CN104164246B - The Coal Blending Expert System of a kind of applicable top dress coke oven - Google Patents
The Coal Blending Expert System of a kind of applicable top dress coke oven Download PDFInfo
- Publication number
- CN104164246B CN104164246B CN201410417811.1A CN201410417811A CN104164246B CN 104164246 B CN104164246 B CN 104164246B CN 201410417811 A CN201410417811 A CN 201410417811A CN 104164246 B CN104164246 B CN 104164246B
- Authority
- CN
- China
- Prior art keywords
- coal
- coke
- blending
- coal blending
- coking
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Coke Industry (AREA)
Abstract
This application discloses the Coal Blending Expert System of a kind of applicable top dress coke oven, comprising: coal data and coal blending data acquisition module, coke property autoregression module, Optimized Coal Blending module and coal blending results acquisition and inspection module.The method that this system adopts multiparameter non-linear regression, mixed coal vitrinite reflectance distribution plan, mixed coal degree of mobilization distribution plan to combine, makes prediction of coke strength deviation control within 2%.This system carries out nonlinear optimization coal blending so that coke pure cost is minimum for target simultaneously, considers coking by-product productive rate, market value, Coal Quality and price to the impact of pure cost, really realizes the coke making and coal blending that cost is minimum.
Description
Technical field
The application belongs to Coking Coal Blending field, particularly relates to the Coal Blending Expert System of a kind of applicable top dress coke oven.
Background technology
Along with the downslide of coke profit, various coal-blending coking system for the purpose of reducing costs is arisen at the historic moment, but existing Coal Blending System only considered caking index, ash content, the volatilization grading factors of mixed coal mostly, or when coking time, coking temperature, quenching mode change, the deviation that predicts the outcome is larger.Patent ZL201010576330.7 adopts the method matching Romax, the degree of mobilization MF that return, the ash index Coke Quality that grades is predicted, but this model only considers ature of coal, but do not consider the coking time of coke oven, coking temperature, and the impact that quenching mode Coke Quality produces, patent 200810013054.6BP neural net method predicts the quality of coke, using the distribution of the vitrinite reflectance of coal and cohesiveness index as the input section of parameters input network, corresponding coke index is as the output terminal of BP neural network, training BP neural network, obtain the nonlinear relationship between input and output parameter, then using index to be predicted as BP nerve network input parameter, and then obtain the intensity of coke prediction, the method have ignored the ash content of coke, volatile matter, maximum thickness of colloidal matter layer, the indexs such as coking technique parameter, coke is caused to predict to there is deviation, and neural net method is more accurate to carrying out prediction coke quality by the coal crossed, but it is larger to new coal prediction deviation.
Summary of the invention
The object of the present invention is to provide the Coal Blending Expert System of a kind of applicable top dress coke oven, to overcome deficiency of the prior art.
For achieving the above object, the invention provides following technical scheme:
The embodiment of the present application discloses the Coal Blending Expert System of a kind of applicable top dress coke oven, comprising:
Coal data and coal blending data acquisition module, gather the coal laboratory index of quality testing department and coke oven, coke property index, coking technique index and quenching mode, automatically for coke property autoregression provides analytical data;
Coke property autoregression module, set up non-linear regression blending models, this blending models selects data to be used for non-linear regression according to the reliable and stable principle of data from coal data and coal blending data acquisition module, realize coke cold strength forecast quality and actual measurement mass deviation < 0.5%, hot performance error < 2%;
Optimized Coal Blending module, is optimized coal blending according to blending models and coal market price, chemical products price according to the minimum principle of pure cost;
Coal blending results acquisition and inspection module, automatically prediction of coke quality result and actual production result are stored, and carry out difference comparison, when producing coke quality and actual production Comparative result deviation > 2%, coke property autoregression system re-establishes Coke Quality Prediction Models.
