CN106709095A - Random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method - Google Patents
Random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method Download PDFInfo
- Publication number
- CN106709095A CN106709095A CN201510777380.4A CN201510777380A CN106709095A CN 106709095 A CN106709095 A CN 106709095A CN 201510777380 A CN201510777380 A CN 201510777380A CN 106709095 A CN106709095 A CN 106709095A
- Authority
- CN
- China
- Prior art keywords
- fraction oil
- matrix
- rate data
- oil
- quality point
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- 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
- G06F2119/18—Manufacturability analysis or optimisation for manufacturability
Abstract
The invention discloses a random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method and system. The method comprises the steps of obtaining distillate oil mass fraction data and a lumped kinetics equation through a random selection mechanism, and determining matrix metadata of a hydrogenation reaction rate matrix; then performing preprocessing, genetic algorithm processing and high-precision finishing processing on the determined matrix metadata through a random function preprocessing method, a genetic algorithm and a quasi-Newton method in sequence to obtain final optimized matrix metadata; and determining a model of the lumped kinetics equation according to the final optimized matrix metadata. According to the method, a plurality of lumped components are defined, so that the flexibility requirement of product cutting in industrial production is met; parameters in a to-be-built model are optimized through three algorithms; the built lumped kinetics model enhances the flexibility of different fraction cutting schemes; and the degree of matching between a distribution curve of the distillate oil mass fraction data calculated through the lumped kinetics model and an experimental result is relatively high.
Description
Technical field
The present invention relates to fraction oil hydrocracking process studying technological domain, and in particular to Yi Zhongsui
The serial heredity lumping kinetics method and system of newton post processing are intended in the pretreatment of machine function.
Background technology
One of vital task of petrochemical industry is by low-quality, high impurity content by hydrogenation reaction
The crude oil of the macromolecular that height is done or its pretreatment fraction oil be processed, with generate high-quality,
Low impurity content, all kinds of fraction oil products of high added value and the original of downstream petrochemicals
Material.Due to world's crude oil price, the price of product oil price and downstream petroleum and petrochemical industry product and
Demand constantly jumbo fluctuation, therefore, oil refining enterprise allows for the work to petroleum refining process
Skill parameter carries out effective adjustment in real time, to adapt to crude oil, product oil and downstream petrochemical industry
The change requirement of the price and demand of product.
Oil refining enterprise carries out in real time the effectively premise of adjustment to the technological parameter of petroleum refining process
Depend on the heightened awareness and the relatively accurate mathematics related to the process to hydrogenation process
The establishment and solution of pattern.
Mentioned lumped reaction kinetics divide relatively easy for the fraction of oil product at present, difficult
With raw material fraction and product involved by the reaction of high-precision description actual industrial and technological experiment
Complex distributions of the product fraction for cutting temperature.And then cause evaporating using different cutting schemes
The oily cutting result of part is very big with the error of result of calculation.
The content of the invention
For defect of the prior art, the present invention provides a kind of random function pretreatment and intends newton
The serial heredity lumping kinetics method and system of post processing, greatly reduce using different cuttings
The fraction oil cutting result of scheme and the error of result of calculation.
In a first aspect, the present invention is provided, a kind of random function pretreatment plan newton post processing is serial to lose
Lumping kinetics method is passed, including：
S1, according to fraction oil quality point rate data and lumping kinetics equation, it is determined that hydrogenation splits
Change all M matrix metadata of reaction rate matrix, the fraction oil quality point rate packet
Include and be simulated the product fraction oil that distillation test is obtained to feedstock oil under different technology conditions
Fraction oil quality point rate data and the feedstock oil is carried out digital simulation product fraction oil
Fraction oil quality point rate data；
S2, M matrix metadata in step S1 is carried out by random function preprocess method
Optimization；
S3, in the step S2 optimize after M matrix metadata by genetic algorithm continuation
Optimize；
S4, in step S3 optimize after M matrix metadata carried out by quasi-Newton method it is excellent
Change, and determine M matrix metadata after optimization；
S5, the M matrix metadata determined according to step S4, determine lumping kinetics equation
Model；
The model of S6, the lumping kinetics equation determined according to the step S5, with to feedstock oil
The fraction oil quality point rate data of the product fraction oil that distillation test is obtained are simulated as first
Beginning condition, calculates the fraction oil quality point rate number of the product fraction oil corresponding to differential responses air speed
According to；
S7, the simulation that step S2-S6 is performed the lumping kinetics equation by serial computing
Work.
Optionally, the step S1 includes：
S11, the division virtual lump component of hydrocracking reaction；
S12, assume being hydrocracked lumped reaction kinetics；
S13, structure hydrocracking reaction network；
It is S14, true according to the lumped reaction kinetics of the hydrocracking reaction network and hypothesis
Determine lumping kinetics equation；
S15, the fraction according to the product fraction oil that distillation test acquisition is simulated to feedstock oil
Oil quality point rate data, the feedstock oil is carried out digital simulation product fraction oil fraction oil
Mass fraction data and lumping kinetics equation, determine the institute of hydrocracking reaction rate matrix
There is M matrix metadata.
Optionally, the step S11 includes：
S111, determine the feedstock oil be simulated under different technology conditions distillation test acquisition
Product fraction oil fraction oil quality point rate data and the mean boiling point of product fraction oil；
S112, the product for being simulated distillation test under different technology conditions according to the feedstock oil
The fraction oil quality point rate data of product fraction oil and the mean boiling point of product fraction oil, divide and add
The virtual lump component of hydrogen cracking reaction.
Optionally, the step S13 includes：
In N number of virtual lump component after division, the 1st product fraction of virtual lump component
The mean boiling point highest of oil, the mean boiling point of the product fraction oil of the virtual lump component of n-th is most
It is low；
I-th (1≤i≤N) virtual lump component includes i-1 in-degree and N-i out-degree；
Wherein, i represents the i-th node of virtual lump component；N represents the number of virtual lump component
Mesh, and each one node of virtual lump component correspondence, common N number of node.
Optionally, lumping kinetics equation is in the step S14：
Wherein, CiAnd CjRepresent the fraction oil quality point rate data of different virtual lump components；γi
The dynamics stoichiometric number of different virtual lump components is represented, different values represent different virtual respectively
The reaction of formation and consumption reaction of lump component；N represents the number of virtual lump component；I and
J represents different virtual lump components respectively；kaiRepresent matrix metadata.
Optionally, the matrix metadata is to include the lower triangular matrix of diagonal element.
