A heating furnace tapping temperature control method based on energy coupling optimization

Abstract

The application provides a heating furnace tapping temperature control method based on energy coupling optimization, relates to the technical field of heating furnace control and comprises the following steps: S1, collecting historical production parameters of a heating furnace and a rolling mill, performing validity processing on the collected data, and forming a data pool; S2, establishing a first relational equation of heating furnace energy consumption and related parameters and a second relational equation of rolling mill energy consumption and related parameters according to the data pool; S3, obtaining a comprehensive energy consumption value of a rolling process; S4, establishing an optimal tapping temperature model under different production processes; S5, controlling the heating furnace to produce according to the optimal tapping temperature output by the optimal tapping temperature model, and re-inputting actual production parameters in the production process into the data pool. The application can determine reasonable tapping temperature and other key parameters by coupling the production of the heating furnace and the rolling mill in the rolling process, and can reduce the overall energy consumption of the hot rolling process.

Description

Technical Field

[0001] This invention relates to the field of furnace control technology, and more particularly to a furnace tapping temperature control method based on energy coupling optimization. Background Technology

[0002] Hot rolling is a crucial step in the steel production process, accounting for over 10% of the total energy consumption in the steel industry. Within hot rolling, the heating furnace consumes the largest share of energy, reaching approximately 70% of the process's total energy consumption. The remainder comes primarily from the electricity consumption of the rolling mill and auxiliary processes such as heat treatment. The heating furnace heats the steel billet to a suitable rolling temperature, while the rolling mill consumes electricity to roll the heated billet into products of specified specifications. The tapping temperature of the heating furnace is a core parameter connecting the furnace and the rolling mill. This temperature determines both the furnace's fuel consumption and directly affects the rolling mill's electricity consumption and roll consumption. Lowering the tapping temperature helps reduce furnace fuel consumption but increases the rolling mill's load and electricity consumption; conversely, raising the tapping temperature reduces rolling mill electricity consumption but increases furnace energy consumption. These two factors form a mutually restrictive coupling relationship. Finding the optimal tapping temperature that minimizes the sum of furnace and rolling mill energy consumption, while ensuring rolling quality, is an effective way to reduce the overall energy consumption of the hot rolling process.

[0003] Currently, several technical solutions have been proposed for controlling the tapping temperature of steel from the heating furnace. CN201410342440.5 determines the required temperature for each rolling stage of the steel based on the mapping relationship between steel grade and critical temperature, adjusts the parameters of the heating furnace, roughing mill, and finishing mill accordingly, and organizes production after setting the rolling schedule according to the yield strength of the steel. CN202310896900.8 determines the billet tapping temperature of the annular furnace by measuring deformation resistance and reduction of area, comparing continuous rolling load and piercing load, and combining the temperature drop from piercing to continuous rolling and the temperature drop from the annular furnace to piercing, using a step-by-step trial approach. CN202010667225.8 improves the control of furnace tapping temperature by determining the target tapping temperature, in-furnace time, and segmented set temperatures. During slab heating, the segmented set temperatures are adjusted based on the mill tapping speed or the minimum of all target tapping temperatures for slabs. The gas flow rate and air-fuel ratio are adjusted according to the residual oxygen content in each segment. Simultaneously, the upper and lower furnace temperature set values ​​and heat load are adjusted based on the temperature deviation between the upper and lower surfaces when the slab tapps. These existing technologies improve the control of furnace tapping temperature from different perspectives, but their main approach is to determine the tapping temperature based on single-dimensional factors such as steel characteristics, rolling load, or furnace operating conditions.

