA model of the target biochar is constructed, and detailed data about the full life cycle of the target biochar is collected, including extraction and processing of raw materials, preparation, use of the biochar, and final disposal and processing. Then, full life cycle assessment is performed on the target biochar based on the LCA model. And analyzing a plurality of environmental impact indexes such as energy consumption, greenhouse gas emission, resource use and the like of the target biochar in the whole life cycle. The energy consumption includes all the energy consumed during the production and use of biochar, the greenhouse gas emissions include all the greenhouse gases released during this process, and the use of resources is concerned with the use of water, soil and other natural resources. And creating a corresponding second preparation parameter optimization strategy according to the energy consumption, greenhouse gas emission and resource use influence indexes of each stage of the target biochar life cycle. This strategy would be designed to reduce the environmental impact of the biochar lifecycle. For example, if LCA assessment shows that the energy consumption of the biochar production phase is particularly high, then the second production parameter optimization strategy would include changing the pyrolysis temperature or extending the pyrolysis time to increase energy utilization efficiency. And carrying out iterative analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy. Different preparation parameter combinations are repeatedly adjusted and tested to find the optimal parameter setting which can maintain or improve the quality of the biochar while meeting the environmental and efficiency targets. For example, iterative analysis may test the carbon fixation rate and greenhouse gas emissions of biochar at different pyrolysis temperatures to determine an optimal pyrolysis temperature setting. By iterative analysis, the optimal biochar preparation parameter combination can be gradually approximated. Finally, obtaining target biochar preparation parameter data through iterative analysis and optimization. These parameter data will reflect the optimal production conditions under dual objectives of environmental impact and biochar quality.
The method for analyzing the preparation of the biochar based on the carbon negative emission in the embodiment of the present application is described above, and the apparatus for analyzing the preparation of the biochar based on the carbon negative emission in the embodiment of the present application is described below, referring to fig. 2, an embodiment of the apparatus for analyzing the preparation of the biochar based on the carbon negative emission in the embodiment of the present application includes:
the acquisition module 201 is configured to acquire historical biochar preparation parameter data of a target preparation container, and perform biochar preparation optimization on the historical biochar preparation parameter data based on a preset genetic algorithm to obtain first biochar preparation parameter data;
the processing module 202 is configured to perform kinetic simulation on the first biochar preparation parameter data to obtain mixed preparation uniformity data and mixed preparation efficiency data, generate a mixed preparation uniformity distribution map according to the mixed preparation uniformity data, and generate a mixed preparation efficiency distribution map according to the mixed preparation efficiency data;
the feature extraction module 203 is configured to perform feature extraction on the mixture preparation uniformity distribution map and the mixture preparation efficiency distribution map, obtain a mixture preparation uniformity distribution feature and a mixture preparation efficiency distribution feature, and generate a mixture preparation distribution feature matrix according to the mixture preparation uniformity distribution feature and the mixture preparation efficiency distribution feature;
The analysis module 204 is configured to input the mixed preparation distribution feature matrix into a preset mixed integer linear programming model to perform preparation parameter adjustment analysis, so as to generate second biochar preparation parameter data;
the updating module 205 is configured to update the preparation parameters of the target preparation container according to the second biochar preparation parameter data, generate target biochar, and perform negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy;
and the iteration module 206 is configured to perform full life cycle evaluation and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and perform iteration analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
Through the cooperation of the components, the optimization is performed by using historical biochar preparation parameter data and a genetic algorithm, so that the optimal adjustment of the biochar preparation parameters is realized. This helps to improve the quality and performance of the biochar. And the uniformity and efficiency of the mixed preparation are analyzed in detail by utilizing a dynamics simulation technology. Generating a mixed preparation uniformity distribution map and a mixed preparation efficiency distribution map, and providing specific data support for subsequent feature extraction and preparation parameter adjustment. And generating a mixed preparation distribution characteristic matrix through characteristic extraction of the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map. This helps to systematically understand key features in the hybrid manufacturing process and provides targeted information for subsequent manufacturing parameter adjustments. And the mixed integer linear programming model is utilized to analyze the mixed preparation distribution characteristic matrix, so that the intelligent adjustment of the preparation parameters is realized. This helps to optimize the process of preparing the biochar and to improve the carbon negative effect of the biochar. By performing multi-scale analysis on the carbon negative emission of the target biochar, factors such as energy consumption, greenhouse gas emission, resource use and the like are systematically considered in combination with full life cycle assessment. This helps to optimize the overall preparation parameters and ensures that the carbon emission effect of the biochar is maximized. The preparation parameters are continuously optimized through iterative analysis, so that the fine adjustment of the preparation parameters of the target biochar is realized, the preparation efficiency of the biochar fertilizer is improved and the preparation parameters of the biochar fertilizer are optimized through combining carbon emission.
The present application also provides a computer device including a memory and a processor, where the memory stores computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the carbon emission-based biochar preparation analysis method in the above embodiments.
