JPS5964705A - Method of detecting condition of blast furnace - Google Patents

Abstract

PURPOSE:To precisely detect the condition of a blast furnace, by selecting factors which have been ascertained as important ones by experience from various sensor information, and performing their readjustment and quantitative control in response to a phenomenum inside the furnace in both long and short-term points of view. CONSTITUTION:Sensors provided at various positions of a furnace casing detect data necessary for evaluating furnace heat and condition to handle said data as indices representing fluctuation, unhomogeneity, a pattern and a level. On the basis of said data, long-term diagnosis 13 of one - a few days order and short-term diagnosis 14 of 30min order are performed. Said long-term diagnosis 13 mainly aiming at the diagnosis 5 of furnace condition is constituted by the diagnosis 6 of the distribution of a gas stream, the diagnosis 7 of the distribution of pressure, the diagnosis 8 of adhesive matter and the diagnosis 9 of the level of furnace heat in addition to it. Said short- term diagnosis 14 is constituted by the diagnosis 10 of the abnormality of furnace condition, the diagnosis 11 of the decrease of furnace heat and the diagnosis 12 of the amount of residual slag. These results are sent to means for judging long and short- term furnace conditions to integrate these indices into an index for diagnosing the furnace condition.

Description

[Detailed description of the invention] The present invention relates to a method for detecting the condition of a blast furnace.

As a method of diagnosing and managing the situation of the blast furnace, conventional -de'r'-'-vr blast furnace 4'hX':'&'%Il
'a M (7) -1= 7 g-Kara no Taku ~ II Ko Determined ↑ Biologically ゛i'11 Determined 11. The method of evaluating the condition of the furnace is omitted, and the operator's skill and experience are used in the wording of the 1' value of the factor. In the snow, there is 1 (Jl Eighth 2, standardization of operational actions!: 1ifi-L, and in addition, the problem is that it is difficult to analyze 1゛ψ work 1'+'+ because the evaluation is not quantitative) Because of this, as a detection power method for the blast furnace situation, from the blast furnace, the whole inside of the furnace 11:, loss index, shaft part 1 (, force distribution index, ]ri) personli; r-to situation index. By detecting each numerical factor of gas utilization rate index, furnace top gas temperature distribution index, shaft furnace wall temperature distribution index, in-furnace "false bell danger" and in-furnace melt retention index, and evaluating the combination of these indices. A method of determining the blast furnace status has been proposed.

However, according to the study of the present inventors, tL
&J:, Uri+'', the method has the following problems and is considered difficult to apply to actual operations.

■ Since it merely grasps short-term changes in reactor conditions, it is difficult to indicate long-term (more than one day) operational actions (the prefecture is different from the prefecture).
There is no Rii ().

The economy has fallen into a slump.
Since it has no bivalence, it cannot be used as an indicator of action.

■ In addition to fluctuations and non-uniformity, the furnace condition is diagnosed by taking into account levels and patterns. However, the diagnosis of furnace conditions is based on the operating methods (e.g. oil injection, all coke, low fuel ratio, If the fuel ratio (high fuel ratio, etc.) changes ->7'j, the standard will change, so
111 - iPL limit value There is fire and gas that perform furnace condition diagnosis by changing the valence 3, ■ Blast furnace 'j': One of the most important factors in A 'j%-4 and 2! The balance in the circumferential direction was evaluated.

(also) There are too few factors to comprehensively evaluate the furnace condition, and 1(
4- does not include information on the "feather L" which is one of the most important factors in determining the furnace condition.

The present invention has been made in view of the above-mentioned problems.
1! l'+, -1-Ichij Saki and 2+0 bright circle I) Six choices L7, all of these in-core phenomena correspond to 9:::;
,, :[Iij,・determining(,: , , , , , , , , , , , , , , , , , , , , , , , , , , , , It is.

