CN107944600B - Method for forecasting thickness of oil film on surface of strip steel before rolling in direct injection system of secondary cold rolling unit - Google Patents

Abstract

A method for forecasting the thickness of an oil film on the surface of a strip steel before rolling in a direct injection system of a secondary cold rolling unit comprises the following steps executed by a computer: (A) constructing a film thickness forecasting model of the upper and lower surfaces of the strip steel before rolling in a secondary cold rolling direct injection system; (B) calculating the optimal value of each influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling by adopting a Powell optimization algorithm; (C) will be lambda_zy、λ_ry、λ_ty、δ_qsy、δ_tsy、δ_qxy、δ_txySubstituting the forecasting model constructed in the step (A) to obtain an optimal forecasting model; (D) collecting preset lubricating process parameters of the strip steel to be produced of the secondary cold rolling unit; (E) forecasting the thickness xi of the oil films on the upper surface and the lower surface of the strip steel before rolling of the secondary cold rolling direct injection system according to the model in the step (C)_s、ξ_x. The invention can predict the flow density of different emulsions, the concentration of the emulsions, the separation distance of the emulsions and the thickness of oil films on the upper and lower surfaces of the strip steel before rolling at the rolling speed of the inlet of the rolling mill, and effectively control and improve the lubricating performance of a direct injection system of a secondary cold rolling mill set.

Description

Method for forecasting thickness of oil film on surface of strip steel before rolling in direct injection system of secondary cold rolling unit

Technical Field

The invention belongs to the technical field of cold rolling, and particularly relates to a method for forecasting the thickness of an oil film on the surface of a strip steel before rolling.

Background

In recent years, with the continuous expansion of the market of the packaging and can-making industry, the tin-plated and chromium-plated plate strip industry is rapidly developed. Meanwhile, in order to save material consumption cost and reduce environmental pollution, packaging can manufacturing is continuously developed towards the direction of thinning and weight reduction, the demand of thin-specification and high-strength strip steel is continuously increased, and the development of secondary cold-rolled products is greatly promoted. The secondary cold rolling refers to that the primary cold-rolled strip steel is subjected to annealing treatment and then is subjected to reduction and thinning again, compared with the traditional primary cold-rolled product, the secondary cold-rolled product has the advantages of thinner thickness, higher strength and better processing performance, the material consumption is effectively reduced under the condition of ensuring the performance requirement of the tank body, and the secondary cold-rolled strip steel can better adapt to the development trend of the packaging and tank making industry. Because the secondary cold-rolled product is thin in thickness and high in strength, an emulsion direct injection system is adopted for rolling lubrication.

In the production process of a secondary cold rolling product, before strip steel enters a rolling roll gap, the emulsion sprayed on the surface of the strip steel separates out a lubricating oil film with a certain thickness on the surface of the strip steel and enters the roll gap along with the strip steel for lubrication, and the thickness of the oil film separated out on the surface of the strip steel by the emulsion is an important influence factor of the lubricating performance of a secondary cold rolling unit and is a basis for realizing the rolling lubrication of the secondary cold rolling unit. In the past, researchers mainly aim at a cold rolling emulsion circulation system in the research of a rolling lubrication process, the research on the oil film thickness of a direct injection system is still in a laboratory measurement stage, and a quantitative expression model of the oil film thickness precipitated on the surface of a strip steel before rolling of a secondary cold rolling direct injection system is not formed. Therefore, how to fully combine the equipment and process characteristics of a direct injection system of a secondary cold rolling unit and establish a set of method for forecasting the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold rolling unit, and the method has important significance for controlling and improving the rolling lubricating performance of the secondary cold rolling unit.

Disclosure of Invention

The invention aims to provide a method for forecasting the thickness of an oil film on the surface of a strip steel before rolling by a direct injection system of a secondary cold rolling unit, which can forecast the thickness of the oil films on the upper surface and the lower surface of the strip steel before rolling at the rolling speed of a rolling mill inlet and at different emulsion flow densities, emulsion concentrations, emulsion precipitation distances and different emulsion precipitation distances.

