CN110580545A - method and device for optimizing blending formula of multi-component gasoline - Google Patents
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Abstract
the invention relates to the technical field of gasoline blending, and discloses a method for optimizing a blending formula of multi-component gasoline, which comprises the following steps: establishing a gasoline blending property prediction model, and adjusting the property prediction model according to a historical blending formula; taking the property requirement of the blend oil as a constraint condition and the total profit of the blend oil as an optimization target to establish a gasoline blending optimization model; and obtaining an optimal blending formula according to the gasoline blending optimization model. The method has the technical effects of accurate prediction of gasoline properties, standard gasoline blending quality and lower cost.
Description
Technical Field
the invention relates to the technical field of gasoline blending, in particular to a method and a device for optimizing a blending formula of multi-component gasoline.
Background
The profit of gasoline blending accounts for more than 60% of the total profit of the oil refining enterprise. Oil blending operations rely on accurate blending recipes and control implementation systems. The optimized blending formula can achieve the purposes of blending index blocking of the blended oil and reduction of blending property index margin. By applying an intelligent optimization algorithm, the optimal formula can be rapidly obtained, the blending cost is reduced, and the maximum profit is obtained. The existing blending technology has the following defects: the property prediction model is inaccurate and lacks a feedback adjustment mechanism; the quality of the blending oil is easy to be excessive, and the cost is not saved.
Disclosure of Invention
the invention aims to overcome the technical defects, provides a method and a device for optimizing a blending formula of multi-component gasoline, and solves the technical problems of inaccurate property prediction of the blended oil, excessive quality of the blended oil and cost waste in the prior art.
in order to achieve the technical purpose, the technical scheme of the invention provides a method for optimizing a blending formula of multi-component gasoline, which comprises the following steps:
Establishing a gasoline blending property prediction model, and adjusting the property prediction model according to a historical blending formula;
Taking the property requirement of the blend oil as a constraint condition and the total profit of the blend oil as an optimization target to establish a gasoline blending optimization model;
And obtaining an optimal blending formula according to the gasoline blending optimization model.
the invention also provides a device for optimizing the blending formula of the multi-component gasoline, which comprises a processor and a memory, wherein a computer medium is stored in the memory, and when the computer medium is executed by the processor, the method for optimizing the blending formula of the multi-component gasoline is realized.
compared with the prior art, the invention has the beneficial effects that: after the gasoline blending property prediction model is established, the property prediction model is adjusted according to the historical blending formula, so that the property prediction accuracy is improved. Meanwhile, the property requirement of the blended oil is used as a constraint condition, the total profit of the blended oil is used as an optimization target, a gasoline blending optimization model is established, and an optimal blending formula is obtained, so that the cost of the optimal blending formula is reduced as far as possible on the premise of meeting the property requirement, and the profit maximization is achieved.
Drawings
FIG. 1 is a flow chart of one embodiment of a method for optimizing a blended blend formula for a multi-component gasoline provided in accordance with the present invention;
FIG. 2 is a flow chart of an embodiment of a method for adjusting an octane prediction model according to the present invention.
Detailed Description
in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
example 1
As shown in fig. 1, embodiment 1 of the present invention provides a method for optimizing a blend formula of a multi-component gasoline, comprising the steps of:
s1, establishing a gasoline blending property prediction model, and adjusting the property prediction model according to a historical blending formula;
s2, establishing a gasoline blending optimization model by taking the property requirement of the blending oil as a constraint condition and the total profit of the blending oil as an optimization target;
And S3, obtaining an optimal blending formula according to the gasoline blending optimization model.
The embodiment of the invention aims at the problems that the property prediction model is inaccurate and a feedback adjustment mechanism is lacked in the conventional gasoline blending process; the blending oil is easy to be excessive in quality and the cost is not saved, and provides an optimization method of a blending formula of the multi-component gasoline. Firstly, establishing a property prediction model, and adjusting the property prediction model according to a historical harmonic formula so as to improve the property prediction accuracy; on the basis of the property prediction model, gasoline which meets specified property requirements is produced as a constraint condition, and meanwhile, the total profit of the blend oil is used as an optimization target, namely the lowest total cost of the blend oil is used as the optimization target, so that a gasoline blending optimization model is established, and an optimal blending formula is obtained.
the method has the technical effects of accurately predicting the quality of the blended oil, utilizing the economic benefit of the conventional component oil to the maximum extent, reducing the waste of the component oil, reducing the cost, improving the profit and improving the economic benefit.
