CN106709094A - Random seed number preprocessing, simplex postprocessing and parallel genetic lumped kinetics method - Google Patents

Abstract

The invention discloses a random seed number preprocessing, simplex postprocessing and parallel 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 seed number preprocessing method, a genetic algorithm and a simplex algorithm 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; 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; and while errors of distillate oil cutting results adopting different cutting schemes and calculation results are reduced, the calculation efficiency of the model is improved.

Description

Random seed number pretreatment simple form post processing parallel genetic lumping kinetics method

Technical field

The present invention relates to fraction oil hydrocracking process studying technological domain, and in particular to Yi Zhongsui Machine subnumber pretreatment simple form post processing parallel genetic lumping kinetics method and system.

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.In addition, anti-in lumped reaction kinetics Answer speed constant generally using Guass-Newton methods, Marquardt methods or its change Enter deformation method to be solved.Solution procedure is generally solved in internal layer using Runge-Kutta methods Differential equation group, reaction rate constant is optimized in outer layer using newton variable metric method is intended.Due to It is n*n that Guass-Newton methods or variable metric method need to calculate the scale comprising second dervative The Hessian matrixes or fairly large of (n is to collect the virtual fraction number involved by general equation) Symmetric positive definite matrix after correlating transforms.Therefore, as the increase of lump composition quantities calculates rule Mould rapid expansion.Calculating storage overhead and calculating time overhead rapidly increases.Walked in iterative calculation It is even more so in the case that number is more.

The content of the invention

For defect of the prior art, it is simple that the present invention provides a kind of random seed number pretreatment Type post-processes parallel genetic lumping kinetics method and system, and different cutting sides is used reducing While the error of the oily cutting result of the fraction of case and result of calculation, the computing effect of model is improve Rate.

In a first aspect, the present invention provides a kind of post processing of random seed number pretreatment simple form parallel Hereditary lumping kinetics method, 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 entered by random seed number preprocess method Row 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 simplex algorithm 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, step S2-S6 is performed into the lumping kinetics by polycaryon processor parallel computation The simulation work of equation.

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, C_iAnd C_jRepresent the fraction oil quality point rate data of different virtual lump components；γ_i 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；ka_iRepresent matrix metadata.

Optionally, the matrix metadata is to include the lower triangular matrix of diagonal element.

Optionally, the step S2 includes：

S21, the multiple matrix element numbers of exhaustive acquisition that preset times are carried out to M matrix metadata According to group；

S22, 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；

S23, 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 S23, calculated value is with the residual error err of experiment value：

Wherein, C_{Cal, i}Represent the fraction oil by calculating the i-th virtual lump component for obtaining Amount point rate data, and C_{Test, i}Represent 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, present invention also offers a kind of pretreatment simple form post processing of random seed number Parallel genetic lumping kinetics system, the system includes：

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 seed number 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 simplex algorithm 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；

Parallel computation module, for performing the collection total output by polycaryon processor parallel computation Learn the simulation work of equation.

As shown from the above technical solution, a kind of random seed number pretreatment that the present invention is provided is simple Type post-processes parallel genetic lumping kinetics method and system, is obtained by random selection mechanism and evaporated Part oil quality point rate data and lumping kinetics equation, determine the square of hydrogenation reaction rate matrix Array element data, then pass sequentially through random seed number preprocess method, genetic algorithm, simple form The matrix metadata that algorithm pair determines is pre-processed, genetic algorithm is processed and high accuracy refine Treatment obtains the matrix metadata after final optimization pass, and according to the matrix element number after the final optimization pass According to the model for determining lumping kinetics equation.The method divides multiple lump components, meets industry The requirement on flexibility of production product cutting, and treat the ginseng set up in model by three kinds of algorithms Number is optimized, and the lumped reaction kinetics after the foundation enhance different faction cut schemes Flexibility, and the fraction oil quality point rate data calculated by the lumped reaction kinetics point The fraction oil quality point rate data of the product fraction oil that cloth curve is obtained with simulation distillation test The distribution curve goodness of fit is higher.Reducing using the fraction oil cutting result of different cutting schemes While with the error of result of calculation, the operation efficiency of model is improve.

