CN113780810A - Coke purchasing decision method - Google Patents
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- 239000000571 coke Substances 0.000 title claims abstract description 395
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000012946 outsourcing Methods 0.000 claims abstract description 115
- 230000008901 benefit Effects 0.000 claims abstract description 59
- 238000010298 pulverizing process Methods 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000004519 manufacturing process Methods 0.000 claims description 30
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 23
- 229910052717 sulfur Inorganic materials 0.000 claims description 23
- 239000011593 sulfur Substances 0.000 claims description 23
- 230000009257 reactivity Effects 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 4
- 238000004939 coking Methods 0.000 abstract description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 78
- 229910052742 iron Inorganic materials 0.000 description 39
- 230000000694 effects Effects 0.000 description 35
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 8
- 229910000831 Steel Inorganic materials 0.000 description 7
- 239000010959 steel Substances 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 4
- 238000005299 abrasion Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 238000005272 metallurgy Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000008188 pellet Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
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Abstract
The invention provides a coke purchasing decision method, and belongs to the technical field of coking. The method comprises the following steps: acquiring sample data, wherein the sample data comprises the price of the purchased coke and the pulverization rate of the purchased coke; establishing a model according to the relation between the sample data and the use cost of the purchased coke, and training the model; acquiring the price of the outsourcing coke and the pulverization rate of the outsourcing coke as target data, and processing the trained model of the target data to obtain the use cost of the outsourcing coke; and comparing the use cost of the outsourcing coke with the use cost of the reference coke, and if the use cost of the outsourcing coke is lower than or equal to the use cost of the reference coke, purchasing, otherwise, not purchasing enough. The method can evaluate each purchased coke so as to make a decision on the purchase of the purchased coke, reduce the dependence on personal experience and be beneficial to the blast furnace to obtain the highest benefit.
Description
Technical Field
The invention relates to the technical field of coking, in particular to a coke purchasing decision method.
Background
Coke is a product for coke ovens and a raw material for blast furnaces. The quantity of the coke produced by part of the steel and iron integrated enterprises can not meet the production, and part of the coke needs to be purchased to improve the yield of the blast furnace. Meanwhile, the price of the self-produced coke is higher than that of the outsourced coke in a special time period. At this time, it is a very difficult problem to purchase outsourcing coke and how to select the coke with high cost performance from a plurality of cokes. Currently, evaluation is often performed according to experience, which depends on personal experience, and if the experience of an evaluator is insufficient, an optimal decision is difficult to make.
Disclosure of Invention
In view of the above disadvantages of the prior art, the present invention is directed to a method for deciding a purchase of coke, which is used to solve the problem that the purchase decision of coke depends on personal experience and is difficult to make an optimal decision in the prior art.
To achieve the above and other related objects, the present invention provides a method for coke procurement decision,
the method comprises the following steps:
acquiring sample data, wherein the sample data comprises the price of the purchased coke and the pulverization rate of the purchased coke;
establishing a model according to the relation between the sample data and the use cost of the purchased coke, and training the model;
acquiring the price of the outsourcing coke and the pulverization rate of the outsourcing coke as target data, and processing the trained model of the target data to obtain the use cost of the outsourcing coke;
and comparing the use cost of the outsourcing coke with the use cost of the reference coke, and if the use cost of the outsourcing coke is lower than or equal to the use cost of the reference coke, purchasing, otherwise, not purchasing enough.
Optionally, the sample data and the target data further include quality parameters of outsourcing coke.
Optionally, the quality parameters of the outsourcing coke at least comprise moisture, ash, sulfur, crushing strength, abrasion resistance, thermal reactivity and coke thermal strength.
Optionally, a model is established according to the relation between the sample data and the blast furnace coke ratio and the blast furnace yield, and the model is trained.
Optionally, the trained model of the target data is processed to obtain a blast furnace coke ratio and a blast furnace yield of the outsourced coke, and the use cost of the outsourced coke is calculated according to the blast furnace coke ratio and the blast furnace yield.
Optionally, target data of two or more outsourcing cokes are obtained, the trained models are processed respectively, and the use cost of each outsourcing coke is obtained respectively;
and selecting the outsourcing cokes, and when the use cost is lower than or equal to the use cost of the reference coke, sequentially purchasing the outsourcing cokes according to the sequence of the use cost from low to high.
