WO2020199666A1 - 一种原油调合的调合效应参数的计算方法 - Google Patents
一种原油调合的调合效应参数的计算方法 Download PDFInfo
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- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- the invention relates to the field of crude oil processing in refineries, and in particular to an intelligent calculation method for blending effect parameters of crude oil blending.
- the formulation of the blending effect model is precisely to quantify this qualitative empirical blending effect. For example, the least squares method is used to calculate the blending effect parameter. This method adds the equation constraint that the blending effect parameter is equal to 0. Although it is easy to solve, it does not match the actual meaning.
- the invention aims to establish a mathematical model of the blending effect.
- Another object of the present invention is to transform the solution of the blending effect parameters into an optimization problem by establishing a mathematical model of blending effects, and then using the objective function of blending parameter prediction.
- a mathematical model of blending effect parameters for crude oil blending including the steps:
- pb j,i represents the blending effect parameter of each attribute of the component oil
- O j,i is the attribute measured by the near-infrared analyzer of the i-th component oil
- Qx(O j,i ) represents the compensation function of the blending effect
- r i represents the formula of the i-th component oil
- P j represents the predicted value of crude oil properties obtained by blending at the blending head
- w s represents the solution stability weight
- [r m,1 r m,2 ... r m,n ] represents a historical blending formula involving n component oils
- m m sets of blending formulas.
- a method for calculating blending effect parameters of crude oil blending comprising the following steps:
- the adaptive differential evolution intelligent optimization algorithm is used to solve the above-mentioned blending effect parameter model provided by the present invention.
- the operating parameters include the component oils involved in blending and the corresponding blending head numbers.
- the objective function includes solution accuracy and solution stability.
- the method is applicable to all crude oil properties, including density, sulfur content, acid value, naphtha yield, and nitrogen content.
- the adaptive differential evolution (Adaptive Differential Evolution, JADE for short) intelligent optimization algorithm includes the steps:
- randci represents the normal distribution
- randci represents the Cauchy distribution
- u j,i,g represent the j-th component of the test vector u i of the i-th individual in the g-th generation;
- v j, i, g denotes the j-th component of the i-th g Generation individual variation of the vector V i;
- x j,i,g represent the j-th component of the i-th individual x i in the g-th generation
- j rank represents a j randomly selected to ensure the introduction of mutation information.
- a crude oil blending method includes the steps of: sending the blending effect parameters obtained by the method provided by the present invention as described above to the blending control system for execution; Predict the blending attributes of a certain formula and guide the formulation of production formulas.
- a computer-readable storage medium on which a computer program is stored.
- the program When the program is executed by a processor, it can be used to implement any one of the methods provided by the present invention or Various calculation methods.
- a computer device including a memory, a processor, and a computer program stored in the memory and running on the processor.
- the computer program executes the method provided by the present invention as described above. The calculation.
- the present invention provides an intelligent optimization algorithm to find the global optimal solution of the problem.
- Figure 1 is a structural diagram of an intelligent calculation system for blending effect parameters (a system that uses intelligent calculation methods to calculate blending effect parameters).
- Figure 2 is a flow chart of the intelligent calculation method of blending effect parameters.
- FIG. 3 is a flowchart of the adaptive differential evolution intelligent optimization algorithm.
- the updated and revised features provide an intelligent calculation method for the blending effect parameters of crude oil blending.
- the objective of the calculation method provided by the present invention is to quantitatively express the qualitative positive and negative effects of blending in the blending process, establish a mathematical model of blending effects, and read component oil properties, blending formulas, and The attributes of the crude oil blended at the blending head are used to dynamically calculate blending effect parameters using an adaptive differential evolution intelligent optimization algorithm, and then the blending effect parameters obtained are sent to the system to guide the control, prediction and optimization of the blending process. On this basis, the present invention has been completed.
- the present invention establishes a mathematical model of the blending effect, and then takes the minimum error between the crude oil at the blending head predicted by the blending parameter and the actual value and the result of the obtained blending effect parameter to be stable as the goal to convert the solution of the blending effect parameter into An optimization problem.
- the present invention establishes the mathematical model of blending effect as follows:
- pb j,i represent the blending effect parameters of the various properties of the component oil
- O j,i are the properties measured by the near-infrared analyzer of the i-th component oil
- Qx( O j,i ) represents the compensation function of the blending effect.