Preferably, in the Coal Blending Expert System of above-mentioned applicable top dress coke oven, set up described blending models and Optimized Coal Blending comprises three steps:
(1) set up Coke Quality Prediction Models, adopt the method for multifactor non-linear regression, consider coking time, coking temperature, quenching mode that Coke Quality impact is larger;
(2) setting coal blending restricted condition is required according to working condition and coke quality;
(3) contrast the degree of mobilization distribution plan of mixed coal and ginseng coal blending, find the temperature range of mixed coal degree of mobilization deficiency, corresponding raising joins chlorine adding ratio preferably in this interval mobility, finally determines Optimized Coal Blending ratio and cost.
Preferably, in the Coal Blending Expert System of above-mentioned applicable top dress coke oven, in described step (1), the Nonlinear regression equation between Indexes on Coke Strength and mixed coal performance index:
M40＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
M10＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
CSR＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
CRI＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
G in above formula: caking index, Vdaf: dry ash-free basis volatile matter, Ad: dry basis ash content, Ro: maximum reflectance of vitrinite, MF: maximum fluidity, h: coking time, T: coking temperature, MCI: coal ash catalytic index ψ: the burnt modified index of wet breath.
Preferably, in the Coal Blending Expert System of above-mentioned applicable top dress coke oven, Optimized Coal Blending system adopts nonlinear programming approach, namely there is the mathematic programming methods of Nonlinear Constraints or objective function, study the extreme-value problem (coke pure cost minimum) of a n unit (n kind coal) real function under the constraint condition of one group of equation or inequality, Nonlinear programming Model is as follows:
·ming(x)
·s.t.fi(x)≥0i＝1，2，3…，m
·hj(x)＝0j＝1，2，3…，p
·x＝(x1，…,xn)∈D
X=(x1 ..., xn): be coking coal usage quantity;
G (x): coke pure cost (the burnt by-product benefit of the burnt mixed coal cost-ton of coke pure cost=ton), the funtcional relationship namely between coke pure cost and coal coking coal quantity;
Min: represent " minimizing ";
S.t.: represent " be tied in ";
Fi (x): non-constraint function such as grade;
Hj (x): equality constraints functions.
Compared with prior art, the invention has the advantages that: the Coal Blending Expert System of a kind of applicable top of the present invention dress coke oven, the method that this system adopts multiparameter non-linear regression, mixed coal vitrinite reflectance distribution plan, mixed coal degree of mobilization distribution plan to combine, makes prediction of coke strength deviation control within 2%.This system carries out nonlinear optimization coal blending so that coke pure cost is minimum for target simultaneously, considers coking by-product productive rate, market value, Coal Quality and price to the impact of pure cost, really realizes the coke making and coal blending that cost is minimum.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, the accompanying drawing that the following describes is only some embodiments recorded in the application, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Figure 1 shows that in the specific embodiment of the invention impact not considering the coking technique such as coking time, coking temperature parameter Coke Quality, coke cold strength M40 measured value and regressand value comparison diagram;
Figure 2 shows that in the specific embodiment of the invention, regression equation introduces the coking technique such as coking time, coking temperature parameter, coke cold strength M40 measured value and regressand value comparison diagram;
Figure 3 shows that 7.63m coke oven M10 measured value and regressand value comparison diagram in the specific embodiment of the invention;
Figure 4 shows that 7.63m coke oven CSR measured value and regressand value comparison diagram in the specific embodiment of the invention;
Figure 5 shows that the mixed coal distribution graph of reflectivity of 7.63m unmodified in the specific embodiment of the invention;
Figure 6 shows that in the specific embodiment of the invention and join coal blending vitrinite reflectance distribution plan, wherein,
A: coking coal 1; B: rich coal 1; C: rich coal 2; D: coking coal 2; E: coking coal 3; F: bottle coal;
G:1/3 coking coal 1; H:1/3 coking coal 2; I: rich coal 3; J: lean coal 1; K: lean coal 2;
L:1/3 coking coal 3;
