CN1173542A - Blast furnace operating consulting system - Google Patents
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- CN1173542A CN1173542A CN 97112469 CN97112469A CN1173542A CN 1173542 A CN1173542 A CN 1173542A CN 97112469 CN97112469 CN 97112469 CN 97112469 A CN97112469 A CN 97112469A CN 1173542 A CN1173542 A CN 1173542A
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- 238000000034 method Methods 0.000 claims abstract description 38
- 238000009826 distribution Methods 0.000 claims abstract description 24
- 239000000463 material Substances 0.000 claims abstract description 16
- 238000007781 pre-processing Methods 0.000 claims abstract description 12
- 238000004364 calculation method Methods 0.000 claims abstract description 5
- 239000004744 fabric Substances 0.000 claims description 38
- 239000003034 coal gas Substances 0.000 claims description 31
- 238000013528 artificial neural network Methods 0.000 claims description 18
- 238000012937 correction Methods 0.000 claims description 11
- 238000013178 mathematical model Methods 0.000 claims description 10
- 238000005516 engineering process Methods 0.000 claims description 9
- 239000000203 mixture Substances 0.000 claims description 7
- 230000007812 deficiency Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000007726 management method Methods 0.000 claims description 5
- 230000033228 biological regulation Effects 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000013473 artificial intelligence Methods 0.000 claims description 3
- 238000002203 pretreatment Methods 0.000 claims description 3
- 230000005144 thermotropism Effects 0.000 claims description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 8
- 230000003628 erosive effect Effects 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 229910000805 Pig iron Inorganic materials 0.000 description 4
- 230000000875 corresponding effect Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 239000000571 coke Substances 0.000 description 3
- 230000004069 differentiation Effects 0.000 description 3
- 229910052742 iron Inorganic materials 0.000 description 3
- 238000009423 ventilation Methods 0.000 description 3
- 229910001018 Cast iron Inorganic materials 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000002893 slag Substances 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 241001417490 Sillaginidae Species 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 208000002352 blister Diseases 0.000 description 1
- 230000036760 body temperature Effects 0.000 description 1
- 239000011449 brick Substances 0.000 description 1
- 239000002826 coolant Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 238000007711 solidification Methods 0.000 description 1
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- 230000003068 static effect Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 239000008400 supply water Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
A suggestion system for operating blast furnace is composed of database, date pre-processing module, furnace state forecast module, display module, print module, suggestion module on gas flow distribution and materials distribution, the module to deduce shape and position of molten soft band, key technique calculation module, operation prediction module, and the module to inquire technical operation specification and job standard, and features complete system functions, and high correctness.
Description
The present invention relates to a kind of blast furnace operating consulting system, belong to field of smelting of cast iron by blast-furnace.
Modern big-and-middle-sized blast furnace whenever round the clock pig iron output be more than 2000～10000 ton.Be difficult to avoid conditions of blast furnace to change in process of production, in this case, if can not in time grasp the operation of blast furnace situation, make corresponding adjustment, can make unusual working of a furnace generation vicious cycle and tend to deterioration, cause the minimizing of pig iron output, downgrade, directly have influence on the economic benefit of whole iron and steel enterprise.According to statistics: 2000m
3Conditions of blast furnace is once undesired, will the underproduction pig iron about about 1000 tons.If the unusual scaling loss of body of heater cupola well masonry more can cause shutdown maintenance, financial loss is bigger; Do not understand cloth virtual condition and coal gas distributions and then be difficult to effective cloth etc., therefore blast furnace is well monitored, predicts fault, prediction process so that blast furnace operating consulting, Operating Guideline and in time to take measures be that modern blast furnace is produced high yield, high-quality, low consumption, efficient and long-lived key.Chinese patent application 87103633 discloses a kind of " (method of control operation of blast furnace) ", it is based on data that the transmitter on the blast furnace provides and by the knowledge base that the experience of operation of blast furnace forms, is inferred by the various processing units in small-size computer and the computer.It comes down to a kind of blase furnace cast iron and smelts expert systems, and it only comprises forecast of stove heat and unusual working of a furnace prediction expert system, in order to infer blast furnace whether the meet accident accident and the unusual working of a furnace such as hanging and pipeline.Chinese patent CN1043765A discloses a kind of " (blast furnace operation management method and device) ", and it is the expert systems that whether unusual a differentiation blast furnace point out corresponding actions when, unusual.But these expert system functions are single, only can infer guidance with regard to the working of a furnace, stove heat, be not enough to comprehensive multiple goal and instruct blast furnace operating, and owing to can not make adapt to some to changing responsive parameter, thereby cause the inferred results accuracy not high.
