CN112750505A - Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel - Google Patents
Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel Download PDFInfo
- Publication number
- CN112750505A CN112750505A CN202110082631.2A CN202110082631A CN112750505A CN 112750505 A CN112750505 A CN 112750505A CN 202110082631 A CN202110082631 A CN 202110082631A CN 112750505 A CN112750505 A CN 112750505A
- Authority
- CN
- China
- Prior art keywords
- reaction
- model
- chemical reaction
- submodel
- simplified
- 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.)
- Pending
Links
- 238000006243 chemical reaction Methods 0.000 title claims abstract description 122
- 239000000446 fuel Substances 0.000 title claims abstract description 24
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000006757 chemical reactions by type Methods 0.000 claims abstract description 40
- 238000010206 sensitivity analysis Methods 0.000 claims abstract description 19
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- BANXPJUEBPWEOT-UHFFFAOYSA-N 2-methyl-Pentadecane Chemical compound CCCCCCCCCCCCCC(C)C BANXPJUEBPWEOT-UHFFFAOYSA-N 0.000 claims description 20
- 229940043268 2,2,4,4,6,8,8-heptamethylnonane Drugs 0.000 claims description 10
- KUVMKLCGXIYSNH-UHFFFAOYSA-N isopentadecane Natural products CCCCCCCCCCCCC(C)C KUVMKLCGXIYSNH-UHFFFAOYSA-N 0.000 claims description 10
- 125000004432 carbon atom Chemical group C* 0.000 claims description 7
- 229920002521 macromolecule Polymers 0.000 claims description 7
- 150000003384 small molecules Chemical class 0.000 claims description 7
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 6
- 230000035945 sensitivity Effects 0.000 claims description 5
- 150000002605 large molecules Chemical class 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 3
- 238000010168 coupling process Methods 0.000 claims description 3
- 238000005859 coupling reaction Methods 0.000 claims description 3
- 239000006185 dispersion Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 230000037361 pathway Effects 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000004088 simulation Methods 0.000 abstract description 11
- 238000002485 combustion reaction Methods 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 2
- 239000000295 fuel oil Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- SYSQUGFVNFXIIT-UHFFFAOYSA-N n-[4-(1,3-benzoxazol-2-yl)phenyl]-4-nitrobenzenesulfonamide Chemical class C1=CC([N+](=O)[O-])=CC=C1S(=O)(=O)NC1=CC=C(C=2OC3=CC=CC=C3N=2)C=C1 SYSQUGFVNFXIIT-UHFFFAOYSA-N 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
-
- 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
- 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
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- 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)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Analytical Chemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Crystallography & Structural Chemistry (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Physical Or Chemical Processes And Apparatus (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the field of combustion subject numerical simulation, and relates to a method for simplifying a large-scale detailed chemical reaction model of a high-carbon fuel, which is used for simplifying the large-scale detailed chemical reaction model of the high-carbon fuel and obtaining a simplified reaction kinetic model which meets the requirement of combustion multidimensional simulation on precision and scale. The method takes the sub-models/reaction classes as objects to simplify the large-scale detailed chemical reaction model of the high-carbon fuel, greatly reduces the number of input variables, reduces the calculation time of global sensitivity analysis, and makes the application of the global sensitivity analysis to the large-scale detailed chemical reaction model possible.
Description
Technical Field
The invention belongs to the field of combustion subject numerical simulation, and relates to a method for simplifying a large-scale detailed chemical reaction model of a high-carbon fuel.
Background
Engines require higher thermal efficiency and lower pollutant emissions to meet increasingly stringent emission regulations. With the development of computer technology, multi-dimensional combustion simulation becomes an important tool for the design and optimization of new engines. In order to ensure the reliability of the multidimensional combustion simulation, a simplified chemical reaction model with compact structure and reliable performance is particularly important. The components of real fuel oil are extremely complex, and in order to reproduce the physicochemical characteristics of the real fuel oil, the characterization fuel is usually composed of components with huge molecular structures, so that the detailed chemical reaction model is extremely large in scale. The simplified chemical reaction model which can satisfy the multidimensional combustion simulation at the same time of scale and performance cannot be obtained based on the current detailed chemical reaction model simplification method. To solve this problem, a method for simplifying a large-scale detailed chemical reaction model of high-carbon fuel is needed to obtain a simplified reaction model satisfying the needs of multi-dimensional combustion simulation.