Figure 7 shows that each seed ginseng coal blending degree of mobilization distribution plan in the mixed coal of unmodified in the specific embodiment of the invention, wherein, A: coking coal 1; C: coking coal 2; D: coking coal 3; G: rich coal 2; H:1/3 coking coal 3; K: lean coal 2; L: bottle coal;
Figure 8 shows that in the specific embodiment of the invention and join coal blending degree of mobilization distribution plan, wherein, A: coking coal 1; B:1/3 coking coal 1; C: coking coal 2; D: coking coal 3; E:1/3 coking coal 2; F: rich coal 1; G: rich coal 2; H:1/3 coking coal 3; I: rich coal 3; J: lean coal 1; K: lean coal 2; L: bottle coal;
To Figure 9 shows that in the specific embodiment of the invention mixed coal vitrinite reflectance distribution plan after the correction of 7.63m coke oven;
To Figure 10 shows that in the specific embodiment of the invention after revising each seed ginseng coal blending degree of mobilization distribution plan in mixed coal, wherein, A: coking coal 1; B:1/3 coking coal 1; C: coking coal 2; D: coking coal 3; E:1/3 coking coal 2; F: rich coal 1; G: rich coal 2; H:1/3 coking coal 3; I: rich coal 3; J: lean coal 1; K: lean coal 2.
Embodiment
The embodiment of the present invention provides a kind of novel method, and prediction of coke quality both considered coal index, considers again coking technique parameter, the factors such as quenching mode, by the method prediction coke quality of nonlinear fitting.Compare with existing method, the method is accurate, comprehensively, and by the Optimized Coal Blending of system, greatly can reduce coal blending cost.The following technical scheme of concrete employing:
(1), realize quality testing department, the automatic data collection in coal blending workshop and correction, set up coal and coal-blending coking data collecting system.Set up quality control system, coal blending production system is connected with the interface of this Coal Blending System, realize the automatic collection of coal, coal blending, coke data, if there is the interdepartmental data inconsistence problems of difference, as problems such as name of vessel are different, the date is inconsistent, as shown in table 1, can be contrasted by character string, to port, production, the multiple stage circulation of proving time contrast, auto modification name of vessel, and image data, realize the collection that data accurately, not repeat, do not omit.
The contrast of table 1 coke-oven plant and quality testing department data and collection
(2) coke strenth autoregression system, is set up.Coal carries out calculating in the data that coal-blending coking data collecting system is collected and returns by this system.First according to performance, the ratio of ginseng coal blending, add and calculate the caking index G of mixed coal
coordinate, dry ash-free basis volatile matter Vdaf
coordinate, dry basis ash content Ad
coordinate, maximum vitrinite reflectance Ro
coordinate, maximum fluidity MF, catalytic index MCI.Then combine with coke oven processing parameter, as coking time h, coking temperature T, quenching mode, set up coke strenth and the Nonlinear regression equation between these mixed coal parameter and coke oven processing parameter, predictive equation is as follows:
M40＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
M10＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
CSR＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
CRI＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
G in above formula: caking index, V
daf: dry ash-free basis volatile matter, A
d: dry basis ash content, Ro: maximum reflectance of vitrinite, MF: maximum fluidity, h: coking time, T: coking temperature, ψ: the burnt modified index of wet breath, MCI: coal ash catalytic index, wherein,
(3), Optimized Coal Blending
1), require to arrange coal blending restricted condition according to scene production conditions and coke quality: the G value, the V that arrange mixed coal
daf, Ad, MF, Ro fluctuation range, i.e. mixed coal G>=75,25≤Vdaf≤29,1.1≤Ro≤1.3 etc.; Simultaneously according to stock and buying condition, from coal data collecting system, automatically select ginseng coal blending kind, the usage quantity restricted condition of every seed ginseng coal blending is set; According to hot strength of coke requirement, mixed coal vitrinite reflectance is set and is distributed in vitrinite's volume percent in [0 ~ 0.9], [0.9 ~ 1.6], [>1.6] scope, Re
[0 ~ 0.9]<30, Re
[0.9 ~ 1.6]>45, Re
[>1.6]<20; Meet mixed coal being greater than 470 DEG C has enough degree of mobilization simultaneously, the coking coal quantity of Tm>=470 DEG C in chlorine adding ratio>=8%.