The present application also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, in which instructions are stored which, when executed on a computer, cause the computer to perform the steps of the carbon emission based biochar preparation analysis method.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, systems and units may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. The biochar preparation analysis method based on the carbon emission is characterized by comprising the following steps of:
acquiring historical biochar preparation parameter data of a target preparation container, and performing biochar preparation optimization on the historical biochar preparation parameter data based on a preset genetic algorithm to obtain first biochar preparation parameter data;
performing dynamics simulation on the first biochar preparation parameter data to obtain mixed preparation uniformity data and mixed preparation efficiency data, generating a mixed preparation uniformity distribution map according to the mixed preparation uniformity data, and generating a mixed preparation efficiency distribution map according to the mixed preparation efficiency data;
Respectively carrying out feature extraction on the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map to obtain mixed preparation uniformity distribution features and mixed preparation efficiency distribution features, and generating a mixed preparation distribution feature matrix according to the mixed preparation uniformity distribution features and the mixed preparation efficiency distribution features;
inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model for preparation parameter adjustment analysis, and generating second biochar preparation parameter data;
updating preparation parameters of the target preparation container through the second biochar preparation parameter data to generate target biochar, and performing negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy;
and carrying out full life cycle assessment and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and carrying out iterative analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
2. The carbon emission-based biochar production analysis method of claim 1, wherein the obtaining the historical biochar production parameter data of the target production vessel and performing the biochar production optimization on the historical biochar production parameter data based on a preset genetic algorithm to obtain the first biochar production parameter data comprises:
obtaining historical biochar preparation parameter data of a target preparation container, the historical biochar preparation parameter data comprising: preparation temperature, preparation humidity and preparation time;
carrying out standardized treatment on the historical biochar preparation parameter data to obtain standard biochar preparation parameter data;
carrying out population initialization on the standard biochar preparation parameter data based on a preset genetic algorithm to obtain a plurality of candidate biochar preparation parameter data;
and respectively calculating the adaptation data of each candidate biochar preparation parameter data, and selecting, crossing and mutating the plurality of candidate biochar preparation parameter data according to the adaptation data to obtain first biochar preparation parameter data.
3. The carbon emission-based biochar preparation analysis method of claim 1, wherein the performing kinetic simulation on the first biochar preparation parameter data to obtain a mixture preparation uniformity data and a mixture preparation efficiency data, generating a mixture preparation uniformity profile from the mixture preparation uniformity data, and generating a mixture preparation efficiency profile from the mixture preparation efficiency data comprises:
Performing kinetic simulation on the target preparation container through the first biochar preparation parameter data to obtain an initial CFD model;
setting initial conditions and boundary conditions of the initial CFD model to obtain a target CFD model;
performing grid division and dynamic parameter setting on the target CFD model, and performing operation simulation on the target CFD model;
performing mixed preparation uniformity analysis on the target CFD model to obtain mixed preparation uniformity data, and performing mixed preparation efficiency analysis on the target CFD model to obtain mixed preparation efficiency data;
extracting a plurality of first data points in the mixed preparation uniformity data, and performing distribution map mapping on the plurality of first data points to obtain a mixed preparation uniformity distribution map;
and extracting a plurality of second data points in the mixed preparation efficiency data, and performing distribution map mapping on the plurality of second data points to obtain a mixed preparation efficiency distribution map.
4. The method according to claim 1, wherein the performing feature extraction on the mixture preparation uniformity profile and the mixture preparation efficiency profile to obtain a mixture preparation uniformity distribution feature and a mixture preparation efficiency distribution feature, and generating a mixture preparation distribution feature matrix according to the mixture preparation uniformity distribution feature and the mixture preparation efficiency distribution feature, respectively, comprises:
Performing extremum analysis on the mixed preparation uniformity distribution map to obtain a first maximum value point and a first minimum value point, and performing extremum analysis on the mixed preparation efficiency distribution map to obtain a second maximum value point and a second minimum value point;
determining a plurality of first characteristic point positions of the mixed preparation uniformity distribution map according to the first maximum value point and the first minimum value point, and determining a plurality of second characteristic point positions of the mixed preparation efficiency distribution map according to the second maximum value point and the second minimum value point;
extracting the feature point values of the first feature point positions to obtain a mixed preparation uniformity distribution feature, and extracting the feature point values of the second feature point positions to obtain a mixed preparation efficiency distribution feature;
constructing a corresponding relation between the uniformity distribution characteristic of the mixed preparation and the efficiency distribution characteristic of the mixed preparation;
taking the mixed preparation uniformity distribution characteristic as a first matrix element according to the corresponding relation, generating a first row of matrix according to the first matrix element, taking the mixed preparation efficiency distribution characteristic as a second matrix element, and generating a second row of matrix according to the second matrix element;