Figure 1 shows the overall structure of the law. , 1 to several days (e.g. 5 days)
[ ]) Period of about 11'1 and the period of 11'1 and the period of time -) Jl, mainly from the information of 1 to 1 to detect the stability of the reactor condition by t・1, and the short-term condition of 30 minutes Rj degree Between Jii, place and (-ta(
From the W report, perform periodic furnace status detection to detect abnormal furnace conditions that require wind reduction, increase in wind, etc. mainly in L2 ” (to j.

Of these, long-term reactor condition detection is mainly aimed at assessing the stability of the reactor condition based on fluctuations and non-uniformity, as well as understanding the causes of instability in the reactor condition through level comparisons, pattern comparisons, etc. The condition of the furnace can be detected from the overall J'r value obtained from these diagnoses. As a result, a constant 9+" evaluation of the furnace condition is carried out, and changes are made to the lifting platform to change the charge distribution, to balance the air flow rate, etc.
An effective 111 disconnection (4-tabble) is used to diagnose the condition of the furnace.The changes in gas, Q, pressure, etc. in the furnace are uniform in the circumferential direction.-
It is possible to determine the cause of the instability of the furnace condition by performing f+' values of the operation results. Diagnosis, Furnace heat level diagnosis 1 (1, How many tests were performed)
, Among these, gas flow distribution diagnosis is based on a of the gas flow distribution shape 1λj1 in the radial direction obtained from a horizontal sonde, belt sonde, etc.
``V value, pressure distribution diagnosis is 7 Yaft pressure d1, evaluation of furnace air permeability from pressure information obtained from blowing pressure gauge etc. Diagnosis of the growth state of kimono and furnace heat level are performed by evaluating the furnace heat level from the corrected fuel ratio, gas utilization rate, G iron temperature, etc.

On the other hand, short-term reactor condition inspection ILI is a reactor condition abnormality diagnosis that evaluates the reactor condition in a short period of 1υ based on the variation 1υ of each extraction factor f-, nonuniformity, 1 novel comparison, etc., 7 variations of various factors, nonuniformity, etc. , level comparison, diagnosis of furnace heat drop by A'F11 [i] at every short heating interval from wall fall of shaft, cooling slurp, etc., and extraction of iron slag from 1st color iron slag 11 and furnace lower part permeability. It consists of a residual pig iron slag amount diagnosis that evaluates irregularities at short intervals. Each of the above evaluation values is compared with the standard level and is 1.
At the 43rd minute, an H alarm was issued and arrests were made! A 4iJ meeting of business action will be given.

All of the various diagnostic results constituting the long-term reactor condition detection and short-term reactor condition detection described above are obtained as indexes, and constant evaluation of each evaluation factor is possible. The basic data for all diagnostics is obtained from sensors installed throughout the furnace body.

Each sensor detects sufficient data to evaluate the furnace heat and furnace condition at short intervals (for example, every few seconds to several minutes), and indexes representing fluctuations, non-uniformities, patterns and levels are obtained.

In long-term furnace condition detection, an index is derived from data for one to several days, and in short-term furnace condition detection, an index is derived from short-time data at predetermined time intervals.

In addition, in this short-term furnace condition detection, the furnace condition is detected every several tens of minutes (about aO minutes), but if an index based on data every several minutes is used, for example, errors due to short-term changes may occur. There is a risk of not knowing what is happening in the reactor or overlooking gradual changes in the furnace condition on the order of 3 to 4 hours. For this reason, in the present invention, an index is derived by performing a time-series Ichikawa search of the previous several hours every several tens of minutes. As a result of the inventors' investigation of the sensor change start time before the furnace temperature and furnace condition become abnormal and wind reduction is performed,
It has been found that it is sufficient to understand sensor changes over a period of 3 to 4 hours on average. In addition, when the periodicity of the furnace condition was separately investigated using the gas chromatography CO concentration as an example by power spectrum analysis, it was found that the furnace condition fluctuated in a period of up to 4 to 5 hours. For this reason, short-term reactor condition detection includes sensor information from 3 to 5 hours before IH1 (average 4 hours) for each index (47 hours).
It is preferable to do so.