Preferably, the gasoline blending property prediction model specifically includes:
Oxygen content prediction model:
a sulfur content prediction model:
aromatic hydrocarbon content prediction model:
Olefin content prediction model:
A benzene content prediction model:
vapor pressure prediction model:
An octane number prediction model:
Wherein m (o) is the mass fraction of oxygen in the blend oil, oiIs the mass fraction of oxygen in the base oil i, xiIs the volume fraction of base oil i, piis the density in the base oil i, m is the base oil quantity, m(s) is the mass fraction of sulfur in the blend oil, siIs the mass fraction of sulfur in the base oil i, and Aro is the volume fraction of aromatic hydrocarbon in the blend oiliis the volume fraction of aromatic hydrocarbon in the base oil i, Ole is the volume fraction of olefin in the blend oiliis the volume fraction of olefin in the base oil i, Ben is the volume fraction of benzene in the blend oiliis the volume fraction of benzene in the base oil i, P is the vapor pressure in the blend oil, PiIs the vapor pressure in the base oil i, RON is the octane number of the blend oil, RONiin order to be able to determine the octane number of the base oil i,As a function of the olefin volume fraction, the aromatic volume fraction, and the base oil volume fraction.
In the embodiment, the gasoline blending property prediction mainly considers properties such as sulfur content, oxygen content, benzene content, aromatic hydrocarbon content, olefin content, octane number, steam pressure index and the like; wherein the octane number and vapor pressure harmonic model is a nonlinear model, and other property indexes are linear superposition models according to mass or volume fractions.
It should be understood that the gasoline blending property prediction model may also include other sets of property indicators, which may be set according to blending component requirements, and this embodiment is merely an example. The invention can optimize the formula of the oil product with unfixed components, optimize the formula of the multi-target multi-component oil and fully exert the market effect.
Preferably, the property prediction model is adjusted according to a historical blending formula, specifically, regression parameters of the octane number prediction model are adjusted according to the historical blending formula;
expressing the octane prediction model as:
Wherein RON is the predicted octane number of the blend oil,Is the sum of the octane volume fractions of the base oil,Is the sum of the volume fractions of the olefin contents of the base oil,Is the sum of the square of the volume fraction of the olefin content of the base oil,Is the sum of the volume fractions of the aromatic hydrocarbon content of the base oil,Is the sum of the squares of the volume fractions of the aromatic hydrocarbon content of the base oil, a2、a3is the regression parameter;
Simplifying the octane number prediction model:
y＝a2x+a3
Wherein y is a dependent variable and x is an independent variable.
the core of the gasoline blending property prediction model is that the properties of blending oil are predicted according to the properties of all blending components, certain blending properties such as octane number and Reid vapor pressure have nonlinear blending effects in the gasoline blending process, different blending components have different properties, different grades of blending oil have different index requirements, the accuracy of the octane number prediction model is ensured by automatically updating regression parameters of the octane number prediction model, the accuracy of the octane number prediction model is ensured, and the one-time blending success rate is improved.
preferably, the regression parameters of the octane number prediction model are adjusted according to a historical blending formula, specifically:
s11, selecting a set number of historical harmonic formulas, respectively calculating a pair of independent variable actual values and dependent variable actual values as sample points according to each historical harmonic formula to obtain a sample point set, and selecting analysis points from the sample point set;
s12, carrying out regression analysis according to the analysis points to calculate the regression parameters to obtain an adjusted octane number prediction model;
S13, calculating the octane prediction value of the historical blend oil according to the adjusted octane number prediction model, judging whether the difference value between the octane prediction value of the historical blend oil and the actual octane value of the historical blend oil is smaller than a first set threshold value, if so, outputting the adjusted octane number prediction model, otherwise, selecting analysis points in the sample point set again, and turning to the step S12;
S14, if there is no analysis point meeting the requirement in the sample point set, selecting more than the set number of history blending recipes to reconstruct the sample point set, and going to step S11.
Preferably, a historical blending formula of blending oil for 6-11 days closest to the current date is selected, data processing is carried out, abnormal points are removed, and then regression parameter correction is carried out.
preferably, the analysis points are selected from the sample point set, specifically:
Selecting two sample points as straight lines to obtain expressions of the straight lines, substituting each independent variable actual value into the expressions to obtain corresponding dependent variable theoretical values, judging whether the difference value between each dependent variable theoretical value and the corresponding dependent variable actual value is smaller than a second set threshold value, counting a count value of which the difference value between the dependent variable theoretical value and the dependent variable actual value is smaller than the second set threshold value, judging whether the count value is larger than a third set threshold value, if so, taking the two selected sample points as analysis points, and if not, re-selecting the other two sample points in the sample point set for judgment.
