CN103878186A - Method for determining hot rolled strip steel laminar cooling temperature - Google Patents

Abstract

The invention discloses a method for determining a hot rolled strip steel laminar cooling temperature and belongs to the field of steel metallurgy. The method includes the following steps that multiple data in a laminar cooling process are collected to establish a database, and a laminar cooling temperature mixed model is used for calculating the coiling temperature in the laminar cooling process. According to the method for determining the hot rolled strip steel laminar cooling temperature, a temperature value which cannot be collected in practice can be predicted, and the method can be combined with practical production data to accurately calculate the coiling temperature of a steel plate. Meanwhile, parameters can be continuously corrected through self-learning according to aging of a production line, equipment updating and the like, and errors in calculating the laminar cooling temperature can be reduced to a low level. According to the method for determining the hot rolled strip steel laminar cooling temperature, production data can be quite high in precision and consistent with the production line, and a good guiding function can be achieved in the laminar cooling production process.

Description

A kind of method of definite TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature

Technical field

The invention belongs to Ferrous Metallurgy field, more specifically, relate to a kind of method of definite TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature.

Background technology

Steel and iron industry is to support the mainstay industry of the national economic development, and modern steel industrial expansion level is the important embodiment of a national technological progress and overall national strength.TEMPERATURE FOR HOT STRIP LAMINAR is cooling is a kind of process of utilizing water-cooled to lower the temperature to high-temperature steel plate, these variations of variation that steel plate crystalline solid can occur in this process can directly affect the performance of steel plate, so the accurate control of steel plate laminar cooling process temperature is played a very important role for the production of high-quality steel plate.Its performance not only depends on hot rolling technology, is more decided by rolling control cooling technology afterwards.Within can hot coiling temperature be controlled at claimed range, depend primarily on the control to hot strip steel cooling system after finishing mill.

TEMPERATURE FOR HOT STRIP LAMINAR cooling procedure is that the system being intercoupled by a complex nonlinear, Multiinputoutput, each variable completes, and relates to belt steel temperature, strip speed, belt steel thickness, spray flow, many-sided problem such as institutional framework and phase transformation with steel inside.How to control the production process of complexity like this well, and the belt steel product of accurately producing on request different size, different performance is the problem that iron and steel production field studies for a long period of time.Wherein, Erecting and improving accurately temperature model be to realize the good key of controlling.

Existing laminar flow chilling temperature calculating method mainly contains two kinds, and the one, taking mechanism model as main, identification model is auxiliary, utilizes the mechanism formula in steel plate laminar cooling process to set up, and then uses the time-varying parameter picking out to calculate; The 2nd, taking Experimental Identification model as main, mechanism model is auxiliary, result of calculation by measured value and mechanism model is forecast coiling temperature as the input/output argument of Experimental Identification model, realizes the coiling temperature prediction based on a large amount of historical datas and current produced on-site measured data.

But, be that mechanism model or identification model all have merits and demerits separately.Mechanism model more approaches the theoretical value of industrial process, can calculate the physical quantity that cannot measure in more reality, utilizes Analysis on Mechanism can more fully understand whole system; But under existence conditions, mechanism model can not comprise all physics chemistry variation occurring in reality completely, and the phenomenon that certainly has actual generation is present stage the unknown, and the mechanism model of building so just exists leak and is difficult to make up.Identification model is to rely on data-driven, only sets up the relation of input and output, and the data that obtain more approach actual conditions and also have more ageing; But identification model can not detect and calculate the data that do not detect in production process.

Summary of the invention

For the deficiencies in the prior art, the invention provides a kind of method of definite TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature, its object is to calculate accurately the curling temperature of steel plate, can, according to phenomenons such as the renewals of the aging and equipment of production line by the continuous corrected parameter of self study, there is good directive function to the cooling production process of laminar flow simultaneously.