[0004] However, all of the aforementioned existing technologies share common shortcomings. Firstly, these methods treat the furnace tapping temperature as an independent variable, failing to consider the furnace fuel consumption and mill power consumption as a coupled whole. The determination of the tapping temperature relies solely on constraints from either the furnace or mill side, lacking a systematic optimization of the combined energy consumption of the furnace and mill. While lowering the furnace tapping temperature reduces fuel consumption, it simultaneously increases mill power consumption; ignoring this coupling relationship prevents the determined tapping temperature from achieving the goal of minimizing overall energy consumption. Secondly, existing technologies often rely on empirical trials or comparative experiments to determine temperature parameters. When equipment conditions or process conditions change, the original temperature settings require extensive adjustments, disrupting normal production and potentially causing rolling quality problems due to improper adjustments. Furthermore, existing methods determine a wide range of tapping temperatures with insufficient precision, failing to accommodate the varying temperature requirements of different steel grades and specifications, resulting in high furnace power consumption and significant energy waste. Summary of the Invention

[0005] In view of this, the purpose of this invention is to propose a heating furnace tapping temperature control method based on energy coupling optimization, so as to solve the existing technical problem that does not consider the coupling between heating furnace fuel consumption and rolling mill power consumption.

[0006] The technical means employed in this invention are as follows: A method for controlling the tapping temperature of a heating furnace based on energy coupling optimization includes the following steps: S1. Collect historical production parameters of heating furnace and rolling mill, perform validity processing on the collected data, and form a data pool; S2. Based on the data pool, establish the first relationship equation between the energy consumption of the heating furnace and related parameters, and the second relationship equation between the energy consumption of the rolling mill and related parameters; S3. Based on the first and second relational equations, the energy consumption of the heating furnace process and the energy consumption of the rolling process are summed to obtain the comprehensive energy consumption calculation formula for the steel rolling process. S4. Optimize the coupled production parameters of the heating furnace and rolling mill with the goal of minimizing the comprehensive energy consumption value in the comprehensive energy consumption value calculation formula, and establish the optimal tapping temperature model under different production processes. S5. Control the heating furnace to produce according to the optimal tapping temperature output by the optimal tapping temperature model, re-input the actual production parameters in the production process into the data pool, and use them to update the first relational equation and the second relational equation.

[0007] Furthermore, the historical production parameters of the heating furnace collected in S1 include output, oxidation loss, heat load, steel grade and heating temperature, and the historical production parameters of the rolling mill collected include power consumption, roll consumption and rolling specifications, with a collection cycle of one week or more.

[0008] Furthermore, the first relational equation in S2 is:

[0009] in, b j Energy consumption of the heating furnace; G For the output of the heating furnace; The billet tapping temperature; The billet loading temperature; For steel grade category parameters; For overall thermal efficiency; Furnace time; α Oxidation burn-off rate; a 1 represents the billet temperature regression coefficient; a 2 represents the regression coefficient for steel grade category; a 3 represents the regression coefficient for output; b 1 represents the regression coefficient for overall thermal efficiency; b 2 represents the regression coefficient for furnace time; b 3 represents the no-load heat load of the heating furnace; b 4 represents the heat load regression coefficient; k 1 represents the overall correction factor; k 2 represents the production correction factor; k 3 represents the furnace time correction factor.

[0010] Furthermore, the calculation formula for the steel grade parameter C is as follows:

[0011] Wherein, [C]% is the mass percentage of carbon in the billet, [Mn]% is the mass percentage of manganese in the billet, and [Cr]% is the mass percentage of chromium in the billet.

[0012] Furthermore, the second relational equation in S2 is:

[0013] in, Energy consumption of the rolling mill; For the power consumption of the rolling mill; Consumed by the rolling mill rolls; This is the amount of pressure applied; The target thickness of the billet; For steel grade category parameters; For rolling width; T jZ0 is the initial rolling temperature; Z1 is the total rolling energy consumption correction factor; Z2 is the billet temperature correction factor; Z3 is the mill power consumption correction factor; Z4 is the roll consumption correction factor; Z5 is the rolling thickness correction factor; Z6 is the steel grade correction factor; Z7 is the rolling width correction factor.

[0014] Furthermore, the optimal tapping temperature model in S4 is:

[0015] in, This is the optimal tapping temperature.