The invention also provides a coke purchasing decision method, which is used for increasing the production scale of a blast furnace by outsourcing coke due to insufficient use amount of self-produced coke and comprises the following steps:
acquiring sample data, wherein the sample data comprises the purchase price and the pulverization rate of the purchased coke;
establishing a model according to the relation between the sample data and the using benefits of the purchased coke, and training the model;
acquiring the purchase price and the pulverization rate of the outsourced coke as target data, and processing the trained model of the target data to obtain the use benefit of the outsourced coke;
and comparing the use benefit of the outsourcing coke with the purchase price of the outsourcing coke, and if the use benefit of the outsourcing coke is higher than or equal to the price of the outsourcing coke, purchasing, otherwise, not acquiring.
Optionally, a model is established and trained according to the relation between the sample data and the yield increase of the blast furnace and the scale benefit of the yield increase of the blast furnace respectively.
Optionally, the trained model of the target data is processed to obtain the blast furnace yield increase scale benefit of the outsourcing coke and the blast furnace yield increase of the outsourcing coke, and the use benefit of the outsourcing coke is calculated through the blast furnace yield increase and the blast furnace yield increase scale benefit.
Optionally, target data of two or more outsourcing cokes are obtained, the trained models are processed respectively, and the use benefits of the outsourcing cokes are obtained respectively;
and selecting the maximum value obtained by subtracting the corresponding purchase price from the use benefit of each outsourcing coke, if the maximum value is greater than or equal to zero, purchasing the outsourcing coke corresponding to the maximum value, and otherwise, not acquiring the outsourcing coke.
As described above, the method for deciding the procurement of the coke according to the present invention has the following beneficial effects: the method can evaluate each purchased coke so as to make a decision on the purchase of the purchased coke, reduce the dependence on personal experience and be beneficial to the blast furnace to obtain the highest benefit.
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.
To achieve the above and other related objects, the present embodiment provides a method for determining a procurement of coke, comprising the steps of:
s1, obtaining sample data, wherein the sample data comprises the price of the outsourcing coke and the pulverization rate of the outsourcing coke;
s2, establishing a model according to the relation between the sample data and the use cost of the purchased coke, and training the model;
s3, acquiring the price of the outsourced coke and the pulverization rate of the outsourced coke as target data, and processing the trained model of the target data to obtain the use cost of the outsourced coke;
and S4, comparing the use cost of the outsourcing coke with the use cost of the reference coke, and if the use cost of the outsourcing coke is lower than or equal to the use cost of the reference coke, purchasing the outsourcing coke, otherwise, not acquiring the outsourcing coke.
In step S1, the sample data and the target data further include quality parameters of the outsourced coke. Specifically, the quality parameters of the outsourcing coke at least comprise moisture, ash content, sulfur content, crushing strength, wear resistance, thermal reactivity and coke thermal strength.
In step S2, a model may be established according to the relationship between the sample data and the coke ratio and the output of the blast furnace, respectively, and trained.
Correspondingly, in step S3, the trained model of the target data is processed to obtain the blast furnace coke ratio and the blast furnace output of the outsourced coke, and the usage cost of the outsourced coke is calculated according to the blast furnace coke ratio and the blast furnace output.
In step S3, target data of two or more outsourced cokes can be obtained simultaneously, and then the trained models are processed respectively to obtain the corresponding use cost of each outsourced coke;
correspondingly, in step S4, the lowest value of the usage costs of the outsourcing cokes is selected to be compared with the usage cost of the reference coke, if the lowest value is lower than or equal to the usage cost of the reference coke, the outsourcing coke corresponding to the lowest value is purchased, and if the lowest value is greater than the usage cost of the reference coke, the outsourcing coke is not purchased sufficiently.
When the quantity of the self-produced coke is sufficient, whether the outsourcing coke is purchased to replace the self-produced coke is decided according to the use price of the outsourcing metallurgical coke, and when the method is actually applied, the specific steps are as follows:
1. and (4) determining an outsourcing coke evaluation standard, namely determining various parameters of the standard coke.
Reference coke: the coke price is 2430 yuan/ton, the pulverization rate is 10%, the coke ash content is 12.15%, the sulfur content is 0.84%, the moisture content is 3.8%, M4088.7%, M105.8%, CRI 22.3% and CSR 68.6%.