A kind of method that the invention provides definite TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature, comprising:

Step 1 gathers the multiple data building databases in laminar cooling process, and wherein, described multiple data comprise steel plate micronutrient levels, steel plate initial temperature T ₀, steel plate thickness, steel plate width, steel plate set curling temperature T ' _cwith jet density q _w;

Step 2 utilizes laminar flow chilling temperature mixed model to calculate the curling temperature of described laminar cooling process, and wherein, described laminar flow chilling temperature mixed model comprises:

Mechanism model, calculate the calorie value of air cooling heat exchange and the calorie value of heat-radiation heat-dissipating according to steel plate input quantity, and the total amount of heat calculating in laminar cooling process in conjunction with the calorie value of water-cooled heat exchange is scattered and disappeared, thereby calculate the curling temperature of described laminar cooling process, wherein, described steel plate input quantity comprises described steel plate micronutrient levels, described steel plate initial temperature T ₀, described steel plate thickness, described steel plate width and described steel plate set curling temperature T ' _c, the calorie value of described water-cooled heat exchange is according to water-cooled coefficient of heat transfer α _wcalculate;

TS fuzzy model, utilizes fuzzy rule, according to the membership function u of input _i, consequent parameter Θ and input matrix calculate corresponding described water-cooled coefficient of heat transfer α _w, and by the described water-cooled coefficient of heat transfer α calculating _wbe input to the calculating of carrying out the calorie value of described water-cooled heat exchange in described mechanism model, wherein said input matrix is x (k)=[q _wt ₀t ' _c], by described jet density q _w, described steel plate initial temperature T ₀with described steel plate set curling temperature T ' _ccomposition; And

Self learning model selects a up-to-date N data to carry out C clustering processing from described database when each run, wherein, 10000≤N≤40000, calculate the described membership function u of a described up-to-date N data _iand described consequent parameter Θ, and by the described membership function u calculating _iand described consequent parameter Θ inputs to described TS fuzzy model

Because the time-varying parameter in laminar cooling process, temperature being had the greatest impact is the water-cooled coefficient of heat transfer, so set up the TS fuzzy model taking the water-cooled coefficient of heat transfer as output.Utilize former piece and the consequent parameter of the algorithm identification TS fuzzy model based on C cluster; Utilize the actual production parameter in laminar cooling process to carry out identification to TS fuzzy model, wherein actual production parameter comprises jet density q _w, steel plate initial temperature T ₀, steel plate set curling temperature T ' _c.In the time that TS fuzzy model moves, utilize fuzzy rule, calculate corresponding water-cooled coefficient of heat transfer α according to the input quantity being input in TS fuzzy model _w.

Further, described step 2 specifically comprises following sub-step:

(2.1) from described database, selected a described up-to-date N data to carry out C clustering processing by described self learning model when each run, wherein, 10000≤N≤40000, calculate the described membership function u of a described up-to-date N data _iand described consequent parameter Θ, and by the described membership function u calculating _iand described consequent parameter Θ inputs to described TS fuzzy model;

(2.2) described TS fuzzy model is according to described membership function u _i, described consequent parameter Θ and described input matrix, calculate corresponding described water-cooled coefficient of heat transfer α _w, and by the described water-cooled coefficient of heat transfer α calculating _wbe input to described mechanism model;

(2.3) described mechanism model is according to described water-cooled coefficient of heat transfer α _wcalculate the calorie value of described water-cooled heat exchange;

(2.4) described mechanism model calculates the calorie value of described air cooling heat exchange and the calorie value of described heat-radiation heat-dissipating according to described steel plate input quantity, and the described total amount of heat calculating in described laminar cooling process in conjunction with the calorie value of described water-cooled heat exchange is scattered and disappeared;

(2.5) described mechanism model scatters and disappears and calculates the described curling temperature of described laminar cooling process according to described total amount of heat.