The objective of the invention is to overcome above-mentioned shortcoming of the prior art, provide that a kind of function is complete, inferred results accuracy height, can make unskilled operator reach the level of skilled operator whereby, thereby can handle timely and accurately the unusual working of a furnace, make the blast furnace abnormal time drop to minimum blast furnace operating consulting system.
The object of the present invention is achieved like this:
A kind of blast furnace operating consulting system, comprise database, data preprocessing module, working of a furnace forecast module, display module, print module, this system also comprises coal gas distributions and cloth suggestion module, cohesive zone shape and position inference module, body of heater and cupola well masonry scaling loss inference module, gordian technique computing module, blast furnace operating prediction module and blast-furnace technique working specification and operation or work standard enquiry module; Raw data in the database is recycled to database again after data preprocessing module is handled, working of a furnace forecast module, coal gas distributions link to each other with database respectively with cloth suggestion module, cohesive zone shape and position inference module, body of heater and cupola well masonry scaling loss inference module, gordian technique computing module, the back links to each other with display module, the blast furnace operating prediction module links to each other with display module with the operation or work standard enquiry module with the blast-furnace technique working specification, connects print module behind the display module.
Database also joins with a neural network threshold value self study and dynamic management piece, and each threshold value in the database is returned each threshold setting of revising in the database again after the neural network self study.
Described working of a furnace forecast module is the method forecast working of a furnace that adopts mathematical model and artificial intelligence to combine; Carrying out the working of a furnace through the data preprocessing module data processed for working of a furnace judgement mathematical model in the database and comprehensively judge, is " well " as judged result, and then demonstration, print result are waited for next cycle arrival judgement more then; As judged result is " attention " or " bad ", then changes unusual working of a furnace expert systems over to, is differentiated for which kind of unusual or not normal working of a furnace and is exported confidence value by it, shows then, print result, waits for the arrival of next cycle.
Above-mentioned unusual working of a furnace expert systems is based on the possibility that the production rule of listing according to expertise is judged the various unusual working of a furnaces and the not normal working of a furnace " not taking place ", " have and tend to " or " being about to take place ".
The above-mentioned unusual working of a furnace is that edge coal gas deficiency, edge coal gas too develop, center coal gas deficiency, center coal gas too develop, partially material, pipeline, to cool, thermotropism; The above-mentioned not normal working of a furnace is hanging, collapse material, stove cold, stove heat.
Display module also shows 1～5 judged result in the past simultaneously except that showing when time unusual or not normal working of a furnace result of determination.
Described coal gas distributions and cloth suggestion module are to take out cross temperature marginal flow center stream, thermal load and gas utilization rate complex data and shaft detector data earlier from database, these data one are to draw coal gas distributions situation through the identification of stochastic method neural network, show coal gas distributions situation then, these data are also judged the cloth situation through the cloth expert systems simultaneously, propose to revise the cloth schema recommendation then, show that then the operator revises the cloth pattern then.
Coal gas distributions and cloth suggestion module also comprise a neural network, revise each observed value that draws behind the correction cloth in back data and the database and send neural network, after the neural network self study, deliver to the cloth expert systems to revise the threshold setting of cloth expert systems.