- r i represents the formula of the i-th component oil
- P j represents the predicted attribute value of crude oil blended at the blending head.
- the blending effect parameters can be obtained by linear regression. Assuming that there are m sets of historical blending data, including m sets of blending formulas, the measured values of each attribute of the m set of component oils and the crude oil blended at the blending head, according to formulas (1), (2), we can get:
- the solution of the parameters of the blending effect is to solve the optimal solution of the following objective function, and considering the stability of the solution, there are the following objective functions:
- w p represents the solution accuracy weight
- w s represents the solution stability weight
- the adaptive differential evolution intelligent optimization algorithm is used to solve the blending effect model and obtain the blending effect parameters.
- the first step is to initialize the operating parameters
- the second step is to set the operating period and the weight of the objective function
- the third step is to obtain the properties of the component oils, the blending formula and the properties of the raw oil blended at the blending head according to the operating cycle;
- the fourth step is to use the adaptive differential evolution intelligent optimization algorithm to solve the blending effect model and obtain the blending effect parameters;
- the fifth step is to send the blending effect parameters to the blending control system for control or optimization.
- the component oil products participating in the blending and the corresponding blending head number are selected in the first step.
- At least two component oils are selected.
- the settable operating period of the second step is not less than the number of components ⁇ the data sampling time, and the actual operating period is determined according to the needs of the production formula, preferably 3-6 minutes.
- the objective function in the second step of the present invention may be solution accuracy, solution stability, etc.; the solution accuracy and solution stability are preferred.
- the above method of the present invention is applicable to all crude oil properties, including but not limited to density, sulfur content, acid value, naphtha yield, nitrogen content, etc.
- the data obtained in the above steps are established according to the formulas (1), (2), (3), (4), (5) and (6) provided by the present invention.
- the adaptive differential evolution intelligent optimization algorithm solves the blending effect model and obtains the blending effect parameters. It includes the following steps:
- randci represents normal distribution
- randci represents Cauchy distribution
- mean A represents ordinary arithmetic average
- mean L represents ordinary Lehmer average, as shown in the following formula (7):
- test vectors were successfully generated for storing x i as compared to the parent better test vector F i, and u i CR i.
- the method of generating test vectors is shown in the following formula (8):
- u j,i,g represent the j-th component of the test vector u i of the i-th individual in the g-th generation
- v j,i,g represent the j-th component of the variation vector v i of the i-th individual in the g-th generation Component
- x j, i, g represents the j-th component of the i-th individual x i of the g-th generation
- j rank represents a j randomly selected to ensure the introduction of mutation information.
- control or optimization of the fifth step includes judging whether the operation is completed, and if it is completed, stop there; if not, wait for an optimization cycle and return to the third step.
- the present invention proposes an intelligent calculation method for the blending effect parameters of crude oil blending.
- the blending effect parameters are calculated according to a certain operating period, and the qualitative blending effect in the blending process is positive and negative based on experience.
- the effect is expressed in a quantitative manner, and the dynamic update and correction of the effect parameters are realized, so as to better guide the control, prediction and optimization of the blending process.
- the present invention uses an intelligent algorithm to decompose the effect parameter to solve the target (for example, but not limited to the two major goals of accuracy and stability), and set the weight according to the actual application object or the needs of different working conditions to adjust the target weight, considering its non- Linear factors are solved by adaptive differential evolution intelligent optimization algorithm to ensure that there are solutions in online applications and meet the needs of online updates.
- Step 1 Initialize operating parameters.
- Step 2 Set the operating period and the weight of the objective function.
- Step 3 Obtain component oil properties, blending formulas, and crude oil properties blended at the blending head according to the operating cycle.
- the sulfur content measurement data of four kinds of oils are taken out, 1# component oil sulfur content is 1.68%, 2# component oil sulfur content is 2.36%, 3# component oil sulfur content is 1.79%, and 4# component oil sulfur content is 2.22%.
- the sulfur content of the crude oil obtained by mixing the 7 blending formula with the blending head is as follows:
- Step 4 Establish a mathematical model of blending effect.
- Step 5 Calculate the parameters of the blending effect.
- the blending effect parameter of 1# component oil is 0.0260%
- the blending effect parameter of 2# component oil is 0.0348%
- the blending effect parameter of 3# component oil is 0.0032%
- the blending effect parameter of 4# component oil is 0.0495%.
- Step 6 Send the blending effect parameters into the system.