Further, TS fuzzy model calculates water-cooled coefficient of heat transfer α _wstep comprise following sub-step:

(2.2.1) according to the described membership function u calculating _iset up i bar TS fuzzy rule, wherein, i article of TS fuzzy rule is expressed as:

$\begin{array}{c}{R}^i:\mathrm{If}{x}_1\left(k\right)\mathrm{is}{A}_1^i\mathrm{and}{x}_2\left(k\right)\mathrm{is}{A}_2^i\mathrm{and}...\mathrm{and}{x}_n\left(k\right)\mathrm{is}{A}_n^i\\ \mathrm{Then}{y}^i(k+1)=p_0^i+p_1^i{x}_1\left(k\right)+...+p_n^i{x}_n\left(k\right);\end{array}i=\mathrm{1,2},...c$

Wherein, c is fuzzy rule sum; N is the input variable number of described TS fuzzy model; x ₁(k), x ₂(k) ..., x _n(k) be the input data in k moment; X (k)=[x ₁(k), x ₂(k) ..., x _n(k)] be the described input matrix of described TS fuzzy model;

for representing the linear membership function u of having of each fuzzy subspace _ifuzzy set, be used for carrying out the fuzzy reasoning of described i article of fuzzy rule;

for the consequent parameter of described i article of fuzzy rule; y ⁱ(k+1) be the output of described TS fuzzy model;

(2.2.2) definition β _ifor the fitness of described i article of fuzzy rule, have:

${\mathrm{\β}}_i=\underset{j=1}{\overset{c}{\mathrm{\Σ}}}\left(\frac{u_i}{u_j}\right),i=\mathrm{1,2},...,c$

Described TS fuzzy model in the computing formula of (k+1) inferior output y (k+1) is:

$\begin{array}{c}y(k+1)=\underset{i=1}{\overset{c}{\mathrm{\Σ}}}{\mathrm{\β}}_i\·y^i(k+1)=\underset{i=1}{\overset{c}{\mathrm{\Σ}}}{\mathrm{\β}}_i\·(p_0^i+p_1^i{x}_1\left(k\right)+...+p_n^i{x}_n\left(k\right))\\ =\underset{i=1}{\overset{c}{\mathrm{\Σ}}}(p_0^i+p_1^i+...+p_n^i)({\mathrm{\β}}_i+{\mathrm{\β}}_i{x}_1\left(k\right)+...+{\mathrm{\β}}_i{x}_n\left(k\right){)}^T\end{array}$

Definition consequent parameter Θ (k) and former piece parameter Φ (k) are:

$\left\{\begin{array}{c}\mathrm{\Θ}\left(k\right)={[{\mathrm{\θ}}_1,{\mathrm{\θ}}_2,...,{\mathrm{\θ}}_r]}^T={[p_{10},p_{20},...,p_{c0},p_{11},p_{12},...,p_{c1},...,p_{\mathrm{cn}}]}^T;\\ \mathrm{\Φ}\left(k\right)={[{\mathrm{\β}}_1,...,{\mathrm{\β}}_c,{\mathrm{\β}}_1{x}_1\left(k\right),...,{\mathrm{\β}}_c{x}_1\left(k\right),...,{\mathrm{\β}}_1{x}_n\left(k\right),...,{\mathrm{\β}}_c{x}_n\left(k\right)]}^T;\end{array}\right.$

Wherein, r=c (n+1), can obtain:

y(k+1)＝Φ(k) ^T·Θ(k)；

(2.2.3) define described output y (k+1)=a _w(k), calculate by the formula in step (2.2.2) the described water-cooled coefficient of heat transfer α that described input matrix x (k) is corresponding _w(k).

Radiating mode in laminar cooling process mainly comprises: heat-radiation heat-dissipating and heat convection, and the computational methods of every kind of radiating mode are as follows:

(1) heat radiation

High temperature hot-rolling stock unit are and unit interval heat radiation energy are followed Stefen-Boltzman law, and in the unit interval, the thermal-radiating heat of steel plate is described below:

${\mathrm{dQ}}_R=A_r\·\mathrm{\ϵ}\·\mathrm{\σ}[{\left(\frac{T_s+273}{100}\right)}^4-{\left(\frac{T_a+273}{100}\right)}^4]\mathrm{d\τ}$

Wherein, A _rfor surface of steel plate area, unit is m ²; DQ _rthe thermal-radiating heat of steel plate in the representation unit time, unit is J/s; ε is the blackness with steel, and its value is 0～1, when belt steel surface oxide skin is more, gets 0.8, and surface smoothing gets 0.55～0.65; σ is heat emissivity coefficient, σ=5.67W/ (m ²k ⁴); T _sfor belt steel surface temperature, unit is DEG C; T _afor environment temperature, unit is DEG C.