Brief description of the drawings

After a kind of random seed number pretreatment simple form that Fig. 1 is provided for one embodiment of the invention Process the schematic flow sheet of parallel genetic lumping kinetics method；

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 parallel algorithms that Fig. 7 is provided for one embodiment of the invention；

Fig. 8 is calculated for the random initial seed number of use that one embodiment of the invention is provided, heredity is calculated Method optimizes and intends the product that the lumping kinetics equation group of simple method refine calculating is obtained The distribution map of fraction oil quality point rate data and the product fraction oil quality point rate acquired in experiment The comparison diagram of the distribution map of data；

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；

After a kind of random seed number pretreatment simple form that Figure 10 is provided for one embodiment of the invention Process the structural representation of parallel genetic lumping kinetics system.

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 is place after a kind of random seed number pretreatment simple form provided in an embodiment of the present invention The schematic flow sheet of parallel genetic lumping kinetics method is managed, as shown in figure 1, the method includes Following steps：

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 entered by random seed number preprocess method Row 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 simplex algorithm 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 entered to each matrix metadata successively by simplex algorithm Row 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, step S2-S6 is performed into the lumping kinetics by polycaryon processor parallel computation The simulation work of equation.

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 subnumber preprocess method, genetic algorithm, simplex algorithm pair determine is entered Row pretreatment, genetic algorithm treatment and high accuracy refine treatment obtain the matrix after final optimization pass Metadata, and the mould of lumping kinetics equation is determined according to the matrix metadata after the final optimization pass Type.The method divides multiple lump components, meets the requirement on flexibility of industrial production product cutting, And treat the parameter set up in model by three kinds of algorithms to optimize, the lump after the foundation Kinetic model enhances the flexibility of different faction cut schemes, and by the collection total output Learn the distribution curve of the fraction oil quality point rate data that model is calculated and the experimental result goodness of fit compared with It is high.

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；

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.

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 reactant For system, i-1 in-degree represents the i-th node virtual lump component and receives and come from than the described i-th section The pyrolysis of oil fractions of the virtual lump component of the heavier i-1 node of virtual lump component of point Product, N-i out-degree represents the virtual lump component self-cleavage of the i-th node to come from than institute The virtual lump component for stating the lighter N-i node of virtual lump component of the i-th node provides former 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, C_iAnd C_jRepresent the fraction oil quality point rate data of different virtual lump components；γ_i 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；ka_iRepresent 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 multiple matrix element numbers of exhaustive acquisition that preset times are carried out to M matrix metadata According to group；

S22, 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；

S23, 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-S23 Initial reaction rate matrix matrix metadata.Specific optimization calculating process is as follows：

(1) assume that 5049 parameters to be optimized are the condition random number between 0-100, this group of bar Part random number meets element absolute value on each column diagonal and is equal to other all elements in respective column Algebraical sum this constraints.

(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 shown below.Herein In calculating process, parameter p=1, q=1 are taken, and record error between the two.

Calculated value is with the residual error err of experiment value：

Formula (2)

Wherein, C_{Cal, i}Represent the fraction oil by calculating the i-th virtual lump component for obtaining Amount point rate data, and C_{Test, i}Represent by testing i-th fraction of virtual lump component for obtaining Oil quality point rate data, N is the number of virtual lump component, and p and q is 0,1,2 or infinite Greatly.

(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) using object function to be optimized it is reciprocal or to the positive related function of the function as fitting 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, previous step is optimized by genetic algorithm based on simplex algorithm The matrix metadata of establishment.Specific optimization calculating process is as follows：

(1) calculated corresponding to the maximum individuality of the fitness function for obtaining with by genetic algorithm 5050 (M+1s) group disturbance array of the matrix metadata in contiguous range is calculated as simple form Initial higher dimensional space summit x (i) the ∈ E that method is calculated^M, i=1...M+1.

(2) the follow-up reflectance factor α ＞ 0 for calculating, spreading coefficient γ ＞ 1 and the compressed coefficient are set 0 ＜ β ＜ 1 and allowable error ε ＞ 0.

(3) functional value f (x are calculated successively⁽ⁱ⁾), i=1,2 ..., M+1.Make k=1.K is represented The iterations that simple form is calculated.

