CN114266526A - Ingredient optimization method and system - Google Patents
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- 238000005457 optimization Methods 0.000 title claims abstract description 48
- 239000004615 ingredient Substances 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 30
- 239000002994 raw material Substances 0.000 claims abstract description 73
- 239000000203 mixture Substances 0.000 claims abstract description 15
- 239000002245 particle Substances 0.000 claims abstract description 11
- 238000004519 manufacturing process Methods 0.000 claims abstract description 10
- 239000000463 material Substances 0.000 claims description 46
- 238000002156 mixing Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000009826 distribution Methods 0.000 claims description 6
- 230000002068 genetic effect Effects 0.000 claims description 6
- 230000035772 mutation Effects 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 239000000843 powder Substances 0.000 claims description 4
- 238000009472 formulation Methods 0.000 claims description 3
- 238000009401 outcrossing Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000011161 development Methods 0.000 abstract description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
- 238000005272 metallurgy Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- CWYNVVGOOAEACU-UHFFFAOYSA-N Fe2+ Chemical compound [Fe+2] CWYNVVGOOAEACU-UHFFFAOYSA-N 0.000 description 1
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 description 1
- 229910052681 coesite Inorganic materials 0.000 description 1
- 229910052593 corundum Inorganic materials 0.000 description 1
- 229910052906 cristobalite Inorganic materials 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 239000011664 nicotinic acid Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000000377 silicon dioxide Substances 0.000 description 1
- 238000005245 sintering Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 229910052682 stishovite Inorganic materials 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 229910052905 tridymite Inorganic materials 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
- 238000004876 x-ray fluorescence Methods 0.000 description 1
- 229910001845 yogo sapphire Inorganic materials 0.000 description 1
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Abstract
The invention provides a method and a system for optimizing ingredients, which comprise the following steps: raw material data are obtained, and corresponding raw material stability indexes are obtained according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index; and constructing an objective function and a conditional constraint of a batching optimization model according to the raw material stability index, wherein the conditional constraint comprises: ratio constraints, composition constraints, and particle size constraints; inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme; the invention can realize an on-line optimized batching scheme and effectively guide the smooth development of production activities.
Description
Technical Field
The invention relates to the field of intelligent metallurgy, in particular to a burdening optimization method and a burdening optimization system.
Background
In the traditional batching method, the objectives of component requirement constraint, constraint of material matching experience and batching cost of the product are mainly considered, and optimization of a batching scheme is realized by selecting linear programming, traversal solution and a bionic algorithm on the basis.
However, in actual production, not only the above requirements but also the continuity and stability of industrial production need to be considered, especially continuous flow manufacturing represented by metallurgy and chemical industry has a high requirement on the stability of product quality, and the stability of the batching scheme is an important index for evaluating the batching scheme, including the requirements on three dimensions of raw material component stability, stock stability and price stability. The traditional method depending on experience is difficult to ensure the accuracy and stability of the acquisition scheme and is difficult to be suitable for production activities with higher requirements on stability.
Disclosure of Invention
In view of the problems in the prior art, the invention provides a batching optimization method and system, and mainly solves the problem that the stability and accuracy of a proportioning scheme generated by relying on experience in the prior art are not enough.
In order to achieve the above and other objects, the present invention adopts the following technical solutions.
A method of ingredient optimization, comprising:
raw material data are obtained, and corresponding raw material stability indexes are obtained according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index;
and constructing an objective function and a conditional constraint of a batching optimization model according to the raw material stability index, wherein the conditional constraint comprises: ratio constraints, composition constraints, and particle size constraints;
and inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme.
Optionally, the calculation manner of the ingredient stability index includes:
wherein q iskiRepresents the feed quantity, σ, of the material K in the feed time period iki(a1)Denotes the standard deviation, σ, of the component a1 of the Material Kki(a2)Denotes the standard deviation, σ, of the component a2 of the Material Kki(an)Represents the standard deviation of the composition an of material K; ckjIs the ratio of the kth material in the blending scheme j.