(2) heat convection

Heat convection refers to when fluid (comprising gas and liquid) is flowed through solid, the heat transport phenomenon between fluid and the surface of solids.In laminar cooling process, the water-cooling of steel plate and cross-ventilation heat radiation are all the forms of heat convection, and the two all can be described with Newtonian Cooling formula, and determines by the different coefficients of heat transfer.In unit interval, the thermal change of heat convection comprises water-cooled heat exchange and air cooling heat exchange two aspects, and wherein, the calorie value of the air cooling heat exchange in the unit interval is:

dQ _ca＝-F·α _a·(T _s-T _w)·dτ

The calorie value of the water-cooled heat exchange in the unit interval is:

dQ _cw＝-F·α _w·(T _s-T _w)·dτ

Wherein, F is steel plate and cooling water contact surface area, and unit is m ²; T _sfor belt steel temperature, unit is DEG C; T _wfor cooling water temperature, unit is DEG C; D τ is the cooling time of contact, and unit is h; α _afor the air cooling coefficient of heat transfer, value is 20; α _wfor the water-cooled coefficient of heat transfer, calculate by TS fuzzy model.

The formula that mechanism model calculates the curling temperature of laminar cooling process is:

T _c＝-ΔQ/cm-T ₀

Wherein, T _crepresent the curling temperature of steel plate; Δ Q represents that the total amount of heat in laminar cooling process is lost, is drawn by calorie value, the calorie value of air cooling heat exchange and the calorie value of water-cooled heat exchange of heat-radiation heat-dissipating; C represents the specific heat of steel plate; M represents the quality of steel plate; T ₀represent steel plate initial temperature.

Beneficial effect of the present invention is embodied in: the theoretical value that can more approach industrial process, can calculate the physical quantity that cannot measure in more reality, utilize Analysis on Mechanism more fully to understand whole system, can make again to export data and actual production has uniformity, and will be in reality occur and theoretical unknown composition has covered in the water-cooled coefficient of heat transfer and goes, make the water-cooled coefficient of heat transfer there is the function of the theoretical error of making up.The present invention can also constantly follow the tracks of production line by Data Update and parameter identification, makes to export data and is consistent with production line all the time.

Generally speaking, the overall process that the present invention not only can obtain the dynamic temperature of laminar flow cooling metal sheets changes, and can make creation data have very high precision and the uniformity with production line, and the cooling production process of laminar flow is had to good guiding value.

Brief description of the drawings

Fig. 1 is the schematic diagram of laminar flow chilling temperature mixed model of the present invention;

Fig. 2 is the method flow diagram that the present invention determines TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature.

Detailed description of the invention

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.In addition,, in each embodiment of described the present invention, involved technical characterictic just can combine mutually as long as do not form each other conflict.

Figure 1 shows that the schematic diagram of laminar flow chilling temperature mixed model of the present invention, specifically comprise three parts:

(1) mechanism model

Utilize quality and the conservation of energy in TEMPERATURE FOR HOT STRIP LAMINAR cooling procedure to build, also comprise and utilize the functional relation of experience to represent the time-varying parameter in laminar cooling process, wherein the conservation of energy mainly comprises: the parts such as heat-radiation heat-dissipating, cross-ventilation heat radiation and water-cooled heat exchange.Wherein, the water-cooled coefficient of heat transfer α that the calorie value of calculating water-cooled heat exchange needs _wcalculate and provide by TS fuzzy model.

(2) TS fuzzy model

Because the time-varying parameter in laminar cooling process, temperature being had the greatest impact is the water-cooled coefficient of heat transfer, so set up the TS fuzzy model taking the water-cooled coefficient of heat transfer as output.Utilize former piece and the consequent parameter of the algorithm identification TS fuzzy model based on C cluster; Utilize the actual production parameter in laminar cooling process, i.e. jet density q _w, steel plate initial temperature T ₀with steel plate set curling temperature T ' _c, carry out identification to set up TS fuzzy model.In the time that TS fuzzy model moves, utilize fuzzy rule to calculate corresponding water-cooled coefficient of heat transfer α according to the input quantity being input in this TS fuzzy model _w, and result of calculation is input to the calculating of carrying out the calorie value of water-cooled heat exchange in mechanism model.Wherein, calculate water-cooled coefficient of heat transfer α _wkey parameter, i.e. membership function u _icalculate and provide by self learning model with consequent parameter Θ.