Disclosure of Invention
In order to simplify a large-scale detailed chemical reaction model of the high-carbon fuel and obtain a simplified reaction kinetic model which meets the multi-dimensional simulation of combustion at the same time of precision and scale, the invention provides a method for simplifying the large-scale detailed chemical reaction model of the high-carbon fuel.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for simplifying a large-scale detailed chemical reaction model of high-carbon fuel comprises the following specific steps:
(1) large scale detailed chemical reaction model pretreatment of high carbon fuels
Dividing the reaction into different sub-models according to the large-scale detailed chemical reaction model structure of the high-carbon fuel and the maximum number of carbon atoms of the components involved in the reaction; then, dividing the reactions in the submodels into different reaction classes according to a rate rule; in order to reduce the amount of model simplification, in the subsequent calculations, the submodels/reaction classes are considered as a whole, i.e. for all reactions belonging to the jth submodel/reaction class
Wherein m isjFor the total number of reactions contained in the jth sub-model/reaction class,standard uncertainty rate parameter for nth reaction
knReaction rate for the nth reaction in a single simulation, fnAs the uncertainty factor f of the nth reactionn=log10(kn,max/kn,0)=log10(kn,0/kn,min),kn,0、kn,maxAnd kn,minRespectively, the standard reaction rate, the maximum value and the minimum value of the nth reaction;
(2) assessing importance of submodels using global sensitivity analysis
To perform a global sensitivity analysis, first, an input variable x is inputjThe dispersion is (0,1/(p-1),2/(p-1), …, 1); subsequently, a set of x is randomly generated, and the change Δ of the elements in x one by one is calculated, and a reduction target y is calculatedi(ii) a The jth input variable pair simplification target yiHas a single influence of
The average effect is obtained through k times of calculation
Sum variance
The larger the influence of the rate constant for disturbing the reaction in the jth submodel/reaction class on the ith simplified target predicted value is, the larger the sigma isijThe larger the reaction class is, the stronger nonlinear relation exists between the jth reaction class and the ith simplified target or the stronger coupling relation exists between the jth reaction class and other reaction classes;
based onAnd the number of carbon atoms in the submodel, and dividing the submodel into a small molecule submodel (the number of carbon atoms is less than or equal to 4), an important large molecule submodel and an unimportant large molecule submodel;
(3) assessment of the importance of reactive species in important macromolecular submodels using global sensitivity and pathway sensitivity analysis
The path sensitivity coefficient is calculated by equation (6),
yi,jand removing the predicted value of the simplified chemical reaction model of the ith reaction class to the jth simplified target.
Based on xijDeleting the reaction classes one by one from small to large until the predicted value of the simplified chemical reaction model reaches the uncertain prediction boundary of the detailed chemical reaction model on any simplified target;
(4) construction of framework macromolecular submodel
Firstly, collecting isomers in the reaction classes reserved in the step (3) as a representative component; then, reactions in the unimportant submodel are lumped to obtain a framework macromolecule submodel;
(5) simplified small molecule submodels
The reactions in the small molecule submodels were evaluated using equation (7) and based on ξjDeleting the reactions one by one until the predicted value of the simplified chemical reaction model reaches the uncertain boundary of the detailed chemical reaction model for predicting any simplified target, and obtaining an initial simplified model;
(6) reaction rate optimization
And (3) optimizing the reaction rate constant in the fuel submodel within an uncertain range by using a multi-objective genetic algorithm to obtain a final simplified model.
The preferred embodiment of the above process is where the high carbon fuel is isohexadecane.
Compared with the prior art, the invention has the beneficial effects that:
1. the sub-models/reaction classes are taken as objects to simplify the large-scale detailed chemical reaction model of the high-carbon fuel, so that the number of input variables is greatly reduced, the calculation time of global sensitivity analysis is shortened, and the application of the global sensitivity analysis to the large-scale detailed chemical reaction model is possible.