And performing matrix fusion on the first row matrix and the second row matrix to generate a mixed preparation distribution characteristic matrix.
5. The method for analyzing the preparation of biochar based on carbon emission as set forth in claim 1, wherein the step of inputting the mixed preparation distribution feature matrix into a preset mixed integer linear programming model for preparation parameter adjustment analysis to generate second biochar preparation parameter data includes:
inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model, and constructing an objective function of the mixed integer linear programming model;
carrying out preparation parameter feasibility solving on the mixed preparation distribution feature matrix through the objective function to obtain a plurality of feasible solutions;
inputting the plurality of feasible solutions into a decision variable layer to conduct decision variable index prediction respectively, so as to obtain decision variable evaluation indexes of each feasible solution;
judging whether the decision variable evaluation index accords with a preset index reference value or not to obtain a target judgment result, and carrying out optimization analysis on the plurality of feasible solutions according to the target judgment result to obtain an optimal solution;
defining a preparation parameter adjustment range of the optimal solution, and dividing a plurality of parameter subsets according to the preparation parameter adjustment range;
And adjusting the technological parameters of the target preparation container according to the plurality of parameter subsets to generate second biochar preparation parameter data.
6. The method for analyzing the preparation of the biochar based on the carbon negative emission according to claim 1, wherein the updating of the preparation parameters of the target preparation container by the second biochar preparation parameter data to generate the target biochar, and the performing the carbon negative emission multi-scale analysis and the preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy comprises:
updating the preparation parameters of the target preparation container through the second biochar preparation parameter data to generate target biochar;
carrying out negative carbon emission index analysis on the target biochar to obtain negative carbon emission index data, and carrying out multi-scale analysis on the negative carbon emission index data to obtain multi-scale characteristic weights;
and carrying out preparation parameter optimization strategy analysis on the carbon emission index data according to the multi-scale feature weights to obtain a first preparation parameter optimization strategy.
7. The method for analyzing the preparation of the biochar based on the carbon emission of claim 1, wherein the performing full life cycle evaluation and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and performing iterative analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data comprises:
Constructing an LCA model of the target biochar, and carrying out full life cycle assessment on the target biochar based on the LCA model to obtain energy consumption, greenhouse gas emission and resource use influence indexes of each stage of the life cycle of the target biochar;
creating a corresponding second preparation parameter optimization strategy according to energy consumption, greenhouse gas emission and resource use influence indexes of each stage of the target biochar life cycle;
and carrying out iterative analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
8. A carbon-emission-based biochar preparation and analysis device, characterized in that the carbon-emission-based biochar preparation and analysis device comprises:
the acquisition module is used for acquiring historical biochar preparation parameter data of the target preparation container, and carrying out biochar preparation optimization on the historical biochar preparation parameter data based on a preset genetic algorithm to obtain first biochar preparation parameter data;
the processing module is used for carrying out dynamics simulation on the first biochar preparation parameter data to obtain mixed preparation uniformity data and mixed preparation efficiency data, generating a mixed preparation uniformity distribution map according to the mixed preparation uniformity data, and generating a mixed preparation efficiency distribution map according to the mixed preparation efficiency data;
The characteristic extraction module is used for respectively carrying out characteristic extraction on the mixed preparation uniformity distribution map and the mixed preparation efficiency distribution map to obtain mixed preparation uniformity distribution characteristics and mixed preparation efficiency distribution characteristics, and generating a mixed preparation distribution characteristic matrix according to the mixed preparation uniformity distribution characteristics and the mixed preparation efficiency distribution characteristics;
the analysis module is used for inputting the mixed preparation distribution characteristic matrix into a preset mixed integer linear programming model to carry out preparation parameter adjustment analysis and generate second biochar preparation parameter data;
the updating module is used for updating the preparation parameters of the target preparation container through the second biochar preparation parameter data to generate target biochar, and carrying out negative carbon emission multi-scale analysis and preparation parameter optimization strategy analysis on the target biochar to obtain a first preparation parameter optimization strategy;
the iteration module is used for carrying out full life cycle assessment and preparation parameter optimization strategy analysis on the target biochar to obtain a second preparation parameter optimization strategy, and carrying out iteration analysis on the second biochar preparation parameter data according to the first preparation parameter optimization strategy and the second preparation parameter optimization strategy to obtain target biochar preparation parameter data.
9. A computer device, the computer device comprising: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the computer device to perform the carbon emission-based biochar preparation analysis method of any one of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the carbon emission-based biochar preparation analysis method of any one of claims 1-7.
CN202410056680.2A 2024-01-16 2024-01-16 Biochar preparation analysis method based on negative carbon emission and related device Active CN117574690B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202410056680.2A CN117574690B (en) 2024-01-16 2024-01-16 Biochar preparation analysis method based on negative carbon emission and related device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410056680.2A CN117574690B (en) 2024-01-16 2024-01-16 Biochar preparation analysis method based on negative carbon emission and related device