Wherein M40 is the crushing strength of the coke, M10 is the abrasion strength of the coke, CRI is the thermal reactivity of the coke, and CSR is the thermal strength of the coke.
The reference coke was used: the blast furnace coke ratio is 330 kg/molten iron, the yield of the blast furnace molten iron is 7800 tons in 1 day, and the benefit of the molten iron is 1000 yuan/ton. The price of the reference coke metallurgy coke is (2430-.
2. And calculating the prices of the reference coke and the metallurgical coke after the outsourced coke is put into the factory according to the price of the coke, the price of the outsourced coke powder and the pulverization rate of the coke when the coke is put into the factory. Wherein, the coke with the granularity of more than or equal to 25mm is metallurgical coke, the coke with the granularity of less than 25mm is coke powder, and the pulverization rate is the ratio of the coke powder in the whole coke.
And (3) outsourcing of coke: the price of outsourcing coke is 2400 yuan/ton, the pulverization rate of the coke is 20%, the ash content of the coke is 12.39%, the sulfur content is 0.98%, the moisture content is 5.1%, M4087.8%, M105.5%, CRI 23.9% and CSR 72%.
The purchased coke powder is 1300 yuan/ton, namely the coke powder can be sold at the price of 1300 yuan/ton or transferred to other production departments for use, and the price of the purchased coke metallurgical coke needs to be deducted when being calculated. Namely the price of the metallurgical coke of the outsourced coke (quoted price of the outsourced coke-price of the outsourced coke powder x pulverization rate)/(1-pulverization rate).
In this example, the price of the metallurgical coke purchased from outsourcing was calculated as (2400 + 1300 x 20%)/(1-20%)/(2675 yuan/ton).
3. Determining the influence of coke moisture, ash, sulfur, M40, M10, CRI, CSR changes on the blast furnace coke ratio and the blast furnace yield.
In this embodiment, the model is a deep learning neural network model, and the sample data is data accumulated by production. After model training, the influence coefficients of moisture, ash, sulfur, M40, M10, CRI, CSR changes on the blast furnace coke ratio and the blast furnace yield are shown in the following table.
| Quality index | Amount of self-fluctuation | Influence coefficient of focus ratio | Influence coefficient on blast furnace yield |
| Ash content | ↑1％ | ↑2％ | ↓3％ |
| M40 | ↑1％ | ↓1％ | ↑1.8％ |
| M10 | ↑1％ | ↑3％ | ↓4％ |
| CRI | ↑1％ | ↑0.5％ | ↓1％ |
| CSR | ↑1％ | ↓0.5％ | ↑1％ |
| Sulfur | ↑0.1％ | ↑1.5％ | ↓2％ |
| Moisture content | ↑1％ | ↑0.5％ | ↓1％ |
4. Determining the influence on the blast furnace according to the indexes of the coke
1) Influence of coke quality parameter change on coke ratio:
the effect of ash on blast furnace coke ratio (outsourced coke ash-reference coke ash)/1 x 2%;
the effect of M40 on blast furnace coke ratio (extra coke M40 — benchmark coke M40)/1 x (-1%);
the effect of M10 on blast furnace coke ratio (outsourced coke M10 — benchmark coke M10)/1 × 3%;
the effect of CRI on blast furnace coke ratio (outsourced coke CRI-reference coke CRI)/1 x 0.5%;
the effect of CSR on blast furnace coke ratio (extra coke CSR-reference coke CSR)/1 x (-0.5%);
the effect of sulfur on blast furnace coke ratio (outsourcing coke sulfur-reference coke sulfur)/0.1 x 1.5%;
the influence of moisture on the blast furnace coke ratio is (outsourced coke moisture-reference coke moisture)/1 x 0.5%;
the total influence of coke quality change on coke ratio: j outer coke + effect of ash on focus ratio + effect of M40 focus ratio + effect of M10 focus ratio + effect of CRI focus ratio + effect of CSR focus ratio + effect of sulfur focus ratio + effect of moisture on focus ratio.
2) The effect of changes in coke quality parameters on blast furnace output.