Optionally, the calculation manner of the price stability indicator includes:
acquiring price data of a corresponding material in a historical time period T, acquiring price distribution of the price data, and dividing the price distribution into a plurality of continuous price intervals;
acquiring the probability that the price data fall into each price interval according to the occurrence frequency of the price data in each price interval;
and counting the price stability index according to the probability that the price data falls into each price interval.
Optionally, the price stability indicator is expressed as:
wherein, PkThe probability of the price interval to which the current price of the material k belongs; ckjThe corresponding percentage of the material k in the batching scheme j is shown; deltapjIs the price stability index of the batching scheme j.
10. The ingredient optimization method of claim 1, wherein the inventory stability indicator is calculated by:
wherein Y is the daily average planned yield; d is a planned material using period; wkThe current stock of the material k; ckjThe corresponding percentage of the material k in the batching scheme j is shown; mu is the yield coefficient in the production process.
11. The ingredient optimization method according to claim 5, wherein the yield coefficient is in a range of 0.6 to 0.9, or is calculated by taking the ratio of the residual amount of the mixture of the ingredient scheme j after the moisture and the burnout are deducted, and the calculation method is as follows:
μ1＝(1-Loij)*(1-H2Oj)
wherein, H2OjDeducting water ratio, LoijIndicating the burn-out ratio.
Optionally, constructing an objective function and a conditional constraint of a blending optimization model according to the raw material stability index includes:
the objective function is expressed as:
the proportioning constraints are expressed as:
∑Xk＝1
XLk≤Xk≤XHk
the compositional constraint is represented as:
the granularity constraint is expressed as:
wherein M isiTon product consumption of kth material for dosing schedule; pkThe current price of the kth raw material is unit of yuan/ton; xkThe ratio of the kth raw material is; XLiAnd XHiRespectively the lower limit and the upper limit of the formulation of the ith raw material, and the unit is%; ck,jThe percentage content of the component j of the raw material k is shown, Loi is the burning loss, and H2O is the water content; CLjAnd CHjThe lower limit and the upper limit of the component j are respectively expressed in unit; gk,dIs the percentage content of the granularity index d of the raw material k; GLdAnd GHdThe lower limit and the upper limit of the blending powder particle size index d are respectively.
Optionally, inputting the blending cost and the raw material stability index into the blending optimization model to obtain an optimal blending scheme, including:
solving the optimal solution of the batching optimization model through a genetic algorithm to serve as the optimal batching scheme, and the method comprises the following steps of:
randomly initializing to generate parent individuals with the size of Q, wherein each parent individual corresponds to one batching scheme;
calculating a fitness value of each individual;
carrying out crossing and mutation operations on the individuals according to the fitness value to generate newly added Qx individuals, wherein the crossing probability is set as p1, and the mutation probability is set as p 2;
selecting Q-Qx individuals before the fitness value sorting in the parent as parent elite, and forming a next generation population with the newly added individuals;
recording the iteration times k as k + 1;
repeating the steps until the iteration number K is the limited total iteration number K;
and sequencing the most-finally-formed offspring population individuals and selecting the individual with the highest fitness as the optimal batching scheme.
An ingredient optimization system comprising:
the index acquisition module is used for acquiring raw material data and acquiring corresponding raw material stability indexes according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index;
the model building module is used for building an objective function and conditional constraints of the ingredient optimization model according to the raw material stability indexes, wherein the conditional constraints comprise: ratio constraints, composition constraints, and particle size constraints;
and the scheme optimization module is used for inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme.
As described above, the ingredient optimization method and system of the present invention have the following advantages.
Real-time components of each raw material actually blended are measured through online component detection, stability indexes of the components of each raw material are calculated, a set of multi-target evaluation system is established by combining blending cost, stock of the raw material and price stability indexes for blending, a blending scheme can be optimized in real time in the production process, and smooth development of production activities is guaranteed.