2), Optimized Coal Blending adopts nonlinear programming approach, study the extreme-value problem of a n unit (n kind coal) real function under the constraint condition of one group of equation or inequality (ton Jiao pure cost is minimum).
Nonlinear programming Model is as follows:
·ming(x)
·s.t.f
i(x)≥0i＝1,…,m
·h
j(x)＝0j＝1,…,p
·x＝(x1，…,xn)∈D
X=(x1 ..., xn): be coking coal usage quantity, as the usage quantity of the various coals such as A coking coal, B coking coal, C rich coal, D lean coal, E1/3 Jiao, F bottle coal, belong to field of definition D.
G (x): coke pure cost (coke pure cost=mixed coal cost/t coke-coking by-product benefit/t coke), i.e. coke pure cost and ginseng coal blending kind and the funtcional relationship of joining between coal blending kind quantity, symbol min represents " minimizing ".
S.t.: represent " be tied in ".
F
i(x): non-constraint function such as grade, as coke quality index M40>85, M10<7 function.
H
jx (): equality constraints functions, as equation function is waited in ten thousand tons, coke oven coke monthly output=18.5.
Above Nonlinear programming Model is utilized to solve how coal blending (determining x value), the minimum Coal Blending Schemes of coke pure cost could be realized, i.e. globally optimal solution under the condition meeting the constraint of all coke qualities, silo or supply constraint, coke output constraint, working condition constraint.
3), automatically calculate and generate vitrinite reflectance distribution plan and the degree of mobilization distribution plan of mixed coal and ginseng coal blending according to the result of Optimized Coal Blending, the vitrinite reflectance distribution plan of contrast mixed coal and ginseng coal blending, the chlorine adding ratio of adjustment ginseng targetedly, eliminates irrational groove in mixed coal distribution graph of reflectivity.The degree of mobilization distribution plan of contrast mixed coal and ginseng coal blending, finds the temperature range of mixed coal degree of mobilization deficiency, and corresponding raising joins coal blending preferably in this interval mobility.
4), Optimized Coal Blending data detection and collection
After Optimized Coal Blending ratio-dependent, automatically can preserve coal blending data, mixed coal index, cost, prediction of coke quality result.The Field Production Data of prediction of coke quality result and follow-up automatic collection can contrast, when producing coke quality and actual production Comparative result deviation > 2%, coke property autoregression system re-establishes Coke Quality Prediction Models.
For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.The example of these preferred implementations illustrates in the accompanying drawings.Shown in accompanying drawing and the embodiments of the present invention described with reference to the accompanying drawings be only exemplary, and the present invention is not limited to these embodiments.
At this, also it should be noted that, in order to avoid the present invention fuzzy because of unnecessary details, illustrate only in the accompanying drawings with according to the closely-related structure of the solution of the present invention and/or treatment step, and eliminate other details little with relation of the present invention.
Figure 1 shows that in the specific embodiment of the invention impact not considering the coking technique such as coking time, coking temperature parameter Coke Quality, coke cold strength M40 measured value and regressand value comparison diagram.
Figure 2 shows that in the specific embodiment of the invention, regression equation introduces the coking technique such as coking time, coking temperature parameter, coke cold strength M40 measured value and regressand value comparison diagram.
For 7.63m coke oven, embodiments of the invention are described, step is as follows:
(1) automatically gather the data in quality testing department, coal blending workshop, and the data of collection are carried out automatic classification according to Coal rank, coke oven.As carried out automatic data collection and classification according to 7.63m coke oven, calculate the mixed coal index of each coal blending list.