preferably, the property requirements of the blend oil are taken as constraints, and the method specifically comprises the following steps:
And (3) oxygen content constraint:
and (3) sulfur content constraint:
Aromatic hydrocarbon content constraint:
olefin content constraint:
and (3) restricting the benzene content:
Vapor pressure constraints:
octane number constraint:
wherein, m (o)minis the minimum value of the mass fraction of oxygen in the blend oil, m (o)maxIs the maximum value of the mass fraction of oxygen in the blend oil, m(s)minis the minimum value of the mass fraction of sulfur in the blend oil, m(s)maxIs the maximum value of the mass fraction of sulfur in the blend oil, ArominIs the minimum volume fraction of aromatic hydrocarbons in the blend oil, AromaxThe maximum volume fraction of aromatic hydrocarbons in the blend oil, OleminOle is the minimum volume fraction of olefins in the blendmaxIs the maximum volume fraction of olefins in the blend, BenminIs the minimum volume fraction of benzene in the blend, BenmaxIs the maximum volume fraction of benzene in the blend oil, PminIs the minimum vapor pressure of the blend oil, PmaxRON being the maximum vapor pressure of the blend oilminThe minimum value of the octane number in the blend, RONmiaxIs the maximum value of the octane number in the blend oil.
preferably, the constraint condition further includes:
and (4) library capacity constraint: cxi≤Vi
restraining with oil: x is the number ofi≥xmin
wherein C is the volume of the blend oil, ViIs the usable volume of the base oil, xminis the minimum oil standard of the base oil.
and increasing inventory constraint and oil utilization constraint, wherein the property prediction model takes the maximization of the benefit output value as an optimization target on the basis of the inventory at a certain moment and the required quantity of blended oil products, predicts the nonlinear quality characteristics, handles the inventory constraint, reduces blending cost, outputs an optimized blending formula in a scheduling period, optimizes a scheduling scheme, and maximizes the economic benefit of blending operation under the condition of meeting various constraints.
Preferably, the total profit of the blend oil is taken as an optimization target, and specifically, the method comprises the following steps:
Y＝Max(∑((yj·pj)-∑(xi,j·bi)))-Pc-Tax
wherein Y is the total profit, YjYield of base oil j, pjIs the selling price, x, of base oil ji,jis the blending amount of component i in base oil j, biis the cost price of component i in base oil j, Pc is the processing fee, and Tax is the other fees in addition to the cost fee and the processing fee.
Preferably, the optimal blending formula is obtained according to the gasoline blending optimization model, and specifically comprises the following steps:
Setting an initial random formula, and carrying out normalization processing on the initial random formula to obtain a group of initial random particles;
Performing iterative operation on the initial random particles by adopting a particle swarm optimization algorithm:
v(i+1)
＝v(i)+c1*rand()*(pbest[i]-present[i])+c2*rand()*(gbest[i]-present[i])(a)
Wherein v (i +1) is the particle velocity after iteration, v (i) is the particle velocity before iteration, rand () is a random number between (0,1), present [ i ] is the current position of the particle, pbest [ i ] is an individual extreme value, gbest [ i ] is a global extreme value, and c1 and c2 are both adjusting parameters;
and setting the total iteration times, selecting the optimal particles in the iteration process according to the gasoline blending optimization model within the total iteration times, and obtaining the optimal blending formula according to the optimal particles.
After the gasoline blending optimization model is established, a particle swarm optimization algorithm is adopted, an initial random formula is normalized and initialized into a group of initial random particles, namely random solutions, then an optimal solution is found through iteration, in each iteration, the particles update themselves by tracking two extreme values, the first extreme value is the optimal solution found by the particles, namely an individual extreme value pbest [ i ], and the other extreme value is the optimal solution found by the whole group, namely a global extreme value gbest [ i ]. And the particle swarm optimization algorithm is utilized to optimize the initial random particles, so that the optimal blending formula of the blended oil is realized, the cost is further reduced, and the blending profit is improved.
the existing gasoline blending optimization algorithm has the problems of low speed, complex modeling and difficult solution, and the global optimal solution is difficult to obtain. In this embodiment, the constraint conditions of the gasoline blending optimization model include linear constraint and nonlinear constraint, and the optimization target of the gasoline blending optimization model is a linear function. The number of equality constraints in the constraint conditions is small, the form is simple, and most of the constraints are inequality constraints. Aiming at the characteristics of the gasoline blending optimization model, the particle swarm optimization algorithm is adopted for solving in the optimization process, the convergence rate is high, and the global optimal solution can be obtained.