- Step 7 Check whether the operation is over, if it is not over, wait for one operation cycle and then return to step 3.
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Abstract
一种原油调合的调合效应参数的计算方法。所述方法包括步骤：首先，进行运行参数初始化；其次，设置运行周期以及目标函数权重；再次，根据运行周期获取组分油属性、调合配方以及调合头处调合所得原油属性；和最后，利用自适应差分进化智能优化算法对调合效应参数模型进行求解。
Description
本发明涉及炼油企业原油加工领域，尤其涉及原油调合的调合效应参数智能计算方法。
在原油调合过程中，各组分油对最终调合所得原油的属性的贡献度，除去线性部分以外，往往表现出额外的正效应或者负效应。因此在现场调合的过程中，技术人员经常会根据调合经验定性的估计各组分油的正负效应，用以指导配方的计算。调合效应模型的提出正是将这种定性的经验化的调合效应定量化，如采用最小二乘法进行计算求解调合效应参数，这种方法添加调合效应参数等于0的等式约束，虽然便于求解，但与实际意义不符。
因此，本领域迫切需要提供一种智能优化算法，以便找到问题的全局最优解。
发明内容
本发明旨在建立调合效应数学模型。
本发明的另一个目的是通过建立调合效应数学模型，再以调合参数预测的目标函数将对调合效应参数的求解转化为一个优化问题。
在本发明的第一方面，提供一种关于原油调合的调合效应参数数学模型的建立，包括步骤：
(a)采用式(1)对各组分油的属性进行补偿：
Qx(O
j,i)＝O
j,i+pb
j,i (1)
其中，
i(i＝1,2,…,n)为各组分油编号；
j(j＝1,2,…,n)为油品的需要进行非线性补偿的属性；
pb
j,i表示组分油各属性的调合效应参数；
O
j,i为第i种组分油的近红外分析仪在线测量的属性；
Qx(O
j,i)表示调合效应补偿函数；
(b)采用式(2)得到调合头处调合所得原油属性的预测值：
其中，
r
i表示第i种组分油的配方；
P
j表示调合头处调合所得原油属性预测值；
(c)求解式(6)的目标函数得到调合效应参数：
其中，
w
p表示求解精度权值；
w
s表示求解稳定性权值；
Y＝R
m,n·PB，
PB＝[pb
j,1 pb
j,2 … pb
j,n]
T (4)
且
[r
m,1 r
m,2 ... r
m,n]表示有n个组分油参与调合的一个历史调合配方；
m表示m组调合配方。
在本发明的第二方面，提供一种原油调合的调合效应参数计算方法，所述方法包括以下步骤：
首先，进行运行参数初始化；
其次，设置运行周期以及目标函数权重；
再次，根据运行周期获取组分油属性、调合配方以及调合头处调合所得原油属性；和
最后，利用自适应差分进化智能优化算法对如上所述的本发明提供的调合效应参数模型进行求解。
在另一优选例中，所述运行参数包括参与调合的组分油品及相应调合头编号。
在另一优选例中，所述目标函数包括求解精度、求解稳定性。
在另一优选例中，该方法适用于所有原油属性，包括密度、硫含量、酸值、石脑油收率以及氮含量等。
在另一优选例中，所述自适应差分进化(Adaptive Differential Evolution，简称JADE，)智能优化算法包括步骤：
(1)初始化种群P，种群大小为NP；
(2)初始化μ
CR＝0.5,μ
F＝0.5，A＝Ф；
(3)循环开始，当算法终止条件尚未满足时，进行：
(i)设置S
F＝Ф，S
CR＝Ф；
(ii)针对种群P中的每一个个体x
i，生成对应的比例因子F
i＝randc
i(μ
F,0.1)和交叉概率CR
i＝randn
i(μ
CR,0.1)；
(iii)从适应度值前100p％的个体中随机挑选一个个体，记为x
p,best，从种群P中选择个体x
r1，x
r1≠x
i，从种群P∪A中选择个体x
r2，x
r2≠x
r1≠x
i；
(iv)生成变异向量v
i＝x
i+F
i·(x
p,best-x
i)+F
i·(x
r1-x
r2)；
(v)生成试验向量u
i；
(vi)比较x
i和u
i的适应度函数值，若x
i优于u
i，则x
i进入下一代种群；若u
i优于x
i，则u
i替换x
i进入下一代种群，并将x