Optionally, the step S2 includes：
S21, the M matrix metadata be by set up with fraction oil cutting temperature be from become
Amount, what 5 power functions with the numerical value of matrix metadata as functional value were calculated, and to institute
Stating the coefficient of power function carries out the exhaustion of preset times；
After S22, the coefficient to the power function carry out the exhaustion of preset times, obtain and exhaustion
It is the same number of comprising the M matrix element data group of matrix metadata；
S23, M matrix metadata in each matrix element data group substituted into respectively it is described
Lumping kinetics equation calculates the fraction oil quality point rate data of product fraction oil；
S24, the fraction oil quality point rate data of the product fraction oil that will be calculated with corresponding technique
Under the conditions of according to experiment obtain product fraction oil fraction oil quality point rate data contrasted,
Obtain the calculating product fraction oil fraction oil quality point rate data with corresponding technique bar
The residual error of the fraction oil quality point rate data of the product fraction oil obtained according to experiment under part is minimum
When corresponding matrix metadata, redefine M matrix metadata.
Optionally, in the step S24, calculated value is with the residual error err of experiment value：
Wherein, CCal, iRepresent the fraction oil by calculating the i-th virtual lump component for obtaining
Amount point rate data, and CTest, iRepresent by testing evaporating for the i-th virtual lump component for obtaining
Part oil quality point rate data, N is the number of virtual lump component, p and q be 0,1,2 or
It is infinitely great.
Optionally, the step S3 includes：
S31, the error function expressed by the residual error err are used as object function to be optimized；
S32, in the step S2 optimize after M matrix metadata in each matrix
Metadata carries out the multiple populations of disturbance generation in the range of default value；
S33, according to disturbance after multiple populations obtain the residual error err respectively；
S34, using the reciprocal function of the residual error err as the fitness function of genetic algorithm, choosing
Take population at individual corresponding during fitness maximum；
S35, the population at individual is carried out into population duplication, it is individual as population male parent；
S36, the population male parent individuality is intersected and is made a variation and produced new population at individual,
Using the new population at individual as M matrix metadata after optimization.
Second aspect, newton post processing string is intended present invention also offers a kind of pretreatment of random function
Row heredity lumping kinetics system, including：
Parameter primarily determines that module, for according to fraction oil quality point rate data and collection total output
Equation is learned, all M matrix metadata of hydrocracking reaction rate matrix are determined, it is described to evaporate
Part oil quality point rate data are simulated distillation to feedstock oil in fact under being included in different technology conditions
Test the fraction oil quality point rate data of the product fraction oil of acquisition and the feedstock oil is counted
Calculate the fraction oil quality point rate data of the product fraction oil of fitting；
Parameter pretreatment module, for primarily determining that M matrix metadata in module to parameter
Optimized by random function preprocess method；
Parameter optimization module, for M matrix metadata after the optimization of parameter pretreatment module
Optimization is proceeded by genetic algorithm；
Parameter determination module, for leading to M matrix metadata after the optimization of parameter optimization module
Cross quasi-Newton method to optimize, and determine M matrix metadata after optimization；
Model building module, for the M matrix metadata determined according to parameter determination module,
Determine the model of lumping kinetics equation；
Mass fraction computing module, for the collection total output determined according to the model building module
The model of equation is learned, to be simulated evaporating for the product fraction oil that distillation test is obtained to feedstock oil
Part oil quality point rate data calculate the product corresponding to differential responses air speed and evaporate as primary condition
The fraction oil quality point rate data of part oil；
Serial computing module, the mould for performing the lumping kinetics equation by serial computing
Intend work.
As shown from the above technical solution, newton is intended in a kind of random function pretreatment that the present invention is provided
The serial heredity lumping kinetics method and system of post processing, fraction is obtained by randomly choosing mechanism
Oil quality point rate data and lumping kinetics equation, determine the matrix of hydrogenation reaction rate matrix
Metadata, then passes sequentially through random function preprocess method, genetic algorithm, quasi-Newton method pair
The matrix metadata of determination is pre-processed, genetic algorithm treatment and high accuracy refine treatment are obtained
The matrix metadata after final optimization pass is taken, and is determined according to the matrix metadata after the final optimization pass
The model of lumping kinetics equation.The method divides multiple lump components, meets industrial production product
The requirement on flexibility of product cutting, and treat the parameter set up in model by three kinds of algorithms and carry out
Optimization, the lumped reaction kinetics after the foundation enhance the flexible of different faction cut schemes
Property, and the distribution of the fraction oil quality point rate data calculated by the lumped reaction kinetics is bent
The distribution of the fraction oil quality point rate data of the product fraction oil that line is obtained with simulation distillation test
Curves degree is higher.
Brief description of the drawings
Locate after a kind of random function pretreatment plan newton that Fig. 1 is provided for one embodiment of the invention
The schematic flow sheet of the serial heredity lumping kinetics method of reason；
Fig. 2 distills figure for the simulation of the raw material fraction oil that one embodiment of the invention is provided；
Fig. 3 distills figure for the simulation of the product fraction oil that one embodiment of the invention is provided；
The hydrocracking reaction network topological diagram that Fig. 4 is provided for one embodiment of the invention；
I-th virtual component of hydrocracking reaction network that Fig. 5 is provided for one embodiment of the invention
Node digraph；
The structure letter of the hydrocracking reaction rate matrix that Fig. 6 is provided for one embodiment of the invention
Figure；
The flow chart of the serial computing algorithm that Fig. 7 is provided for one embodiment of the invention；
The calculating of use random function, genetic algorithm optimization that Fig. 8 is provided for one embodiment of the invention
And the product fraction oil that the lumping kinetics equation group of quasi-Newton method refine calculating is obtained
The distribution map of amount point rate number divides the distribution of rate data with the product fraction oil quality acquired in experiment
The comparison diagram of figure；
Fig. 9 divides calculating acquisition product fraction for the different lumps that one embodiment of the invention is provided
The comparison diagram of the distribution map of oil quality point rate data；
Locate after a kind of random function pretreatment plan newton that Figure 10 is provided for one embodiment of the invention
The structural representation of the serial heredity lumping kinetics system of reason.
Specific embodiment
Below in conjunction with the accompanying drawings, the specific embodiment invented is further described.Hereinafter implement
Example is only used for clearly illustrating technical scheme, and can not limit this hair with this
Bright protection domain.
Lumping kinetics method is exactly applied to be hydrocracked or the relative of catalytic cracking process has
One of effect and feasible mathematic(al) mode.The basic thought of the method is will to react all kinds of oil being related to
Product simplify according to certain principle and are divided into specific virtual lump component, and dividing mode can be according to
The boiling point of fraction oil, boiling range are divided.Also can be according to the component of fraction oil (especially with four components more
For common) divided.Splitting scheme has larger flexibility in itself.Then lump is established
The virtual reaction network of component, and according to the reaction network and the kinetics of correlation established
Relation establishes the equation of lumped reaction, and the form of equation is general with corresponding with first order reaction
Linear differential equation system it is most commonly seen.Then on the premise of reaction primary condition is brought into, lead to
The equation of the lumped reaction that the parsing and Numerical Methods Solve for crossing correlation are established, so as to obtain each
Parsing or numerical function form of the virtual lump fraction oil product of group with Annual distribution.And then realize
For being hydrocracked or catalytic cracking reaction product is for reaction velocity (soaking periods) relation
Dynamic detailed description.