(3) self learning model:

Self learning model comprises database and parameter identification device.Database is for storing the data of the cooling production process of laminar flow, i.e. steel plate micronutrient levels, steel plate initial temperature T ₀, steel plate thickness, steel plate width, steel plate set curling temperature T ' _cwith jet density q _w, and constantly gather new data and replace the oldest data, in database, preserve 20000 up-to-date data.Parameter identification device is, by the algorithm of C cluster, the data in database are carried out to cluster analysis, calculates the membership function u of these data _iand consequent parameter Θ, and the result at every turn calculating is input in TS fuzzy model.

After production process, self learning model imports to the creation data newly gathering in the cooling database of laminar flow each time, utilizes up-to-date data to calculate former piece and the consequent parameter of water-cooled coefficient of heat transfer TS model, and TS fuzzy model and actual production line are kept consistency.

Figure 2 shows that the present invention determines the method flow diagram of TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature.

Gather the multiple data in described laminar cooling process, i.e. steel plate micronutrient levels, steel plate initial temperature T ₀, steel plate thickness, steel plate width, steel plate set curling temperature T ' _cwith jet density q _w, building database; Operation self learning model carries out identification technique to 20000 data up-to-date in database, obtains TS fuzzy model and calculates two required important parameters, i.e. membership function u _iwith consequent parameter Θ, and these two important parameters are input in TS fuzzy model; TS fuzzy model is according to the input message of steel plate, i.e. jet density q _w, steel plate initial temperature T ₀with steel plate set curling temperature T ' _c, calculate the water-cooled coefficient of heat transfer α under this condition _w, and by water-cooled coefficient of heat transfer a _wbe input in mechanism model; Mechanism model is according to water-cooled coefficient of heat transfer α _wcalculate the calorie value of water-cooled heat exchange; Mechanism model is also according to steel plate input quantity, and steel plate micronutrient levels, steel plate initial temperature, steel plate thickness, steel plate width and steel plate are set curling temperature, calculates the calorie value of air cooling heat exchange and the calorie value of heat-radiation heat-dissipating; The total amount of heat that mechanism model obtains laminar cooling process in conjunction with calorie value, the calorie value of heat-radiation heat-dissipating and the calorie value of water-cooled heat exchange of the air cooling heat exchange calculating is scattered and disappeared, thereby calculates the cooling curling temperature of laminar flow.

TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature mixed model provided by the invention, the advantage of identification model and mechanism model is combined and proposes the mixed model that a kind of mechanism model combines with identification model, both can calculate the physical quantity that cannot measure in the cooling production process of laminar flow, also can make the physical and chemical process that comprises present stage the unknown in model output data.And this model has self-learning capability, can regulate model parameter according to the variation of production line, make the calculating output of model and the output of production line have good uniformity.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (5)

1. a method for definite TEMPERATURE FOR HOT STRIP LAMINAR chilling temperature, comprising:

Step 1 gathers the multiple data building databases in laminar cooling process, and wherein, described multiple data comprise steel plate micronutrient levels, steel plate initial temperature T ₀, steel plate thickness, steel plate width, steel plate set curling temperature T ' _cwith jet density q _w;

Step 2 utilizes laminar flow chilling temperature mixed model to calculate the curling temperature of described laminar cooling process, and wherein, described laminar flow chilling temperature mixed model comprises:

Mechanism model, calculate the calorie value of air cooling heat exchange and the calorie value of heat-radiation heat-dissipating according to steel plate input quantity, and the total amount of heat calculating in laminar cooling process in conjunction with the calorie value of water-cooled heat exchange is scattered and disappeared, thereby calculate the curling temperature of described laminar cooling process, wherein, described steel plate input quantity comprises described steel plate micronutrient levels, described steel plate initial temperature T ₀, described steel plate thickness, described steel plate width and described steel plate set curling temperature T ' _c, the calorie value of described water-cooled heat exchange is according to water-cooled coefficient of heat transfer α _wcalculate;

TS fuzzy model, utilizes fuzzy rule, according to the membership function u of input _i, consequent parameter Θ and input matrix calculate corresponding described water-cooled coefficient of heat transfer α _w, and by the described water-cooled coefficient of heat transfer α calculating _wbe input to the calculating of carrying out the calorie value of described water-cooled heat exchange in described mechanism model, wherein said input matrix is x (k)=[q _wt ₀t ' _c], by described jet density q _w, described steel plate initial temperature T ₀with described steel plate set curling temperature T ' _ccomposition; And

Self learning model selects a up-to-date N data to carry out C clustering processing from described database when each run, wherein, 10000≤N≤40000, calculate the described membership function u of a described up-to-date N data _iand described consequent parameter Θ, and by the described membership function u calculating _iand described consequent parameter Θ inputs to described TS fuzzy model.

2. the method for claim 1, is characterized in that, described step 2 specifically comprises following sub-step:

(2.1) from described database, selected a described up-to-date N data to carry out C clustering processing by described self learning model when each run, wherein, 10000≤N≤40000, calculate the described membership function u of a described up-to-date N data _iand described consequent parameter Θ, and by the described membership function u calculating _iand described consequent parameter Θ inputs to described TS fuzzy model;

(2.2) described TS fuzzy model is according to described membership function u _i, described consequent parameter Θ and described input matrix, calculate corresponding described water-cooled coefficient of heat transfer α _w, and by the described water-cooled coefficient of heat transfer α calculating _wbe input to described mechanism model;

(2.3) described mechanism model is according to described water-cooled coefficient of heat transfer α _wcalculate the calorie value of described water-cooled heat exchange;

(2.4) described mechanism model calculates the calorie value of described air cooling heat exchange and the calorie value of described heat-radiation heat-dissipating according to described steel plate input quantity, and the described total amount of heat calculating in described laminar cooling process in conjunction with the calorie value of described water-cooled heat exchange is scattered and disappeared;

(2.5) described mechanism model scatters and disappears and calculates the described curling temperature of described laminar cooling process according to described total amount of heat.

3. method as claimed in claim 1 or 2, is characterized in that, described TS fuzzy model calculates described water-cooled coefficient of heat transfer α _wstep comprise following sub-step:

(2.2.1) according to the described membership function u calculating _iset up i bar TS fuzzy rule, wherein, i article of TS fuzzy rule is expressed as:

$\begin{array}{c}{R}^i:\mathrm{If}{x}_1\left(k\right)\mathrm{is}{A}_1^i\mathrm{and}{x}_2\left(k\right)\mathrm{is}{A}_2^i\mathrm{and}...\mathrm{and}{x}_n\left(k\right)\mathrm{is}{A}_n^i\\ \mathrm{Then}{y}^i(k+1)=p_0^i+p_1^i{x}_1\left(k\right)+...+p_n^i{x}_n\left(k\right);\end{array}i=\mathrm{1,2},...c$

Wherein, c is fuzzy rule sum; N is the input variable number of described TS fuzzy model; x ₁(k), x ₂(k) ..., x _n(k) be the input data in k moment; X (k)=[x ₁(k), x ₂(k) ..., x _n(k)] be the described input matrix of described TS fuzzy model;

for representing the linear membership function u of having of each fuzzy subspace _ifuzzy set, be used for carrying out the fuzzy reasoning of described i article of fuzzy rule;

for the consequent parameter of described i article of fuzzy rule; y ⁱ(k+1) be the output of described TS fuzzy model;

(2.2.2) definition β _ifor the fitness of described i article of fuzzy rule, have:

${\mathrm{\β}}_i=\underset{j=1}{\overset{c}{\mathrm{\Σ}}}\left(\frac{u_i}{u_j}\right),i=\mathrm{1,2},...,c$

Described TS fuzzy model in the computing formula of (k+1) inferior output y (k+1) is:

$\begin{array}{c}y(k+1)=\underset{i=1}{\overset{c}{\mathrm{\Σ}}}{\mathrm{\β}}_i\·y^i(k+1)=\underset{i=1}{\overset{c}{\mathrm{\Σ}}}{\mathrm{\β}}_i\·(p_0^i+p_1^i{x}_1\left(k\right)+...+p_n^i{x}_n\left(k\right))\\ =\underset{i=1}{\overset{c}{\mathrm{\Σ}}}(p_0^i+p_1^i+...+p_n^i)({\mathrm{\β}}_i+{\mathrm{\β}}_i{x}_1\left(k\right)+...+{\mathrm{\β}}_i{x}_n\left(k\right){)}^T\end{array}$

Definition consequent parameter Θ (k) and former piece parameter Φ (k) are:

$\left\{\begin{array}{c}\mathrm{\Θ}\left(k\right)={[{\mathrm{\θ}}_1,{\mathrm{\θ}}_2,...,{\mathrm{\θ}}_r]}^T={[p_{10},p_{20},...,p_{c0},p_{11},p_{12},...,p_{c1},...,p_{\mathrm{cn}}]}^T;\\ \mathrm{\Φ}\left(k\right)={[{\mathrm{\β}}_1,...,{\mathrm{\β}}_c,{\mathrm{\β}}_1{x}_1\left(k\right),...,{\mathrm{\β}}_c{x}_1\left(k\right),...,{\mathrm{\β}}_1{x}_n\left(k\right),...,{\mathrm{\β}}_c{x}_n\left(k\right)]}^T;\end{array}\right.$

Wherein, r=c (n+1), can obtain:

y(k+1)＝Φ(k) ^T·Θ(k)；

(2.2.3) define described output y (k+1)=a _w(k), calculate by the formula in step (2.2.2) the described water-cooled coefficient of heat transfer α that described input matrix x (k) is corresponding _w(k).

4. method as claimed in claim 1 or 2, is characterized in that, the formula that described mechanism model calculates the calorie value of described heat-radiation heat-dissipating is:

${\mathrm{dQ}}_R=A_r\·\mathrm{\ϵ}\·\mathrm{\σ}[{\left(\frac{T_s+273}{100}\right)}^4-{\left(\frac{T_a+273}{100}\right)}^4]\mathrm{d\τ}$

Wherein, dQ _rthe thermal-radiating heat of steel plate in the representation unit time, unit is J/s; A _rfor surface of steel plate area, unit is m ²; ε is the blackness with steel, and its value is 0～1, when belt steel surface oxide skin is more, gets 0.8, and surface smoothing gets 0.55～0.65; σ is heat emissivity coefficient, σ=5.67W/ (m ²k ⁴); T _sfor belt steel surface temperature, unit is DEG C; T _afor environment temperature, unit is DEG C;

The formula of the calorie value of the air cooling heat radiation in the described mechanism model unit of account time is:

dQ _ca＝-F·α _a·(T _s-T _w)·dτ

The formula of the calorie value of the water-cooled heat exchange in the described mechanism model unit of account time is:

dQ _cw＝-F·α _w·(T _s-T _w)·dτ

Wherein, F is steel plate and cooling water contact surface area, and unit is m ²; T _sfor belt steel surface temperature, unit is DEG C; T _wfor cooling water temperature, unit is DEG C; D τ is the cooling time of contact, and unit is h; α _afor the air cooling coefficient of heat transfer, value is 20; α _wfor the described water-cooled coefficient of heat transfer, calculate by described TS fuzzy model;

The formula that described mechanism model calculates the described curling temperature of described laminar cooling process is:

T _c＝-ΔQ/cm-T ₀

Wherein, T _crepresent the curling temperature of steel plate; Δ Q represents that the total amount of heat in laminar cooling process is lost; C represents the specific heat of steel plate; M represents the quality of steel plate; T ₀represent steel plate initial temperature.

5. the method for claim 1, it is characterized in that, former piece parameter and the consequent parameter of TS fuzzy model described in the algorithm identification of utilization based on C cluster, utilize the actual production parameter in described laminar cooling process to carry out identification to set up described TS fuzzy model, wherein said actual production parameter comprises described jet density q _w, described steel plate initial temperature T ₀with described steel plate set curling temperature T '.

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