Example 2
embodiment 2 of the present invention provides a multi-component gasoline blending formula optimization apparatus, which includes a processor and a memory, where the memory stores a computer medium, and when the computer medium is executed by the processor, the multi-component gasoline blending formula optimization method provided in any of the above embodiments is implemented.
the method for optimizing the blending formula of the multi-component gasoline specifically comprises the following steps:
Establishing a gasoline blending property prediction model, and adjusting the property prediction model according to a historical blending formula;
Taking the property requirement of the blend oil as a constraint condition and the total profit of the blend oil as an optimization target to establish a gasoline blending optimization model;
and obtaining an optimal blending formula according to the gasoline blending optimization model.
The device for optimizing the blending formula of the multi-component gasoline provided by the invention is used for realizing the method for optimizing the blending formula of the multi-component gasoline, so that the device for optimizing the blending formula of the multi-component gasoline has the technical effects of the method for optimizing the blending formula of the multi-component gasoline, and the device for optimizing the blending formula of the multi-component gasoline is also provided, and is not repeated herein.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A method for optimizing a blending formula of multi-component gasoline is characterized by comprising the following steps:
establishing a gasoline blending property prediction model, and adjusting the property prediction model according to a historical blending formula;
taking the property requirement of the blend oil as a constraint condition and the total profit of the blend oil as an optimization target to establish a gasoline blending optimization model;
And obtaining an optimal blending formula according to the gasoline blending optimization model.
2. The method of optimizing a multi-component gasoline blending blend formula of claim 1 wherein the gasoline blending property prediction model specifically comprises:
Oxygen content prediction model:
A sulfur content prediction model:
Aromatic hydrocarbon content prediction model:
olefin content prediction model:
A benzene content prediction model:
vapor pressure prediction model:
An octane number prediction model:
wherein m (o) is the mass fraction of oxygen in the blend oil, oiis the mass fraction of oxygen in the base oil i, xiIs the volume fraction of base oil i, piis the density in the base oil i, m is the base oil quantity, m(s) is the mass fraction of sulfur in the blend oil, siIs the mass fraction of sulfur in the base oil i, and Aro is the volume fraction of aromatic hydrocarbon in the blend oiliIs the volume fraction of aromatic hydrocarbon in the base oil i, Ole is the volume fraction of olefin in the blend oiliIs the volume fraction of olefin in the base oil i, Ben is the volume fraction of benzene in the blend oiliIs the volume fraction of benzene in the base oil i, P is the vapor pressure in the blend oil, PiIs the vapor pressure in the base oil i, RON is the octane number of the blend oil, RONiin order to be able to determine the octane number of the base oil i,As a function of the olefin volume fraction, the aromatic volume fraction, and the base oil volume fraction.
3. the method for optimizing a multi-component gasoline blending blend formula of claim 1, wherein the property prediction model is adjusted according to a historical blending blend formula, in particular, the regression parameters of the octane prediction model are adjusted according to the historical blending blend formula;
Expressing the octane prediction model as:
wherein RON is the predicted octane number of the blend oil,Is the sum of the octane volume fractions of the base oil,Is the sum of the volume fractions of the olefin contents of the base oil,Is the sum of the square of the volume fraction of the olefin content of the base oil,is the sum of the volume fractions of the aromatic hydrocarbon content of the base oil,Is the sum of the squares of the volume fractions of the aromatic hydrocarbon content of the base oil, a2、a3is the regression parameter;
simplifying the octane number prediction model:
y＝a2x+a3
wherein y is a dependent variable and x is an independent variable.
4. The method for optimizing a multi-component gasoline blending formula of claim 3, wherein the regression parameters of the octane number prediction model are adjusted according to historical blending formulas, specifically:
S11, selecting a set number of historical harmonic formulas, respectively calculating a pair of independent variable actual values and dependent variable actual values as sample points according to each historical harmonic formula to obtain a sample point set, and selecting analysis points from the sample point set;
s12, carrying out regression analysis according to the analysis points to calculate the regression parameters to obtain an adjusted octane number prediction model;
s13, calculating the octane prediction value of the historical blend oil according to the adjusted octane number prediction model, judging whether the difference value between the octane prediction value of the historical blend oil and the actual octane value of the historical blend oil is smaller than a first set threshold value, if so, outputting the adjusted octane number prediction model, otherwise, selecting analysis points in the sample point set again, and turning to the step S12;
S14, if there is no analysis point meeting the requirement in the sample point set, selecting more than the set number of history blending recipes to reconstruct the sample point set, and going to step S11.