2. By taking the sub-model/reaction class as an object, the problems of too many isomers and huge reaction models caused by reaction path analysis can be effectively avoided. The global sensitivity analysis can accurately capture the nonlinear behavior in the chemical reaction model and the coupling relation information during the reaction, and can ensure the reliability of the final simplified model. Therefore, the simplified chemical reaction kinetic model based on the method can maintain a compact structure and reliable performance and meet the requirement of multi-dimensional combustion simulation.
Drawings
FIG. 1 is a flow diagram of a large scale detailed chemical reaction model using the present method to simplify high carbon fuels.
FIG. 2 is a block diagram of a large scale detailed chemical reaction model of isohexadecane.
FIG. 3 is a comparison of a large scale detailed chemical reaction model and a simplified chemical reaction model of isohexadecane to the predicted value of the stagnation period.
FIG. 4 is a comparison of a large scale detailed chemical reaction model and a simplified chemical reaction model of isohexadecane to the predicted values of the concentrations of the HMN, CO and CO2 components in JSR.
Detailed Description
The invention is described in detail below with reference to the accompanying drawings and technical solutions.
A method for simplifying a large-scale detailed chemical reaction model of high-carbon fuel is used for simplifying a detailed chemical reaction model of isohexadecane, and a flow chart is shown in figure 1.
(1) Large-scale detailed chemical reaction model pretreatment of isohexadecane
Firstly, dividing an isohexadecane large-scale detailed chemical reaction model into 17 sub-models according to the maximum number of carbon atoms of components involved in the reaction; the reactions in the submodel are then divided into 26 reaction classes according to the rate rule, as shown in FIG. 2. In the subsequent calculations, the reaction class/submodel as a whole, i.e. for all reactions belonging to the jth reaction class/submodel
Wherein m isjFor the total number of reactions contained in the jth sub-model/reaction class,standard uncertainty rate parameter for nth reaction
knReaction rate for the nth reaction in a single simulation, fnAs an indeterminate factor of the nth reaction, fn=log10(kn,max/kn,0)=log10(kn,0/kn,min),kn,0、kn,maxAnd kn,minThe standard reaction rate, maximum and minimum values for the nth reaction, respectively. By the formula (2), the reaction rate of each reaction in the detailed chemical reaction model can be converted into a number between 0 and 1 in an uncertain space for subsequent global sensitivity analysis; uncertain factor f of reaction in C0-C4 submodelnUncertainty factor f from reaction in NIST database, C5-C16 submodelnSet to 0.6.
The simplification aims are as follows: t is 600-,And a flame lag period at p-50 atm and T-600-1500K,And the concentration of isohexadecane (HMN), CO and CO2 components in JSR at p ═ 10 atm.
(2) Assessing importance of submodels using global sensitivity analysis
Inputting variable x by using sub-model as object and through global sensitivity analysis methodjThe dispersion is (0,1/(p-1),2/(p-1), …, 1); subsequently, a set of x is randomly generated, and the change Δ of the elements in x one by one is calculated, and a reduction target y is calculatedi(ii) a The jth input variable pair simplification target yiHas a single influence of
The average effect is obtained through k times of calculation
Sum variance
Calculating sub-models under different simplified objectivesOnly the C0-C4 submodel and the C16 submodel were found to be superior
(3) Assessment of the importance of reactive species in important macromolecular submodels using global sensitivity and pathway sensitivity analysis
Calculating PSCs of the reaction classes under different simplification targets by using global sensitivity analysis and path sensitivity analysis and using formulas (4) to (6) by taking the reaction class in the C16 submodel as an objectij、And σij,
yi,jThe importance of the simplified chemical reaction model to remove the ith reaction class to the predicted value reaction class of the jth simplified target is normalizedAndare jointly determined, i.e.
Xi of each reaction class is obtained by the formula (7)jBased on xijAnd deleting the reaction classes one by one from small to large, and calculating and introducing errors until the predicted value of the simplified chemical reaction model for any simplified target reaches the prediction uncertainty boundary of the detailed chemical reaction model. A total of 10 reactive groups, namely reactive groups 1-3, 5, 10, 13, 14, 23-25, are retained.