The invention includes the following computer-implemented steps:

(A) constructing a film thickness forecasting model of the upper and lower surfaces of the strip steel before rolling in a secondary cold rolling direct injection system:

in the formula, xi_sThe thickness of an oil film on the upper surface of the strip steel before rolling of a direct injection system of a secondary cold rolling unit; xi_xThe thickness of an oil film on the lower surface of the strip steel before rolling of a direct injection system of the secondary cold rolling unit is determined; q is the emulsion flow density; c is the concentration of the emulsion; l is the emulsion separation distance; v is the rolling speed of the inlet of the rolling mill; lambda [ alpha ]_zThe impact influence coefficient is the precipitation rate; lambda [ alpha ]_rThe precipitation rate is the wettability influence coefficient; lambda [ alpha ]_tThe time influence coefficient of the precipitation rate is used; delta_qsThe influence coefficient of the residual rate and the flow of the upper surface of the strip steel is shown; delta_tsThe influence coefficient of the residual rate time of the upper surface of the strip steel is shown; delta_qxThe influence coefficient of the residual rate and the flow on the lower surface of the strip steel is shown; delta_txIs the influence coefficient of the residual rate time of the lower surface of the strip steel.

(B) Calculating the impact influence coefficient lambda of the precipitation rate by adopting Powell optimization algorithm_zAnd precipitation rate wettability influence coefficient lambda_rTime influence coefficient lambda of precipitation rate_tThe influence coefficient delta of the residual rate and the flow of the upper surface of the strip steel_qsInfluence coefficient delta of time of residual rate of upper surface of strip steel_tsInfluence coefficient delta of residual rate and flow on lower surface of strip steel_qxInfluence coefficient delta of residual rate and time of lower surface of strip steel_txThe optimal value of (2) specifically comprises the following steps:

B1) the method for collecting the rolling technological parameters of N groups of strip steels produced on the site of the secondary cold rolling unit and the corresponding thickness of the oil film on the surface of the strip steel before rolling comprises the following steps: flow density q of emulsion_iEmulsion concentration C_iThe separation distance L of the emulsion_iRolling speed V at inlet of rolling mill_iAnd the actually measured value xi of the oil film thickness on the upper surface of the strip steel before rolling_smiAnd the actually measured value xi of the oil film thickness of the lower surface of the strip steel before rolling_xmi(ii) a Wherein i is the serial number of the strip steel groups, i is 1,2, L and N;

B2) defining the influence coefficient array X of the thickness of the oil film on the surface of the strip steel before rolling as { lambda ═ lambda_z,λ_r,λ_t,δ_qs,δ_ts,δ_qx,δ_txGiving an initial value X of an array of influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling₀＝{λ_z0,λ_r0,λ_t0,δ_qs0,δ_ts0,δ_qx0,δ_tx0The initial value of search step Δ X ═ Δ λ_z,Δλ_r,Δλ_t,Δδ_qs,Δδ_ts,Δδ_qx,Δδ_txThe convergence precision epsilon;

B3) calculating the oil film thickness xi of the upper and lower surfaces of the produced N groups of strip steel before rolling according to the model constructed in the step (A)_si、ξ_xi：

B4) Calculating an optimized objective function G (X) of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling:

in the formula (I), the compound is shown in the specification,

the thickness weight coefficient of the oil film on the upper surface of the strip steel before rolling,

is the weight coefficient of the oil film thickness on the lower surface of the strip steel before rolling,

B5) determine whether Powell conditions hold? If yes, go to step B6); if not, updating the array X and the search step length delta X thereof, and turning to the step B3);

B6) outputting the optimal value of the pre-rolling strip steel surface oil film thickness influence coefficient array corresponding to the minimum value of the optimization objective functionX_y＝{λ_zy,λ_ry,λ_ty,δ_qsy,δ_tsy,δ_qxy,δ_txy}。

(C) The optimal value lambda of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling_zy、λ_ry、λ_ty、δ_qsy、δ_tsy、δ_qxy、δ_txySubstituting the forecasting model constructed in the step (A) to obtain an optimal forecasting model of the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold rolling direct injection system:

(D) the method for collecting preset lubricating process parameters of the strip steel to be produced of the secondary cold rolling unit comprises the following steps: the flow density q of the emulsion, the concentration C of the emulsion, the separation distance L of the emulsion and the rolling speed V of the inlet of the rolling mill.

(E) Forecasting the thickness xi of the oil films on the upper surface and the lower surface of the strip steel before rolling of the secondary cold rolling direct injection system according to the model in the step (C)_s、ξ_x。

Compared with the prior art, the invention has the following advantages:

the method can predict the flow density of different emulsions, the concentration of the emulsions, the separation distance of the emulsions and the thickness of oil films on the upper surface and the lower surface of the strip steel before rolling at the rolling speed of the inlet of the rolling mill, effectively control and improve the lubricating performance of a direct injection system of a secondary cold rolling unit, reduce the production cost and improve the production efficiency.