The above indexes are compared with predetermined limit values or target values for each factor, and a graded evaluation (for example, five-level rating) is performed for each factor according to the comparison result. Then, the resulting i=F value index is weighted based on empirical rules, and added together for each factor representing all of the furnace conditions and furnace heat, to perform an evaluation based on, for example, an io-point perfect score satisfaction evaluation. Through this evaluation, in long-term reactor condition detection, the burden descent condition index, pressure fluctuation condition index, gas flow condition index, circumferential balance condition index, and furnace heat fluctuation condition index, which are the evaluation factors for furnace condition diagnosis, can be obtained. A gas flow distribution diagnostic index, a pressure distribution diagnostic index, a furnace heat level diagnostic index, and a deposit diagnostic index, which are clues for diagnosing abnormalities in the furnace condition, can be obtained. In addition, in short-term reactor condition detection, the pressure fluctuation situation index, which is an evaluation factor for furnace condition abnormality diagnosis, the level comparison index for furnace condition abnormality diagnosis, and 11. which is an evaluation factor for furnace heat drop diagnosis. Charging that should become a common evaluation factor for the sagging situation index and the level comparison index for diagnosing furnace condition abnormality and diagnosing furnace heat drop! l? 2I IQ
The bottom condition index, gas flow fluctuation index, and circumferential balance condition index are determined, and roughly the residual pig iron slag amount index is determined. In long-term furnace condition detection, the burden drop condition index, pressure fluctuation condition index, gas flow fluctuation condition index, circumferential balance condition index, and furnace heat fluctuation condition index are weighted and added, and the score is, for example, 100 points. Determine the furnace condition diagnostic index by drop foot evaluation. 10 Furthermore, this furnace condition diagnosis index, the gas flow distribution diagnosis index, the pressure distribution diagnosis index, the furnace heat level diagnosis index, and the deposit diagnosis index are weighted and added, and the score is 100 points, for example. Obtain long-term reactor condition diagnostic index through evaluation. On the other hand, in short-term reactor condition detection, the burden drop condition index, pressure fluctuation condition index, gas flow fluctuation condition index, circumferential balance condition index, and level comparison index for furnace condition diagnosis are weighted and added to give, for example, 100 points. ? Obtain the furnace condition abnormality diagnosis index by W"J drip foot evaluation, and also obtain the charge descent condition index, wall fall condition index, gas flow change 1IIh condition index, circumferential balance condition index, and level comparison index for furnace heat drop diagnosis. For example, calculate the furnace condition abnormality diagnosis index based on the 100-point drip foot evaluation Q.Then, each of the above-mentioned furnace condition abnormality diagnosis index, furnace heat decrease diagnosis index, and residual pig iron slag amount index is determined. For example, the level of A (good), B (bad), C
(deterioration), and if any index is C, ti K-艮 is issued.

An example of classification of factors that form the basis of each evaluation and diagnosis is as follows.

(1) 艮jtll furnace condition detection (a) ri;
・”-171′ for people′l, 1j・′f1 song j
Lower blade yield:・、Scream J So';1. i'i'F f curved gas j4i+,'lνji+11 situation correction [I] Iji+
Balance situation evaluation Furnace thermal change NbtY value (b) Gas flow distribution diagnosis ←9 Pressure distribution diagnosis (in -) Furnace d, 1 to level diagnosis''・Correction fuel ratio (・Hot metal temperature (j ri・1)::f 'i Naru Diagnosis (2) Short-term reactor condition diagnosis Charge fall t' status evaluation (・Charging nose descent speed σ ・・Slip points\ Pressure cuff [rule υ status I;'Y (i'i Gas θ: Rayu Moga γ1 in back nine-H curved circumference Banonnu's situation correction 1 / I.Stock line Reher unrestricted - strong U Reactor condition abnormality diagnosis level comparative evaluation' (P2-Pi) / V17 ( 'rmki IK-
Index)・Blade 1-1 Brightness %%ζ Fall status evaluation Cooling stave temperature abnormal fluctuation strength .7 (
Among the factors above and beyond the amount of iron slag (air permeability), slip point breakage is scored based on the degree and number of slips of the charge within a predetermined period of time, and the various abnormal fluctuation points are as follows: The number of fluctuations or culm sputum changes above a predetermined level within a predetermined period of time is scored. Further, the below-bell sonde σ is obtained by taking the average σ of the sonde at one point or several points. In addition, each inconstraint regarding the circumferential non-uniformity −φt n+
1 is the index of the deflection strength of the factor in the circumferential direction of the blast furnace.