The effect of ash on blast furnace production (outsourced coke ash-baseline coke ash)/1 x (-3%);
the effect of M40 on blast furnace production ═ (extra coke M40 — benchmark coke M40)/1.8%;
effect of M10 on blast furnace production ═ (extra coke M10 — benchmark coke M10)/1 x (-4%);
effect of CRI on blast furnace production ═ (outsourced coke CRI-reference coke CRI)/1 x (-1)%;
the effect of CSR on blast furnace production (outsourced coke CSR-baseline coke CSR)/1 x 1%;
the effect of sulfur on blast furnace production ═ 0.1 x (-2%) of (outsourcing coke sulfur-baseline coke sulfur);
the effect of moisture on blast furnace production (outsourced coke moisture-reference coke moisture)/1 x (-1%);
the total influence of coke quality change on the blast furnace yield: the C external yield is the influence of ash on the blast furnace yield, the influence of M40 on the blast furnace yield, the influence of M10 on the blast furnace yield, the influence of CRI on the blast furnace yield, the influence of CSR on the blast furnace yield, the influence of sulfur on the blast furnace yield and the influence of moisture on the blast furnace yield.
When the influence of the quality change of the coke on the coke ratio of the blast furnace and the yield of the blast furnace is more than or equal to 4 percent, taking 4 percent.
Specifically, the coke mass change has a total influence on the coke ratio:
the influence of ash on blast furnace coke ratio is (12.39-12.15)/1 × 2% ═ 0.48%;
the effect of M40 on blast furnace coke ratio was (87.8-88.7)/1 x (-1%) -0.9%;
the effect of M10 on blast furnace coke ratio is (5.5-5.8)/1 × 3% — 0.9%;
the effect of CRI on blast furnace coke ratio is (23.9-22.3)/1 x 0.5% ═ 0.8%;
the effect of CSR on blast furnace coke ratio is (72-68.6)/1 x (-0.5%) -1.7%;
the effect of sulfur on blast furnace coke ratio is (0.98-0.84)/0.1 x 1.5% ═ 2.1%;
the influence of moisture on the blast furnace coke ratio is (5.1-3.8)/1 x 0.5% ═ 0.65%;
the total effect of coke quality change on coke ratio is 0.48% + 0.9% + (-0.9%) + 0.8% + (-1.7%) + 2.1% + 0.65% -. 2.33%
Influence of coke index change on blast furnace yield:
the effect of ash on blast furnace production (12.39-12.15)/1 x (-3%) -0.72%;
the effect of M40 on blast furnace production (87.8-88.7)/1 x 1.8% — 1.62%;
the effect of M10 on blast furnace production (5.5-5.8)/1 x (-4%) ═ 1.2;
the effect of CRI on blast furnace production (23.9-22.3)/1 x (-1)% -1.6%;
the effect of CSR on blast furnace production (72-68.6)/1 × 1% — 3.4%;
the effect of sulfur on blast furnace production (0.98-0.84)/0.1 x (-2%) -2.8%;
the effect of moisture on blast furnace production (5.1-3.8)/1 x (-1%) -1.3%;
the total influence of coke quality change on the blast furnace yield: yield outside C (-0.72%) + (-1.62%) + 1.2% + (-1.6%) 3.4% + (-2.8%) + (-1.3%) -3.44%;
5. and calculating the blast furnace coke ratio and the blast furnace yield of the outsourcing coke according to the blast furnace coke ratio and the blast furnace yield under the reference conditions, and calculating the use price of the outsourcing coke.
Blast furnace coke ratio of the purchased coke is (1+ J outer coke) reference coke blast furnace coke ratio;
blast furnace output using outsourced coke (1+ C outsource output) baseline coke blast furnace output;
the coke consumption per unit time is the blast furnace yield and the blast furnace coke ratio;
the iron production per unit time consumes coke cost, which is the coke consumption per unit time and the metallurgical coke price;
the iron creation benefit per unit time is the iron creation benefit per unit weight and the iron yield per unit time;
wherein, the benefit created by the iron with unit weight refers to the benefit obtained by selling steel products produced by the iron with unit weight according to the current market quotation. The iron creating benefit per unit weight is related to the production scale of a company, the yield of sinter, the purchase quantity of pellet ore, the iron-steel ratio, the variety and structure distribution of steel products and the market price of the steel products with different unit weights.