Described cohesive zone shape and position inference module are based on the method for mathematical model, find out the cohesive zone position by technology theory; These model concrete steps are
A) fetch data from database read;
B) data are carried out pre-treatment;
C) speed of material descent calculates;
D) material balance and heat calculation;
E) furnace top gas temperature and composition calculate;
Is f) the furnace top gas temperature correction carried out in judgement? then carry out furnace top gas temperature in this way from revising, carry out step e) then again, as otherwise carry out next step;
Is g) correction of stock gas composition carried out in judgement? then carry out blanking velocity in this way from revising, carry out step c), d then again), e), f), as otherwise carry out next step;
H) furnace charge and gas temperature distributed computation;
I) show output;
Does j) judgement finish operation? then finish operation in this way, as otherwise carry out next step;
K) do you judge whether arrive lock in time? continue to judge to wait for lock in time and finish as not arriving then, as to then carrying out above-mentioned steps again successively.
Described blast-furnace technique working specification and operation or work standard enquiry module comprise technical process and major equipment technical parameter, blast-furnace burden and furnace charge correction, blast furnace technology operation, stokehold regulations for technical operation and blast furnace plant post operation or work standard.
The present invention is described further below in conjunction with drawings and Examples:
Fig. 1: the pie graph of system of the present invention.
Fig. 2: system of the present invention working of a furnace forecast module schema.
Fig. 3: system of the present invention coal gas distributions and cloth suggestion module schema.
Fig. 4: system of the present invention cohesive zone shape and position inference module schema.
Fig. 5: system of the present invention body of heater and the regional Ω synoptic diagram of the permanent heat transfer of cupola well masonry scaling loss inference module.
A kind of blast furnace operating consulting system of the present invention, comprise database, data preprocessing module, working of a furnace forecast module, display module, print module, this system also comprises coal gas distributions and cloth suggestion module, cohesive zone shape and position inference module, body of heater and cupola well masonry scaling loss inference module, gordian technique computing module, blast furnace operating prediction module and blast-furnace technique working specification and operation or work standard enquiry module; Raw data in the database is recycled to database again after data preprocessing module is handled, working of a furnace forecast module, coal gas distributions link to each other with database respectively with cloth suggestion module, cohesive zone shape and position inference module, body of heater and cupola well masonry scaling loss inference module, gordian technique computing module, the back links to each other with display module, the blast furnace operating prediction module links to each other with display module with the operation or work standard enquiry module with the blast-furnace technique working specification, connects print module behind the display module.Database also joins with a neural network threshold value self study and dynamic management module, and each threshold value in the database is returned each threshold setting of revising in the database again after the neural network self study.
Database deposit various spot sensor raw data and these data after data preprocessing module is handled the data that form and each parameter threshold.
Data preprocessing module realize to the spot sensor data check, revise and do feature extraction, the invalid or error message to the blast furnace transmitter automatically compensates, editor and smoothing processing, the input data that the working of a furnace is judged are more reliable.
Described working of a furnace forecast module is the method forecast working of a furnace that adopts mathematical model and artificial intelligence to combine, carrying out the working of a furnace through the data preprocessing module data processed for working of a furnace judgement mathematical model in the database comprehensively judges, the judgement cycle is adjustable in 2～60 minutes, also can require instant judgement according to operator, present embodiment is to judge once per half an hour, as judged result is " well ", then shows, prints judged result, waits for next cycle arrival judgement more then; As judged result is " attention " or " bad ", then changes unusual working of a furnace expert systems over to, is differentiated for which kind of unusual or not normal working of a furnace and is exported confidence value by it, shows then, print result, waits for that next cycle arrives to remake differentiation.The working of a furnace judges that the method that mathematical model is theoretical with blast furnace technology and operating experience combines sets up, and adopts the short-term, mid-term of the level of eight class parameters and four class parameters, the change value is comprehensively judged and drawn the conclusion of the working of a furnace " well ", " attention " or " bad " for a long time.Eight class parameters are: (1) full stove ventilation property, and it is made up of total permeability index, top and the bottom permeability index again; (2) local ventilation property, it is characterized by each layer shaft static pressure; (3) the hot state of stove, it is characterized by FeO content in molten iron temperature, stove heat number, the slag; (4) stock