i放入A中，F
i放入S
F中，CR
i放入S
CR中；
(vii)在每一代更新结束后，随机移除A中的个体，使得|A|≤NP；
(viii)更新μ
F和μ
CR：μ
F＝(1-c)·μ
F+c·mean
L(S
F),μ
CR＝(1-c)·μ
CR+c·mean
A(S
CR)；
(4)算法停止，得到最终种群NP，种群中适应度值最优的个体即为优化问题的解；
其中，
randci表示正态分布；
randci表示柯西分布；
mean
A表示普通的算术平均；
mean
L表示普通的Lehmer平均；
S
F与S
CR分别用于储存成功产生相比于父代x
i更优秀试验向量u
i的F
i与CR
i。
在另一优选例中，采用式(7)得到普通的Lehmer平均：
采用式(8)得到试验向量：
其中，
u
j,i,g表示第g代第i个个体的试验向量u
i的第j个分量；
v
j,i,g表示第g代第i个个体的变异向量v
i的第j个分量；
x
j,i,g表示第g代第i个个体x
i的第j个分量；
j
rank表示保证变异信息的引入而随机选定的一个j。
在本发明的第三方面，提供一种原油调合方法，所述方法包括步骤：将通过如上所述的本发明提供的方法得到的调合效应参数送入调合控制系统执行；可用于对某一配方下的调合属性进行预测，指导生产配方的制定。
在本发明的第四方面，提供一种计算机可读存储介质，其上存有计算机程序，该程序被处理器执行时能用于实施如上所述的本发明提供的方法中的任意一种或多种计算方法。
在本发明的第五方面，提供一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述计算机程序执行如上所述的本发明提供的方法中所述的计算。
据此，本发明提供了一种智能优化算法，以便找到问题的全局最优解。
图1是调合效应参数智能计算系统(利用智能计算方法计算调合效应参数的 系统)结构图。
图2是调合效应参数智能计算方法流程图。
图3是自适应差分进化智能优化算法流程图。
发明人经过广泛而深入的研究，针对原油调合过程中各组分油对最终调合所得原油的属性的贡献度除去线性部分以外表现出的额外的正效应或者负效应，且效应参数需要动态更新和修正的特点，提供一种原油调合的调合效应参数智能计算方法。
本发明提供的计算方法的目标是将调合过程中的依靠经验的定性的调合正负效应以定量方式表现出来，建立调合效应数学模型，在线读取组分油属性、调合配方以及调合头处调合所得原油属性，利用自适应差分进化智能优化算法动态地计算调合效应参数，再将所得调合效应参数送往系统，指导调合过程的控制、预测与优化。在此基础上，完成了本发明。
具体地，本发明通过建立调合效应数学模型，再以调合参数预测的调合头处原油与实际值误差最小与求得的调和效应参数结果稳定作为目标把对调合效应参数的求解转化为一个优化问题。
本发明根据调合过程的特点建立调合效应数学模型如下：
假设有n个组分油参与调合，其调合后的属性可以按照以下方式预测。首先，对各组分油的属性进行补偿：
Qx(O
j,i)＝O
j,i+pb
j,i (1)
其中，i(i＝1,2,…,n)为各组分油编号；j(j＝1,2,…,n,对应收率、酸值、硫含量、密度、氮含量等)为油品的需要进行非线性补偿的属性；pb
j,i表示组分油各属性的调合效应参数；O
j,i为第i种组分油的近红外分析仪在线测量的属性；Qx(O
j,i)表示调合效应补偿函数。
其次，对补偿后的各组分油的属性进行线性叠加，以得到调合头处调合所得原油属性的预测值，如：
其中，r
i表示第i种组分油的配方；P
j表示调合头处调合所得原油属性预测值。
在上述的模型中，仅有调合效应pb
j,i为待定参数，参数数量由参与调合的组分油个数及属性数量决定(其数量为组分数与属性数量的乘积)。根据调合的历史数据可以通过线性回归的方法求得调合效应参数。假设共有m组历史调合数据，包括m组调合配方，m组组分油与调合头处调合所得原油的各属性测量值，则根据公式(1)，(2)可得：
若将公式(3)中的配方矩阵记为R
m,n，公式(3)可转换为线性回归形式R
m,n·PB＝Y，其中PB＝[pb
j,1 pb
j,2 … pb
j,n]
T (4)
且
因此调合效应参数的求解即为求解以下目标函数的最优解，并考虑到解的稳定性，有如下目标函数：
其中，w
p表示求解精度权值；w
s表示求解稳定性权值。