FIGS. 2 and 3 show J! of the present invention! : offered as alms: ij
This shows that lJgoj>tj77. Of these, FIG. 2 first shows a device for long-term reactor condition detection. From the blast furnace, there are various factors, namely, the charge descending rate σ (X8)
, charging interval σ (X2), number of Surinobu points (X3), blowing pressure σ (X, ), Nyaft pressure σ (X5), number of furnace top pressure abnormal fluctuation points (X6), number of vertical sonde abnormal fluctuation points (X7),
Gas utilization rate (ηco) σ (X8), bell-bottom sonde σ (x
9) Furnace top temperature σ (X1o), strike line level non-uniform strength (X□□), furnace top temperature non-uniform strength (X1□),
Non-uniform strength of Si concentration in hot metal (X□3), non-uniform intensity of tuyere brightness (X 14 ), Si concentration σ in hot metal (Xt, ), tuyere brightness (X1a), horizontal sonde temperature distribution (X1□), Bell temperature distribution (X□8), shaft pressure (X1
9), Blow pressure (X2ok furnace JJ'j pressure (X21),
Corrected fuel ratio (X2z), gas utilization rate ■23), hot metal f!
Data for each factor of 1 degree (X24), shaft temperature (X2□), and cooling tape temperature (X26) is imported. Most of these data are taken in at intervals of several seconds to several minutes, and some of them are taken several times a day. The data captured in this manner is sent to the indexing devices 1a to 1y, and each device 1 outputs an index corresponding to the data of each factor. As mentioned above, long-term reactor condition detection uses the information obtained at short intervals for one to several days as an index. For example, the following! A similar treatment method is used.

・Charge descending speed σ (X□) ...Take σ of one day's worth of raw data.

・Charging interval 1’? Aσ(X2) ...Take σ of one day's worth of raw data.

・Slip score (X3) ... 500 or more slips
, 77-1 Hiro, 1000 or more and less than 2000 = 2
Points, 2000111111 or more = 3 points are added in each direction.

・Blowing pressure σ (X4) ...Take σ of one day's worth of raw data.

・Shaft pressure σ (X5) ...Take the 2σ of one day's worth of raw data.

・Furnace top pressure abnormal fluctuation point 1 (X-s)...If the furnace top pressure increases by 0.03 Kg/cM or more, it will be scored as 1 point.

・Vertical sonde abnormal fluctuation score (X7) ... Convert the degree of temperature gradient in abnormal temperature rise into a score.

・Gas utilization rate (ηco)σ(X8) ...Take σ of ηco for one day.

・Bottom bell sonde σ(X,) ...Take the σ of large wave fluctuations excluding the influence of charging.

・Furnace] ri　i書组〔σ　(X-工.) ...Take the σ of large wave fluctuations excluding the influence of charging.

- Storsocline level non-uniform intensity (Xll), furnace top temperature non-uniform intensity (X1□) and tuyere brightness non-uniform intensity (
X14> ...Take the non-uniform intensity of one day's worth of raw data.

- Non-uniform strength of Si concentration in hot metal (X□3)...Take the non-uniform strength of data for each tap hole for several days.

・Si concentration in hot metal (Xts) ...Take the σ of the Sl concentration for each pot for one day.