gas situation comprises stock gas CO utilization ratio and furnace top gas temperature; (5) furnace charge decline situation comprises density index, charge interval index, collapses the material index; (6) stock gas distribution situation comprises cross temperature situation, lip temperature proportion, core temperature proportion; (7) furnace body temperature comprises temperature of furnace wall, the thermal load of restoration of lower stack, furnace bosh; (8) cupola well slag iron state comprises the pitch time of slagging tap, tap a blast furnace, the coefficient of slagging tap.Four class parameters are blast, each layer shaft pressure, stove heat number and stock gas CO and N2 concentration.The method of comprehensive judgement is to adopt the method for giving a mark, and each subitem and the normal value of eight class parameters compare, and are " attention " when surpassing a certain threshold value, and get 1 fen; If when surpassing bigger threshold value be " bad ", and must 0 minute; As then be " well " when being normal value, and must 2 minutes.Four class parameters also are respectively threshold ratio, get 0 fen when good, get when bad-2 fens.Such eight class parameters each the subitem and four class parameters all respectively with threshold ratio than and score.Because the importance of each parameter is also inequality, so score will be taken advantage of different weight coefficients.This model regulation is judged when eight class parameter levels that each is itemized and should be adjusted every weight coefficient when all being " well " to make total score GSN1 be 100 minutes, it is 0 minute that four class parameters change the GSN2 that always scores when differentiation is every all to be " well ", the GSN2 that always scores when being " bad " is-30 minutes, divide GSN1 and GSN2 addition to draw total points GSN two subtotals, the present embodiment model is decided to be GSN＞70～80 timesharing, the working of a furnace is " well ", 60～65＜GSN＜70～80 timesharing are " attention ", GSN＜60～65 o'clock, the working of a furnace is " bad ".Unusual working of a furnace expert systems is based on the presentation rule of listing according to expertise, be the mode of If (prerequisite) Then (conclusion) CF (confidence level), judge " not taking place " of eight kinds of unusual working of a furnaces and four kinds of not normal working of a furnaces, the possibility of " have and tend to " or " being about to take place ".Eight kinds of unusual working of a furnaces are: edge coal gas deficiency, edge coal gas too develop, center coal gas deficiency, center coal gas too develop, partially material, pipeline, to cool, thermotropism; Four kinds of not normal working of a furnaces are hanging, collapse material, stove cold, stove heat.The result of determination of expert systems adopts the rod figure with color and two features of height to represent, " do not take place " is that green, " have and tend to " are red for yellow, " being about to generation ", the confidence value size is with the high expression of rod, except that showing when time judged result, also show 5 judged results in the past, thereby find out working of a furnace trend.
Coal gas distributions and cloth suggestion module are to take out cross temperature marginal flow center stream earlier from database, thermal load and gas utilization rate complex data and shaft detector data, these data one are to draw coal gas distributions situation through the identification of stochastic method neural network, show coal gas distributions situation by display module then, these data are also judged the cloth situation through the cloth expert systems simultaneously, propose to revise the cloth schema recommendation then, show by display module, revise the cloth pattern by the operator then, revise each observed value that draws behind the cloth in revised data and the database and send neural network, after the neural network self study, deliver to the cloth expert systems to revise the threshold setting of cloth expert systems.The neural network self study will accumulate 3 day data and just carry out self study.The cloth expert systems is based on blast furnace expert's cloth operation tricks of the trade and is write as production rule, and fetching data by above-mentioned institute provides the cloth schema recommendation.
Cohesive zone shape and position inference module are based on the method for mathematical model, blast furnace radially is divided into the cylinder of some amount by technology theory, find the solution by material balance and the thermal equilibrium of setting up cylinder, draw furnace charge and temperature distribution, temperature distribution history just can draw the cohesive zone position in the stove.These model concrete steps are
A) fetch data from database read;
B) data are carried out pre-treatment;
C) speed of material descent calculates;
D) material balance and heat calculation;
E) furnace top gas temperature and composition calculate;
Is f) the furnace top gas temperature correction carried out in judgement? then carry out furnace top gas temperature in this way from revising, carry out step e) then again, as otherwise carry out next step;
Is g) correction of stock gas composition carried out in judgement? then carry out blanking velocity in this way from revising, carry out step c), d then again), e), f), as otherwise carry out next step;
H) furnace charge and gas temperature distributed computation;
I) show output;
Does j) judgement finish operation? then finish operation in this way, as otherwise carry out next step;
K) do you judge whether arrive lock in time? continue to judge to wait for lock in time and finish as not arriving then, as to then carrying out above-mentioned steps again successively.