(4) Construction of framework macromolecular submodel
Using linear lumped method, isomers in 10 reaction classes were lumped, and each class of isomers was lumped as a representative component. Reactions in the C5-C15 submodel were then lumped to obtain the framework macromolecule submodel.
(5) Simplified small molecule submodels
Calculation of PSC for each reaction in the C0-C4 submodels at different simplified targets using Global sensitivity analysis and Path sensitivity analysis equations (4) - (6)ij、And σijAnd calculated by the equations (8) to (10)Andxi of each reaction is obtained by the formula (7)j. Based on xijAnd deleting the reactions from small to large one by one, and calculating and introducing errors until the predicted value of the simplified chemical reaction model for any simplified target reaches the prediction uncertain boundary of the detailed chemical reaction model, thereby obtaining the initial simplified chemical reaction model.
(6) Reaction rate optimization
And (3) optimizing the rate constant of the reaction in the C16 submodel in the initial simplified chemical reaction model within an uncertain range by using a multi-objective genetic algorithm NSGA-II to obtain the final simplified chemical reaction model.
The detailed chemical reaction model of isohexadecane contained 1107 components and 4469 reactions, and the simplified chemical reaction model obtained using the present method contained only 56 components and 131 reactions. The number of reactions, the number of components and the introduced errors of each sub-model in the process of simplifying the chemical reaction model are shown in table 1. Then at T600-,And p is 10-80 atm to compare detailed chemical model and simplify chemical reactionModel pair stagnation and component concentrations of HMN, CO and CO2 in JSR, as shown in fig. 3 and 4, it can be seen that simplifying the chemical reaction model can well reproduce the predicted performance of the detailed chemical reaction model over the entire operating regime.
TABLE 1 variation of reaction number, component number and introduced error in different submodels during model simplification
Claims (2)
1. A method of simplifying a large-scale detailed chemical reaction model of a high carbon fuel, characterized by: the method comprises the following steps:
(1) large scale detailed chemical reaction model pretreatment of high carbon fuels
Dividing the reaction into different sub-models according to the large-scale detailed chemical reaction model structure of the high-carbon fuel and the maximum number of carbon atoms of the components involved in the reaction; then, dividing the reactions in the submodels into different reaction classes according to a rate rule; in order to reduce the amount of model simplification, in the subsequent calculations, the submodels/reaction classes are considered as a whole, i.e. for all reactions belonging to the jth submodel/reaction class
Wherein m isjFor the total number of reactions contained in the jth sub-model/reaction class,standard uncertainty rate parameter for nth reaction
knFor the nth in a single simulationReaction rate of the reaction, fnAs the uncertainty factor f of the nth reactionn=log10(kn,max/kn,0)=log10(kn,0/kn,min),kn,0、kn,maxAnd kn,minRespectively, the standard reaction rate, the maximum value and the minimum value of the nth reaction;
(2) assessing importance of submodels using global sensitivity analysis
To perform a global sensitivity analysis, first, an input variable x is inputjThe dispersion is (0,1/(p-1),2/(p-1), …, 1); subsequently, a set of x is randomly generated, and the change Δ of the elements in x one by one is calculated, and a reduction target y is calculatedi(ii) a The jth input variable pair simplification target yiHas a single influence of
The average effect is obtained through k times of calculation
Sum variance
The larger the influence of the rate constant for disturbing the reaction in the jth submodel/reaction class on the ith simplified target predicted value is, the larger the sigma isijThe larger the reaction class is, the stronger nonlinear relation exists between the jth reaction class and the ith simplified target or the stronger coupling relation exists between the jth reaction class and other reaction classes;
based onAnd the number of carbon atoms in the submodel, and dividing the submodel into a small molecule submodel (the number of carbon atoms is less than or equal to 4), an important large molecule submodel and an unimportant large molecule submodel;
(3) assessment of the importance of reactive species in important macromolecular submodels using global sensitivity and pathway sensitivity analysis
The path sensitivity coefficient is calculated by equation (6),
yi,jand removing the predicted value of the simplified chemical reaction model of the ith reaction class to the jth simplified target.