Drawings
Fig. 1 is a schematic flow chart of an ingredient optimization method according to an embodiment of the present invention.
FIG. 2 is a flow chart illustrating genetic algorithm optimization according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, the present invention provides a method for optimizing ingredients, comprising the following steps:
step S01, raw material data are obtained, and corresponding raw material stability indexes are obtained according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index;
step S02, constructing an objective function and conditional constraints of a burdening optimization model according to the raw material stability indexes, wherein the conditional constraints comprise: ratio constraints, composition constraints, and particle size constraints;
and step S03, inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme.
Specifically, for the example of the optimization of a ferrous metallurgical sintering process batch, the metallurgical production feedstock components typically include TFe, Al, and Si.
1. Data acquisition
(1) A weight detector and an online component detector are additionally arranged on the batching belt, and components and weight data of different supplied mineral powder raw materials are collected and stored in real time.
(2) Acquiring and storing actual execution information of different raw material supplies in the batching process by an external MES system, wherein the actual execution information mainly comprises a supply raw material name, a supply starting time and a supply ending time;
(3) collecting and storing the price and inventory information data of raw materials used in the production process;
2. raw material ingredient stability calculation
(1) Calculating the feeding amount q of the material k in each feeding time period ikiStandard deviation σ of TFeki(TFe)Standard deviation of Alki(Al)Standard deviation of Siki(si)。
(2) During a set time T (T ═ 90 days, assuming R feeding periods during time T), there were a total of 10 materials participating in the batching, and an index of ingredient stability for material k was calculated based on its individual ingredients.
In the formula, CkjThe ratio of the kth material in the scheme j is percent.
3. Raw material price stability calculation
Material price stability index deltaPDividing the historical price distribution of the material k into 20 continuous intervals according to price data in the historical time T;
Maxkis the historical maximum price of material k;
Minkis the historical minimum price of material k;
n is the price interval step length, and is set to be 5;
respectively calculating the probability P of each interval according to the price data frequency in 20 intervalski。
mkiFor the historical price of material k in the interval iThe frequency of the inner frequency;
Mkthe total frequency is recorded for the price of material k.
PkThe probability of the historical price interval to which the current price of the material k belongs is percent;
Ckjis the corresponding percentage percent of the material k in the burdening scheme j;
δpjis the price stability index of the batching scheme j.
4. Raw stock stability calculation
Stock stability index deltaSThe method is used for evaluating the degree that the raw materials related to the batching scheme can meet the planned material using period and evaluating the quality of the batching scheme by using the inventory stability index, so that the inventory stability of the raw materials corresponding to a certain scheme j is deltaSj。
Y is the daily average planned output, ton;
d is the planned material using period, day;
Wkis the current stock of material k, ton;
Ckjis the corresponding percentage percent of the material k in the burdening scheme j;
μ＝0.86。
5. establishing a material distribution calculation model
An objective function:
and (3) proportioning constraint:
∑Xk＝1
XLk≤Xk≤XHk
component constraint:
and (3) particle size constraint:
wherein M isiTon iron consumption of kth material for dosing schedule; pkThe current price of the kth raw material is unit of yuan/ton; xkThe ratio of the kth raw material is; XLiAnd XHiRespectively the lower limit and the upper limit of the formulation of the ith raw material, and the unit is%; ck,jThe percentage content of the component j of the raw material k is shown, loi is the burning loss, H2O is the water content, and the unit is; CLjAnd CHjThe lower limit and the upper limit of the component j are respectively expressed in unit; gk,dIs the percentage content of the granularity index d of the raw material k in unit percent; GLdAnd GHdThe lower limit and the upper limit of the blending powder particle size index d are respectively expressed in percentage.