Fig. 1 intends newton post processing for a kind of random function pretreatment provided in an embodiment of the present invention
The schematic flow sheet of serial heredity lumping kinetics method, as shown in figure 1, the method include with
Lower step：
S1, according to fraction oil quality point rate data and lumping kinetics equation, it is determined that hydrogenation splits
Change all M matrix metadata of reaction rate matrix, the fraction oil quality point rate packet
Include and be simulated the product fraction oil that distillation test is obtained to feedstock oil under different technology conditions
Fraction oil quality point rate data and the feedstock oil is carried out digital simulation product fraction oil
Fraction oil quality point rate data；
S2, M matrix metadata in step S1 is carried out by random function preprocess method
Optimization；
S3, in the step S2 optimize after M matrix metadata by genetic algorithm continuation
Optimize；
S4, in step S3 optimize after M matrix metadata carried out by quasi-Newton method it is excellent
Change, and determine M matrix metadata after optimization；
It will be appreciated that in above-mentioned steps S4, M matrix metadata after optimizing to step S3
The generalized error function for being constituted is carried out to each matrix metadata successively by quasi-Newton method
Optimization is calculated, and M matrix element number when redefining error function value within a preset range
According to, wherein, the generalized error function by with M matrix metadata as independent variable, with root
Fraction oil quality point rate data according to the product fraction oil of digital simulation are real with by simulating distillation
The absolute value of fraction oil quality point rate data difference of the product fraction oil of acquisition is tested as function
What value determined.
S5, the M matrix metadata determined according to step S4, determine lumping kinetics equation
Model.
The model of S6, the lumping kinetics equation determined according to the step S5, with to feedstock oil
The fraction oil quality point rate data of the product fraction oil that distillation test is obtained are simulated as first
Beginning condition, calculates the fraction oil quality point rate number of the product fraction oil corresponding to differential responses air speed
According to.
That is it is empty with the difference corresponding to the simulation distillation figure of the raw material fraction oil shown in Fig. 2
The fraction oil quality point rate data for intending component react differential equation group as lumping kinetics is solved
Initial value, using Runge-Kutta methods calculate solve the differential equation group.The interval of integration
From 0 to reaction velocity inverse.
S7, the simulation that step S2-S6 is performed the lumping kinetics equation by serial computing
Work.
The above method obtains fraction oil quality point rate data and lump by randomly choosing mechanism
Kinetics equation, determines the matrix metadata of hydrogenation reaction rate matrix, then pass sequentially through with
The matrix metadata that machine function preprocess method, genetic algorithm, quasi-Newton method pair determine carries out pre-
Treatment, genetic algorithm treatment and high accuracy refine treatment obtain the matrix element number after final optimization pass
According to, and the model of lumping kinetics equation is determined according to the matrix metadata after the final optimization pass.
The method divides multiple lump components, meets the requirement on flexibility of industrial production product cutting, and
And treat the parameter set up in model by three kinds of algorithms and optimize, the lump after the foundation is moved
Mechanical model enhances the flexibility of different faction cut schemes, and by the lumping kinetics
The curve of the fraction oil quality point rate data that model is calculated is higher with the experimental result goodness of fit.
Describe the above method in detail below by specific embodiment, following examples are only used for
The method of the present invention is illustrated, but is not used to limit protection scope of the present invention.
Above-mentioned steps S1 is specifically included：
S11, the division virtual lump component of hydrocracking reaction；
Step S11 specifically includes following steps：
S111, determine the feedstock oil be simulated under different technology conditions distillation test acquisition
Product fraction oil fraction oil quality point rate data and the mean boiling point of product fraction oil；
S112, the product for being simulated distillation test under different technology conditions according to the feedstock oil
The fraction oil quality point rate data of product fraction oil and the mean boiling point of product fraction oil, divide and add
The virtual lump component of hydrogen cracking reaction.
First from 40ml small hydrogenation devices according to different technology conditions successively Extracting temperature, treatment
Amount and the different corresponding fraction oil product of 27 groups of experiments of hydrogen dividing potential drop power, gather 27 kinds of works respectively
The fraction oil quality point rate number that distillation test is obtained is simulated under skill operating condition to feedstock oil
According to the fraction oil quality point rate data of digital simulation are carried out according to the feedstock oil as this mould
The parameter fitting data set that type method is calculated.By the fraction oil corresponding to feedstock oil and checking test
Simulation distillation data are drawn as shown in Figures 2 and 3.In fig. 2, wherein X-coordinate representative simulation
Distillation cutting temperature/degree Celsius, Y-coordinate represents raw material fraction oil quality point rate data, in Fig. 3,
X-coordinate representative products fraction oil cutting temperature/degree Celsius, Y-coordinate representative products fraction oil quality
Divide rate data.27 groups of simulation distillation test data are divided, by all oil product initial boiling points
The maximum that minimum value simultaneously does all oil products as the initial boiling point of model of fit system temperature
Value is done as model of fit system temperature, and so all fraction oil boiling ranges are in model of fit
The boiling point range for being calculated in.Then to the boiling range involved by calculating according to experiment and calculate
Need to carry out lump division, carrying out 100 lumps to the scope at this divides.
S12, assume being hydrocracked lumped reaction kinetics；
Step S12 comprises the following steps：
S121, react fraction and generation fraction belong to same boiling range when, then the reaction is refused to examine
Consider；
S122, hydrocracking reaction have one-way and irreversibility；
S123, the speed constant of the hydrocracking reaction are influenced by temperature and meet Arrhenius
Equation；
Compared with Light ends for not had an effect compared with heavy component in S124, each virtual lump component,
And the reacted accumulation of reaction and component compared with heavy component is to the generation quantity and product compared with Light ends
Raw speed makes a difference；
S125, using first order reaction kinetics model describe；
S126, course of reaction are only controlled by dynamic process, not by other process influences.
S13, structure hydrocracking reaction network；
In N number of virtual lump component after division, the 1st product fraction of virtual lump component
The mean boiling point highest of oil, the mean boiling point of the product fraction oil of the virtual lump component of n-th is most
It is low；
I-th (1≤i≤N) virtual lump component includes i-1 in-degree and N-i out-degree；
Wherein, i represents the i-th node of virtual lump component；N represents the number of virtual lump component
Mesh, and each one node of virtual lump component correspondence, common N number of node.