[0016] Furthermore, in S4, when optimizing with the goal of minimizing the overall energy consumption value, the optimization range of the steel tapping temperature of the heating furnace is limited by the constraint of satisfying the rolling quality.

[0017] Furthermore, the regression coefficients and correction coefficients of the first and second relational equations in S2 are established by using historical production data in the data pool through big data analysis.

[0018] Furthermore, the regression coefficients are dynamically updated as the actual production parameters re-enter the data pool in S5 increase.

[0019] Furthermore, the data validity processing in S1 includes removing outliers and imputing missing values.

[0020] Compared with the prior art, the present invention has the following advantages: First, this invention breaks through the limitations of traditional methods that treat the furnace tapping temperature as an independent variable. It considers the furnace fuel consumption, mill power consumption, and roll consumption as a coupled whole system. By establishing separate furnace energy consumption equations and mill energy consumption equations and performing weighted summation, the optimal tapping temperature is calculated by reverse calculation with the goal of minimizing overall energy consumption. This overcomes the technical bias of simply reducing furnace energy consumption while ignoring the resulting increase in mill energy consumption in existing technologies, thus ensuring that the determined tapping temperature is optimal at the system level.

[0021] Secondly, this invention employs big data analysis to establish a quantitative relationship equation between energy consumption and production parameters. By using regression coefficients, it integrates multiple dimensions such as steel composition, furnace time, rolling specifications, and reduction into the optimization model, providing a clear calculation path. Compared to existing technologies that rely on empirical trial-and-error to determine temperature parameters, when process conditions change, the optimal tapping temperature can be recalculated simply by inputting the new parameters into the model, eliminating the need for repeated on-site adjustments.

[0022] Third, this invention constructs a complete closed loop of data acquisition, model building, comprehensive optimization, production execution and data feedback. Actual production parameters are continuously fed back to the data pool and the regression equation is dynamically updated, so that the model continuously approaches the real energy consumption characteristics as production data accumulates. This solves the problem in the prior art that the temperature setting system is difficult to adaptively adjust with changes in production line conditions. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] like Figure 1 As shown, this invention provides a method for controlling the tapping temperature of a heating furnace based on energy coupling optimization, comprising the following steps: S1. Collect historical production parameters of the heating furnace and rolling mill, and perform data validation to form a data pool. Historical production parameters of the heating furnace include output, oxidation loss, heat load, steel grade, and heating temperature. Historical production parameters of the rolling mill include power consumption, roll consumption, and rolling specifications. The collection cycle is one week or more. Data validation includes removing outliers and filling in missing values.

[0028] S2. Based on the data pool, establish the first relationship equation between the energy consumption of the heating furnace and related parameters, and the second relationship equation between the energy consumption of the rolling mill and related parameters; The first relational equation is:

[0029] in, b j Energy consumption of the heating furnace, GJ / t; G The output of the heating furnace is expressed in tons (t). The billet tapping temperature is ℃; The billet loading temperature is ℃; Heating parameters for steel type; For overall thermal efficiency, % The furnace time is in hours (h). α Oxidation loss rate, % a 1 represents the billet temperature regression coefficient; a 2 represents the regression coefficient for steel grade category; a 3 represents the regression coefficient for output; b 1 represents the regression coefficient for overall thermal efficiency; b 2 represents the regression coefficient for furnace time; b 3 represents the no-load heat load of the heating furnace; b 4 represents the heat load regression coefficient; k 1 represents the overall correction factor; k 2 represents the production correction factor; k 3 represents the furnace time correction factor.

[0030]

[0031] Wherein, [C]% is the mass percentage of carbon in the billet, [Mn]% is the mass percentage of manganese in the billet, and [Cr]% is the mass percentage of chromium in the billet.