The index values regarding the variation, level, pattern, and non-uniformity of each factor determined based on the data are taken in by the comparators 2a to 2□, and then input to the setters 6a to 7. is compared with a pre-given limit value or target value. Here, for factors mainly related to variation or heterogeneity of (Xl) or Xl,), the limit value and (X17)
~(X26), factors mainly related to patterns or levels are compared with target values. As a result of this comparison, a graded evaluation such as a five-level evaluation is performed for each factor, and an index value corresponding to the evaluation is output. Among these index values, those related to furnace condition diagnosis are determined by multipliers 4a to 4. be taken in. Among these multipliers, weighting for multipliers 4a to 4c is performed by an exponent generator 5A.
, weighting is performed by an exponent generator 5 to the multipliers 4d to 4f.
From B, the weighting to multipliers 4g to 4j is from an exponent generator 5c, the weighting to multipliers 4 to 4n is from an exponent generator 5D, and the weighting to multipliers 4° and 4p is from an exponent generator. An exponent for the 7'c weighting corresponding to each factor is input from the device 5E, and a multiplication is performed between the exponent and the exponent corresponding to each factor. ? The index of - IU + Kui;] is determined as 'Cj-men' according to the conditions of the blast furnace, etc. (↓
,・S,Each 1-4i ii(?+ (:
) υzu finger; same as place) 3FC2゛1waru5, this J Unadunamiima]ge's line, convex person, 1- own 1, 1 human 11,
~So-”Add for each group of L);l);
6 A, I, 6): Manpower 'J'1 is added to.

and - no J11 (charged! 12/Jl'yf lower condition index WA, pressured). These indices are, for example, 1
0 points perfect score M+ h = Depends on the evaluation method. These index values WA or Wot, and -y1℃!゛λ) The wealth 7 to 7E are manually input, and here, the tsume miri manually generated from the 11 number generator 8 is multiplied by the exponent. Then, the weights are added by the u-1 adder 10, and the furnace condition diagnosis index VA is obtained.

On the other hand, each index value for the factors other than those that form the basis of the furnace condition is taken into R Diagnosis 4q to 47. These multipliers4. And 4z is weighted from exponent generator 5F, and from 48 to 4u.
, J, and h are from the exponent generator 5n, and are multiplied by 1,000 and 1"3.
-43 to Σ. and 47-\ is] 1 (corresponding to each factor from the counting generator 5). After such heavy calculation, the exponents of each factor are manually added and added for each group. tQ' + index ■., pressure distribution proverb H; rr4?number vc
, Furnace heat level meter 17i index ■. , Imma 1 Kimono Diagnostic Index V
= is obtained.

The index values of 1 to VE obtained in this way are given by a common =Y value, such as the total -C1, for example, 10 points in points and 11゛6 feet d゛1' value. Each of these diagnostic indices vA to VE is applied to multipliers 12, 11A to 11.
It is input to D, where the index ≧+l'i generator 16
The jk readings, which are performed manually, are multiplied by the exponent η. After this weighting, each index is
5, and by adding trowel 2, long-term
A reactor condition diagnosis index is determined as the total value of the condition. This diagnostic index is 100 points rsl 1 point i
14 feet iFt' 111i -] and wait. In this way, 'fJ:' is used as a method of displaying the index, for example, a spider graph is used.
thl (shown in Figure 4 Q) 1, long 1 (1) furnace condition diagnosis t[J" index display -i: □lI is shown 3, this A score of 72 points was achieved in the furnace condition a' value.

FIG. 6 shows the shift in the J number jff when the long-term reactor 61 detection as shown below is carried out at 1 fE and 141st position in the actual operating conduit, and the reactor condition is managed. This graph)]
After the mid-year period, the overall J'' value (Furnace Status Examination 14JI index) had been hovering at 85-90 points, but since the mid-year period, it has dropped to around 60 stores. As for the phenomenon, the situation of the burden descent has deteriorated7, and the gas flow fluctuation and the furnace heat change have increased by 1υ.As a result, in the latter half of the month, the charge change due to the 1-movable armor fICactual Mσ occurred. The situation in which the burden was lowered gradually improved to 11, and after about 1A, the evaluation score turned around 80 points (J) in the trench.

Further, FIG. 3 shows a device for detecting the condition of the furnace during the lighting period.

In this device, the above-mentioned (X, L (X3) to 1X7), (X□.)t, (Xtechnical 2), (X14).