Body of heater and cupola well masonry scaling loss inference module are to set up model with technology theory, mathematical analysis method to release cupola well scaling loss curve.Furnace bottom is regarded as the rotational symmetry zone Ω that permanent heat transfer process is arranged, and its border is Γ
1, furnace bottom sidewall Γ
2, base plate Γ
3, and free boundary Γ
4Furnace bottom sidewall Γ
2With base plate Γ
3Near be provided with water coolant and cool off, at XOZ plane and Γ
2, Γ
3Intersection on be embedded with thermometer m.If observed temperature is u=(u
1, u
2... u
m), utilize these observed temperature information u to infer the position that encroachment line promptly determines free boundary Γ 4.Because being conducted heat through solidification layer (erosion curve) refractory brick by supply water temperature, the temperature of point for measuring temperature comes, the depth of erosion difference, temperature is also just different, so suppose a depth of erosion earlier, calculate the point for measuring temperature temperature, compare with observed temperature then, if any difference, suppose another depth of erosion again, calculate, till accounting temperature and observed temperature difference minimum, this supposition degree of depth is exactly the depth of erosion that will try to achieve again, the temperature of calculating each point for measuring temperature will draw each depth of erosion, and linking up is exactly actual erosion curve.
The gordian technique computing module carries out four big class technique computes, and promptly a) feed proportioning optimization is calculated, and except that an example calculation is arranged, also can change the input data and carry out new calculating; B) data computation in the stove comprises reacting weight, ventilation property etc.; C) turnout calculates; D) Intake Quantity calculates, and comprises coke ratio, ore deposit/coke ratio, pig iron growing amount etc.
The blast furnace operating prediction module is that the preservation of supposition Fu Shi body coexistence heat is with under the condition that exists, calculate blast furnace top and the bottom thermal equilibrium and material balance, calculate to change each operations factor prediction coke ratio, stock gas composition, temperature, help operation to estimate the economy that changes various operations and security.
Blast-furnace technique working specification and operation or work standard enquiry module comprise technical process and major equipment technical parameter, blast-furnace burden and furnace charge correction, blast furnace technology operation, stokehold regulations for technical operation and blast furnace plant post operation or work standard, inquire about at any time for furnace superintendent and other staff.
Furnace superintendent or other staff can understand the pairing operation of blast furnace situation of any one functions of modules according to demand, and numerical value or the Operating Guideline given according to native system are made corresponding adjustment, guarantee that blast furnace normally moves.
The present invention has following advantage than prior art: (1) function is complete, can make the operator Understand, grasp the information of various relevant blast furnaces, be convenient to accurate judgement, make corresponding adjustment, Thereby it is minimum that the blast furnace abnormal time is dropped to; (2) using artificial neural metanetwork technology, thus right Important parameter carries out self study, self-correcting, has strengthened the dynamic management of native system to blast furnace, protects Demonstrate,proved the accuracy of inferred results; (3) side that adopts Mathematical Modeling to combine with expert system Method, so that inferred results accuracy height, the Operating Guideline that provides is with a high credibility; (4) have rule The query function of journey, standard etc. makes unskilled operator can reach very soon skilled operator Level.
Claims (10)
1, a kind of blast furnace operating consulting system, comprise database, data preprocessing module, working of a furnace forecast module, display module, print module, it is characterized in that: this system also comprises coal gas distributions and cloth suggestion module, cohesive zone shape and position inference module, body of heater and cupola well masonry scaling loss inference module, gordian technique computing module, blast furnace operating prediction module and blast-furnace technique working specification and operation or work standard enquiry module; Raw data in the database is recycled to database again after data preprocessing module is handled, working of a furnace forecast module, coal gas distributions link to each other with database respectively with cloth suggestion module, cohesive zone shape and position inference module, body of heater and cupola well masonry scaling loss inference module, gordian technique computing module, the back links to each other with display module, the blast furnace operating prediction module links to each other with display module with the operation or work standard enquiry module with the blast-furnace technique working specification, connects print module behind the display module.