Publications (2)

Publication Number Publication Date
CN117574690A true CN117574690A (en) 2024-02-20
CN117574690B CN117574690B (en) 2024-04-12

Family

ID=89864762

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202410056680.2A Active CN117574690B (en) 2024-01-16 2024-01-16 Biochar preparation analysis method based on negative carbon emission and related device

Country Status (1)

Country Link
CN (1) CN117574690B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118246608A (en) * 2024-05-27 2024-06-25 深圳碳中和生物燃气股份有限公司 Carbon emission strategy optimization method and system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104129777A (en) * 2014-08-26 2014-11-05 武汉科技大学 Multifunctional bio-charcoal and preparation method thereof
CN106971080A (en) * 2017-04-21 2017-07-21 上海交通大学 A kind of modification biological charcoal adsorbs the response surface optimization method of Phosphorus From Wastewater
CN111807453A (en) * 2020-07-17 2020-10-23 长沙理工大学 Modified biochar for adsorbing phosphorus in water body and preparation method and application thereof
US20210155481A1 (en) * 2017-12-15 2021-05-27 Cool Planet Energy Systems, Inc. Biochars, biochar extracts and biochar extracts having soluble signaling compounds and method for capturing material extracted from biochar
CN115049095A (en) * 2022-03-23 2022-09-13 上海创能国瑞新能源科技股份有限公司 Dynamic optimization system for improving energy efficiency and carbon emission of equipment and related device
CN116116449A (en) * 2023-01-17 2023-05-16 四川大学 Nitrogen-doped biochar-based bio-oil upgrading catalyst and preparation method and application thereof