(2) carry out coke quality recurrence according to the mixed coal index of different coke ovens, regression equation is as follows, and automatically can draw out recurrence and measured data comparison diagram, as shown in Figure 3, Figure 4.
M40＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
M10＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
CSR＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
CRI＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
(3) Optimized Coal Blending
1. enter Optimized Coal Blending system, from coal data system, automatically call in ginseng coal blending kind and corresponding performance and price data according to market supply and inventories;
2. require setting Coal Blending Technology restricted condition, as shown in table 2, table 3 according to the coke quality of different coke-oven:
Table 2 coke quality restricted condition
| Coke oven | Coke oven output/t | Indicator conditions | M40 | M10 | CSR | CRI | Ash content | S |
| 7.63m coke oven | 6000 | The upper limit | 6 | 24 | 12.6 | 0.8 |
| Lower limit | 88.5 | 68 |
Table 3 mixed coal index restricted condition
3. according to market and stock's condition, arrange ginseng coal blending quantity limitation condition, i.e. the quantity upper and lower limit of often kind of coal, namely x=(x1 ..., xn) and ∈ D;
4. minimum for target with coke pure cost, inequation or equality constraint is set up according to coke quality index, mixed coal performance limitations condition, buying restricted condition, output demand etc., adopt the method for nonlinear programming, automatic calculation goes out to meet all restricted conditions, Coal Blending Schemes that pure cost is minimum, and automatically generate ginseng coal blending, mixed coal vitrinite reflectance distribution plan and degree of mobilization distribution plan, as shown in Fig. 5 ~ Fig. 8.
Table 4 Optimized Coal Blending result and ginseng coal blending performance table
5. contrast mixed coal and ginseng coal blending vitrinite reflectance distribution plan, find there is abnormal groove in mixed coal vitrinite reflectance distribution plan, as shown in Figure 5, coke microtexture can be caused not up to standard, hot strength reduces.Therefore find out the ginseng coal blending F that groove is corresponding, improve the consumption of ginseng coal blending F, reduce the consumption of rich coal G of the same type, adjust the ratio of H coal and I coal simultaneously, as shown in table 3.As shown in Figure 9, Figure 10, vitrinite reflectance, close to normal distribution, has enough degree of mobilization between the high-temperature zone more than 470 DEG C, and mixed coal indices meets coal blending index request for mixed coal vitrinite reflectance distribution plan after adjustment and degree of mobilization distribution plan.The coke pure cost of Optimized Coal Blending reduces by 14.6 yuan/t coke compared with traditional coal blending, the quality conformance with standard requirement of coke, as shown in table 5.
Table 5 Optimized Coal Blending coke predicted intensity and observed strength contrast
| M40 | M10 | CSR | CRI | |
| Optimized Coal Blending predicted intensity/% | 89.3 | 5.7 | 66.7 | 24.2 |
| Coke oven production data/% | 89.6 | 5.6 | 66.1 | 24.7 |
| Prediction deviation/% | 0.3 | 1.7 | 0.9 | 2.0 |
Finally, also it should be noted that, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.