In addition to the factors (X23) and (X24), N2i'fi in the top gas: [1' (X27), furnace IJ-t ti
C07(,”'H degree cl (X2.), /Kino1
Fusho) Low 5″ bi'powder', number of points (X29), Coolings Dave?! l',', IJt-, Y4 constant drive number (X3o), (p 2-p. 2) 7 y 1.7(
X3□), grain brightness (X, S□), S i l in hot metal (
: Data of each factor of 1g degree (X33) and iron slag -F+1 (X34) are imported. In this way, the data is taken into account from index generation ff516a to 1
6W, and each device 16 outputs an index corresponding to the data of each prisoner.

Knowledge 1. II reactor condition detection uses information that is collected every few seconds to several minutes and converts it into an index of 1σ per 30 minutes.7 As mentioned above, when converting to an index, -C is used for 10-odd hours. The data are time-series weighted. For example,
When calculating the index using data at 4 hours old, the following formula is used.

1 = aXIaomin+b×Ixhr−1−C×I
zhr + d × 1 < hr, a, b, c, d: Car protection 4I 3ornin: From 30 minutes ago, index by boot ■□hr” hours ago 〃 Izhr: ff! hours ago〃 Inhr ” hours The index values regarding the variation, level, pattern and non-uniformity of each factor determined based on the index data from previous data are sent to the comparators 17a to 17w.
Here, it is compared with a limit value given in advance by the setters 18a to 18w. As a result of this comparison,
For each factor, a graded evaluation such as a five-level evaluation is performed, and an index value according to the evaluation is output. Among these index values, those related to abnormality diagnosis of the reactor condition are multiplier 1.
9a to 19n. Of these multipliers, weighting for multipliers 19a and 19b is performed by an exponent generator 20A.
From there, weighting is applied to multipliers 19c to 198 from exponent generator 20B, weighting is applied to multipliers 19f to 19i from exponent generator 20c, and weighting is applied to multipliers 19j to 19t from exponent generator 20D. Then, to the multipliers 19m and 19n, an exponent corresponding to each factor is input from the exponent generator 20E, and multiplication is performed between the exponent and the exponent corresponding to each factor. After such weighting, the exponents of each factor are input to adders 21A to 21E for each group and added.

By this addition, the charge descent status index YA1, the pressure fluctuation status index YR1, the gas flow rate 1υ status index Yc1, the circumferential balun 7 status index YD, and the level ratio soft index Y for furnace condition abnormality diagnosis.

is required. These indices are obtained, for example, by the io point perfect satisfaction evaluation method. These index values YA or YE
is further input to a multiplier 22A or 22E, where the weighted phrase input from the index generator 26 is multiplied by an index. Then, after this weighting,
The adder 25 adds jI-, thereby obtaining the furnace condition abnormality diagnosis indicator UA.

This furnace condition abnormality diagnosis index UA is obtained as a satisfaction evaluation with a perfect score of 100, for example.

On the other hand, regarding the diagnosis of furnace heat drop, comparator 1
The exponent value used from 7o to 17u is sent to the multiplier 19o.
or 19. be taken in. Among these multipliers, multipliers 1, 9o and 19p-\ are weighted by the exponent generator 20.
From F, multiplier 19. For the weightings 19u to 19u, exponents are input from the index generator 20G for weighting corresponding to each factor, and multiplication is performed with the exponent corresponding to each factor. After such weighting, the exponents of each factor are input to adders 21F to 21° for each group and added. By this addition, the level comparison index YG for diagnosing the fall of wall condition index YF1 and the decrease in furnace heat is obtained. These indices are also obtained by the same method as YA to YE described above, for example, using the IO point perfect satisfaction evaluation method. These index values YF and YGl, as well as the charge descent status index YA1, the gas flow fluctuation status index Yc, and the circumferential balance status index Yb, are input to multipliers 22F to 22J,
Here, the weighting input from the index generator 24 is multiplied by an index. And after this weighting,
The values are added by an adder 26, thereby obtaining a furnace heat reduction diagnostic index UB. This furnace heat drop diagnostic index U is also
For example, it is obtained as a 100-point drip foot evaluation.