For example, the reaction network for drawing course of reaction is as shown in Figure 4.Wherein, from a left side to
The mean boiling point of the product fraction oil of right virtual lump component is from low to high.In the reaction network
The total N of virtual lump component is 100.In partition process, the 1st virtual lump component
The mean boiling point highest of product fraction oil, it is understood that for virtual lump component is most heavy void
Intend lump component, and the mean boiling point of the product fraction of n-th virtual lump component oil is minimum,
It can be appreciated that virtual lump component is most light virtual lump component.Now by i-th lump
Component is extracted as shown in Figure 5 from reaction network schematic diagram.For i-th virtual lump group
For part, it can be considered comprising 100 i-th nodes of the digraph of node.For the node
For, amount to and include i-1 in-degree and N-i out-degree.For reaction system, i-1 enters
Degree represents the i-th node virtual lump component and receives the virtual lump group come from than i-th node
The product of the pyrolysis of oil fractions of the virtual lump component of i-1 heavier node of part, N-i out-degree
Represent the virtual lump component self-cleavage of the i-th node more virtual than i-th node to come from
The virtual lump component of N-i lighter node of lump component provides raw material.
It is S14, true according to the lumped reaction kinetics of the hydrocracking reaction network and hypothesis
Determine lumping kinetics equation；
According to involved by reaction network figure and lumped watershed hydrologic model equation assume that establishing description reacts
And ordinary differential system.In the present embodiment, equation group includes 100 ODEs.
Specifically, lumping kinetics equation is：
Formula (1)
Wherein, CiAnd CjRepresent the fraction oil quality point rate data of different virtual lump components；γi
The dynamics stoichiometric number of different virtual lump components is represented, different values represent different virtual respectively
The reaction of formation and consumption reaction of lump component；N represents the number of virtual lump component；I and j
Different virtual lump components are represented respectively；kaiRepresent matrix metadata.
S15, the fraction according to the product fraction oil that distillation test acquisition is simulated to feedstock oil
Oil quality point rate data, the feedstock oil is carried out digital simulation product fraction oil fraction oil
Mass fraction data and lumping kinetics equation, determine the institute of hydrocracking reaction rate matrix
There is M matrix metadata.
As shown in fig. 6, the matrix metadata is to include the lower triangular matrix of diagonal element, reaction
The mathematical form of rate matrix is through virtual lump component from gently including to can be converted into after permutatation
The upper triangular matrix of diagonal element.
It is assumed that all matrix metadata of hydrocracking reaction rate matrix, the matrix is
100x100 scales.But the matrix is lower triangular matrix according to preamble model hypothesis.Simultaneously because
The characteristics of reaction itself, each element characterizes the virtual lump group on reaction rate diagonal of a matrix
The heating rate coefficient of part, its absolute value should be equal to the generation of every other element in respective column
Number and.The hydrogenation reaction rate matrix structure diagram involved by the embodiment is now listed in Fig. 6 institutes
Show.Its matrix metadata of position a little is not zero in figure 6, and blank space its matrix element number
According to being zero.Include 5049 nonzero elements in whole matrix, these nonzero elements are this mould
Type parameter to be optimized is M matrix metadata to be optimized.
Above-mentioned steps S2 includes：
S21, the M matrix metadata be by set up with fraction oil cutting temperature be from become
Amount, what 5 power functions with the numerical value of matrix metadata as functional value were calculated, and to institute
Stating the coefficient of power function carries out the exhaustion of preset times；
After S22, the coefficient to the power function carry out the exhaustion of preset times, obtain and exhaustion
It is the same number of comprising the M matrix element data group of matrix metadata；
S23, M matrix metadata in each matrix element data group substituted into respectively it is described
Lumping kinetics equation calculates the fraction oil quality point rate data of product fraction oil；
S24, the fraction oil quality point rate data of the product fraction oil that will be calculated with corresponding technique
Under the conditions of according to experiment obtain product fraction oil fraction oil quality point rate data contrasted,
Obtain the calculating product fraction oil fraction oil quality point rate data with corresponding technique bar
The residual error of the fraction oil quality point rate data of the product fraction oil obtained according to experiment under part is minimum
When corresponding matrix metadata, redefine M matrix metadata.
In order to subsequent descriptions are convenient, the fraction oil quality point rate data of the product fraction oil that will be calculated
Calculated value is defined as, the fraction oil quality with the product fraction oil for calculating divides rate data in corresponding work
The fraction oil quality point rate data definition obtained according to experiment under the conditions of skill is experiment value.
Specifically, it is minimum with the residual error err of calculated value that experiment value is established in above-mentioned steps S21-S24
Initial reaction rate matrix matrix metadata.Specific optimization calculating process is as follows：
(1) assume that 600 parameters to be optimized are the condition random number between -2~2, by these with
Machine number as the power function that 100 groups of most high orders are 5 coefficient, the independent variable of these power functions is to evaporate
The boiling point of part oil, functional value is the numerical value of reaction rate matrix metadata.By random generation
Power function calculates reaction rate matrix, and result of calculation is met on reaction rate matrix each column diagonal
Element absolute value is equal to this constraints of the algebraical sum of other all elements in respective column.
(2) parameter to be optimized by assuming is simulated with feedstock oil using Runge-Kutta methods and steamed
Evaporate the fraction corresponding to curve and be distributed as primary condition, the inverse with 0 to reaction velocity is integration
Interval solve established comprising 100 lumping kinetics ODEs of ODE
Group.
(3) by solving result with corresponding process conditions product fraction oil simulation distillation curve institute
Corresponding fraction oil distribution curve carries out error calculation, and its computing formula is as follows.Count herein
During calculation, parameter p=1, q=1, N=100 and record error between the two are taken.
Calculated value is with the residual error err of experiment value：
Formula (2)
Wherein, CCal, iRepresent by calculating the i-th fraction oil quality of virtual lump component for obtaining
Point rate data, and CTest, iRepresent the fraction oil by testing the i-th virtual lump component for obtaining
Mass fraction data, N is the number of virtual lump component, and p and q is 0,1,2 or infinitely great.
(4) (1)-(3) process 10 is repeated6It is secondary, choose error most in all multiple errors
It is small to be worth as the matrix metadata of initial reaction rate matrix.As the initial of next step calculating
Value.
Above-mentioned steps S3 includes：
S31, with the mistake expressed by the absolute value of residual error err namely the difference of calculated value and experiment value
Difference function is used as object function to be optimized；
S32, in the step S2 optimize after M matrix metadata in each matrix
Metadata carries out the multiple populations of disturbance generation in the range of default value；
S33, according to disturbance after multiple populations obtain the residual error err respectively；
S34, using the reciprocal function of the residual error err as the fitness function of genetic algorithm, choosing
Take population at individual corresponding during fitness maximum；
S35, the population at individual is carried out into population duplication, it is individual as population male parent；
S36, the population male parent individuality is intersected and is made a variation and produced new population at individual,
Using the new population at individual as M matrix metadata after optimization.