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104129777A (en) * 2014-08-26 2014-11-05 武汉科技大学 Multifunctional bio-charcoal and preparation method thereof
CN106971080A (en) * 2017-04-21 2017-07-21 上海交通大学 A kind of modification biological charcoal adsorbs the response surface optimization method of Phosphorus From Wastewater
US20210155481A1 (en) * 2017-12-15 2021-05-27 Cool Planet Energy Systems, Inc. Biochars, biochar extracts and biochar extracts having soluble signaling compounds and method for capturing material extracted from biochar
CN111807453A (en) * 2020-07-17 2020-10-23 长沙理工大学 Modified biochar for adsorbing phosphorus in water body and preparation method and application thereof
CN115049095A (en) * 2022-03-23 2022-09-13 上海创能国瑞新能源科技股份有限公司 Dynamic optimization system for improving energy efficiency and carbon emission of equipment and related device
CN116116449A (en) * 2023-01-17 2023-05-16 四川大学 Nitrogen-doped biochar-based bio-oil upgrading catalyst and preparation method and application thereof

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
鲍宏;刘光复;王吉凯;: "采用碳足迹分析的产品低碳优化设计", 计算机辅助设计与图形学学报, no. 02, 15 February 2013 (2013-02-15) *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118246608A (en) * 2024-05-27 2024-06-25 深圳碳中和生物燃气股份有限公司 Carbon emission strategy optimization method and system
CN118246608B (en) * 2024-05-27 2024-08-06 深圳碳中和生物燃气股份有限公司 Carbon emission strategy optimization method and system

Also Published As

Publication number Publication date
CN117574690B (en) 2024-04-12

Similar Documents

Publication Publication Date Title
CN117574690B (en) Biochar preparation analysis method based on negative carbon emission and related device
Meng et al. Multi-objective hydropower station operation using an improved cuckoo search algorithm
Kumar et al. The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization
CN110544011B (en) Intelligent system combat effectiveness evaluation and optimization method
CN103605909B (en) It is a kind of based on gray theory and the water quality prediction method of support vector machine
CN109214449A (en) A kind of electric grid investment needing forecasting method
CN103605611A (en) Test case optimization method based on genetic algorithm
CN112070356A (en) Method for predicting anti-carbonization performance of concrete based on RF-LSSVM model
CN102542051A (en) Design method for multi-target cooperative sampling scheme of randomly-distributed geographic elements
EP4288966A1 (en) Drug optimisation by active learning
CN109165081A (en) Web application adaptive resource allocation method based on machine learning
CN112364560A (en) Intelligent prediction method for working hours of mine rock drilling equipment
CN103164631B (en) A kind of intelligent coordinate expression gene analyser
Huo et al. Evaluation method of multiobjective functions’ combination and its application in hydrological model evaluation
CN115600492A (en) Laser cutting process design method and system
CN115458060A (en) Method and device for processing mRNA untranslated region sequence
Passone et al. Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation
CN110222133A (en) A kind of multiclass land use simulating and predicting method and device considering landscape index
CN105426959A (en) Aluminium electrolysis energy conservation and emission reduction method based on BP neural network and self-adaptive MBFO algorithm
CN102708298B (en) A kind of Vehicular communication system electromagnetic compatibility index distribution method
CN111813669A (en) Adaptive random test case generation method based on multi-target group intelligence
CN116108963A (en) Electric power carbon emission prediction method and equipment based on integrated learning module
Stark et al. A new strategy for adapting the mutation probability in genetic algorithms
Schardong et al. Water Resources Research Report
Cahyadi et al. Salt Fields Productivity Forecasting Based On Sunlight Duration, Wind Speed and Temperature Data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant