CN103160626A - Method for judging whether blast furnace hearth is too cold - Google Patents
Method for judging whether blast furnace hearth is too cold Download PDFInfo
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Abstract
The invention discloses a method for judging the supercooling of a blast furnace hearth, which comprises the steps of respectively establishing an RBF neural network calculation model, a blast furnace material speed calculation model and a theoretical combustion temperature calculation model of Si content in blast furnace molten iron, then comprehensively judging whether the blast furnace hearth is supercooling by utilizing 3 models-Index 1, Index2 and Index3 for judging the supercooling of the blast furnace hearth, and if delta 1. Index1+ delta 2. Index2+ delta 3. Index3 is more than or equal to 1, wherein the delta 1 belongs to [0, 1], the delta 2 belongs to [0, 1] and the delta 3 belongs to [0, 1], the blast furnace hearth is supercooling. The method comprehensively considers the physical factors and the chemical factors in the blast furnace to judge the supercooling of the blast furnace hearth, can judge the supercooling of the blast furnace hearth more early and accurately compared with the prior art, can take measures to control the temperature of the blast furnace within a reasonable range, avoids the deviation or the misjudgment of different operators on the operation condition of the blast furnace, and is very beneficial to the high-efficiency blast furnace operation.
Description
Technical field
The present invention relates to blast furnace ironmaking and monitor field, especially a kind of method that judges that blast furnace hearth is excessively cool.
Background technology
Blast furnace ironmaking is the living ferriferous major way of modern steel enterprise, its production process is accompanied by the physical and chemical reaction of a large amount of tight couplings, non-linear, large dead time, and the characteristics of this complex process of high furnace interior have caused the very difficult physics and chemistry state that directly obtains high furnace interior with conventional test set and detection means.And the hot state of high furnace interior is the most directly embodying of blast furnace stable smooth operation, and blast furnace is excessively cool to be unfavorable for tapping a blast furnace smoothly and to separate with slag iron, overheatedly not only causes blast furnace to be difficult to walk but also causes a large amount of energy dissipations.Therefore; the hot state that obtains ahead of time blast furnace hearth can be earlier to the blast furnace Intervention; can guarantee to reduce energy consumption under the prerequisite of smooth operation of furnace; reduce coke ratio; reduce the discharging of obnoxious flavour, realize green low-carbon production and adapt to national environmental protection policy important in inhibiting for iron and steel enterprise.Be the important indicator of reflection blast furnace working order in view of heat state of blast furnace, judge that as early as possible blast furnace hearth crosses cool extremely importantly for the blast furnace operating personnel, cross cool situation and can not directly be obtained by the blast furnace operating personnel but high furnace interior complicated and changeable determined blast furnace hearth.Conventional judges that it is all to judge indirectly by means of the silicone content in blast-melted whether blast furnace hearth is excessively cool that blast furnace hearth is crossed the means of cool situation, but this method need to find the solution the various process variables of blast furnace and blast-melted in silicone content between mathematical model, and mathematical model is series model or the model such as end user's artificial neural networks judges that all there is the undesirable situation of judged result in blast furnace hearth duration of service.
Summary of the invention
The purpose of this invention is to provide a kind of method that judges that blast furnace hearth is excessively cool, being intended to treat with a certain discrimination affects the excessively cool influence factor of blast furnace hearth, reality from blast furnace iron-making process, consider blast furnace material distribution, blast furnace blast, historical molten iron silicon content to the excessively cool impact of blast furnace hearth, in conjunction with RBF neural network and blast furnace expertise advantage separately, form comprehensive mathematical model efficiently, thereby judge as early as possible the situation that blast furnace hearth is excessively cool, result is implemented to control to blast furnace accordingly.
Realizing that purpose of the present invention designs three technical schemes altogether, is respectively that Si content in blast-melted and blast furnace hearth excessively cool judgement scheme, high furnace charge speed computation model calculation result and blast furnace hearth are crossed the excessively cool judgement scheme of cool judgement scheme, theoretical combustion temperature computation model calculation result and blast furnace hearth.
1, the judgement scheme that the Si content in blast-melted and blast furnace hearth are excessively cool:
This scheme comprises the following steps:
1) choose the input variable of RBF calculating model of neural networks.Because the parameter that relates in the blast furnace actually operating is numerous, therefore for the structure of simplifying model, improve arithmetic speed and the generalization ability of model, the input variable of determining after deleting comprises that cold flow, hot blast temperature, blast, pressure reduction, ventilation property, top are pressed, the Si content-Si (i-1) when top temperature, hour coal powder blowing amount, front tap a blast furnace for twice in molten iron, Si (i).
2) input variable is implemented to carry out normalized.Due to the order of magnitude between input variable differ large and the RBF neural network to the requirement of input data, determine to adopt Mean Method processing input variable:
Wherein:
Be the data after normalization method,
X is input variable, X
minBe the minimum value of input variable, X
maxMaximum value for input variable.
3) determine the RBF neural network structure.The choosing of the number of determining mainly to comprise input layer of RBF neural network structure, the number of hidden layer neuron, the neuronic number of output layer, RBF center, RBF center width.The number of input layer is actual is exactly the number of network input variable, and the neuronic number of output layer can be chosen according to prediction ability and the actual demand of network.RBF center width r can first elect 1 as, and value can suitably be relaxed or reduce, and impact is not very large on prediction ability of network reality for it, and its impact can also compensate by the optimization at network weight and RBF center.The center of RBF neural network can learn to obtain by the nearest neighbor classifier method.
The detailed process of nearest neighbor classifier method is as follows:
At first select a suitable Gaussian function width r, define a vector A (l) and be used for depositing the output vector sum that belongs to all kinds of, define a counter B (l) and belong to all kinds of number of samples for statistics, wherein l is the classification number.From first data to (X
1, Y
1) beginning, set up a cluster centre on X1, make C
1=X
1, A (1)=Y
1, B (1)=1.The RBF network of setting up like this only has an implicit unit, and the center of this implicit unit is C
1, this implicit unit is W to the weight vector of output layer
1=A (1)/B (1).Consider that second sampled data is to (X
2, Y
2), obtain X
2To C
1The distance of this cluster centre || X
2-C
2||.If || X
2-C
2||≤r, C
1Be X
2The most contiguous cluster.Make A (1)=Y
1+ Y
2, B (1)=2, W
1=A (1)/B (1); If || X
2-C
2||＞r, with X
2As a new cluster centre.Make C
2=X
2, A (2)=Y
2, B (2)=1 is adding a hidden unit in the RBF of above-mentioned foundation neural network, and this hidden unit is W to the weight vector of output layer
2=A (2)/B (2).Suppose that we consider that k sampled data is to (X
k, Y
k) (k=3,4 ..., in the time of N), there be H cluster centre, its central point is respectively C
1, C
2..., C
H, existing H hidden unit in the RBF of above-mentioned foundation network.Obtain respectively again X
kDistance to this H cluster centre || X
k-C
i||, i=1,2 ..., H, establish || X
k-C
j|| be the minor increment in these distances, i.e. C
jBe X
kNearest neighbor classifier: if || X
k-C
j||＞r, with X
kAs a new cluster centre.C (H+1)=X
k, A (H+1)=Y
k, B (H+1)=1, and keep A (i), the value of B (i) is constant, i=1, and 2 ..., H adds H+1 hidden unit again in the RBF of above-mentioned foundation network, and this hidden unit is W to the weight vector of output layer
H+1=A (H+1)/B (H+1).If || X
k-C
j||≤r, order: A (j)=A (j)+Y
k, B (j)=B (j)+1.When i ≠ j, i=1,2 ..., H keeps A (i), and the value of B (i) is constant.Hidden unit is W to the weight vector of output layer
i=A (i)/B (i), i=1,2 ..., H.
The RBF neural network of setting up like this is output as:
Wherein, f (X
k) be required-silicone content in molten iron during next blast furnace casting.
4) input variable after the normalization method in the collection unit sampling period, be input in the RBF neural network, and forecast obtains the silicone content Si (i+1) that next time taps a blast furnace blast-melted.The blast-melted silicone content that once taps a blast furnace before Si (i-1) representative, Si (i) represents the current blast-melted silicone content that once taps a blast furnace:
The 1:Si if satisfy condition (i-1)≤a1, Si (i)＜a2, Si (i+1)＜a3
: Index1=1
If do not satisfy condition 1, and the 2:Si that satisfies condition (i)≤a4, Si (i+1)≤a5
: Index1=0.5
If namely do not satisfy condition 1, do not satisfy condition 2 again
: Index1=0
Wherein, the threshold value of Si when once tapping a blast furnace before the a1 representative, the threshold value 1 of Si when a2 represents current tapping a blast furnace, the threshold value 1 of Si when the a3 representative is tapped a blast furnace next time, the threshold value 2 of Si when a4 represents current tapping a blast furnace, the threshold value 2 of Si when the a5 representative is tapped a blast furnace next time, a1＜a2＜a3;
The result of index Index1 is directly connected to final to the excessively cool judgement of blast furnace hearth.
2, high furnace charge speed computation model calculation result and blast furnace hearth are crossed cool judgement scheme
The input variable of high furnace charge speed computation model comprises the degree of depth that the stock rod of blast furnace descends, and output is this model to the excessively cool judgement of blast furnace hearth.According to the principle of blast furnace blanking and blast furnace hearth temperature variation relation, the algorithm of model is as follows: extract eigenwert from identification stock rod shape, stock rod degree of depth time series data, do discharging chi translational speed, the acceleration abnormal working of a furnace of judgement and estimate the blanking state.Eigenwert is the data that represent the stock rod shape facility, identification different time sequence data.At first calculate the data such as momentary velocity, top speed, minimum velocity, speed variation, transient acceleration, peak acceleration, minimum acceleration and acceleration bias, data all derive from the stock rod depth data of collecting take Δ T as the cycle, then, data value and lowering speed are pressed the different stock rod degree of depth-time series array data.
v
max＝max(v
i(j))
v
min＝min(v
i(j))
a
max＝max(a
i(j))
a
min＝min(a
i(j))
In formula: i=1,2,3; J=1,2,3...n.V, v
max, v
min, v
Aver, v
stdRepresent respectively blanking velocity, speed maximum value, speed minimum value, speed average, velocity standard deviation; A, a
max, a
min, a
Aver, a
stdRepresent respectively blanking acceleration, acceleration maximum value, acceleration minimum value, accelerate mean value, acceleration standard deviation, wherein speed variation and acceleration bias are got the value of one-period.
1:a (j)＞(b1+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.3
2:a (j)＞(b2+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.5
3:a (j)＞(b3+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.7
If above three conditions do not satisfy,
: Index2=0
Wherein: a (j) represents stock rod at j blanking acceleration constantly, and b1, b2, b3 represent respectively threshold value 1, threshold value 2, the threshold value 3 of blanking acceleration, a
stdRepresent the acceleration bias data, j=1,2,3...n.
The result of index Index2 is directly connected to final to the excessively cool judgement of blast furnace hearth.
3, the judgement scheme that theoretical combustion temperature computation model calculation result and blast furnace hearth are excessively cool
Carbon element (comprising other combustiblesubstance) the air port zonal combustion be containing the hot blast of hygroscopic water in incomplete combustion, products of combustion is CO, H2 and N2, theoretical tuyere combustion temperature is that the heat that measurement burning heat release and air blast are brought into is all passed to the level that coal gas can reach, and the hot state of theoretical combustion temperature and blast furnace is closely related.Theoretical combustion temperature is the transient function under the various operating parameters effects of blast furnace, lifetime hysteresis quality not, it can be used as a kind of reference of grasping at any time heat state of blast furnace, but be subjected to various blast furnace effect of parameters comparatively frequent just because of its variation, therefore the index of heat state of blast furnace is weighed in the conduct that theoretical combustion temperature can not be single, needs to coordinate other means jointly to obtain the hot state of blast furnace.According to blast furnace theoretical combustion temperature calculation formula:
Wherein: Q
CBurn before the air port for carbon element and produce CO liberated heat, KJ/t;
Q
FBe the physical thermal that air blast and coal powder injection carrier gas are brought into, KJ/t;
Q
RThe sensible heat of bringing into when entering zone of combustion for coke, KJ/t;
Q
XBe minute heat of desorption of moisture decomposition and fuel injection in air blast, KJ/t;
V
g,
For burning production coal gas volume and at T
LSpecific heat capacity during temperature, m
3/ t and KJ/ (m
3. ℃).
If satisfy condition: c1＜T
L≤ c2
: Index3=0.3
If satisfy condition: T
L≤ c1
: Index3=0.5
Other situation: Index3=0
Wherein, c1 and c2 be threshold value 1 and the threshold value 2 of representation theory temperature of combustion respectively, and the result of index Index3 is directly connected to final to the excessively cool judgement of blast furnace hearth.
4, judge that comprehensively blast furnace hearth is excessively cool
Can be obtained respectively separately the excessively cool judgement of blast furnace hearth by three computation models such as RBF calculating model of neural networks, high furnace charge speed computation model, theoretical combustion temperature computation model, be respectively Index1, Index2, Index3.Therefore can be defined as follows rule:
If: δ 1Index1+ δ 2Index2+ δ 3Index3 〉=1, wherein δ 1 ∈ [0,1], δ 2 ∈ [0,1], δ 3 ∈ [0,1]
: blast furnace hearth is excessively cool
Enforcement of the present invention can judge early that blast furnace hearth is excessively cool, the blast furnace temperature of can taking measures to improve accordingly, control blast furnace temperature in rational scope, avoided the different operating personnel to deviation or erroneous judgement that the operation of blast furnace situation produces, efficient blast furnace operating is highly profitable.
Description of drawings
Fig. 1 is that blast furnace hearth is crossed cool judgment models schematic diagram;
Fig. 2 is RBF neural network training schema in the present invention.
Embodiment
Below in conjunction with specific embodiment, the excessively cool method of judgement blast furnace hearth of the present invention is further described:
The purpose of this invention is to provide a kind of method that judges that early blast furnace hearth is excessively cool.Realizing that purpose of the present invention designs three technical schemes altogether, is respectively that Si content in blast-melted and blast furnace blast furnace hearth excessively cool judgement scheme, high furnace charge speed computation model calculation result and blast furnace hearth are crossed the excessively cool judgement scheme of cool judgement scheme, theoretical combustion temperature computation model calculation result and blast furnace hearth.According to the Computing Principle of scheme, as shown in Figure 1, need altogether three computation models, be respectively RBF calculating model of neural networks, high furnace charge speed computation model, theoretical combustion temperature computation model.The high low degree of the RBF calculating model of neural networks can calculate the blast furnace next one when tapping a blast furnace blast-melted middle silicone content, thus draw this model to the too low judgement of blast furnace blast furnace hearth temperature.High furnace charge speed computation model can draw according to the decline situation of high furnace charge chi this model, and to judge the blast furnace hearth temperature too low.The theoretical combustion temperature computation model can obtain the physical thermal that burning production heat and fuel brings into according to products of combustion burning and draw this model to the excessively cool judgement of blast furnace hearth.Because three models consider to cause the too low reason of blast furnace hearth temperature from physics heat transfer, chemistry heat transfer, historical data equal angles respectively, substantially all of blast furnace hearth furnace temperature factors that affects have been included, therefore the judged result of comprehensive three models, just can judge more accurately that the blast furnace hearth furnace temperature is excessively cool.
1, the judgement scheme that the Si content in blast-melted and blast furnace hearth are excessively cool:
Utilize this nonlinear approximation capability of RBF to forecast the height of the blast-melted middle silicone content of next smelting cycle.Comprise following steps:
1) choose the input variable of RBF calculating model of neural networks.Because the parameter that relates in the blast furnace actually operating is numerous, therefore for the structure of simplifying model, improve arithmetic speed and the generalization ability of model, the input variable of determining after deleting comprises that cold flow, hot blast temperature, blast, pressure reduction, ventilation property, top are pressed, the Si content-Si (i-1) when top temperature, hour coal powder blowing amount, front tap a blast furnace for twice in molten iron, Si (i).
2) input variable is implemented to carry out normalized.Determine to adopt Mean Method to process input variable:
Wherein:
Be the data after normalization method,
X is input variable, X
minBe the minimum value of input variable, X
maxMaximum value for input variable.Cold flow, hot blast temperature, blast, pressure reduction, ventilation property, top are pressed, the Si content-Si (i-1) when top temperature, hour coal powder blowing amount, front tap a blast furnace for twice in molten iron, the input such as Si (i) data are through after normalized, its numerical value can all fall between [0,1].
3) determine the RBF neural network structure.Because the sample size that network training needs is many, select altogether 300 stove data in this example, wherein 200 blast furnace data are used for network structure is trained, 100 stove data are used for network is verified, sampled data is input variable and the output variable after normalization method, represent with vector (X, Y):
(X, Y)=[cold flow hot blast temperature blast pressure reduction ventilation property is pushed up temperature hour coal powder blowing amount Si (i-1) Si (i) Si (i+1) that bears down on one]
Use the detailed process of nearest neighbor classifier method Training RBF Neural Network as shown in Figure 2:
At first Gaussian function width r is 0.06, defines a vector A (l) and is used for depositing the output vector sum that belongs to all kinds of, defines a counter B (l) and belongs to all kinds of number of samples for statistics, and wherein l is the classification number.From first data to (X
1, Y
1) beginning, set up a cluster centre on X1, make C
1=X
1, A (1)=Y
1, B (1)=1.The RBF network of setting up like this only has an implicit unit, and the center of this implicit unit is C
1, this implicit unit is W to the weight vector of output layer
1=A (1)/B (1).Consider that second sampled data is to (X
2, Y
2), obtain X
2To C
1The distance of this cluster centre || X
2-C
1||.If || X
2-C
1||≤r, C
1Be X
2The most contiguous cluster.Make A (1)=Y
1+ Y
2, B (1)=2, W
1=A (1)/B (1); If || X
2-C
2||＞r, with X
2As a new cluster centre.Make C
2=X
2, A (2)=Y
2, B (2)=1 is adding a hidden unit in the RBF of above-mentioned foundation neural network, and this hidden unit is W to the weight vector of output layer
2=A (2)/B (2).Suppose that we consider that k sampled data is to (X
k, Yk) (k=3,4 ..., in the time of N), there be H cluster centre, its central point is respectively C
1, C
2..., C
H, existing H hidden unit in the RBF of above-mentioned foundation network.Obtain respectively again X
kDistance to this H cluster centre || X
k-C
i||, i=1,2 ..., H, establish || X
k-C
j|| be the minor increment in these distances, i.e. C
jBe X
kNearest neighbor classifier: if || X
k-C
j||＞r, with X
kAs a new cluster centre.C (H+1)=X
k, A (H+1)=Y
k, B (H+1)=1, and keep A (i), the value of B (i) is constant, i=1, and 2 ..., H adds H+1 hidden unit again in the RBF of above-mentioned foundation network, and this hidden unit is W to the weight vector of output layer
H+1=A (H+1)/B (H+1).If || X
k-C
j||≤r, order: A (j)=A (j)+Y
k, B (j)=B (j)+1.When i ≠ j, i=1,2 ..., H keeps A (i), and the value of B (i) is constant.Hidden unit is W to the weight vector of output layer
i=A (i)/B (i), i=1,2 ..., H.
The RBF neural network of setting up like this is output as:
Wherein, f (X
k) be required-silicone content in molten iron during blast furnace casting next time.
In the present embodiment, the neural network structure after training is that the input layer number is 10, and the middle layer neuron number is 14, and the output layer neuron number is 1.
4) the silicone content Si (i+1) that next time taps a blast furnace blast-melted that obtains according to the RBF neural network prediction.Simultaneously draw the RBF neural network to the excessively cool judgement of blast furnace blast furnace hearth according to following rule:
The 1:Si if satisfy condition (i-1)≤a1, Si (i)＜a2, Si (i+1)＜a3
: Index1=1
If do not satisfy condition 1, and the 2:Si that satisfies condition (i)≤a4, Si (i+1)≤a5
: Index1=0.5
If namely do not satisfy condition 1, do not satisfy condition 2 again
: Index1=0
In this example, a1=0.2, a2=0.25, a3=0.3, a4=0.2, a5=0.2.
2, the excessively cool judgement scheme of high furnace charge speed computation model calculation result and blast furnace hearth
According to the principle of blast furnace blanking and blast furnace hearth temperature relation, the algorithm of model is as follows: extract eigenwert from identification stock rod shape, stock rod degree of depth time series data, do discharging chi translational speed, the acceleration abnormal working of a furnace of judgement and estimate the blanking state.Eigenwert is the data that represent the stock rod shape facility, identification different time sequence data.At first calculate the data such as momentary velocity, top speed, minimum velocity, speed variation, transient acceleration, peak acceleration, minimum acceleration and acceleration bias, data all derive from the stock rod depth data of collecting take Δ T as the cycle, then, data value and lowering speed are pressed the different stock rod degree of depth-time series array data.
Suppose that blast furnace has 3 stock rods, the degree of depth that the definition stock rod descends is respectively l
1, l
2, l
3, in unit period, the variation of 3 stock rod descending depths is respectively Δ l
1(j), Δ l
2(j), Δ l
3(j), a computation period is Δ T, and the blast furnace hearth temperature is too low judgment result is that Index2.Can calculate the speed V that in the unit time, stock rod descends
i(j) and acceleration alpha
i(j).
v
max＝max(v
i(j))
v
min＝min(v
i(j))
a
max＝max(a
i(j))
a
min＝min(a
i(j))
In formula: i=1,2,3; J=1,2,3...n.V, v
max, v
min, v
Aver, v
stdRepresent respectively blanking velocity, speed maximum value, speed minimum value, velocity standard deviation; A, a
max, a
min, a
Aver, a
stdRepresent respectively blanking acceleration, acceleration maximum value, acceleration minimum value, acceleration standard deviation.Wherein speed variation and acceleration bias are got one-period.
According to the impact of blast furnace blanking velocity on heat state of blast furnace:
1:a (j)＞(b1+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.3
2:a (j)＞(b2+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.5
3:a (j)＞(b3+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.7
If above three conditions do not satisfy,
: Index2=0
Wherein: a (j) represents stock rod at j blanking acceleration constantly, and b1, b2, b3 represent respectively threshold value 1, threshold value 2, the threshold value 3 of blanking acceleration, a
stdRepresent the acceleration bias data, j=1,2,3...n.
In this example, b1=0.01, b2=0.02, b3=0.03 both can obtain high furnace charge speed computation model to the excessively cool judgement of blast furnace blast furnace hearth by above calculating.
3, theoretical combustion temperature computation model calculation result and blast furnace blast furnace hearth are crossed cool judgement scheme
According to blast furnace theoretical combustion temperature calculation formula:
Wherein: Q
CBurn before the air port for carbon element and produce CO liberated heat, KJ/t;
Q
FBe the physical thermal that air blast and coal powder injection carrier gas are brought into, KJ/t;
Q
RThe sensible heat of bringing into when entering zone of combustion for coke, KJ/t;
Q
XBe minute heat of desorption of moisture decomposition and fuel injection in air blast, KJ/t;
V
g,
For burning production coal gas volume and at T
LSpecific heat capacity during temperature, m
3/ t and KJ/ (m
3. ℃).
If satisfy condition: c1＜T
L≤ c2
: Index3=0.3
If satisfy condition: T
L≤ c1
: Index3=0.5
Other situation: Index3=0
Wherein, c1 and c2 be threshold value 1 and the threshold value 2 of representation theory temperature of combustion respectively, in this example, and c1=1950, c2=2100, the result of index Index3 is directly connected to final to the excessively cool judgement of blast furnace hearth.
4, judge that comprehensively blast furnace hearth is excessively cool
Can be obtained respectively separately the excessively cool judgement of blast furnace hearth by three computation models such as RBF calculating model of neural networks, high furnace charge speed computation model, theoretical combustion temperature computation model, be respectively Index1, Index2, Index3.
If: δ 1Index1+ δ 2Index2+ δ 3Index3 〉=1, wherein δ 1 ∈ [0,1], δ 2 ∈ [0,1], δ 3 ∈ [0,1]
: blast furnace hearth is excessively cool
In the blast furnace actual production, the burden structure of blast furnace, operating duty etc. all can change, so the judgement no less important of the self-teaching capability for correcting of model and model.So degree of agreement of the meeting periodical survey judged result of the computation model in the present invention and actual result.If computation model is after having moved for some time, when its judgement precision can not satisfy the blast furnace Production requirement, the RBF calculating model of neural networks can be upgraded and revise network structure from new collection sample, forms new RBF calculating model of neural networks.The theoretical combustion temperature computation model also can recomputate to satisfy the blast furnace operating mode according to the situation of blast furnace crude fuel.The model that uses in present method has adaptability and robustness preferably, can satisfy the requirement that now large blast furnace is produced.
Claims (4)
1. method that judges that blast furnace hearth is excessively cool, it is characterized in that setting up respectively RBF calculating model of neural networks, high furnace charge speed computation model, the theoretical combustion temperature computation model of the Si content in blast-melted, then judgement-Index1, the Index2, the Index3 that utilize 3 models to cross cool result to blast furnace hearth judge comprehensively whether blast furnace hearth is excessively cool, if δ 1Index1+ δ 2Index2+ δ were 3Index3 〉=1, δ 1 ∈ [0 wherein, 1], δ 2 ∈ [0,1], δ 3 ∈ [0,1], can judge that blast furnace hearth is excessively cool.