Claims (1)
1. be applicable to a Coal Blending Expert System for top dress coke oven, it is characterized in that, comprising:
Coal data and coal blending data acquisition module, gather the coal laboratory index of quality testing department and coke oven, coke property index, coking technique index and quenching mode, automatically for coke property autoregression provides analytical data;
Coke property autoregression module, set up non-linear regression blending models, this blending models selects data to be used for non-linear regression according to the reliable and stable principle of data from coal data and coal blending data acquisition module, realize coke cold strength forecast quality and actual measurement mass deviation < 0.5%, hot performance error < 2%;
Optimized Coal Blending module, is optimized coal blending according to blending models and coal market price, chemical products price according to the minimum principle of pure cost;
Coal blending results acquisition and inspection module, automatically prediction of coke quality result and actual production result are stored, and carry out difference comparison, when producing coke quality and actual production Comparative result deviation > 2%, coke property autoregression system re-establishes Coke Quality Prediction Models
Set up described blending models and Optimized Coal Blending comprises three steps:
(1) set up Coke Quality Prediction Models, adopt the method for multifactor non-linear regression, consider coking time, coking temperature, quenching mode that Coke Quality impact is larger;
(2) setting coal blending restricted condition is required according to working condition and coke quality;
(3) the vitrinite reflectance distribution plan of mixed coal and ginseng coal blending is contrasted, the chlorine adding ratio of adjustment ginseng targetedly, eliminate irrational groove in the vitrinite reflectance distribution plan of mixed coal, and contrast the degree of mobilization distribution plan of mixed coal and ginseng coal blending, find the temperature range of mixed coal degree of mobilization deficiency, corresponding raising joins coal blending preferably in this interval mobility, finally determines Optimized Coal Blending ratio and cost
In described step (1), the Nonlinear regression equation between Indexes on Coke Strength and mixed coal performance index:
M40＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
M10＝f(G,V
daf,A
d,MF,Ro,h,T,Ψ)
CSR＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
CRI＝f(G,V
daf,A
d,MF,Ro,h,T,MCI,Ψ)
G in above formula: caking index, Vdaf: dry ash-free basis volatile matter, Ad: dry basis ash content, Ro: maximum reflectance of vitrinite, MF: maximum fluidity, h: coking time, T: coking temperature, MCI: coal ash catalytic index ψ: the burnt modified index of wet breath,
Optimized Coal Blending system adopts nonlinear programming approach, namely has the mathematic programming methods of Nonlinear Constraints or objective function, and study the extreme-value problem of the first real function of n under the constraint condition of one group of equation or inequality, Nonlinear programming Model is as follows:
·ming(x)
·s.t.fi(x)≥0i＝1，2，3…，m
·hj(x)＝0j＝1，2，3…，p
·x＝(x1，…,xn)∈D
X=(x1 ..., xn): be coking coal usage quantity;
G (x): coke pure cost, the funtcional relationship namely between coke pure cost and coal coking coal quantity;
Min: represent " minimizing ";
S.t.: represent " be tied in ";
Fi (x): non-constraint function such as grade;
Hj (x): equality constraints functions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410417811.1A CN104164246B (en) | 2014-08-22 | 2014-08-22 | The Coal Blending Expert System of a kind of applicable top dress coke oven |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410417811.1A CN104164246B (en) | 2014-08-22 | 2014-08-22 | The Coal Blending Expert System of a kind of applicable top dress coke oven |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104164246A CN104164246A (en) | 2014-11-26 |
| CN104164246B true CN104164246B (en) | 2016-02-03 |
Family
ID=51908230
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410417811.1A Active CN104164246B (en) | 2014-08-22 | 2014-08-22 | The Coal Blending Expert System of a kind of applicable top dress coke oven |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104164246B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106479549B (en) * | 2015-08-31 | 2019-07-23 | 宝山钢铁股份有限公司 | Mixed coal Giseeler fluidity prediction technique |