Specifically, above-mentioned steps S31-S36 is based on genetic algorithm establishes experiment value with calculated value
The matrix metadata of the minimum reaction rate matrix of residual error.Specific optimization calculating process is as follows：
(1) each to M matrix metadata initial value matrix after optimization in the step S2
Element carries out small-scale perturbation, and range of disturbance is each of each matrix metadata initial value matrix
0.8-1.2 times of element numerical value.The initial matrix unit number that scale is 800 is generated by random perturbation
According to population.The purpose of this disturbance both ensure that " the outstanding property " of population oeverall quality, protect again
Demonstrate,prove " variation " of population, and the initial kind calculated as genetic algorithm optimization using the population
Group.
(2) reciprocal function of the error function shown in formula (2) fitting as genetic algorithm
Response function, calculates the fitness function value of each population at individual successively.Wherein error function meter
P=1 and q=1 is taken during calculation.Ensure that error is smaller, its related fitness is bigger.So that by mistake
The small matrix metadata population of difference is more obtained in that reservation.
(3) selecting individuality using the selective algorithm of genetic algorithm carries out population duplication, under
The population male parent of secondary calculating is individual；
(4) the population male parent individuality chosen is intersected and the generation new population that made a variation
Body, related crossing-over rate is 0.1-0.9, preferably 0.5, aberration rate is 0.0001-0.05, excellent
Elect 0.001 as；
(5) new population at individual is substituted into old population at individual, repeat step (2)-(4)
Until evolve reaching optimization aim, the computer algebra of whole genetic algorithm was 10000 generations.
Retain the population at individual of all different algebraically being related in calculating process 8x10 altogether6It is individual,
It is that the minimum individuality of error function value is joined as next step to choose fitness function maximum wherein
The initial value that number refine is calculated.
Specifically, above-mentioned steps rate matrix metadata is excellent based on genetic algorithm as primary data
Changing can also in concrete numerical value this detailed process of computation rate matrix correlation matrix metadata
It is interpreted as comprising the following steps：
A) using as experiment value with calculate the norm of value difference expressed by error function as to be optimized
The form of object function；
B) it is directed to the matrix metadata of the initial reaction rate matrix of each optimization aim generation
The population of random generation respective objects in numerical value neighborhood, population at individual number is 20-400000；
C stochastic transformation) is carried out according to associated binary codes to each population, new population is formed；
D) by C) in the way of sequentially generate new individual, new individual number is 20-400000；
E) function reciprocal with object function to be optimized or related to function forward direction is as suitable
Response function, calculates the fitness function value of each population at individual successively；
F ideal adaptation) is selected according to probabilistic manner using " roulette " algorithm of genetic algorithm
The larger population of degree functional value carries out population duplication, individual as the population male parent for calculating next time；
G) the population male parent individuality chosen is intersected and the generation new population that made a variation
Body, related crossing-over rate is 0.1-0.9, and aberration rate is 0.0001-0.05；
H new population) is substituted into old population, repeat step C)-H) until evolving reaches optimization
Target, the computer algebra of whole genetic algorithm is 100-1000000 generations.
Specifically, in above-mentioned steps S4, based on plan newton mutative scale (BFGS) algorithm optimization
The matrix metadata that previous step is established by genetic algorithm.Specific optimization calculating process is as follows：
(1) calculated corresponding to the maximum individuality of the fitness function for obtaining with by genetic algorithm
Matrix metadata is used as the iteration initial value for intending newton mutative scale (BFGS) calculating.
(2) the Hessian matrixes at this initial value are calculated in second order diff method.
(3) to calculating the symmetrical Hessian matrix computations characteristic value for obtaining, if it is decided that Hessian
Matrix is positive definite matrix, then using second-order matrix that it is used as Newton iteration.If sentenced
It is not positive definite matrix to determine Hessian matrixes, then be multiplied by a unit matrix with the coefficient of a disturbance
It is then added to construct so-called positive definite matrix B* on corresponding Hessian matrixes in this approach approach
Hessian matrixes.Using the second-order matrix that matrix B * is used as Newton iteration.
(4) Newton iterative calculation of variable step is carried out to iteration initial value.Obtaining has minimum value
New optimal value, calculate both error by formula (2), p=2, q=1 are taken in calculating process.
Stop iterative calculation if the front and rear absolute value of error calculated twice is less than 0.001.Otherwise will
New optimal value replaces initial value, repeats this process of (2)-(4) until both errors are small
Untill 0.001.
Using final calculation result as the matrix element by the end reaction rate matrix after refine
Data.And then establish the mathematic(al) mode that lumping kinetics reacts differential equation group.
As shown in fig. 7, above-mentioned steps S7 specifically includes following steps：
S71,1000000 groups are serially produced using computer comprising 600-2~2 between with
Machine power function coefficient array, and reaction rate matrix is calculated by power function with the array for producing
Matrix metadata；
S72,1000000 groups of generator matrix metadata monokaryons to producing serially assemble spanning set
Total output reaction matrix；
S73, primary condition is distributed as with zero to corresponding anti-with feedstock oil mass fraction data
Answer air speed reciprocal for integrating range is calculated according to the matrix serial of generation with Runge-Kutta methods
1000000 groups evaporate comprising the product corresponding to 100 lumping kinetics equation groups of the differential equation
The distribution curve of the fraction oil quality point rate data of part oil；
S74, the fraction oil that 1000000 set product fractions oil is calculated according to above-mentioned formula (2)
The distribution curve of amount point rate data and the fraction oil quality point rate data of actual product fraction oil
The difference of distribution curve, selection wherein corresponding to difference reckling comprising 5049 random numbers
Array as reaction rate matrix initial driving force parameter；
S75, it is carried out to each element in the initial driving force parameter array that is obtained
0.8-1.2 times of random perturbation serially produces 800 groups of initial populations of genetic algorithm, and to each
Population at individual carries out binary coding；
S76, the packet of the binary coding of generation is serially intersected, make a variation generates new two
Carry system code, and generation binary code is converted into corresponding genetic algorithm calculating new population,
According to the adaptive response function as genetic algorithm reciprocal of formula (2), serial computing successively
The adaptive response functional value of initial population and new population；
S77, the adaptive response functional value for obtaining calculating are according to the mode of " roulette " by general
Rate randomly selects 800 groups of new populations, and follow-up calculating is participated in as population of future generation；
S78, the new population that will be generated are instead of initial population and record the two of all population at individual
Scale coding and all individual adaptive response functions and error function value, repeat S76-S77
The population that step 10000 time is obtained is used as final population；
S79, choose in calculating process that auto-adaptive function numerical value in all individualities is maximum namely error
The minimum individual initial value calculated as next step refine of function；
S80, each element for the optimum individual selected according to genetic algorithm are using three points value differences
The method divided is using serial computing mode approximate calculation error function for each matrix metadata
First derivative vector and second order Hessian matrixes；
S81, the two-dimentional Hessian matrixes to calculating acquisition carry out feature decomposition, ask for its right
The characteristic value answered, because Hessian matrixes have symmetry, therefore its characteristic value must be real number；
S82, judgement is carried out to Hessian matrixes see whether it is positive definite matrix, if the square
Battle array is positive definite matrix, then be directly iterated calculating using Newton and linear search method,
If the matrix is nonpositive definite matrix, a unit matrix is multiplied by with a disturbed value and is added to this
A positive definite matrix is constructed on Hessian matrixes to enter using Newton and linear search method again
Row iteration is calculated, and new optimal solution vector can be obtained by iterative calculation；
S83, the solution vector used instead of S80 with new solution vector, repeat S80-S82 steps
Untill error function value is less than 0.001, stop iterative calculation, and by the solution vector of gained
As the solution vector after refine, whole undetermined parameters of related lumping kinetics are thereby determined that, really
The mathematical form of vertical lumping kinetics equation；
S84, according to establish lumping kinetics equation group with feedstock oil mass fraction data (i.e.