Drawings

FIG. 1 is a total calculation flow diagram of the present invention;

FIG. 2 is a flow chart of step (B) of the present invention.

Detailed Description

Example 1:

taking a certain secondary cold rolling unit as an example, according to the total calculation flow chart of the method for forecasting the thickness of the oil film on the surface of the strip steel before rolling of the direct injection system of the secondary cold rolling unit shown in fig. 1:

firstly, in step (A), constructing a prediction model of the thicknesses of the oil films on the upper surface and the lower surface of the strip steel before rolling in a secondary cold rolling direct injection system:

subsequently, as shown in fig. 2, in step (B), a segregation rate impact influence coefficient λ is calculated using a Powell optimization algorithm_zAnd precipitation rate wettability influence coefficient lambda_rTime influence coefficient lambda of precipitation rate_tThe influence coefficient delta of the residual rate and the flow of the upper surface of the strip steel_qsInfluence coefficient delta of time of residual rate of upper surface of strip steel_tsInfluence coefficient delta of residual rate and flow on lower surface of strip steel_qxInfluence coefficient delta of residual rate and time of lower surface of strip steel_txThe optimal value of (2) specifically comprises the following steps:

firstly, in step B1), collecting rolling process parameters of 10 groups of strip steel produced on the site of the secondary cold rolling mill group and corresponding thickness of the surface oil film of the strip steel before rolling, comprising: flow density q of emulsion_i1, {9.5, 8.0, 10.3, 12.5, 12.2, 11.0, 8.4, 9.9, 9.5, 8.9}, with the unit of L/min/m; concentration of emulsion C_i{ 4.5%, 5.0%, 4.8%, 6.3%, 9.3%, 7.2%, 9.5%, 8.8%, 4.3%, 7.5% }; separation distance L of emulsion_i0.5,0.5, 0.6, 0.8, 0.6, 1.0,1.0, 1.0,0.5, 0.8} in m; rolling speed V at inlet of rolling mill_i-560,440,580,520,650,460,820,780,740,800 in m/min; actually measured value xi of oil film thickness on upper surface of band steel before rolling_smi0.172,0.213,0.199,0.376,0.395,0.480,0.252,0.295,0.121,0.205} in μm; actually measured value xi of oil film thickness of lower surface of band steel before rolling_xmi0.158,0.193,0.175,0.323,0.355,0.411,0.238,0.265,0.116,0.191, in μm; wherein i is the serial number of the strip steel groups, i is 1,2, L and 10;

subsequently, in step B2), an array X ═ λ { λ } of the influence coefficient of the thickness of the oil film on the surface of the strip before rolling is defined_z,λ_r,λ_t,δ_qs,δ_ts,δ_qx,δ_txGiving an initial value X of an array of influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling₀＝{0.1,0.1,0.1,0.1,0.1,0.1,0.1The initial value Δ X of the search step is {0.1,0.1,0.1,0.1,0.1,0.1,0.1}, and the convergence accuracy ∈ is 0.001;

subsequently, in step B3), the oil film thickness ξ of the upper and lower surfaces of the strip before rolling of the produced 10 groups of strips was calculated in accordance with the model constructed in step (A)_si、ξ_xi：

Subsequently, in step B4), the film thickness weight coefficient of the upper surface of the strip before rolling is selected

Weight coefficient of oil film thickness on lower surface of strip steel before rolling

Calculating an optimized objective function G (X) of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling:

subsequently, in step B5), it is determined that the Powell condition is satisfied, and the process proceeds to step B6);

subsequently, in step B6), outputting the optimal value X of the array of the influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling corresponding to the minimum value of the optimization objective function_y＝{0.205,0.689,119.4,10.31,29.36,14.84,45.65}。

Subsequently, in step (C), the optimum value λ of the influence coefficient of the thickness of the oil film on the surface of the strip before rolling is set_zy、λ_ry、λ_ty、δ_qsy、δ_tsy、δ_qxy、δ_txySubstituting the forecasting model constructed in the step (A) to obtain an optimal forecasting model of the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold rolling direct injection system:

subsequently, in step (D), collecting preset lubrication process parameters of the strip steel to be produced in the secondary cold rolling mill, including: the emulsion flow density q was 10.5L/min/m, the emulsion concentration C was 9.0%, the emulsion separation distance L was 1.0m, and the rolling speed V at the mill entrance was 780 m/min.