The steel products generally comprise commercial blanks, thick plates, medium plates, hot coils, bars, double-high bars, wires and the like. Different products have different production costs and different market prices, which results in different benefits created by different products. And calculating different products produced by the iron per unit weight according to a theory to carry out benefit sequencing to obtain the highest benefit value and the lowest benefit value, namely the highest benefit created by the iron per unit weight and the lowest benefit created by the iron per unit weight. Or different steel products are produced after the iron yield is improved, and benefits are created by calculating iron per unit weight according to different commodity benefits.
The change of the cost of the iron liquid for coke outsourcing per unit weight is (the cost of the iron output per unit time consuming the coke outsourcing-the cost of the iron output per unit time consuming the standard coke)/the consumption of the coke outsourcing per unit time;
change in the scale of coke purchased per unit weight (benefit created by using iron purchased per unit time-benefit created by using iron purchased per unit time with reference coke)/consumption of coke purchased per unit time;
the using cost of the coke purchased from outside per unit weight is equal to the molten iron cost change of the coke purchased from outside per unit weight-the scale benefit change of the coke purchased from outside per unit weight;
the price of the outsourced metallurgical coke is equal to the price of the outsourced metallurgical coke-the cost of the outsourced coke per unit weight;
specifically, in this embodiment: the unit time is 1 day, and the unit weight is 1 ton.
Using the outsourcing coke blast furnace coke ratio of (1+2.33) × 330 of 337.689 kg/molten iron;
the blast furnace output of the outsourcing coke (1+ (-3.44%)) 7800 ═ 7531.68 tons of molten iron was used;
reference coke: the iron yield consumed 7800 × 330/1000 to 2574 tons of coke for 1 day;
and (3) outsourcing of coke: the coke consumption of iron production in 1 day is 7531.68 × 337.689/1000 is 2543.4 tons;
the cost change of the external coke hot metal per ton is (2543.4 × 2675 × 2574 × 2555.56)/2543.4 is 88.7 yuan;
the change in the scale benefit of the coke purchased outside per ton is (7531.68 × 1000-;
the use cost of the externally purchased coke is 88.7- (-105.5) ═ 194.2 yuan per ton;
the price of the outsourced metallurgical coke is 2675+194.2, 2869.2 yuan is more than 2555.56 (the price of the benchmark metallurgical coke), and the price of the outsourced coke has no cost performance and is not recommended to be purchased.
In practical situations, the purchase price of the outsourcing coke can be adjusted by means of negotiation and the like, and when the use price of the outsourcing coke is less than 2555.56 yuan (the price of the reference metallurgical coke), the price of the outsourcing coke has cost performance and can be purchased. For example, when the price of the outsourced coke is 2274 yuan/ton, the price of the outsourced metallurgical coke is 2554.2 yuan/ton, and 2554.2 yuan < 2555.56 yuan, the price of the outsourced coke is cost-effective and can be purchased.
When the target data of two or more outsourcing cokes are acquired simultaneously, the following steps are implemented:
reference coke: the coke price is 2430 yuan/ton, the pulverization rate is 10%, the coke ash content is 12.15%, the sulfur content is 0.84%, the moisture content is 3.8%, M4088.7%, M105.8%, CRI 22.3% and CSR 68.6%.
The reference coke was used: the blast furnace coke ratio is 330 kg/molten iron, the yield of the blast furnace molten iron is 7800 tons in 1 day, and the benefit of the molten iron is 1000 yuan/ton.
1300 yuan/ton of coke powder purchased from outsourcing.
1# outsourcing coke (outsourcing coke in example 1): the price of outsourcing coke is 2400 yuan/ton, the pulverization rate of the coke is 20%, the ash content of the coke is 12.39%, the sulfur content is 0.98%, the moisture content is 5.1%, M4087.8%, M105.5%, CRI 23.9% and CSR 72%.
2# outsourcing coke: the price of outsourcing coke is 2560 yuan/ton, the pulverization rate of the coke is 15%, the ash content of the coke is 12.5%, the sulfur content is 0.5%, the moisture content is 5.5%, M4088.5%, M105.9%, CRI 23% and CSR 70%.