(4) the peak x of calculating functional value is determined^(h), secondary high point x^(l)And minimum point x^(g), H, g, l ∈ { 1,2 ..., M+1 } so that：

f(x^(h))=max { f (x⁽¹⁾),f(x⁽²⁾),...,f(x^(M+1))}

f(x^(g))=max { f (x⁽ⁱ⁾)|x⁽ⁱ⁾≠x^(h))}

f(x^(l))=min { f (x⁽¹⁾),f(x⁽²⁾),...,f(x^(M+1))}

(5) calculate and remove x^(h)The M centre of form of point in additionAnd Calculate

(6) carry out reflection and calculate orderF (x are calculated again^(M+2))。

(7) if f (x^(M+2)) ＜ f (x^(l)) be so extended, make F (x are calculated again^(M+3)) (8th) step is transferred to, if f (x^(l))≤f(x^(M+2))≤f(x^(g)) then make x^(h)=x^(M+2)Simultaneously by f (x^(h))=f (x^(M+2)) it is transferred to (10th) step.If f (x^(M+2)) ＞ f (x^(g)) Then it is compressed calculating orderWherein h' ∈ { h, M+2 } makeFurther calculate f (x^(M+4)), it is transferred to (9th) step.

(8) if f (x^(M+3)) ＜ f (x^(M+2)), then make x^(h)=x^(M+3), f (x^(h))=f (x^(M+3)) it is transferred to (10th) Step, otherwise makes x^(h)=x^(M+2), f (x^(h))=f (x^(M+2)) (10th) step is transferred to again.

(9) if f (x^(M+4)) ＜ f (x^(h'⁾), then make x^(h)=x^(M+4), f (x^(h))=f (x^(M+4)) be transferred to (10th) step.Otherwise carry out shrinking calculation order F (x are calculated successively⁽ⁱ⁾), i=1,2 ..., M+1 are transferred to (10th) step.

(10) detect whether to meet convergence criterion.IfMeter Convergence is calculated to stop calculating, it is linearly most better as required；Otherwise, k=k+1 is made, (4th) is returned Step.

Parameter selection is as follows in whole calculating process：Reflectance factor α=0.001, the compressed coefficient β=0.95, spreading coefficient γ=1.05 and allowable error ε=0.001.

As shown in fig. 7, above-mentioned steps S7 specifically includes following steps：

S71,1000000 groups are produced to include 5049 using the core of multi-core computer 16 is simultaneously parallel The array of the random number between individual 0~100；

S72,1000000 random arrays to producing carry out the generation lump of 16 core parallel compositions Kinetic 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 parallel of generation using 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 produces 800 groups of initial populations of genetic algorithm parallel, and to each Population at individual carries out binary coding；

S76, the binary coding grouping parallel that will be generated intersect, the binary system that variation generation is new 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), parallel computation is initial successively Population and the adaptive response functional value of 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, the reflectance factor that follow-up calculating is set, spreading coefficient, the compressed coefficient and permission Error using computer parallel computation generate at random the initial higher dimensional space of simple form summit and with The error function value on each summit of this parallel computation；

S81, the higher dimensional space vertex function value to calculating acquisition are ranked up, and letter is selected successively Maximum, second largest value and minimum value in numerical value, and calculate the centre of form of related higher-dimension simple form And its centroid function value；

S82, according to configured reflectance factor, spreading coefficient, the compressed coefficient respectively according to Algorithm requirement carries out reflection calculating, expands calculating and the related higher dimensional space of compression calculating generation Coordinate points and the corresponding error function value of these coordinate points, and to generate error function value enter Row judges, to determine that algorithm system is to carry out shrinking calculation or carry out convergence judgement；

S83, according to setting allowable error calculate convergence criterion be such as unsatisfactory for, repeat step S80-S82 stops iterative calculation untill error function value is less than 0.0001, and by gained Solution vector established as the solution vector after refine, thus related lumping kinetics whole it is undetermined Parameter, establishes the mathematical form of 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 represent fraction oil cutting temperature/degree Celsius, Y-coordinate represent evaporate Part oil quality point rate data, are respectively adopted initial seed number, genetic algorithm and simple form in figure Algorithm calculates dividing for the product fraction oil quality point rate data that lumping kinetics equation group is obtained Butut divides the comparison diagram of the distribution map of rate data with the product fraction oil quality acquired in experiment.Can To find due to the numerous product fraction oil calculated by initial seed number of number of parameters to be optimized The distribution map of mass fraction data and the product fraction oil quality point rate data acquired in experiment Distribution map still has very big difference, and passes through dividing for genetic algorithm product fraction oil quality point rate data The distribution map that Butut then divides rate data with the product fraction oil quality acquired in experiment is relatively Approach but still have certain deviation, and eventually pass through seven iterative calculation refine of simplex algorithm and obtain Product fraction oil quality point rate data distribution map then with the product fraction acquired in experiment Oil quality divides the distribution map of rate data suitable steady conjunction.Realize for product fraction oil quality point The high accuracy numerical fitting of the distribution of rate data.