5. the method of optimizing a blending formula of a multi-component gasoline of claim 4 wherein the analysis points are selected from the set of sample points and specifically are:
Selecting two sample points as straight lines to obtain expressions of the straight lines, substituting each independent variable actual value into the expressions to obtain corresponding dependent variable theoretical values, judging whether the difference value between each dependent variable theoretical value and the corresponding dependent variable actual value is smaller than a second set threshold value, counting a count value of which the difference value between the dependent variable theoretical value and the dependent variable actual value is smaller than the second set threshold value, judging whether the count value is larger than a third set threshold value, if so, taking the two selected sample points as analysis points, and if not, re-selecting the other two sample points in the sample point set for judgment.
6. The method for optimizing the blending formula of the multi-component gasoline according to claim 2, wherein the property requirements of the blending oil are taken as constraint conditions, and the method specifically comprises the following steps:
And (3) oxygen content constraint:
And (3) sulfur content constraint:
aromatic hydrocarbon content constraint:
Olefin content constraint:
and (3) restricting the benzene content:
vapor pressure constraints:
octane number constraint:
Wherein, m (o)minis the minimum value of the mass fraction of oxygen in the blend oil, m (o)maxIs the maximum value of the mass fraction of oxygen in the blend oil, m(s)minIs the minimum value of the mass fraction of sulfur in the blend oil, m(s)maxIs the maximum value of the mass fraction of sulfur in the blend oil, ArominIs the minimum volume fraction of aromatic hydrocarbons in the blend oil, Aromaxthe maximum volume fraction of aromatic hydrocarbons in the blend oil, OleminOle is the minimum volume fraction of olefins in the blendmaxIs the maximum volume fraction of olefins in the blend, BenminIs the minimum volume fraction of benzene in the blend, BenmaxIs the maximum volume fraction of benzene in the blend oil, PminIs the minimum vapor pressure of the blend oil, PmaxRON being the maximum vapor pressure of the blend oilminthe minimum value of the octane number in the blend, RONmiaxis the maximum value of the octane number in the blend oil.
7. the method of optimizing a blended gasoline blend formula of claim 6 wherein the constraints further comprise:
and (4) library capacity constraint: cxi≤Vi
restraining with oil: x is the number ofi≥xmin
Wherein C is the volume of the blend oil, Viis the usable volume of the base oil, xminIs the minimum oil standard of the base oil.
8. the method for optimizing a blending formula of a multi-component gasoline according to claim 1, wherein the total profit of the blending oil is taken as an optimization target, and specifically comprises the following steps:
Y＝Max(∑((yj·pj)-∑(xi,j·bi)))-Pc-Tax
wherein Y is the total profit, YjYield of base oil j, pjis the selling price, x, of base oil ji,jis the blending amount of component i in base oil j, biIs the cost price of component i in base oil j, Pc is the processing fee, and Tax is the other fees in addition to the cost fee and the processing fee.
9. The method for optimizing a multi-component gasoline blending formula according to claim 1, wherein an optimal blending formula is obtained according to the gasoline blending optimization model, and specifically comprises:
setting an initial random formula, and carrying out normalization processing on the initial random formula to obtain a group of initial random particles;
Performing iterative operation on the initial random particles by adopting a particle swarm optimization algorithm:
v(i+1)
＝v(i)+c1*rand()*(pbest[i]-present[i])+c2*rand()*(gbest[i]-present[i])(a)
wherein v (i +1) is the particle velocity after iteration, v (i) is the particle velocity before iteration, rand () is a random number between (0,1), present [ i ] is the current position of the particle, pbest [ i ] is an individual extreme value, gbest [ i ] is a global extreme value, and c1 and c2 are both adjusting parameters;
And setting the total iteration times, selecting the optimal particles in the iteration process according to the gasoline blending optimization model within the total iteration times, and obtaining the optimal blending formula according to the optimal particles.
10. a multi-component gasoline blending formula optimization device, comprising a processor and a memory, wherein the memory stores a computer medium, and the computer medium is executed by the processor to implement the multi-component gasoline blending formula optimization method according to any one of claims 1 to 9.
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| CN114399122A (en) * | 2022-01-21 | 2022-04-26 | 浙江中控技术股份有限公司 | Oil blending scheduling optimization method suitable for refinery plant |
| CN114822705A (en) * | 2022-06-30 | 2022-07-29 | 卡松科技股份有限公司 | Intelligent production and blending method for lubricating oil based on big data |
| CN114822705B (en) * | 2022-06-30 | 2022-09-09 | 卡松科技股份有限公司 | Intelligent production and blending method for lubricating oil based on big data |
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