最后，利用自适应差分进化智能优化算法对调合效应模型进行求解，得出调合效应参数。
本发明提供的原油调合的调合效应参数计算方法包括以下步骤：
第一步，进行运行参数初始化；
第二步，设置运行周期以及目标函数权值；
第三步，根据运行周期，获取组分油属性、调合配方以及调合头处调合所得原 油属性；
第四步，利用自适应差分进化智能优化算法对调合效应模型进行求解，得出调合效应参数；
第五步，将调合效应参数送入调合控制系统，用于控制或优化。
在本发明的一种实施方式中，上述第一步中选定参与调合的组分油品以及相应调合头编号。
在本发明的一个实施例中，选择至少两个组分油。
在本发明的一种实施方式中，上述第二步可设置的运行周期不少于组分数×数据采样时间，其实际运行周期根据生产配方制定的需要而定，优选3-6分钟。
本发明上述第二步中的目标函数可以是求解精度、求解稳定性等；优选求解精度和求解稳定性。
本发明上述方法适用于所有原油属性，包括但不限于，密度、硫含量、酸值、石脑油收率、氮含量等。
本发明上述第四步将上述各步获得的数据按照本发明提供的公式(1)、(2)、(3)、(4)、(5)和(6)建立调合效应模型后，采用自适应差分进化智能优化算法对调合效应模型进行求解，得出调合效应参数。包括以下步骤：
(1)初始化种群P，种群大小为NP；
(2)初始化μ
CR＝0.5,μ
F＝0.5，A＝Ф；
(3)循环开始，当算法终止条件尚未满足时，进行：
(i)设置S
F＝Ф，S
CR＝Ф；
(ii)针对种群P中的每一个个体x
i，生成对应的比例因子F
i＝randc
i(μ
F,0.1)和交叉概率CR
i＝randn
i(μ
CR,0.1)；
(iii)从适应度值前100p％的个体中随机挑选一个个体，记为x
p,best，从种群P中选择个体x
r1，x
r1≠x
i，从种群P∪A中选择个体x
r2，x
r2≠x
r1≠x
i；
(iv)生成变异向量v
i＝x
i+F
i·(x
p,best-x
i)+F
i·(x
r1-x
r2)；
(v)生成试验向量u
i；
(vi)比较x
i和u
i的适应度函数值，若x
i优于u
i，则x
i进入下一代种群；若u
i优于x
i，则u
i替换x
i进入下一代种群，并将x
i放入A中，F
i放入S
F中，CR
i放入S
CR中；
(vii)在每一代更新结束后，随机移除A中的个体，使得|A|≤NP；
(viii)更新μ
F和μ
CR：μ
F＝(1-c)·μ
F+c·mean
L(S
F), μ
CR＝(1-c)·μ
CR+c·mean
A(S
CR)
(4)算法停止，得到最终种群NP，种群中适应度值最优的个体即为优化问题的解。
其中，randci表示正态分布；randci表示柯西分布；mean
A表示普通的算术平均；mean
L表示普通的Lehmer平均,如下式(7)所示：
S
F与S
CR分别用于储存成功产生相比于父代x
i更优秀试验向量u
i的F
i与CR
i。产生试验向量的方法如以下式(8)所示：
其中，u
j,i,g表示第g代第i个个体的试验向量u
i的第j个分量；v
j,i,g表示第g代第i个个体的变异向量v
i的第j个分量；x
j,i,g表示第g代第i个个体x
i的第j个分量；j
rank表示保证变异信息的引入而随机选定的一个j。
在本发明的一种实施方式中，上述第五步的控制或优化包括判断运行是否完成，若完成，则止于此；若未完成，则等待一个优化周期，返回第三步。
本发明提到的上述特征，或实施例提到的特征可以任意组合。本案说明书所揭示的所有特征可与任何组合物形式并用，说明书中所揭示的各个特征，可以任何可提供相同、均等或相似目的的替代性特征取代。因此除有特别说明，所揭示的特征仅为均等或相似特征的一般性例子。
本发明的主要优点在于：
1、本发明提出了一种原油调合的调合效应参数智能计算方法，在调合过程中按一定运行周期计算调合效应参数，将调合过程中的依靠经验的定性的调合正负效应以定量方式表现出来，并实现了效应参数的动态更新和修正，从而更好指导调合过程的控制、预测与优化。
2、本发明采用智能算法将效应参数求解目标分解(例如但不限于精度和稳定性两大目标)，并根据实际应用对象或不同工况需求设定权值进行目标权重的调节，考虑其非线性因素，采用自适应差分进化智能优化算法对其进行求解，确保了在线应用时有解，满足在线更新的需要。