The related residual err=24.1186 calculated by genetic algorithm, and pass through the step of simple form 7 and change In generation, calculates the corresponding reaction rate matrixes of final residual error err=3.9568. for obtaining due to matrix element number According to numerous, table 1 is shown to be calculated by various algorithms and obtains residual error and part matrix metadata Numerical value.

Table 1

To choosing the total and different cutting scheme of different collection and use in the above method The time of different computational methods consumption contrasts as follows respectively：

(1) in order to show algorithm involved in the present invention for the virtual lump involved by lumped model The dependence of component number, is calculated with the lump of 20 lump 50 and 100 lumped models respectively herein The distribution curve of related fraction oil quality point rate data, and result of calculation is entered with experimental result Row contrast, comparing result is listed in shown in Fig. 9, and X-coordinate represents fraction oil cutting temperature/take the photograph Family name's degree, Y-coordinate represents fraction oil quality point rate data.

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.

(2) in order to show flexible practicality of the method for different faction cut schemes.It is existing Two kinds of different product fraction oil cutting schemes and its according to the method described above calculating are obtained Result is listed in shown in table 2 and table 3, and wherein table 2 and table 3 are the fraction oil of different cutting fractions The distribution situation of amount point rate data.

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.

(3) in order to show acceleration effect of the parallel calculating method for calculating, herein using difference Lump multi-component model calculated using different concurrent computational systems, by the meter of relative program Evaluation time is listed in shown in table 4.

Table 4

By the data of table 4 it can be found that shortening as the increase for calculating core number calculates the time, Computational efficiency can actually be increased.But for the shortening that unit concurrent computational system calculates the time It is not in simple inversely prroportional relationship with the increase of calculating core number.When in calculating calculation less than etc. This inversely prroportional relationship is obvious when 8, though the calculating time needed for when the center of calculating is for 16 So generally reduce but be simultaneously not up to 1/2 of calculating center when being 8, because parallel computation system Caused by system internal hardware configuration.But from general, the introducing of parallel calculating method is certain The calculating time can be greatly shortened.For example, for the whole calculating of 250 lumped reaction kinetics It is about for process, when mutually being shut down using serial computing 150 days or so, and uses 16 cores parallel Computing system can then ensure to complete identical evaluation work at 11 days or so.

As shown in Figure 10, the embodiment of the present invention additionally provides a kind of random seed number pretreatment merely Type post-processes the structural representation of parallel genetic lumping kinetics system, and as shown in Figure 10, this is System includes：

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 seed number 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 simplex algorithm, 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；

Parallel computation module 107, for performing the lump by polycaryon processor parallel computation The simulation work of kinetics equation.

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. a kind of random seed number pretreatment simple form post-processes parallel genetic lumping kinetics side Method, it is characterised in that 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 entered by random seed number preprocess method Row 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 simplex algorithm 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, step S2-S6 is performed into the lumping kinetics by polycaryon processor parallel computation The simulation work of equation.

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 2, 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：

$\frac{\∂}{\∂t}{C}_j=\underset{i=1}{\overset{N}{\Σ}}{\mathrm{\γ}}_i{\mathrm{ka}}_i{C}_i$

$\left\{\begin{array}{cc}{\mathrm{\&gamma;}}_i=1& i<j\\ {\mathrm{\&gamma;}}_i=-1& i=j\\ {\mathrm{\&gamma;}}_i=0& j<i\end{array}\right.$

Wherein, C_iAnd C_jRepresent the fraction oil quality point rate data of different virtual lump components；γ_i 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；ka_iRepresent 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 multiple matrix element numbers of exhaustive acquisition that preset times are carried out to M matrix metadata According to group；

S22, 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；

S23, 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 S23, Calculated value is with the residual error err of experiment value：

$err={\left(\underset{i=1}{\overset{N}{\&Sigma;}}\right|C_{cal,i}-C_{test,i}{|}^p)}^{\left(\frac{1}{q}\right)}$

Wherein, C_{Cal, i}Represent the fraction oil by calculating the i-th virtual lump component for obtaining Amount point rate data, and C_{Test, i}Represent 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 1, 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. a kind of random seed number pretreatment simple form post-processes parallel genetic lumping kinetics system 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 seed number 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 simplex algorithm 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；

Parallel computation module, for performing the collection total output by polycaryon processor parallel computation Learn the simulation work of equation.