The fraction oil quality point rate number of the product fraction oil that distillation test is obtained is simulated to feedstock oil
According to) as the fraction oil quality point of the product fraction oil corresponding to the different air speeds of primary condition calculating
Rate data, and corresponding fraction oil quality point rate number can be calculated according further to different cutting schemes
According to distribution situation.
As shown in figure 8, X-coordinate representative products fraction oil cutting temperature/degree Celsius, Y-coordinate generation
Table product fraction oil quality point rate data, are respectively adopted initial random function, genetic algorithm in figure
And quasi-Newton method method calculates the product fraction oil quality point that lumping kinetics equation group is obtained
The distribution map of rate data divides the distribution map of rate data with the product fraction oil quality acquired in experiment
Comparison diagram.It can be found that being calculated by initial random function because number of parameters to be optimized is numerous
The distribution of product fraction oil quality point rate data still have very big difference with experiment distribution, and pass through
The curve that genetic algorithm calculates the product fraction oil quality point rate data for determining then is obtained with experiment
The curve of the product fraction oil quality point rate data for taking is relatively close together but still has certain inclined
From, and eventually pass through result of calculations that the iterative calculation refine of quasi-Newton method eight times obtains then with experiment
The suitable steady conjunction of result.Realize the high-precision of distribution for product fraction oil quality point rate data
Degree numerical fitting.
The related residual err=24.1186 calculated by genetic algorithm, and pass through the step of quasi-Newton method 7
The corresponding reaction rate matrixes of final residual error err=3.9568. for obtaining are iterated to calculate due to matrix element
Data are numerous, only enumerate part and are listed in table 1, table 1 be calculated by various algorithms obtain residual error and
The numerical value of part matrix metadata.
Table 1
In order to show algorithm involved in the present invention for the virtual lump group involved by lumped model
Number purpose is relied on, and calculates phase with the lump of 20 lump 50 and 100 lumped models respectively herein
The distribution curve of the fraction oil quality of pass point rate data, and result of calculation is carried out with experimental result
Contrast, comparing result is listed in shown in Fig. 9, and X-coordinate representative products fraction oil cutting temperature/
Degree Celsius, Y-coordinate representative products fraction oil quality point rate data.Above-mentioned BFGS methods correspondence
In quasi-Newton method.
Result of calculation shows that lump component number is intended for the calculating of product fraction oil quality point rate
Close quite important.If it is desired to reaching fitting precision higher must assure that enough virtual group numbers
Amount.When virtual component is 20, result of calculation is very coarse to express product fraction oil
The general trend of amount point rate data, but product fraction oil quality point rate data cannot be given expression to completely
Distribution detailed information, no matter high boiling range fraction namely the virtual lump group of mean boiling point highest
Part fraction oil quality point rate data or low boiling journey fraction namely minimum virtual of mean boiling point
The fraction oil quality point rate data of lump component are not always the case；When virtual component is 50, meter
Calculate result preferable for what the distribution of the fraction oil quality point rate data of high boiling range fraction coincide, this
Because fluctuation of the distribution of the fraction oil quality of high boiling range fraction point rate data with cutting temperature
It is smaller, and model for the distribution and expression of the fraction oil quality point rate data of low boiling journey fraction compared with
It is coarse, because the distribution of the fraction oil quality of low boiling journey fraction point rate data is with the ripple of cutting temperature
It is dynamic larger；When virtual component is 100, fraction oil of the result of calculation for height boiling range fraction
It is preferable that the distribution of mass fraction data coincide, and had both reflected the fraction oil of high boiling range fraction
The gentle distribution of amount point rate data, is also demonstrated by the fraction oil quality point rate data of low boiling journey fraction
Fluctuation distribution.
In order to show flexible practicality of the method for different faction cut schemes.Now by two kinds
The obtained result row of the oily cutting scheme of different product fractions and its according to the method described above calculating
In shown in table 2 and table 3.Table 2 and table 3 are the fraction oil quality point rate data of different cutting fractions
Distribution situation.
Table 2
Table 3
By the comparing of table 2 and table 3 it can be found that because the present invention can use more collection
Total component is divided and calculated to fraction oil, while its result of calculation fitting precision is very high.Cause
This result of calculation of the invention can have very broad sense for the flexible cutting scheme of fraction oil
With flexible applicability.
As shown in Figure 10, the embodiment of the present invention additionally provides a kind of random function pretreatment plan newton
The structural representation of the serial heredity lumping kinetics system of post processing, as shown in Figure 10, the system
Including：
Parameter primarily determines that module 101, for according to fraction oil quality point rate data and lump
Kinetics equation, determines all M matrix metadata of hydrocracking reaction rate matrix, institute
State under fraction oil quality point rate data are included in different technology conditions and steaming is simulated to feedstock oil
Evaporate the fraction oil quality point rate data of the product fraction oil that experiment is obtained and the feedstock oil is entered
The fraction oil quality point rate data of the product fraction oil of row digital simulation；
Parameter pretreatment module 102, for primarily determining that M matrix element in module to parameter
Data are optimized by random function preprocess method；
Parameter optimization module 103, for M matrix element after the optimization of parameter pretreatment module
Data proceed optimization by genetic algorithm；
Parameter determination module 104, for M matrix element number after the optimization of parameter optimization module
Optimized according to by quasi-Newton method, and determine M matrix metadata after optimization；
Model building module 105, for the M matrix element number determined according to parameter determination module
According to determining the model of lumping kinetics equation.
Mass fraction computing module 106, for the lump determined according to the model building module
The model of kinetics equation, to be simulated the product fraction oil that distillation test is obtained to feedstock oil
Fraction oil quality point rate data as primary condition, calculate the product corresponding to differential responses air speed
The fraction oil quality point rate data of product fraction oil；
Serial computing module 107, for performing the lumping kinetics equation by serial computing
Simulation work.