[0032] The second relational equation is:

[0033] in, Energy consumption of the rolling mill, GJ / t; The power consumption of the rolling mill is kWh / t; For consumption of rolling mill rolls, kg / t; The reduction amount is in mm; Tj represents the target thickness of the billet, in mm; Tj represents the initial rolling temperature, in °C. For steel grade category parameters; Z0 is the rolling width in mm; Z1 is the total rolling energy consumption correction factor; Z2 is the billet temperature correction factor; Z3 is the mill power consumption correction factor; Z4 is the roll consumption correction factor; Z5 is the steel grade correction factor; and Z6 is the rolling width correction factor.

[0034] S3. Based on the first and second relational equations, the energy consumption of the heating furnace process and the energy consumption of the rolling process are weighted and calculated to obtain the comprehensive energy consumption value of the steel rolling process. S4. Optimize the coupled production parameters of the heating furnace and rolling mill with the goal of minimizing the overall energy consumption, and establish the optimal tapping temperature model under different production processes. The optimal tapping temperature model is:

[0035] in, This is the optimal tapping temperature.

[0036] S5. Control the heating furnace to produce according to the optimal tapping temperature output by the optimal tapping temperature model, re-input the actual production parameters in the production process into the data pool, and use them to update the first relational equation and the second relational equation.

[0037] Example 1 A factory's thick plate production line uses mixed coal gas as fuel in its heating furnace, with a calorific value of 8778 kJ / Nm³. 3 Heat load 20140 Nm 3 / h, under the condition that the rolled product is Q235B, the average furnace temperature in the soaking section of the original heating furnace is 1210℃, and the average billet tapping temperature is T out =1150℃, heating furnace cold loading, loading temperature is room temperature T in =20℃, average furnace time τ=5h, oxidation loss rate α=0.98%, statistical furnace output G=114t / h, average thermal efficiency η0=52.3%, mill power consumption per ton of steel P=25kWh / t, average reduction Δh=17mm / pass, statistical roll consumption W=0.6kg / t, target rolling thickness h m =15mm, rolling width B=3m, calculated steel grade parameter C=1.2, average initial rolling temperature T jThe regression parameters for the energy consumption formula of the furnace heated to 1000℃ using this method are a1=0.0000045, a2=0.000098, a3=0.00012, b1=0.14, b2=0.000087, b3=98.12, b4=0.000094, k1=0.00011, k2=0.0055, k3=0.0526, c =1.2, z0=0.41, z1=0.12, z2=0.051, z3=0.14, z4=0.11, z5=1.115, z6=0.74, calculate the rolling mill energy consumption bz=0.92GJ / t, the heating furnace energy consumption bj=1.83GJ / t, and the comprehensive energy consumption 2.75GJ / t; based on the regression equation and relevant parameters, calculate the optimal tapping temperature T. out1 =1081℃, adjusting production parameters reduced the comprehensive energy consumption per ton of steel to 2.61GJ / t, with an energy saving rate of 5.01%.