In addition, regarding the evaluation of residual pig iron slag amount, comparator 1
The index values output from 7v and 17w are taken into multipliers 19v and 19w, which are attached to index generator 20 II.
The value inputted from 11 (Mitsuke) is multiplied by the exponent.

After this weighting, the adder 21 Tl adds the
The residual iron slag amount evaluation index YH is determined. This index is also obtained by, for example, the 10-point satisfaction evaluation method, which is common to the evaluation indices for diagnosing abnormalities in the furnace condition and diagnosing the decrease in furnace heat.

The furnace condition abnormality diagnosis index UA1, furnace heat drop diagnosis index UB, and residual iron slag amount index Y)I obtained in the above manner are taken into the short-term furnace condition determination device 27, where the short-term furnace condition is determined. It will be done. The judgment made here is, as mentioned above, for example, by ranking the level of each index as A (good), B (fair), or C (deteriorating), and if any index level is C, a warning will not be issued. It will be done. This method of displaying the short-term furnace status also uses, for example, a spider web graph and is displayed on a CRT display device.

Figure 5 shows an example of an index display for short-term reactor condition diagnosis using such a Kunosu graph, and shows an example of the index display for short-term reactor condition diagnosis using such a Kunosu graph.
(solid line in the figure) and the furnace heat reduction diagnostic index UB (dashed line in the figure). Diagnosis 1 (17 index' 70 points, furnace -, nohe low 1 [1 number 58 small is raised T)...su, -, y p-7, 42q4. ,, (:I
-,7<, 4HG, 'C4 points are given and 2), then K,[;go-if 4 songs get B rank 6, (2', l)'tf introduction;Ti For example, the value is determined as shown in the table below.

Figures 7 (a) to (e) are spider web graphs of the changes in reactor conditions detected by short-term reactor condition ship breaks in actual operations. shows the condition of the furnace 5 hours ago, ←) V shows 2 hours before, ni) shows 1 hour before, and (e) shows the condition of each furnace at the time of wind reduction. Figure 8 also shows the 7th I-1
It was measured at the time of (a), ishiku, and /ξ is Char 1 of the burden descent situation, and is not (a) in the figure. This corresponds to the period of 19. At this time, 10 hours before the wind decrease, at the stage (a), the total score was 84 points, rank A, -Mata continuation < (I=) and the stage of Even for 1 cost, the total score was 64 points (B rank), and the total score was 73 points (B rank), but as shown in Figure 8, due to the deterioration of the burden descent situation, ) at the stage of V
The total score for I was 55 points (rank B), and the total score for stage I (e) was reduced to 27 points, rank C. Therefore, in actual construction, wind reduction was required at this stage. “Action has been taken.

Actually, the above is a comprehensive explanation of the furnace condition detection. ：'In addition to the F number, individual indexes of one rank overall evaluation 17
, may be a factor in the furnace condition evaluation3. Therefore, -F
The index of the position is also displayed by the CRT display device iN. Figure 9 (t) shows an example of this, and (a) is
Each factor index of reactor condition diagnosis in long-term reactor condition detection is also (
口)C'', which also display the furnace condition diagnostic index.

This depends on the control of the -tN stage reactor condition (6) and early stage reactor condition detection, and the furnace condition is determined based on the detected urine condensation by each machine (,'', Y. However, in the present invention, the judgment results (instruction culture) obtained by each judgment device iJ', j 5 and 27 are further processed and incorporated into the joint publication Jikkaku, 14, and here, both judgments are carried out. A comprehensive evaluation of the reactor condition can be made from the above. Judgment J゛1' Sen [Used as a factor'!L]

61. ! According to "I", there are many things, including inconsistencies in the circumferential direction of the blast furnace and information on the diameter of the blast furnace. -F
In order to detect not only short-term changes in furnace condition but also long-term changes in furnace condition on a daily basis, the overall aY value of the furnace condition is calculated based on the value factor. It can be used as an indicator for furnace condition management.