Said system is one-to-one with the above method, and the implementation detail of the above method is also suitable
In the system, the present embodiment is not described in detail to the system.
In specification of the invention, numerous specific details are set forth.It is to be appreciated, however, that this
Inventive embodiment can be put into practice in the case of without these details.In some examples
In, known method, structure and technology is not been shown in detail, so as not to fuzzy to this specification
Understanding.
Although it will be appreciated by those of skill in the art that some embodiments described herein include it
Some included features are rather than further feature, but the spy of different embodiments in its embodiment
The combination levied means to be within the scope of the present invention and formed different embodiments.Example
Such as, in the following claims, the one of any of embodiment required for protection can
Mode is used in any combination.
Finally it should be noted that：Various embodiments above is merely illustrative of the technical solution of the present invention,
Rather than its limitations；Although being described in detail to the present invention with reference to foregoing embodiments,
It will be understood by those within the art that：It still can be to described in foregoing embodiments
Technical scheme modify, or which part or all technical characteristic are equally replaced
Change；And these modifications or replacement, the essence of appropriate technical solution is departed from of the invention each
The scope of embodiment technical scheme, it all should cover in claim of the invention and specification
In the middle of scope.
Claims (10)
1. the serial heredity lumping kinetics method of newton post processing is intended in a kind of random function pretreatment,
It is characterised in that it includes：
S1, according to fraction oil quality point rate data and lumping kinetics equation, it is determined that hydrogenation splits
Change all M matrix metadata of reaction rate matrix, the fraction oil quality point rate packet
Include and be simulated the product fraction oil that distillation test is obtained to feedstock oil under different technology conditions
Fraction oil quality point rate data and the feedstock oil is carried out digital simulation product fraction oil
Fraction oil quality point rate data；
S2, M matrix metadata in step S1 is carried out by random function preprocess method
Optimization；
S3, in the step S2 optimize after M matrix metadata by genetic algorithm continuation
Optimize；
S4, in step S3 optimize after M matrix metadata carried out by quasi-Newton method it is excellent
Change, and determine M matrix metadata after optimization；
S5, the M matrix metadata determined according to step S4, determine lumping kinetics equation
Model；
The model of S6, the lumping kinetics equation determined according to the step S5, with to feedstock oil
The fraction oil quality point rate data of the product fraction oil that distillation test is obtained are simulated as first
Beginning condition, calculates the fraction oil quality point rate number of the product fraction oil corresponding to differential responses air speed
According to；
S7, the simulation that step S2-S6 is performed the lumping kinetics equation by serial computing
Work.
2. method according to claim 1, it is characterised in that the step S1 includes：
S11, the division virtual lump component of hydrocracking reaction；
S12, assume being hydrocracked lumped reaction kinetics；
S13, structure hydrocracking reaction network；
It is S14, true according to the lumped reaction kinetics of the hydrocracking reaction network and hypothesis
Determine lumping kinetics equation；
S15, the fraction according to the product fraction oil that distillation test acquisition is simulated to feedstock oil
Oil quality point rate data, the feedstock oil is carried out digital simulation product fraction oil fraction oil
Mass fraction data and lumping kinetics equation, determine the institute of hydrocracking reaction rate matrix
There is M matrix metadata.
3. method according to claim 2, it is characterised in that the step S11 includes：
S111, determine the feedstock oil be simulated under different technology conditions distillation test acquisition
Product fraction oil fraction oil quality point rate data and the mean boiling point of product fraction oil；
S112, the product for being simulated distillation test under different technology conditions according to the feedstock oil
The fraction oil quality point rate data of product fraction oil and the mean boiling point of product fraction oil, divide and add
The virtual lump component of hydrogen cracking reaction.
4. method according to claim 3, it is characterised in that the step S13 includes：
In N number of virtual lump component after division, the 1st product fraction of virtual lump component
The mean boiling point highest of oil, the mean boiling point of the product fraction oil of the virtual lump component of n-th is most
It is low；
I-th (1≤i≤N) virtual lump component includes i-1 in-degree and N-i out-degree；
Wherein, i represents the i-th node of virtual lump component；N represents the number of virtual lump component
Mesh, and each one node of virtual lump component correspondence, common N number of node.
5. method according to claim 2, it is characterised in that collect in the step S14
Total output equation is：
Wherein, CiAnd CjRepresent the fraction oil quality point rate data of different virtual lump components；γi
The dynamics stoichiometric number of different virtual lump components is represented, different values represent different virtual respectively
The reaction of formation and consumption reaction of lump component；N represents the number of virtual lump component；I and
J represents different virtual lump components respectively；kaiRepresent matrix metadata.
6. method according to claim 5, it is characterised in that the matrix metadata
It is to include the lower triangular matrix of diagonal element.
7. method according to claim 1, it is characterised in that the step S2 includes：
S21, the M matrix metadata be by set up with fraction oil cutting temperature be from become
Amount, what 5 power functions with the numerical value of matrix metadata as functional value were calculated, and to institute
Stating the coefficient of power function carries out the exhaustion of preset times；
After S22, the coefficient to the power function carry out the exhaustion of preset times, obtain and exhaustion
It is the same number of comprising the M matrix element data group of matrix metadata；
S23, M matrix metadata in each matrix element data group substituted into respectively it is described
Lumping kinetics equation calculates the fraction oil quality point rate data of product fraction oil；
S24, the fraction oil quality point rate data of the product fraction oil that will be calculated with corresponding technique
Under the conditions of according to experiment obtain product fraction oil fraction oil quality point rate data contrasted,
Obtain the calculating product fraction oil fraction oil quality point rate data with corresponding technique bar
The residual error of the fraction oil quality point rate data of the product fraction oil obtained according to experiment under part is minimum
When corresponding matrix metadata, redefine M matrix metadata.
8. method according to claim 7, it is characterised in that in the step S24,
Calculated value is with the residual error err of experiment value：
Wherein, CCal, iRepresent the fraction oil by calculating the i-th virtual lump component for obtaining
Amount point rate data, and CTest, iRepresent by testing evaporating for the i-th virtual lump component for obtaining
Part oil quality point rate data, N is the number of virtual lump component, p and q be 0,1,2 or
It is infinitely great.
9. method according to claim 8, it is characterised in that the step S3 includes：
S31, the error function expressed by the residual error err are used as object function to be optimized；
S32, in the step S2 optimize after M matrix metadata in each matrix
Metadata carries out the multiple populations of disturbance generation in the range of default value；
S33, according to disturbance after multiple populations obtain the residual error err respectively；
S34, using the reciprocal function of the residual error err as the fitness function of genetic algorithm, choosing
Take population at individual corresponding during fitness maximum；
S35, the population at individual is carried out into population duplication, it is individual as population male parent；
S36, the population male parent individuality is intersected and is made a variation and produced new population at individual,
Using the new population at individual as M matrix metadata after optimization.