2, system according to claim 1 is characterized in that: database also joins with a neural network threshold value self study and dynamic management piece, and each threshold value in the database is returned each threshold setting of revising in the database again after the neural network self study.
3, system according to claim 1 is characterized in that: described working of a furnace forecast module is the method forecast working of a furnace that adopts mathematical model and artificial intelligence to combine; Carrying out the working of a furnace through the data preprocessing module data processed for working of a furnace judgement mathematical model in the database and comprehensively judge, is " well " as judged result, and then demonstration, print result are waited for next cycle arrival judgement more then; As judged result is " attention " or " bad ", then changes unusual working of a furnace expert systems over to, is differentiated for which kind of unusual or not normal working of a furnace and is exported confidence value by it, shows then, print result, waits for the arrival of next cycle.
4, system according to claim 3 is characterized in that: described unusual working of a furnace expert systems is based on the possibility that the production rule of listing according to expertise is judged the various unusual working of a furnaces and the not normal working of a furnace " not taking place ", " have and tend to " or " being about to take place ".
5, according to claim 3 or 4 described systems, it is characterized in that: the described unusual working of a furnace is that edge coal gas deficiency, edge coal gas too develop, center coal gas deficiency, center coal gas too develop, partially material, pipeline, to cool, thermotropism; The described not normal working of a furnace is hanging, collapse material, stove cold, stove heat.
6, according to claim 3,4,5 any one described system, it is characterized in that: display module also shows 1～5 judged result in the past simultaneously except that showing when time unusual or not normal working of a furnace result of determination.
7, system according to claim 1, it is characterized in that: described coal gas distributions and cloth suggestion module are to take out cross temperature marginal flow center stream, thermal load and gas utilization rate complex data and shaft detector data earlier from database, these data one are to draw coal gas distributions situation through the identification of stochastic method neural network, show coal gas distributions situation then, these data are also judged the cloth situation through the cloth expert systems simultaneously, propose to revise the cloth schema recommendation then, show that then the operator revises the cloth pattern then.
8, system according to claim 7, it is characterized in that: coal gas distributions and cloth suggestion module also comprise a neural network, revise each observed value that draws behind the correction cloth in back data and the database and send neural network, after the neural network self study, deliver to the cloth expert systems to revise the threshold setting of cloth expert systems.
9, system according to claim 1 is characterized in that: described cohesive zone shape and position inference module are based on the method for mathematical model, find out the cohesive zone position by technology theory; These model concrete steps are
A) fetch data from database read;
B) data are carried out pre-treatment;
C) speed of material descent calculates;
D) material balance and heat calculation;
E) furnace top gas temperature and composition calculate;
Is f) the furnace top gas temperature correction carried out in judgement? then carry out furnace top gas temperature in this way from revising, carry out step e) then again, as otherwise carry out next step;
Is g) correction of stock gas composition carried out in judgement? then carry out blanking velocity in this way from revising, carry out step c), d then again), e), f), as otherwise carry out next step;
H) furnace charge and gas temperature distributed computation;
I) show output;
Does j) judgement finish operation? then finish operation in this way, as otherwise carry out next step;
K) do you judge whether arrive lock in time? continue to judge to wait for lock in time and finish as not arriving then, as to then carrying out above-mentioned steps again successively.