下面结合具体实施例，进一步阐述本发明。应理解，这些实施例仅用于说明本发明而不用于限制本发明的范围。下列实施例中未注明具体条件的实验方法，通常按照常规条件或按照制造厂商所建议的条件。除非另外说明，否则所有的百分数、比率、比例、或份数按重量计。除非另行定义，文中所使用的所有专业与科学用语与本领域熟练人员所熟悉的意义相同。此外，任何与所记载内容相似或均等的方法及材料皆可应用于本发明方法中。文中所述的较佳实施方法与材料仅作示范之用。
下面以本发明在某原油调合过程的实际实施情况并结合一具体算例，给出详细的计算过程与操作流程。本实施例在以本发明技术方案为前提下进行实施，但本发明的保护范围不限于下述的实施例。实施例所涉及的软件结构如图1所示，首先从DCS系统中取出数据，进而建立调合效应模型，通过调用自适应差分进化智能优化算法进行计算，将结果送回系统用于控制、预测与优化。本实施例以硫含量为例，但保护范围不限于硫含量。
根据图2所示，一种原油调合的调合效应参数智能计算方法步骤如下：
步骤一：运行参数初始化。
选定参与调合的组分油品为1#、2#、3#及4#，并选择相应调合头编号。
步骤二：设置运行周期以及目标函数权值。
设置运行周期为5min,设定求解精度权值0.7，求解稳定性权值0.3。
步骤三：按运行周期获取组分油属性、调合配方以及调合头处调合所得原油属性。
取出四种油分的硫含量测量数据，1#组分油硫含量1.68％，2#组分油硫含量2.36％，3#组分油硫含量1.79％，4#组分油硫含量2.22％。取出的7次调合的配方与调合头混合所得原油的硫含量如下表1：
表1
步骤四：建立调合效应数学模型。
通过以上数据按照本发明所涉及公式(1)-(6)，建立调合效应模型。
步骤五：计算调合效应参数。
调用自适应差分进化智能优化算法程序进行求解，算法流程图如图3所示。求得1#组分油调合效应参数0.0260％，2#组分油调合效应参数0.0348％，3#组分油调合效应参数0.0032％，4#组分油调合效应参数0.0495％。
步骤六：将调合效应参数送入系统。
步骤七：查看运行是否结束，若未结束，则等待一个运行周期再返回步骤三。
以上所述仅为本发明的较佳实施例而已，并非用以限定本发明的实质技术内容范围，本发明的实质技术内容是广义地定义于申请的权利要求范围中，任何他人完成的技术实体或方法，若是与申请的权利要求范围所定义的完全相同，也或是一种等效的变更，均将被视为涵盖于该权利要求范围之中。
Claims (10)
- 一种关于原油调合的调合效应参数数学模型的建立，其特征在于，包括步骤：(a)采用式(1)对各组分油的属性进行补偿：Qx(O j,i)＝O j,i+pb j,i (1)其中，i(i＝1,2,…,n)为各组分油编号；j(j＝1,2,…,n)为油品的需要进行非线性补偿的属性；pb j,i表示组分油各属性的调合效应参数；O j,i为第i种组分油的近红外分析仪在线测量的属性；Qx(O j,i)表示调合效应补偿函数；(b)采用式(2)得到调合头处调合所得原油属性的预测值：
其中，r i表示第i种组分油的配方；P j表示调合头处调合所得原油属性预测值；(c)求解式(6)的目标函数得到调合效应参数：
其中，w p表示求解精度权值；w s表示求解稳定性权值；Y＝R m,n·PB，PB＝[pb j,1 pb j,2 … pb j,n] T (4)且
表示第i组历史调合数据中调合头处调合所得原油的属性值；
[r m,1 r m,2 ... r m,n]表示有n个组分油参与调合的一个历史调合配方；m表示m组调合配方。 - 一种原油调合的调合效应参数计算方法，其特征在于，所述方法包括以下步骤：首先，进行运行参数初始化；其次，设置运行周期以及目标函数权重；再次，根据运行周期获取组分油属性、调合配方以及调合头处调合所得原油属性；和最后，利用自适应差分进化智能优化算法对权利要求1所述的调合效应参数模型进行求解。
- 如权利要求2所述的方法，其特征在于，所述运行参数包括参与调合的组分油品及相应调合头编号。
- 如权利要求2所述的方法，其特征在于，所述目标函数包括求解精度、求解稳定性。
- 如权利要求2所述的方法，其特征在于，该方法适用于所有原油属性，包括密度、硫含量、酸值、石脑油收率以及氮含量等。