3# outsourcing coke: the price of the outsourcing coke is 2410 yuan/ton, the pulverization rate of the coke is 18 percent, the ash content of the coke is 12.6 percent, the sulfur content is 0.95 percent, the moisture content is 1.4 percent, the M4087.1 percent, the M106.1 percent, the CRI 22 percent and the CSR 69 percent.
4# outsourcing coke: the price of outsourcing coke is 2580 yuan/ton, the pulverization rate of the coke is 12 percent, the ash content of the coke is 12.2 percent, the sulfur content is 0.71 percent, the moisture content is 6.1 percent, the M4088.1 percent, the M105.2 percent, the CRI 18 percent and the CSR 70 percent.
And similarly calculating:
1# outsourcing coke: the service price of the metallurgical coke is 2869.16 yuan;
2# outsourcing coke: the service price of the metallurgical coke is 2900.35 yuan;
3# outsourcing coke: the influence of the coke mass change on the coke ratio of the blast furnace is 4.53 percent and is more than 4 percent, and the calculation is carried out by taking 4 percent. The using price of the metallurgical coke is 2857.29 yuan;
4# outsourcing coke: if the influence of the coke quality change on the blast furnace coke ratio is 4.75 percent and is more than 4 percent, namely-4 percent is taken in the calculation; if the influence of the coke quality change on the blast furnace yield is 7.17 percent and is more than 4 percent, namely 4 percent is taken in the calculation. The service price of the metallurgical coke is 2828.03 Yuan
According to the calculation: the using price of the No. 4 outsourced coke is less than that of the No. 3 outsourced coke and is less than that of the No. 1 outsourced coke and is less than that of the No. 2 outsourced coke.
Namely, the 4# outsourcing coke has the highest cost performance among the 4 outsourcing coke use prices and is lower than the reference metallurgical coke price, and the 4# outsourcing coke is recommended to be purchased.
The embodiment also provides a coke purchasing decision method, which is used for increasing the production scale of a blast furnace by outsourcing coke due to insufficient utilization amount of self-produced coke, and comprises the following steps:
s01, obtaining sample data, wherein the sample data comprises the purchase price of the outsourced coke and the pulverization rate of the outsourced coke;
s02, establishing a model according to the relation between the sample data and the using benefits of the purchased coke, and training the model;
s03, acquiring the purchase price and the pulverization rate of the outsourced coke as target data, and processing the trained model of the target data to obtain the use benefit of the outsourced coke;
and S04, comparing the use benefit of the outsourcing coke with the purchase price of the outsourcing coke, and if the use benefit of the outsourcing coke is higher than or equal to the price of the outsourcing coke, purchasing, otherwise, not adopting enough.
In step S02, a model may be established according to the relationship between the sample data and the yield increase of the blast furnace and the scale benefit of the yield increase of the blast furnace, respectively, and the model may be trained.
Correspondingly, in step S03, the trained model of the target data is processed to obtain the scale benefit of increasing the blast furnace production of the outsourced coke and the increase of the blast furnace production of the outsourced coke, and the use benefit of the outsourced coke is calculated according to the increase of the blast furnace production and the scale benefit of increasing the blast furnace production.
Specifically, the benefit of using the outsourced metallurgical coke (blast furnace production increase scale benefit + blast furnace production increase rate + iron creation benefit per unit weight of the coke) is used/the amount of the outsourced coke used.
The usage amount of the purchased coke is the blast furnace yield and the blast furnace coke ratio;
wherein, the blast furnace coke ratio of the purchased coke is (1+ J external coke) reference coke blast furnace coke ratio; blast furnace output using outsourced coke (1+ C outsource output) baseline coke blast furnace output;
in step S04, target data of two or more outsourced cokes can be obtained, and the trained models are processed respectively to obtain the use benefits of each outsourced coke;
and selecting the maximum value obtained by subtracting the corresponding purchase price from the use benefit of each outsourcing coke, if the maximum value is greater than or equal to zero, purchasing the outsourcing coke corresponding to the maximum value, and otherwise, not acquiring the outsourcing coke.
In practical application, the unit weight is 1 ton, the unit time is 1 day, and the specific steps are as follows:
reference coke: the coke price is 2430 yuan/ton, the pulverization rate is 10%, the coke ash content is 12.15%, the sulfur content is 0.84%, the moisture content is 3.8%, M4088.7%, M105.8%, CRI 22.3% and CSR 68.6%.