10, system according to claim 1 is characterized in that: described blast-furnace technique working specification and operation or work standard enquiry module comprise technical process and major equipment technical parameter, blast-furnace burden and furnace charge correction, blast furnace technology operation, stokehold regulations for technical operation and blast furnace plant post operation or work standard.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN97112469A CN1052758C (en) | 1997-06-13 | 1997-06-13 | Blast furnace operating consulting system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN97112469A CN1052758C (en) | 1997-06-13 | 1997-06-13 | Blast furnace operating consulting system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1173542A true CN1173542A (en) | 1998-02-18 |
| CN1052758C CN1052758C (en) | 2000-05-24 |
Family
ID=5172283
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN97112469A Expired - Fee Related CN1052758C (en) | 1997-06-13 | 1997-06-13 | Blast furnace operating consulting system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN1052758C (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101792836A (en) * | 2010-03-25 | 2010-08-04 | 济南领航机械设备有限公司 | Blast furnace bell-less furnace top failure diagnosis forecasting system |
| CN102002545A (en) * | 2010-12-09 | 2011-04-06 | 山西太钢不锈钢股份有限公司 | Determination method for root position of soft heat belt in blast furnace |
| CN102758032A (en) * | 2011-04-28 | 2012-10-31 | 宝山钢铁股份有限公司 | Method for real-time predication of blast furnace pipeline fault probability |
| CN103361454A (en) * | 2012-03-30 | 2013-10-23 | 鞍钢股份有限公司 | Blast furnace hanging material judgment method based on data filtering |
| CN104388613A (en) * | 2014-11-13 | 2015-03-04 | 北京首钢股份有限公司 | Method for quantitative evaluation of activeness of blast furnace hearth |
| CN105382216A (en) * | 2015-10-23 | 2016-03-09 | 莱芜钢铁集团泰东实业有限公司 | Online infrared temperature measuring early warning system of continuous casting tundish and early warning method |
| CN109966996A (en) * | 2019-02-26 | 2019-07-05 | 武汉恒力华振科技有限公司 | A kind of system using the melting state in big data analysis prediction hot melt adhesive production process |
| CN110765629A (en) * | 2019-10-31 | 2020-02-07 | 中冶赛迪重庆信息技术有限公司 | Method, system and equipment for calculating reflow zone |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62270708A (en) * | 1986-05-20 | 1987-11-25 | Nippon Kokan Kk <Nkk> | Control system for blast furnace heat |
| JPS62270712A (en) * | 1986-05-20 | 1987-11-25 | Nippon Kokan Kk <Nkk> | System for detecting condition of blast furnace |
| ES2157233T3 (en) * | 1988-12-20 | 2001-08-16 | Nippon Steel Corp | METHOD AND APPARATUS FOR THE MANAGEMENT OF THE OPERATION OF A HIGH OVEN. |
| CN1038146C (en) * | 1993-07-21 | 1998-04-22 | 首钢总公司 | Computerized blast furnace smelting expert system method |
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1997
- 1997-06-13 CN CN97112469A patent/CN1052758C/en not_active Expired - Fee Related
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101792836A (en) * | 2010-03-25 | 2010-08-04 | 济南领航机械设备有限公司 | Blast furnace bell-less furnace top failure diagnosis forecasting system |
| CN102002545A (en) * | 2010-12-09 | 2011-04-06 | 山西太钢不锈钢股份有限公司 | Determination method for root position of soft heat belt in blast furnace |
| CN102758032A (en) * | 2011-04-28 | 2012-10-31 | 宝山钢铁股份有限公司 | Method for real-time predication of blast furnace pipeline fault probability |
| CN102758032B (en) * | 2011-04-28 | 2013-09-25 | 宝山钢铁股份有限公司 | Method for real-time predication of blast furnace pipeline fault probability |
| CN103361454A (en) * | 2012-03-30 | 2013-10-23 | 鞍钢股份有限公司 | Blast furnace hanging material judgment method based on data filtering |
| CN103361454B (en) * | 2012-03-30 | 2015-03-11 | 鞍钢股份有限公司 | Blast furnace hanging material judgment method based on data filtering |
| CN104388613A (en) * | 2014-11-13 | 2015-03-04 | 北京首钢股份有限公司 | Method for quantitative evaluation of activeness of blast furnace hearth |
| CN105382216A (en) * | 2015-10-23 | 2016-03-09 | 莱芜钢铁集团泰东实业有限公司 | Online infrared temperature measuring early warning system of continuous casting tundish and early warning method |
| CN109966996A (en) * | 2019-02-26 | 2019-07-05 | 武汉恒力华振科技有限公司 | A kind of system using the melting state in big data analysis prediction hot melt adhesive production process |
| CN110765629A (en) * | 2019-10-31 | 2020-02-07 | 中冶赛迪重庆信息技术有限公司 | Method, system and equipment for calculating reflow zone |
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