Subsequently, in step (E), the thickness ξ of the oil film on the upper and lower surfaces of the strip before rolling in the secondary cold rolling direct injection system is predicted according to the model in step (C)_s＝0.314μm、ξ_x＝0.290μm。

As shown in Table 1, the forecasting precision of the method for forecasting the thickness of the oil film on the surface of the strip steel before rolling by adopting the secondary cold-rolling direct-injection system reaches more than 90 percent, and the method can meet the requirement of forecasting the thickness of the oil film on the surface of the strip steel before rolling by adopting the secondary cold-rolling direct-injection system.

TABLE 1 comparison of prediction and actual measurement of the thickness of the oil film on the upper and lower surfaces of the strip before rolling in example 1

	Thickness of oil film on upper surface of strip steel before rolling	Thickness of oil film on lower surface of strip steel before rolling
			Forecast value	0.314μm	0.290μm
Measured value	0.293μm	0.272μm
			Error of the measurement	7.17％	6.62％

Example 2:

taking a certain secondary cold rolling mill group as an example, firstly, in step (a), a model for predicting the thickness of oil films on the upper and lower surfaces of a strip steel before rolling in a secondary cold rolling direct injection system is constructed:

subsequently, as shown in fig. 2, in step (B), a segregation rate impact influence coefficient λ is calculated using a Powell optimization algorithm_zAnd precipitation rate wettability influence coefficient lambda_rTime influence coefficient lambda of precipitation rate_tThe influence coefficient delta of the residual rate and the flow of the upper surface of the strip steel_qsInfluence coefficient delta of time of residual rate of upper surface of strip steel_tsInfluence coefficient delta of residual rate and flow on lower surface of strip steel_qxInfluence coefficient delta of residual rate and time of lower surface of strip steel_txThe optimal value of (2) specifically comprises the following steps:

firstly, in step B1), the rolling process parameters of 12 groups of strip steel produced on the site of the secondary cold rolling mill train and the corresponding thickness of the surface oil film of the strip steel before rolling are collected, including: flow density q of emulsion_i1, {7.2, 7.0, 8.3, 8.5,11.2,13.1, 9.7, 8.9, 9.2, 7.8,10.4,10.9}, in L/min/m; concentration of emulsion C_i(vii) { 7.3%, 5.9%, 8.8%, 6.3%, 7.3%, 9.2%, 9.5%, 8.8%, 5.3%, 7.5%, 9.9%, 7.7% }; separation distance L of emulsion_i0.6, 0.6,0.5, 0.5,0.8,0.8, 0.6,0.5,1.0, 1.0,1.0,0.8} in m; rolling speed V at inlet of rolling mill_i-760,840,480,590,850,660,830,750,710,600,920,890 in m/min; actually measured value xi of oil film thickness on upper surface of band steel before rolling_smi0.165,0.115,0.359,0.206,0.228,0.435,0.251,0.236,0.185,0.275,0.280,0.224, { unit μm; actually measured value xi of oil film thickness of lower surface of band steel before rolling_xmi＝{0.151,0.108,0.322,0.191,0.215,0.387,0.236,0.216,0.165,0.250,0.262,0.207}, in μm; wherein i is the serial number of the strip steel groups, i is 1,2, L and 12;

subsequently, in step B2), an array X ═ λ { λ } of the influence coefficient of the thickness of the oil film on the surface of the strip before rolling is defined_z,λ_r,λ_t,δ_qs,δ_ts,δ_qx,δ_txGiving an initial value X of an array of influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling₀0.1,0.1,0.1,0.1,0.1,0.1,0.1}, an initial search step size Δ X of {0.1,0.1,0.1,0.1,0.1,0.1,0.1}, and a convergence accuracy ∈ of 0.001;

subsequently, in step B3), the oil film thickness ξ of the upper and lower surfaces of the strip before rolling of the produced 12 groups of strips was calculated in accordance with the model constructed in step (A)_si、ξ_xi：

Subsequently, in step B4), the film thickness weight coefficient of the upper surface of the strip before rolling is selected

Weight coefficient of oil film thickness on lower surface of strip steel before rolling

Calculating an optimized objective function G (X) of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling:

subsequently, in step B5), it is determined that the Powell condition is satisfied, and the process proceeds to step B6);

subsequently, in step B6), outputting the optimal value X of the array of the influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling corresponding to the minimum value of the optimization objective function_y＝{0.212,0.693,125.4,9.81,30.13,15.44,43.95}。

Subsequently, in step (C), the optimum value λ of the influence coefficient of the thickness of the oil film on the surface of the strip before rolling is set_zy、λ_ry、λ_ty、δ_qsy、δ_tsy、δ_qxy、δ_txySubstituting the forecasting model constructed in the step (A) to obtain an optimal forecasting model of the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold rolling direct injection system:

subsequently, in step (D), collecting preset lubrication process parameters of the strip steel to be produced in the secondary cold rolling mill, including: the flow density q of the emulsion was 12.5L/min/m, the concentration C of the emulsion was 5.8%, the precipitation distance L of the emulsion was 0.5m, and the rolling speed V at the inlet of the mill was 640 m/min.

Subsequently, in step (E), the thickness ξ of the oil film on the upper and lower surfaces of the strip before rolling in the secondary cold rolling direct injection system is predicted according to the model in step (C)_s＝0.249μm、ξ_x＝0.220μm。

As shown in Table 2, the forecasting precision of the method for forecasting the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold-rolling direct-injection system reaches more than 90%, and the method can meet the requirement of forecasting the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold-rolling direct-injection system.

TABLE 2 comparison of prediction and actual measurement of thickness of oil film on upper and lower surfaces of strip steel before rolling in example 2

	Thickness of oil film on upper surface of strip steel before rolling	Thickness of oil film on lower surface of strip steel before rolling
			Forecast value	0.249μm	0.220μm
Measured value	0.235μm	0.204μm
			Error of the measurement	5.96％	7.84％

Claims (2)

1. A method for forecasting the thickness of an oil film on the surface of a strip steel before rolling in a direct injection system of a secondary cold rolling unit is characterized by comprising the following steps: it includes the following steps executed by the computer:

(A) constructing a film thickness forecasting model of the upper and lower surfaces of the strip steel before rolling in a secondary cold rolling direct injection system:

in the formula, xi_sThe thickness of an oil film on the upper surface of the strip steel before rolling of a direct injection system of a secondary cold rolling unit; xi_xThe thickness of an oil film on the lower surface of the strip steel before rolling of a direct injection system of the secondary cold rolling unit is determined; q is the emulsion flow density; c, emulsion concentration; l is the emulsion separation distance; v is the rolling speed of the inlet of the rolling mill; lambda [ alpha ]_zThe impact influence coefficient is the precipitation rate; lambda [ alpha ]_rThe precipitation rate is the wettability influence coefficient; lambda [ alpha ]_tThe time influence coefficient of the precipitation rate is used; delta_qsThe influence coefficient of the residual rate and the flow of the upper surface of the strip steel is shown; delta_tsThe influence coefficient of the residual rate time of the upper surface of the strip steel is shown; delta_qxThe influence coefficient of the residual rate and the flow on the lower surface of the strip steel is shown; delta_txThe influence coefficient of the residual rate time of the lower surface of the strip steel is shown;

(B) calculating the impact influence coefficient lambda of the precipitation rate by adopting Powell optimization algorithm_zAnd precipitation rate wettability influence coefficient lambda_rTime influence coefficient lambda of precipitation rate_tThe influence coefficient delta of the residual rate and the flow of the upper surface of the strip steel_qsInfluence coefficient delta of time of residual rate of upper surface of strip steel_tsInfluence coefficient delta of residual rate and flow on lower surface of strip steel_qxInfluence coefficient delta of residual rate and time of lower surface of strip steel_txThe optimal value of (2) specifically comprises the following steps:

B1) the method for collecting the rolling technological parameters of N groups of strip steels produced on the site of the secondary cold rolling unit and the corresponding thickness of the oil film on the surface of the strip steel before rolling comprises the following steps: flow density q of emulsion_iEmulsion concentration C_iThe separation distance L of the emulsion_iRolling speed V at inlet of rolling mill_iAnd the actually measured value xi of the oil film thickness on the upper surface of the strip steel before rolling_smiAnd the actually measured value xi of the oil film thickness of the lower surface of the strip steel before rolling_xmi(ii) a Wherein i is the serial number of the strip steel groups, i is 1,2, L and N;