2. a kind of method that judges that blast furnace hearth is excessively cool according to claim 1 is characterized in that the excessively cool judgement scheme of Si content in blast-melted and blast furnace hearth comprises the following steps:
1) choose the input variable of RBF calculating model of neural networks;
2) input variable is implemented to carry out normalized;
3) determine the RBF neural network structure, mainly comprise the determining of number, the neuronic number of output layer of number, the hidden layer neuron of input layer, also comprise the choosing of the RBF center of using that the nearest neighbor classifier method determines, RBF center width;
4) input variable after the normalization method in the collection unit sampling period, be input in the RBF neural network, forecast obtains the silicone content Si (i+1) that next time taps a blast furnace blast-melted, the blast-melted silicone content that once taps a blast furnace before Si (i-1) representative, Si (i) represents the current blast-melted silicone content that once taps a blast furnace:
The 1:Si if satisfy condition (i-1)≤a1, Si (i)＜a2, Si (i+1)＜a3
: Index1=1
If do not satisfy condition 1, and the 2:Si that satisfies condition (i)≤a4, Si (i+1)≤a5
: Index1=0.5
If namely do not satisfy condition 1, do not satisfy condition 2 again
: Index1=0
Wherein, the threshold value of Si when once tapping a blast furnace before the a1 representative, the threshold value 1 of Si when a2 represents current tapping a blast furnace, the threshold value 1 of Si when the a3 representative is tapped a blast furnace next time, the threshold value 2 of Si when a4 represents current tapping a blast furnace, the threshold value 2 of Si when the a5 representative is tapped a blast furnace next time, a1＜a2＜a3;
The result of index Index1 is directly connected to final to the excessively cool judgement of blast furnace hearth.
3. a kind of method that judges that blast furnace hearth is excessively cool according to claim 1, the input variable of the high furnace charge speed computation model that it is characterized in that setting up comprises the degree of depth that stock rod descends, output is this model to the excessively cool judgement of blast furnace hearth, according to the blast furnace blanking velocity, the impact of the hot cupola well state of blast furnace is come index access Index2, the result of Index2 is directly connected to final to the excessively cool judgement of blast furnace hearth, according to the impact of blast furnace blanking velocity on the hot state of blast furnace hearth:
1:a (j)＞(b1+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.3
2:a (j)＞(b2+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.5
3:a (j)＞(b3+a if each stock rod of blast furnace all satisfies condition
std)
: Index2=0.7
If above three conditions do not satisfy,
: Index2=0
Wherein: a (j) represents stock rod at j blanking acceleration constantly, and b1, b2, b3 represent respectively threshold value 1, threshold value 2, the threshold value 3 of blanking acceleration, a
stdRepresent the acceleration bias data, j=1,2,3...n;
The result of index Index2 is directly connected to final to the excessively cool judgement of blast furnace hearth.
4. a kind of method that judges that blast furnace hearth is excessively cool according to claim 1, it is characterized in that the theoretical combustion temperature computation model of setting up is based on that the hot state relation degree of cupola well of theoretical combustion temperature and blast furnace designs, according to blast furnace theoretical combustion temperature calculation formula:
Wherein: T
LThe representation theory temperature of combustion, ℃;
Q
CBurn before the air port for carbon element and produce CO liberated heat, KJ/t;
Q
FBe the physical thermal that air blast and coal powder injection carrier gas are brought into, KJ/t;
Q
RThe sensible heat of bringing into when entering zone of combustion for coke, KJ/t;
Q
XBe minute heat of desorption of moisture decomposition and fuel injection in air blast, KJ/t;
V
g,
For burning production coal gas volume and at T
LSpecific heat capacity during temperature, m
3/ t and KJ/ (m
3. ℃);
If satisfy condition: c1＜T
L≤ c2
: Index3=0.3
If satisfy condition: T
L≤ c1
: Index3=0.5
Other situation: Index3=0
Wherein, c1 and c2 be threshold value 1 and the threshold value 2 of representation theory temperature of combustion respectively, and the result of index Index3 is directly connected to final to the excessively cool judgement of blast furnace hearth.
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