[0038] Example 2 In a certain factory's thin plate production line, the heating furnace uses mixed coal gas as fuel, with a calorific value of 7954 kJ / Nm³. 3 Heat load 15380 Nm 3 / h, under the condition that the rolled product is Q235B, the average furnace temperature in the soaking section of the original heating furnace is 1280℃, and the average billet tapping temperature T out =1220℃, hot charging of the heating furnace, charging temperature T in =500℃, average furnace time τ=3h, oxidation loss rate α=1.0%, statistical furnace output G=92.67t / h, average thermal efficiency η0=32.3%, mill power consumption per ton of steel P=68kWh / t, average reduction Δh=20mm / pass, statistical roll consumption W=0.36kg / t, target rolling thickness h m =5mm, rolling width B=1.58m, calculated steel grade parameter C=1.1, average initial rolling temperature T j The regression parameters for the furnace energy consumption formula using this method at 1150℃ are: a1=0.00000284, a2=0.00018, a3=0.00016, b1=0.12, b2=0.000095, b3=86.33, b4=0.000112, k1=0.0014, k2=0.0032, k3=0.064, c=1.1, z0=0.42, z1=0.11, z2=0.054, z3=0.33, z4=0.22, z5=1.026, z6=1.5. The calculated rolling mill energy consumption is bz=0.90GJ / t, the furnace energy consumption is bj=1.56GJ / t, and the total energy consumption is 2.46GJ / t. Based on the regression equation and related parameters, the optimal tapping temperature T is calculated. out1=1180℃, adjusting production parameters reduces the comprehensive energy consumption per ton of steel to 2.32GJ / t, with an energy saving rate of 5.69%.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the tapping temperature of a heating furnace based on energy coupling optimization, characterized in that, Includes the following steps: S1. Collect historical production parameters of heating furnace and rolling mill, perform validity processing on the collected data, and form a data pool; S2. Based on the data pool, establish the first relationship equation between the energy consumption of the heating furnace and related parameters, and the second relationship equation between the energy consumption of the rolling mill and related parameters; S3. Based on the first and second relational equations, the energy consumption of the heating furnace process and the energy consumption of the rolling process are summed to obtain the comprehensive energy consumption calculation formula for the steel rolling process. S4. Optimize the coupled production parameters of the heating furnace and rolling mill with the goal of minimizing the comprehensive energy consumption value in the comprehensive energy consumption value calculation formula, and establish the optimal tapping temperature model under different production processes. S5. Control the heating furnace to produce according to the optimal tapping temperature output by the optimal tapping temperature model, re-input the actual production parameters in the production process into the data pool, and use them to update the first relational equation and the second relational equation.

2. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 1, characterized in that, The historical production parameters of the heating furnace collected in S1 include output, oxidation loss, heat load, steel grade and heating temperature. The historical production parameters of the rolling mill collected include power consumption, roll consumption and rolling specifications. The collection period is one week or more.

3. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 1, characterized in that, The first relational equation in S2 is: in, b j Energy consumption of the heating furnace; G For the output of the heating furnace; The billet tapping temperature; The billet loading temperature; For steel grade category parameters; For overall thermal efficiency; Furnace time; α Oxidation burn-off rate; a 1 represents the billet temperature regression coefficient; a 2 represents the regression coefficient for steel grade category; a 3 represents the regression coefficient for output; b 1 represents the regression coefficient for overall thermal efficiency; b 2 represents the regression coefficient for furnace time; b 3 represents the no-load heat load of the heating furnace; b 4 represents the heat load regression coefficient; k 1 represents the overall correction factor; k 2 represents the production correction factor; k 3 represents the furnace time correction factor.

4. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 3, characterized in that, The formula for calculating the steel grade parameter C is as follows: Wherein, [C]% is the mass percentage of carbon in the billet, [Mn]% is the mass percentage of manganese in the billet, and [Cr]% is the mass percentage of chromium in the billet.

5. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 4, characterized in that, The second relational equation in S2 is: in, Energy consumption of the rolling mill; For the power consumption of the rolling mill; Consumed by the rolling mill rolls; This is the amount of pressure applied; The target thickness of the billet; For steel grade category parameters; For rolling width; T j Z0 is the initial rolling temperature; Z1 is the total rolling energy consumption correction factor; Z2 is the billet temperature correction factor; Z3 is the mill power consumption correction factor; Z4 is the roll consumption correction factor; Z5 is the rolling thickness correction factor; Z6 is the steel grade correction factor; Z7 is the rolling width correction factor.

6. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 5, characterized in that, The optimal tapping temperature model in S4 is: in, This is the optimal tapping temperature.

7. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 1, characterized in that, In S4, when the goal is to minimize the overall energy consumption, the optimization range of the furnace tapping temperature is limited by the constraint of satisfying the rolling quality.

8. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 1, characterized in that, The regression coefficients and correction coefficients of the first and second relational equations in S2 are established by using historical production data in the data pool through big data analysis.

9. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 1, characterized in that, The regression coefficients are dynamically updated as the actual production parameters re-entered into the data pool in S5 increase.

10. The method for controlling the tapping temperature of a heating furnace based on energy coupling optimization according to claim 1, characterized in that, The data validity processing in S1 includes removing outliers and filling in missing values.

Images