- 如权利要求2所述的方法，其特征在于，所述自适应差分进化(Adaptive Differential Evolution，简称JADE，)智能优化算法包括步骤：(1)初始化种群P，种群大小为NP；(2)初始化μ CR＝0.5,μ F＝0.5，A＝Ф；(3)循环开始，当算法终止条件尚未满足时，进行：(i)设置S F＝Ф，S CR＝Ф；(ii)针对种群P中的每一个个体x i，生成对应的比例因子F i＝randc i(μ F,0.1)和交叉概率CR i＝randn i(μ CR,0.1)；(iii)从适应度值前100p％的个体中随机挑选一个个体，记为x p,best，从种群P中选择个体x r1，x r1≠x i，从种群P∪A中选择个体x r2，x r2≠x r1≠x i；(iv)生成变异向量v i＝x i+F i·(x p,best-x i)+F i·(x r1-x r2)；(v)生成试验向量u i；(vi)比较x i和u i的适应度函数值，若x i优于u i，则x i进入下一代种群；若u i优于x i，则u i替换x i进入下一代种群，并将x i放入A中，F i放入S F中，CR i放入S CR中；(vii)在每一代更新结束后，随机移除A中的个体，使得|A|≤NP；(viii)更新μ F和μ CR：μ F＝(1-c)·μ F+c·mean L(S F),μ CR＝(1-c)·μ CR+c·mean A(S CR)；(4)算法停止，得到最终种群NP，种群中适应度值最优的个体即为优化问题的解；其中，randci表示正态分布；randci表示柯西分布；mean A表示普通的算术平均；mean L表示普通的Lehmer平均；S F与S CR分别用于储存成功产生相比于父代x i更优秀试验向量u i的F i与CR i。
- 如权利要求6所述的方法，其特征在于，采用式(7)得到普通的Lehmer平均：
采用式(8)得到试验向量：
其中，u j,i,g表示第g代第i个个体的试验向量u i的第j个分量；v j,i,g表示第g代第i个个体的变异向量v i的第j个分量；x j,i,g表示第g代第i个个体x i的第j个分量；j rank表示保证变异信息的引入而随机选定的一个j。 - 一种原油调合方法，其特征在于，所述方法包括步骤：将通过如权利要求2-7任一项所述的方法得到的调合效应参数送入调合控制系统执行；可用于对某一配方下的调合属性进行预测，指导生产配方的制定。
- 一种计算机可读存储介质，其上存有计算机程序，该程序被处理器执行时能用于实施如权利要求1-7中任一项所述的方法中的任意一种或多种计算方法。
- 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述计算机程序执行如权利要求1-7中任一项所述的方法中所述的计算。
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| CN108733009A (zh) * | 2018-02-26 | 2018-11-02 | 华东理工大学 | 一种多层架构的油品调合在线优化控制方法 |
| CN109994160A (zh) * | 2019-03-29 | 2019-07-09 | 华东理工大学 | 一种原油调合的调合效应参数的计算方法 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113139155A (zh) * | 2021-04-23 | 2021-07-20 | 南京富岛信息工程有限公司 | 一种大范围原油调合选择优化方法 |
| CN113139155B (zh) * | 2021-04-23 | 2024-02-06 | 南京富岛信息工程有限公司 | 一种大范围原油调合选择优化方法 |
| CN114965347A (zh) * | 2022-02-25 | 2022-08-30 | 南京富岛信息工程有限公司 | 一种直接基于近红外光谱的原油调合方法 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109994160A (zh) | 2019-07-09 |
| KR20210144862A (ko) | 2021-11-30 |
| CN109994160B (zh) | 2023-08-15 |
| KR102614179B1 (ko) | 2023-12-14 |
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