1300 yuan/ton of coke powder purchased from outsourcing.
And (3) outsourcing of coke: the price of the outsourcing coke is 3050 yuan/ton, the pulverization rate of the coke is 20%, the ash content of the coke is 12.39%, the sulfur content is 0.98%, the moisture content is 5.1%, M4087.8%, M105.5%, CRI 23.9% and CSR 72%.
When the reference coke is used, the blast furnace coke ratio is 330 kg/molten iron, the blast furnace molten iron yield is only 7200 tons in 1 day, and the molten iron creates 500 yuan/ton benefits.
When the outsourcing coke is used, the molten iron yield of the blast furnace is increased to 7800 tons/day, and the molten iron yield is increased to 600 tons/day. The molten iron is raised by 600 tons, and the benefit of iron per ton is 700 yuan.
It was calculated that the change in the quality of the outsourced coke resulted in a 2.33% increase in the blast furnace coke ratio, i.e. the blast furnace coke ratio was 337.689 kg/molten iron when the outsourced coke was used. The yield of the molten iron is increased by 600 tons, and the coke consumption is increased by 600 × 337.689/1000 to 202.6134 tons. The scale benefit is 40 yuan/ton due to the increase of the yield. The outsourcing coke use benefit is (600 × 700+7200 × 40)/202.6134 ═ 3494.34 > 3487.50. Therefore, it is recommended to purchase outsourcing coke for increasing the yield.
In conclusion, the method and the device can evaluate each purchased coke so as to make a decision on the purchase of the purchased coke, reduce the dependence on personal experience and be beneficial to the blast furnace to obtain the highest benefit.
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 (10)
1. A coke purchasing decision method is characterized by comprising the following steps:
acquiring sample data, wherein the sample data comprises the price of the purchased coke and the pulverization rate of the purchased coke;
establishing a model according to the relation between the sample data and the use cost of the purchased coke, and training the model;
acquiring the price of the outsourcing coke and the pulverization rate of the outsourcing coke as target data, and processing the trained model of the target data to obtain the use cost of the outsourcing coke;
and comparing the use cost of the outsourcing coke with the use cost of the reference coke, and if the use cost of the outsourcing coke is lower than or equal to the use cost of the reference coke, purchasing, otherwise, not purchasing enough.
2. The coke procurement decision method of claim 1 characterized by: the sample data and the target data also comprise quality parameters of the outsourcing coke.
3. The coke procurement decision method of claim 2 characterized by: the quality parameters of the purchased coke at least comprise moisture, ash content, sulfur content, crushing strength, wear resistance, thermal reactivity and coke thermal strength.
4. The coke procurement decision method of claim 1 characterized by: and establishing a model according to the relation between the sample data and the coke ratio and the yield of the blast furnace, and training the model.
5. The coke procurement decision method of claim 4 characterized by: and processing the trained model of the target data to obtain the blast furnace coke ratio and the blast furnace yield of the outsourced coke, and calculating the use cost of the outsourced coke according to the blast furnace coke ratio and the blast furnace yield.
6. The coke procurement decision method of claim 4 characterized by: acquiring target data of two or more outsourcing cokes, and processing the outsourcing cokes by the trained models respectively to obtain the use cost of each outsourcing coke;
and selecting the outsourcing cokes, and when the use cost is lower than or equal to the use cost of the reference coke, sequentially purchasing the outsourcing cokes according to the sequence of the use cost from low to high.
7. A coke purchasing decision method is used for increasing the production scale of a blast furnace by outsourcing coke due to insufficient utilization amount of self-produced coke, and is characterized by comprising the following steps:
acquiring sample data, wherein the sample data comprises the purchase price and the pulverization rate of the purchased coke;
establishing a model according to the relation between the sample data and the using benefits of the purchased coke, and training the model;
acquiring the purchase price and the pulverization rate of the outsourced coke as target data, and processing the trained model of the target data to obtain the use benefit of the outsourced coke;
and comparing the use benefit of the outsourcing coke with the purchase price of the outsourcing coke, and if the use benefit of the outsourcing coke is higher than or equal to the price of the outsourcing coke, purchasing, otherwise, not acquiring.