Based on xijDeleting the reaction classes one by one from small to large until the predicted value of the simplified chemical reaction model reaches the uncertain prediction boundary of the detailed chemical reaction model on any simplified target;
(4) construction of framework macromolecular submodel
Firstly, collecting isomers in the reaction classes reserved in the step (3) as a representative component; then, reactions in the unimportant submodel are lumped to obtain a framework macromolecule submodel;
(5) simplified small molecule submodels
The reactions in the small molecule submodels were evaluated using equation (7) and based on ξjDeleting the reactions one by one until the predicted value of the simplified chemical reaction model reaches the uncertain boundary of the detailed chemical reaction model for predicting any simplified target, and obtaining an initial simplified model;
(6) reaction rate optimization
And (3) optimizing the reaction rate constant in the fuel submodel within an uncertain range by using a multi-objective genetic algorithm to obtain a final simplified model.
2. A method of simplifying a large scale detailed chemical reaction model of high carbon fuel as claimed in claim 1, characterized in that: the high-carbon fuel is isohexadecane.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110082631.2A CN112750505A (en) | 2021-01-21 | 2021-01-21 | Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110082631.2A CN112750505A (en) | 2021-01-21 | 2021-01-21 | Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel |
Publications (1)
Publication Number | Publication Date |
---|---|
CN112750505A true CN112750505A (en) | 2021-05-04 |
Family
ID=75652778
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110082631.2A Pending CN112750505A (en) | 2021-01-21 | 2021-01-21 | Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112750505A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114882957A (en) * | 2022-04-11 | 2022-08-09 | 北京理工大学 | Efficiency evaluation method for binary composite combustion improver |
-
2021
- 2021-01-21 CN CN202110082631.2A patent/CN112750505A/en active Pending
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114882957A (en) * | 2022-04-11 | 2022-08-09 | 北京理工大学 | Efficiency evaluation method for binary composite combustion improver |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111804146A (en) | Intelligent ammonia injection control method and intelligent ammonia injection control device | |
CN111814956B (en) | Multi-task learning air quality prediction method based on multi-dimensional secondary feature extraction | |
CN113205207A (en) | XGboost algorithm-based short-term power consumption load fluctuation prediction method and system | |
CN112101480A (en) | Multivariate clustering and fused time sequence combined prediction method | |
CN110975597B (en) | Neural network hybrid optimization method for cement denitration | |
CN111006240B (en) | Biomass boiler furnace temperature and load prediction method | |
CN112489734B (en) | Simplified method of combustion reaction mechanism model of alternative fuel dimethyl ether of internal combustion engine | |
CN114943372A (en) | Method and device for predicting life of proton exchange membrane based on Bayesian recurrent neural network | |
CN113011660A (en) | Air quality prediction method, system and storage medium | |
CN115860173A (en) | Construction and prediction method and medium of carbon emission prediction model based on Stacking algorithm | |
CN115688581A (en) | Oil gas gathering and transportation station equipment parameter early warning method, system, electronic equipment and medium | |
CN111832839A (en) | Energy consumption prediction method based on sufficient incremental learning | |
CN112750505A (en) | Method for simplifying large-scale detailed chemical reaction model of high-carbon fuel | |
CN111680712A (en) | Transformer oil temperature prediction method, device and system based on similar moments in the day | |
CN113281229B (en) | Multi-model self-adaptive atmosphere PM based on small samples 2.5 Concentration prediction method | |
CN112100759A (en) | Distributed cooperative agent model method for approximation analysis of complex engineering structure system | |
CN114819107B (en) | Mixed data assimilation method based on deep learning | |
CN115762653B (en) | Fuel combustion mechanism optimization method based on evolutionary algorithm and deep learning | |
CN116151469A (en) | Model for forecasting air quality | |
CN116307139A (en) | Wind power ultra-short-term prediction method for optimizing and improving extreme learning machine | |
CN116258234A (en) | BP neural network model-based energy enterprise carbon emission measuring and predicting method | |
CN113486553A (en) | Complex equipment reliability analysis method based on Thiessen polygon area division | |
CN112836431A (en) | Penicillin fermentation process fault prediction method based on PSO-LSTM | |
CN113239495A (en) | Complex structure reliability design method based on vector hybrid agent model | |
CN117912585B (en) | Optimization method for combustion chemical reaction based on deep artificial neural network |
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 |