B2) defining the influence coefficient array X of the thickness of the oil film on the surface of the strip steel before rolling as { lambda ═ lambda_z,λ_r,λ_t,δ_qs,δ_ts,δ_qx,δ_txGiving an initial value X of an array of influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling₀＝{λ_z0,λ_r0,λ_t0,δ_qs0,δ_ts0,δ_qx0,δ_tx0The initial value of search step Δ X ═ Δ λ_z,Δλ_r,Δλ_t,Δδ_qs,Δδ_ts,Δδ_qx,Δδ_txThe convergence precision epsilon;

B3) calculating the oil film thickness xi of the upper and lower surfaces of the produced N groups of strip steel before rolling according to the model constructed in the step (A)_si、ξ_xi：

B4) Calculating an optimized objective function G (X) of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling:

in the formula (I), the compound is shown in the specification,

the thickness weight coefficient of the oil film on the upper surface of the strip steel before rolling,

is the weight coefficient of the oil film thickness on the lower surface of the strip steel before rolling,

B5) determine whether Powell conditions hold? If yes, go to step B6); if not, updating the array X and the search step length delta X thereof, and turning to the step B3);

B6) outputting an optimal value X of a pre-rolling strip steel surface oil film thickness influence coefficient array corresponding to the minimum value of an optimization objective function_y＝{λ_zy,λ_ry,λ_ty,δ_qsy,δ_tsy,δ_qxy,δ_txy}；

(C) The optimal value lambda of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling_zy、λ_ry、λ_ty、δ_qsy、δ_tsy、δ_qxy、δ_txySubstituting the forecasting model constructed in the step (A) to obtain an optimal forecasting model of the thickness of the oil film on the surface of the strip steel before rolling of the secondary cold rolling direct injection system:

(D) the method for collecting preset lubricating process parameters of the strip steel to be produced of the secondary cold rolling unit comprises the following steps: the flow density q of the emulsion, the concentration C of the emulsion, the separation distance L of the emulsion and the rolling speed V of the inlet of the rolling mill;

(E) according to step (C)Model prediction secondary cold rolling direct injection system pre-rolling strip steel upper and lower surface oil film thickness xi_s、ξ_x。

2. The method for forecasting the thickness of the oil film on the surface of the pre-rolling strip steel of the direct injection system of the secondary cold rolling mill set according to claim 1, wherein the method comprises the following steps: the step (B) comprises the following steps:

B1) the method for collecting the rolling technological parameters of N groups of strip steels produced on the site of the secondary cold rolling unit and the corresponding thickness of the oil film on the surface of the strip steel before rolling comprises the following steps: flow density q of emulsion_iEmulsion concentration C_iThe separation distance L of the emulsion_iRolling speed V at inlet of rolling mill_iAnd the actually measured value xi of the oil film thickness on the upper surface of the strip steel before rolling_smiAnd the actually measured value xi of the oil film thickness of the lower surface of the strip steel before rolling_xmi(ii) a Wherein i is the serial number of the strip steel groups, i is 1,2, L and N;

B2) defining the influence coefficient array X of the thickness of the oil film on the surface of the strip steel before rolling as { lambda ═ lambda_z,λ_r,λ_t,δ_qs,δ_ts,δ_qx,δ_txGiving an initial value X of an array of influence coefficients of the thickness of the oil film on the surface of the strip steel before rolling₀＝{λ_z0,λ_r0,λ_t0,δ_qs0,δ_ts0,δ_qx0,δ_tx0The initial value of search step Δ X ═ Δ λ_z,Δλ_r,Δλ_t,Δδ_qs,Δδ_ts,Δδ_qx,Δδ_txThe convergence precision epsilon;

B3) calculating the oil film thickness xi of the upper and lower surfaces of the produced N groups of strip steel before rolling according to the model constructed in the step (A)_si、ξ_xi：

B4) Calculating an optimized objective function G (X) of the influence coefficient of the thickness of the oil film on the surface of the strip steel before rolling:

in the formula (I), the compound is shown in the specification,

the thickness weight coefficient of the oil film on the upper surface of the strip steel before rolling,

is the weight coefficient of the oil film thickness on the lower surface of the strip steel before rolling,

B5) determine whether Powell conditions hold? If yes, go to step B6); if not, updating the array X and the search step length delta X thereof, and turning to the step B3);

B6) outputting an optimal value X of a pre-rolling strip steel surface oil film thickness influence coefficient array corresponding to the minimum value of an optimization objective function_y＝{λ_zy,λ_ry,λ_ty,δ_qsy,δ_tsy,δ_qxy,δ_txy}。

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