10. the serial heredity lumping kinetics system of newton post processing is intended in a kind of random function pretreatment
System, it is characterised in that including：
Parameter primarily determines that module, for according to fraction oil quality point rate data and collection total output
Equation is learned, all M matrix metadata of hydrocracking reaction rate matrix are determined, it is described to evaporate
Part oil quality point rate data are simulated distillation to feedstock oil in fact under being included in different technology conditions
Test the fraction oil quality point rate data of the product fraction oil of acquisition and the feedstock oil is counted
Calculate the fraction oil quality point rate data of the product fraction oil of fitting；
Parameter pretreatment module, for primarily determining that M matrix metadata in module to parameter
Optimized by random function preprocess method；
Parameter optimization module, for M matrix metadata after the optimization of parameter pretreatment module
Optimization is proceeded by genetic algorithm；
Parameter determination module, for leading to M matrix metadata after the optimization of parameter optimization module
Cross quasi-Newton method to optimize, and determine M matrix metadata after optimization；
Model building module, for the M matrix metadata determined according to parameter determination module,
Determine the model of lumping kinetics equation；
Mass fraction computing module, for the collection total output determined according to the model building module
The model of equation is learned, to be simulated evaporating for the product fraction oil that distillation test is obtained to feedstock oil
Part oil quality point rate data calculate the product corresponding to differential responses air speed and evaporate as primary condition
The fraction oil quality point rate data of part oil；
Serial computing module, the mould for performing the lumping kinetics equation by serial computing
Intend work.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510777380.4A CN106709095A (en) | 2015-11-12 | 2015-11-12 | Random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510777380.4A CN106709095A (en) | 2015-11-12 | 2015-11-12 | Random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106709095A true CN106709095A (en) | 2017-05-24 |
Family
ID=58930305
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510777380.4A Pending CN106709095A (en) | 2015-11-12 | 2015-11-12 | Random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106709095A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003073207A2 (en) * | 2002-02-21 | 2003-09-04 | Protein Mechanics, Inc. | Method for providing thermal excitation to molecular dynamics models |
| CN103914595A (en) * | 2014-04-01 | 2014-07-09 | 西北大学 | Modeling method of medium-temperature coal tar total-fraction hydrogen cracking lumping kinetic model |
| CN103995466A (en) * | 2014-04-24 | 2014-08-20 | 燕山大学 | Interval prediction control modeling and optimizing method based on soft constraints |
| CN104318306A (en) * | 2014-10-10 | 2015-01-28 | 西安电子科技大学 | Non-negative matrix factorization and evolutionary algorithm optimized parameter based self-adaption overlapping community detection method |
-
2015
- 2015-11-12 CN CN201510777380.4A patent/CN106709095A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003073207A2 (en) * | 2002-02-21 | 2003-09-04 | Protein Mechanics, Inc. | Method for providing thermal excitation to molecular dynamics models |
| CN103914595A (en) * | 2014-04-01 | 2014-07-09 | 西北大学 | Modeling method of medium-temperature coal tar total-fraction hydrogen cracking lumping kinetic model |
| CN103995466A (en) * | 2014-04-24 | 2014-08-20 | 燕山大学 | Interval prediction control modeling and optimizing method based on soft constraints |
| CN104318306A (en) * | 2014-10-10 | 2015-01-28 | 西安电子科技大学 | Non-negative matrix factorization and evolutionary algorithm optimized parameter based self-adaption overlapping community detection method |
Non-Patent Citations (4)
| Title |
|---|
| 李群勇: "加氢裂化反应器的建模和仿真", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》 * |
| 祝然: "结构导向集总新方法构建催化裂化动力学模型及其应用研究", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 谷云格等: "油浆抽提油在Ni-Mo/SiO2-Al2O3催化剂上加氢脱除PAHs动力学", 《石油化工高等学校学报》 * |
| 赫孝良等: "《最优化与最优控制》", 31 January 2015, 西安交通大学出版社 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104598611A (en) | Method and system for sequencing search entries | |
| CN102594909A (en) | Multi-objective community detection method based on spectrum information of common neighbour matrix | |
| CN108960304A (en) | A kind of deep learning detection method of network trading fraud | |
| CN114360662A (en) | Single-step inverse synthesis method and system based on two-way multi-branch CNN | |
| CN102419549B (en) | Complex chemical process modeling method of hybrid DNA (Deoxyribose Nucleic Acid) genetic algorithm | |
| CN104503420A (en) | Non-linear process industry fault prediction method based on novel FDE-ELM and EFSM | |
| Majumdar et al. | Genetic algorithm parameter optimization using Taguchi robust design for multi-response optimization of experimental and historical data | |
| CN105512755A (en) | Decomposition-based multi-objective distribution estimation optimization method | |
| CN105608295A (en) | Multi-objective evolutionary algorithm (MOEA) and radial basis function (RBF) neural network optimization modeling method of coking furnace pressure | |
| Bi et al. | Proximal alternating-direction-method-of-multipliers-incorporated nonnegative latent factor analysis | |
| CN106709230A (en) | Method for preprocessing serial genetic lumping kinetics by using random function and post-processing serial genetic lumping kinetics by least squares | |
| CN105740960A (en) | Optimization method of industrial hydrocracking reaction condition | |
| CN106708785A (en) | Random seed number preprocessing, quasi-Newton postprocessing and parallel genetic lumped kinetics method | |
| CN104933103A (en) | Multi-target community discovering method integrating structure clustering and attributive classification | |
| CN108596339A (en) | A kind of multi-objective Optimization Genetic Algorithm of structure population and subproblem | |
| CN117334271A (en) | Method for generating molecules based on specified attributes | |
| CN106709095A (en) | Random function preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method | |
| CN106709087A (en) | Random seed number preprocessing, least square postprocessing and parallel lumped kinetics method | |
| CN106709092A (en) | Distribution heredity lumping kinetic method with random function preprocessing and simulated Newton postprocessing | |
| CN106708784A (en) | Lumping kinetics method for random function preprocessing simplex post-processing parallel inheritance | |
| CN106709086A (en) | Random seed number preprocessing, quasi-Newton postprocessing and serial genetic lumped kinetics method | |
| CN106709571A (en) | Random seed number pretreatment and quasi-Newton post-treatment parallel genetic lumped kinetics method | |
| CN106709088A (en) | Lumping kinetics method for random function preprocessing simplex post-processing distribution inheritance | |
| CN106709085A (en) | Lumping kinetics method of random seed number pretreatment quasi-Newton after-treatment distribution inheritance | |
| Caraka | Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170524 |
|
| RJ01 | Rejection of invention patent application after publication |