Table 1 sinter composition constraint value example
| Name of material | TFe | SiO2 | CaO | MgO | Al2O3 | P | S |
| CLj | 53.00 | 4 | 6 | 1 | 0 | 0 | 0 |
| CHj | 62.50 | 5.3 | 7.5 | 2 | 2.7 | 0.01 | 0.02 |
6. Establishing a genetic algorithm to solve
Referring to fig. 2, the constraint programming model in step (5) is solved by using a genetic algorithm, and an optimal solution of the model is solved, which includes the following steps:
(1) randomly initializing to generate a population size of 1000 parents (batching scheme);
(2) calculating a fitness value of each individual;
(3) carrying out crossing and mutation operations on the individuals according to the fitness value to generate newly-added Qx individuals, wherein the crossing probability p1 is 0.3, and the mutation probability p2 is 0.1;
(4) selecting the first 1000-Qx individuals in the parent with the rank of fitness value as parent elite, and forming a next generation population with the newly added individuals;
(5) the iteration number k is k + 1;
(6) repeating the steps 2-5 until the iteration number K is the limited total iteration number K;
(7) and sequencing the most-finally-formed offspring population individuals, and selecting the individuals with the highest fitness as the optimal burdening scheme solution.
It should be noted that the database to which the present invention pertains includes, but is not limited to, ORACLE, DB2, SQL Server, Sybase, Informix, MySQL, VF, and Access. The material weight measuring device includes but is not limited to weighing instruments of different sensor types such as photoelectric type, hydraulic type, electromagnetic type, capacitance type, magnetic pole deformation type, vibration type, gyroscope type, resistance strain type and the like, the material component detecting device includes but is not limited to an X-ray fluorescence spectrum type analyzer, and the constraint processing in the genetic algorithm includes but is not limited to feasible matrix method, penalty function and the like.
The embodiment also provides an ingredient optimization system, which is used for executing the ingredient optimization method in the method embodiment. Since the technical principle of the system embodiment is similar to that of the method embodiment, repeated description of the same technical details is omitted
In one embodiment, an ingredient optimization system, comprising: the index acquisition module is used for acquiring raw material data and acquiring corresponding raw material stability indexes according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index; the model building module is used for building an objective function and conditional constraints of the ingredient optimization model according to the raw material stability indexes, wherein the conditional constraints comprise: ratio constraints, composition constraints, and particle size constraints; and the scheme optimization module is used for inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (9)
1. A method of optimizing ingredients, comprising:
raw material data are obtained, and corresponding raw material stability indexes are obtained according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index;
and constructing an objective function and a conditional constraint of a batching optimization model according to the raw material stability index, wherein the conditional constraint comprises: ratio constraints, composition constraints, and particle size constraints;
and inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme.
2. The ingredient optimization method of claim 1, wherein the ingredient stability indicator is calculated by:
wherein q iskiRepresents the feed quantity, σ, of the material K in the feed time period iki(a1)Denotes the standard deviation, σ, of the component a1 of the Material Kki(a2)Denotes the standard deviation, σ, of the component a2 of the Material Kki(an)Represents the standard deviation of the composition an of material K; ckjIs the ratio of the kth material in the blending scheme j.
3. The ingredient optimization method according to claim 1, wherein the price stability indicator is calculated by:
acquiring price data of a corresponding material in a historical time period T, acquiring price distribution of the price data, and dividing the price distribution into a plurality of continuous price intervals;
acquiring the probability that the price data fall into each price interval according to the occurrence frequency of the price data in each price interval;
and counting the price stability index according to the probability that the price data falls into each price interval.
4. The ingredient optimization method according to claim 3, wherein the price stability indicator is expressed as:
wherein, PkThe probability of the price interval to which the current price of the material k belongs; ckjThe corresponding percentage of the material k in the batching scheme j is shown; deltapjIs the price stability index of the batching scheme j.
5. The ingredient optimization method of claim 1, wherein the inventory stability indicator is calculated by:
wherein Y is the daily average planned yield; d is a planned material using period; wkThe current stock of the material k; ckjThe corresponding percentage of the material k in the batching scheme j is shown; mu is the yield coefficient in the production process.
6. The ingredient optimization method according to claim 5, wherein the yield coefficient is in a range of 0.6 to 0.9, or is calculated by taking the ratio of the residual amount of the mixture of the ingredient scheme j after the moisture and the burnout are deducted, and the calculation method is as follows:
μ＝(1-Loij)*(1-H2Oj)
wherein, H2OjDeducting water ratio, LoijIndicating the burn-out ratio.
7. The ingredient optimization method according to claim 1, wherein constructing an objective function and conditional constraints of an ingredient optimization model according to the raw material stability index comprises:
the objective function is expressed as:
the proportioning constraints are expressed as:
∑Xk＝1
XLk≤Xk≤XHk
the compositional constraint is represented as:
the granularity constraint is expressed as:
wherein M isiTon product consumption of kth material for dosing schedule; pkThe current price of the kth raw material is unit of yuan/ton; xkThe ratio of the kth raw material is; XLiAnd XHiRespectively the lower limit and the upper limit of the formulation of the ith raw material, and the unit is%; ck,jThe percentage content of the component j of the raw material k is shown, Loi is the burning loss, and H2O is the water content; CLjAnd CHjThe lower limit and the upper limit of the component j are respectively expressed in unit; gk,dIs the percentage content of the granularity index d of the raw material k; GLdAnd GHdThe lower limit and the upper limit of the blending powder particle size index d are respectively.
8. The ingredient optimization method according to claim 1, wherein inputting ingredient cost and the raw material stability index into the ingredient optimization model to obtain an optimal ingredient scheme comprises:
solving the optimal solution of the batching optimization model through a genetic algorithm to serve as the optimal batching scheme, and the method comprises the following steps of:
randomly initializing to generate parent individuals with the size of Q, wherein each parent individual corresponds to one batching scheme;
calculating a fitness value of each individual;
carrying out crossing and mutation operations on the individuals according to the fitness value to generate newly added Qx individuals, wherein the crossing probability is set as p1, and the mutation probability is set as p 2;
selecting Q-Qx individuals before the fitness value sorting in the parent as parent elite, and forming a next generation population with the newly added individuals;
recording the iteration times k as k + 1;
repeating the steps until the iteration number K is the limited total iteration number K;
and sequencing the most-finally-formed offspring population individuals and selecting the individual with the highest fitness as the optimal batching scheme.
9. An ingredient optimization system, comprising:
the index acquisition module is used for acquiring raw material data and acquiring corresponding raw material stability indexes according to the raw material data, wherein the raw material stability indexes comprise: a component stability index, a price stability index, and a stock stability index;
the model building module is used for building an objective function and conditional constraints of the ingredient optimization model according to the raw material stability indexes, wherein the conditional constraints comprise: ratio constraints, composition constraints, and particle size constraints;
and the scheme optimization module is used for inputting the batching cost and the raw material stability index into the batching optimization model to obtain an optimal batching scheme.
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| CN111260157A (en) * | 2020-02-21 | 2020-06-09 | 天津开发区精诺瀚海数据科技有限公司 | Smelting ingredient optimization method based on ecological niche optimization genetic algorithm |
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| CN101261704A (en) * | 2007-12-13 | 2008-09-10 | 东华大学 | An intelligent small particle mixing optimization system based on genetic algorithm |
| BR122017001932A2 (en) * | 2008-04-30 | 2018-02-06 | Xyleco, Inc. | METHOD OF CONVERTING AN INTERMEDIARY TO A PRODUCT, PRODUCT AND BIOMASS FEED STOCK PROCESSING SYSTEMS |
| CN111260157A (en) * | 2020-02-21 | 2020-06-09 | 天津开发区精诺瀚海数据科技有限公司 | Smelting ingredient optimization method based on ecological niche optimization genetic algorithm |
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