8. The coke procurement decision method of claim 7 characterized by: and establishing a model according to the relation between the sample data and the yield increasing and the scale benefit increasing of the blast furnace respectively, and training the model.
9. The coke procurement decision method of claim 8 characterized by: and processing the trained model of the target data to obtain the blast furnace yield increase scale benefit of the outsourcing coke and the blast furnace yield increase of the outsourcing coke, and calculating the use benefit of the outsourcing coke according to the blast furnace yield increase and the blast furnace yield increase scale benefit.
10. The coke procurement decision method of claim 7 characterized by: acquiring target data of two or more outsourcing cokes, and processing the outsourcing cokes by the trained models respectively to obtain the use benefits of the outsourcing cokes respectively;
and selecting the maximum value obtained by subtracting the corresponding purchase price from the use benefit of each outsourcing coke, if the maximum value is greater than or equal to zero, purchasing the outsourcing coke corresponding to the maximum value, and otherwise, not acquiring the outsourcing coke.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5506782A (en) * | 1993-01-20 | 1996-04-09 | Mitsubishi Chemical Corporation | Operation management system for coke oven |
| JP2013254329A (en) * | 2012-06-06 | 2013-12-19 | Nippon Steel & Sumitomo Metal | Raw material purchase/use plan creation apparatus, method, and program |
| CN104164246A (en) * | 2014-08-22 | 2014-11-26 | 江苏沙钢集团有限公司 | Coal blending expert system fit for top-loading coke oven |
| US20150039242A1 (en) * | 2012-03-27 | 2015-02-05 | Jfe Steel Corporation | Coal-to-coal adhesiveness evaluation method |
| CN110275007A (en) * | 2019-06-12 | 2019-09-24 | 邯郸钢铁集团有限责任公司 | A kind of method for building up of coking coal cost performance evaluation model |
| CN111401774A (en) * | 2020-03-26 | 2020-07-10 | 武汉钢铁有限公司 | Comprehensive evaluation method for coke quality |
| CN112036628A (en) * | 2020-08-25 | 2020-12-04 | 武汉钢铁有限公司 | Method for establishing model for representing coal blending cost variation |
| CN113238022A (en) * | 2021-05-07 | 2021-08-10 | 重庆钢铁股份有限公司 | Coking coal usability-price ratio evaluation method |
| CN113313394A (en) * | 2021-06-02 | 2021-08-27 | 马鞍山钢铁股份有限公司 | Coke quality evaluation method |
-
2021
- 2021-09-10 CN CN202111063358.5A patent/CN113780810A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5506782A (en) * | 1993-01-20 | 1996-04-09 | Mitsubishi Chemical Corporation | Operation management system for coke oven |
| US20150039242A1 (en) * | 2012-03-27 | 2015-02-05 | Jfe Steel Corporation | Coal-to-coal adhesiveness evaluation method |
| JP2013254329A (en) * | 2012-06-06 | 2013-12-19 | Nippon Steel & Sumitomo Metal | Raw material purchase/use plan creation apparatus, method, and program |
| CN104164246A (en) * | 2014-08-22 | 2014-11-26 | 江苏沙钢集团有限公司 | Coal blending expert system fit for top-loading coke oven |
| CN110275007A (en) * | 2019-06-12 | 2019-09-24 | 邯郸钢铁集团有限责任公司 | A kind of method for building up of coking coal cost performance evaluation model |
| CN111401774A (en) * | 2020-03-26 | 2020-07-10 | 武汉钢铁有限公司 | Comprehensive evaluation method for coke quality |
| CN112036628A (en) * | 2020-08-25 | 2020-12-04 | 武汉钢铁有限公司 | Method for establishing model for representing coal blending cost variation |
| CN113238022A (en) * | 2021-05-07 | 2021-08-10 | 重庆钢铁股份有限公司 | Coking coal usability-price ratio evaluation method |
| CN113313394A (en) * | 2021-06-02 | 2021-08-27 | 马鞍山钢铁股份有限公司 | Coke quality evaluation method |
Non-Patent Citations (1)
| Title |
|---|
| 潘晶等: "外购焦炭性价比模型的开发与应用", 柳钢科技, no. 1, pages 26 * |
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