[Brief explanation of drawings]

FIG. 1 is a flow sheet of the overall structure of the method of the present invention. Figures 2 and 283 show the equipment used to implement the method of the present invention, and Figure 2 shows the control (+i
ll block diagram, 2+ '73 diagram is for short-term reactor condition detection +, j
It is a block diagram of the seven-fingered system 1i111. Figures 4 and 5
The figure shows a display example of comprehensive evaluation. Figure 6 shows the furnace condition index 11 (transfer) when management is performed based on the furnace condition diagnosis ltI'i'i/f'X. Fig. 8 shows Char 1 in the lower charge I17 condition corresponding to the changes in Fig. 7. Fig. 9 (a) and (b) show the lower An example of displaying an index is shown in the figure J<1. In the figure, 1a to 1.Z are indexing devices 17.2a to 1.Z.
2.1. v yo”t:li father:diagnosis, 4 a~47.
C, s"r'?": %V, 5 A~5 Engineering Q"
, jic mi(t keJ'a p occurrencehy, 6 A
~6 F; &, J-K! ='i vessel, 7A~7];
8 is the weighted index generator, 9A to 91), 1
0 is an adder, 11A to i1D, 12 is Me,! 71
j, +: 4.16 is 11L finding J”a number generator, 1
4.1, comprehensive 1' erection device, 15 is long-term reactor condition '11' fixed IE7. ．． 16 a~16 wIt: ↓'' digitization device i'Oi, 17 a~17 w&ma ÷ on the ratio axis,
198~19.7 is here>1'4A, 20A~20H is jlj, the index generator is 21A~2,1, I IJ
, Ka3゛7 device, 22A-22Ji1.1. multiplier, 25
．． 24 is a 111 number generator, 25.26 is a addition 1, a vessel, 27 is a beam, a target 1. +J furnace condition 1'1] is a fixed device. For patent use: 0 Nippon Kokan Co., Ltd. Inventor Saito Confusion Tei Shibuya Sando
Tanaka Dentaka Orientation/Izumi ■5 Department Yoshiyoshi Horiuchi Ryo Kimura, instant patent attorney Masaru Yoshihara Same
Hiroko Yoshihara (lawyer)
705 (9) Figure 4 Figure 5 Figure 6 (0 village) No. (a) -, -36=

Claims (1)

[Claims] Burden drop status index, pressure fluctuation status index, gas flow fluctuation status index, circumferential balance status index, level comparison index for furnace condition abnormality diagnosis 1tf, wall fall status index, level comparison index for furnace heat drop diagnosis, and residual iron The sludge amount index is converted into an index by time-series weighting of the short-term information <jl of multiple factors corresponding to each index, and a comparison of these index values with limit values, graded evaluation into several categories, The nail attachment is determined by addition, and the above-mentioned burden drop condition index, pressure fluctuation condition index, gas flow fluctuation condition index, circumferential balance condition index, and level comparison index for furnace condition abnormality diagnosis are added after weighting. A furnace condition abnormality diagnosis index is calculated, and the above-mentioned burden drop condition index, wall fall condition index, gas flow fluctuation condition index, circumferential balance condition index, and level comparison index for furnace heat drop diagnosis are added after weighting to calculate the furnace condition. A heat drop diagnostic index is obtained, and the short-term furnace condition of the blast furnace is detected at short intervals from the above-mentioned furnace condition abnormality diagnostic index, furnace heat drop diagnostic index, and iron slag amount index. , pressure fluctuation status index, gas flow fluctuation status index, circumferential balance status index, and furnace heat fluctuation status index, long-term information of multiple factors corresponding to each index is converted into an index, and the limit values of these index values are calculated. Comparison, graded evaluation into several categories, weighting are determined by addition, and the determined indexes are added after weighting to determine the furnace condition diagnosis index, gas flow distribution index, pressure distribution index, and furnace heat level index. and the deposit index, by indexing the long-term information of the number factor corresponding to each index, comparing these index values with the target value, graded evaluation into number categories, and calculating the weighted interest by addition. A long-term furnace condition diagnosis index is obtained by weighting and adding these indices and the above-mentioned furnace condition diagnosis index, and the long-term furnace condition of the blast furnace is detected based on the index. Blast furnace condition detection method with R.