| CN110964552B (en) * | 2018-09-30 | 2021-06-01 | 武钢集团昆明钢铁股份有限公司 | Method for classifying main mixed coking coal for coking |
| CN110942208B (en) * | 2019-12-10 | 2023-07-07 | 萍乡市恒升特种材料有限公司 | Method for determining optimal production conditions of silicon carbide foam ceramic |
| CN112521965A (en) * | 2020-12-22 | 2021-03-19 | 鞍钢集团北京研究院有限公司 | Rapid coal blending method |
| CN113719856A (en) * | 2021-08-24 | 2021-11-30 | 河北邯峰发电有限责任公司 | Automatic coal blending and blending combustion control method for coal-fired generator set |
| CN113780810A (en) * | 2021-09-10 | 2021-12-10 | 重庆钢铁股份有限公司 | Coke purchasing decision method |
| CN115368927A (en) * | 2022-09-02 | 2022-11-22 | 安阳钢铁股份有限公司 | Low-volatile coal blending coking method for top-mounted coke oven |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1173006C (en) * | 2002-03-18 | 2004-10-27 | 山西焦化集团有限公司 | Method for coal blending coking by using coal petrography analysis |
| CN101661026B (en) * | 2008-08-30 | 2013-04-24 | 中钢集团鞍山热能研究院有限公司 | Method for predicting mechanical strength and thermal properties of coke |
| KR101185279B1 (en) * | 2010-03-30 | 2012-09-21 | 현대제철 주식회사 | Method for predicting of drum index of cokes |
| CN102021007B (en) * | 2010-12-07 | 2013-09-04 | 江苏沙钢集团有限公司 | Low-cost coking coal blending system |
| CN102424758A (en) * | 2011-10-17 | 2012-04-25 | 开滦（集团）有限责任公司 | Multi-index blended coal coking method |
| JP6056157B2 (en) * | 2012-02-29 | 2017-01-11 | Ｊｆｅスチール株式会社 | Coke blending coal composition determination method and coke manufacturing method |
| CN102746866A (en) * | 2012-06-21 | 2012-10-24 | 徐州伟天化工有限公司 | Accurate prediction method of sulfur content in coke in coal blending for coking |
-
2014
- 2014-08-22 CN CN201410417811.1A patent/CN104164246B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN104164246A (en) | 2014-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104164246B (en) | The Coal Blending Expert System of a kind of applicable top dress coke oven | |
| Menghi et al. | Energy efficiency of manufacturing systems: A review of energy assessment methods and tools | |
| CN101661026B (en) | Method for predicting mechanical strength and thermal properties of coke | |
| CN101863088B (en) | Method for forecasting Mooney viscosity in rubber mixing process | |
| Liu et al. | Innovation networks and r&d allocation | |
| CN105243255A (en) | Evaluation method for soft foundation treatment scheme | |
| CN102021007B (en) | Low-cost coking coal blending system | |
| CN103853915A (en) | Method for predicting coke quality through coking coal nonlinear optimization coal blending | |
| WO2021007871A1 (en) | Alumina production operation optimization system and method employing cloud-side collaboration | |
| Granderson et al. | Assessment of automated measurement and verification (M&V) methods | |
| CN103631140A (en) | Method for automatically regulating temperature of flame path of coke oven in heating and combusting procedures on basis of performance evaluation | |
| CN105259827A (en) | Real-time monitoring system and real-time monitoring method for solid fermentation process condition | |
| CN101447001A (en) | Method for predicting sulfur content in coke | |
| CN104484724A (en) | Extra-high voltage drop point plan optimal selection method based on cloud model | |
| CN110246547A (en) | A kind of sintering process Blending optimization method | |
| CN104504280A (en) | Planning-demand-considered comprehensive evaluation method for communication mode of cluster management system of charging piles of electric automobile | |
| CN102637020A (en) | Integrated-type control system of roll steel heating furnace | |
| CN104134103A (en) | Method for predicting energy consumption of hot oil pipeline by using modified BP neural network model | |
| CN104699957B (en) | Coke making and coal blending based on improved differential evolution algorithm compares optimization method | |
| CN112521965A (en) | Rapid coal blending method | |
| Hu et al. | Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process | |
| CN114662763A (en) | Method and system for evaluating cost performance of single coal for coking coal blending | |
| CN102156405B (en) | Sintered ore chemical component prediction and intelligent control system under small sample poor information | |
| Yang et al. | Modeling and optimal-setting control of blending process in a metallurgical industry | |
| Farla et al. | Industrial long-term agreements on energy efficiency in The Netherlands. A critical assessment of the monitoring methodologies and quantitative results |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |