Direct-current submerged arc furnace smelting simulation method and system based on multi-field coupling optimization

Abstract

The invention relates to the technical field of direct-current submerged arc furnace simulation, in particular to a direct-current submerged arc furnace smelting simulation method and system based on multi-field coupling optimization. Comprising the following steps: establishing an initial structure model according to equipment parameters of the direct current submerged arc furnace, and generating a plurality of auxiliary field models; generating a calling sub-strategy according to a real-time simulation demand, and generating a primary simulation model according to the calling sub-strategy; setting a grid division strategy of the primary simulation model, and setting simulation operation parameters of the primary simulation model according to the grid division strategy; by constructing a plurality of auxiliary simulation fields and setting calling sub-strategies according to different simulation requirements, multi-type simulation sub-models are rapidly constructed, different simulation requirements of the direct-current submerged arc furnace are met, the simulation efficiency and the simulation precision of the direct-current submerged arc furnace are improved, and the simulation efficiency of the direct-current submerged arc furnace is improved. And data support is provided for process optimization, efficiency improvement and fault diagnosis of the direct-current submerged arc furnace.

Description

Technical Field

[0001] The present application relates to the technical field of DC submerged arc furnace simulation, and in particular to a DC submerged arc furnace smelting simulation method and system based on multi-field coupling optimization. Background Art

[0002] A DC submerged arc furnace is a high-temperature device used for smelting metals (such as ferroalloys, ferrosilicon, and ferromanganese). It uses the heat energy generated by direct current to melt ore and reducing agents to extract the target metal. The goal of simulation is to reproduce the complex physical and chemical processes within the furnace through numerical calculations, thereby optimizing process parameters and improving production efficiency and product quality.

[0003] However, at present, during the simulation process of DC ore-fired furnaces, only a single simulation model can be constructed for simulation. In addition, the amount of calculation during the simulation process is huge, which occupies a large amount of hardware resources. The overall simulation takes a long time and cannot meet the rapid needs in actual production. Summary of the Invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a DC submerged arc furnace smelting simulation method and system based on multi-field coupling optimization, aiming to improve the simulation efficiency and simulation accuracy of the DC submerged arc furnace.

[0005] In some embodiments of the present application, by constructing multiple auxiliary simulation fields, calling sub-strategies are set according to different simulation requirements, so as to quickly construct multiple types of simulation sub-models to meet the different simulation requirements of the DC electric arc furnace, improve the simulation efficiency and simulation accuracy of the DC electric arc furnace, and provide data support for the optimization process, efficiency improvement and fault diagnosis of the DC electric arc furnace.

[0006] In some embodiments of the present application, corresponding grid division strategies are generated according to different categories of simulation sub-models, and by analyzing each grid, corresponding simulation resource proportions are generated to improve the efficiency of hardware resource occupancy, thereby improving the simulation efficiency of the DC electric arc furnace. At the same time, resource allocation is continuously optimized based on the results of a single simulation to improve the simulation accuracy of the DC electric arc furnace.

[0007] In some embodiments of the present application, a DC submerged arc furnace smelting simulation method based on multi-field coupling optimization is provided, comprising: Establish an initial structural model based on the equipment parameters of the DC submerged arc furnace and generate multiple auxiliary field models; Generate a call sub-strategy according to real-time simulation requirements, and generate a first-level simulation model based on the call sub-strategy; Setting a grid division strategy for the first-level simulation model, and setting simulation operation parameters for the first-level simulation model according to the grid division strategy; Among them, when generating multiple auxiliary field models, it includes: Establish auxiliary field model sequence A, A=(a1,a2…a i …a n ), where a i is the i-th auxiliary field model; n is the number of auxiliary field models.

[0008] In some embodiments of the present application, when generating a call sub-strategy according to real-time simulation requirements, the process includes: Obtain historical simulation parameters of DC submerged arc furnace; Establish multiple simulation scenarios based on historical simulation parameters; Establish a simulation scenario sequence B, B=(b1,b2…b i …b m ), where b i is the i-th simulation scenario; m is the number of simulation scenarios; Establish first-level sub-strategies for each simulation scenario; Generate real-time simulation requirements and matching evaluation values ​​for each simulation scenario; Establish matching evaluation value sequence P, P=(p1,p2…p i …p m ), where p i is the matching evaluation value between the real-time simulation demand and the i-th simulation scenario; Set the matching evaluation value sequence P to p max The first-level sub-strategy of the corresponding simulation scenario is the calling sub-strategy.

[0009] In some embodiments of the present application, generating a matching evaluation value between a real-time simulation requirement and each simulation scenario includes: Set b in sequence according to the simulation scenario sequence B i Simulate scenarios for your goals; Generate a matching evaluation value p between the real-time simulation requirements and the target simulation scenario; p= η i *(j i -j' i ); Among them, θ1 is the number of demand characteristic indicators; η i is the reference value of the i-th demand characteristic indicator; j i is the reference value of the i-th demand characteristic index generated based on real-time simulation demand; j' i is the reference value of the i-th demand characteristic indicator in the target simulation scenario; Generate matching evaluation values ​​between each simulation scenario and real-time simulation requirements in turn.

[0010] In some embodiments of the present application, when setting simulation operation parameters of a first-level simulation model according to a grid division strategy, the following steps are included: Establish multiple simulation sub-areas based on the grid division strategy; Establish a simulation sub-area sequence C, C=(c1,c2…c i …c r ), where c i is the ith simulation sub-region; r is the number of simulation sub-regions; Generate simulated demand values ​​for each simulated sub-area; Establish a simulation demand value sequence G, G=(g1,g2…g i …g r ), where g i is the simulated demand value of the ith simulation sub-area; According to the simulated demand value sequence G, the simulated resource ratio of each simulated sub-area is set, and the initial simulation strategy of the first-level simulation model is generated according to the ratio of all simulated resources; An initial simulation result is obtained according to the initial simulation strategy, and it is determined whether to generate a correction instruction based on the initial simulation result.

[0011] In some embodiments of the present application, generating the simulated demand value of each simulated sub-area includes: Set c in sequence according to the number of simulation sub-areas C i Simulate sub-regions for the target; Generate the simulated demand value g of the target simulation sub-area according to the real-time demand parameters; g=e1*Q1*[ β 1i *k i ]+e2*Q2*[ β 2i *t i ]; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of demand evaluation indicators; β 1i is the influencing factor of the i-th demand evaluation index; k i is the reference value of the demand evaluation index of the target simulation sub-area generated based on the real-time simulation demand; θ3 is the number of grid evaluation indicators; β 2i is the influencing factor of the i-th grid evaluation index; t i is the reference value of the evaluation index of the i-th grid in the target simulation sub-area.

[0012] In some embodiments of the present application, determining whether to generate a correction instruction based on the initial simulation result includes: Generate the simulation evaluation values for each simulation sub-region according to the initial simulation results; Establish a sequence D of simulation evaluation values, D = (d1, d2…d i …d r ), where d i is the simulation evaluation value of the i-th simulation sub-region; Generate a corrected evaluation value f according to the sequence D of simulation evaluation values; f = e3 * Q3 * α i * h i + e4 * Q4 * Y(i) * s i * (d i - d') 2 ; Where, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; is the number of result indicators; α i is the influence factor of the i-th result indicator; hi is the reference value of the i-th result indicator generated based on the initial simulation results; s i is the weighting coefficient generated based on the simulation demand value of the i-th simulation sub-region; Y(i) is the selection coefficient. If (d i - d') > 0, Y(i) = 0; if (d i - d') < 0, Y(i) = 1; d' is the simulation evaluation value threshold; Judge whether to generate a correction instruction according to the corrected evaluation value f.

[0013] In some embodiments of the present application, when judging whether to generate a correction instruction according to the corrected evaluation value f, it includes: Preset a first corrected evaluation value threshold F1 and a second corrected evaluation value threshold F2, and F1 < F2; If f < F1, no correction instruction is generated; If F1 < f < F2, a first-level correction instruction is generated; If f > F2, a second-level correction instruction is generated.

[0014] In some embodiments of the present application, a DC submerged arc furnace smelting simulation system based on multi-field coupling optimization is provided, including: A central control unit for establishing an initial structure model according to the equipment parameters of the DC submerged arc furnace and generating multiple auxiliary field models; A simulation unit for obtaining real-time simulation requirements and generating a call sub-strategy according to the real-time simulation requirements; The central control unit includes: A first processing module for generating a first-level simulation model according to the call sub-strategy; The second processing module is used to set a grid division strategy of the first-level simulation model and set simulation operation parameters of the first-level simulation model according to the grid division strategy; The third processing module is used to establish the auxiliary field model sequence A, A=(a1, a2…a i …a n ), where a i is the i-th auxiliary field model; n is the number of auxiliary field models; The fourth processing module is used to obtain historical simulation parameters of the DC submerged arc furnace; Establish multiple simulation scenarios based on historical simulation parameters; Establish a simulation scenario sequence B, B=(b1,b2…b i …b m ), where b i is the i-th simulation scenario; m is the number of simulation scenarios; Establish first-level sub-strategies for each simulation scenario.

[0015] In some embodiments of the present application, the simulation unit is further configured to: Set b in sequence according to the simulation scenario sequence B i Simulate scenarios for your goals; Generate a matching evaluation value p between the real-time simulation requirements and the target simulation scenario; p= η i *(j i -j' i ); Among them, θ1 is the number of demand characteristic indicators; η i is the reference value of the i-th demand characteristic indicator; j i is the reference value of the i-th demand characteristic index generated based on real-time simulation demand; j' i is the reference value of the i-th demand characteristic indicator in the target simulation scenario; Generate matching evaluation values ​​between each simulation scenario and real-time simulation requirements in sequence; Establish matching evaluation value sequence P, P=(p1,p2…p i …p m ), where p i is the matching evaluation value between the real-time simulation demand and the i-th simulation scenario; Set the matching evaluation value sequence P to p max The first-level sub-strategy of the corresponding simulation scenario is the calling sub-strategy.

[0016] In some embodiments of the present application, the second processing module is further configured to: Establish multiple simulation sub-areas based on the grid division strategy; Establish a simulation sub-region sequence C, C = (c1, c2…ci…cr), where ci is the i-th simulation sub-region; r is the number of simulation sub-regions; Set c in sequence according to the number of simulation sub-areas C i Simulate sub-regions for the target; Generate the simulated demand value g of the target simulation sub-area according to the real-time demand parameters; g=e1*Q1*[ β 1i *k i ]+e2*Q2*[ β 2i *t i ]; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of demand evaluation indicators; β 1i is the influencing factor of the i-th demand evaluation index; k i is the reference value of the demand evaluation index of the target simulation sub-area generated based on the real-time simulation demand; θ3 is the number of grid evaluation indicators; β 2i is the influencing factor of the i-th grid evaluation index; t i is the reference value of the evaluation index of the i-th grid in the target simulation sub-area; Generate simulated demand values ​​for each simulated sub-area; Establish a simulated demand value sequence G, G = (g1, g2…gi…gr), where gi is the simulated demand value of the i-th simulation sub-area; According to the simulated demand value sequence G, the simulated resource ratio of each simulated sub-area is set, and the initial simulation strategy of the first-level simulation model is generated according to the ratio of all simulated resources; An initial simulation result is obtained according to the initial simulation strategy, and it is determined whether to generate a correction instruction based on the initial simulation result.

[0017] Compared with the prior art, the DC submerged arc furnace smelting simulation method and system based on multi-field coupling optimization in the embodiment of the present application has the following beneficial effects: By constructing multiple auxiliary simulation fields and setting calling sub-strategies according to different simulation requirements, we can quickly build multiple types of simulation sub-models to meet the different simulation requirements of DC submerged arc furnaces, improve the simulation efficiency and accuracy of DC submerged arc furnaces, and provide data support for the optimization process, efficiency improvement and fault diagnosis of DC submerged arc furnaces.

[0018] Corresponding grid division strategies are generated according to different categories of simulation sub-models, and by analyzing each grid, the corresponding simulation resource ratio is generated to improve the efficiency of hardware resource occupancy, thereby improving the simulation efficiency of the DC submerged arc furnace. At the same time, resource allocation is continuously optimized according to the results of a single simulation to improve the simulation accuracy of the DC submerged arc furnace. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a DC submerged arc furnace smelting simulation method based on multi-field coupling optimization in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0020] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0021] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0024] like Figure 1 As shown, a DC submerged arc furnace smelting simulation method based on multi-field coupling optimization in a preferred embodiment of the present application is characterized by comprising: S101: Establish an initial structural model based on the equipment parameters of the DC submerged arc furnace and generate multiple auxiliary field models; S102: Generate a call sub-strategy according to the real-time simulation requirement, and generate a first-level simulation model according to the call sub-strategy; S103: setting a grid division strategy for the first-level simulation model, and setting simulation operation parameters of the first-level simulation model according to the grid division strategy; Among them, when generating multiple auxiliary field models, it includes: Establish auxiliary field model sequence A, A=(a1,a2…a i …a n ), where a i is the i-th auxiliary field model; n is the number of auxiliary field models.

[0025] Specifically, multiple auxiliary field models are established based on the historical operating parameters of the DC ore furnace. The auxiliary field models include but are not limited to temperature field, electromagnetic field, flow field, chemical reaction field, etc., and there are multiple temperature fields of each category.

[0026] Specifically, an initial structural model is constructed according to the structural parameters and material properties of the DC submerged arc furnace.

[0027] Specifically, when generating a call sub-strategy based on real-time simulation requirements, it includes: Obtain historical simulation parameters of DC submerged arc furnace; Establish multiple simulation scenarios based on historical simulation parameters; Establish a simulation scenario sequence B, B=(b1,b2…b i …b m ), where b i is the i-th simulation scenario; m is the number of simulation scenarios; Establish first-level sub-strategies for each simulation scenario; Generate real-time simulation requirements and matching evaluation values ​​for each simulation scenario; Establish matching evaluation value sequence P, P=(p1,p2…p i …p m ), where p i is the matching evaluation value between the real-time simulation demand and the i-th simulation scenario; Set the matching evaluation value sequence P to p max The first-level sub-strategy of the corresponding simulation scenario is the calling sub-strategy.

[0028] Specifically, all historical simulation parameters of the current simulation scenario are obtained to generate the degree of fit between the simulation scenario and each auxiliary simulation field. When the real-time fit is greater than the fit, the auxiliary simulation field is set as the calling simulation field of the current simulation scenario. Based on all the called simulation fields, the first-level sub-strategy of the current simulation scenario is generated.

[0029] Specifically, the greater the degree of fit, the more the operating status of the current auxiliary simulation field construction conforms to the operating status of the DC blast furnace in the simulation scenario.

[0030] Specifically, when generating the matching evaluation values ​​of real-time simulation requirements and various simulation scenarios, it includes: Set b in sequence according to the simulation scenario sequence B i Simulate scenarios for your goals; Generate a matching evaluation value p between the real-time simulation requirements and the target simulation scenario; p= η i *(j i -j' i ); Among them, θ1 is the number of demand characteristic indicators; η i is the reference value of the i-th demand characteristic indicator; j i is the reference value of the i-th demand characteristic index generated based on real-time simulation demand; j' i is the reference value of the i-th demand characteristic indicator in the target simulation scenario; Generate matching evaluation values ​​between each simulation scenario and real-time simulation requirements in turn.

[0031] Specifically, demand characteristic indicators include, but are not limited to, simulation demand categories (e.g., efficiency optimization categories, fault diagnosis categories), key simulation areas, and other parameters. Each demand characteristic indicator is quantified so that its quantified value falls within the same range.

[0032] Specifically, the larger the matching evaluation value, the higher the similarity between the real-time simulation needs and the corresponding simulation scenario, and the better the simulation effect of constructing the corresponding first-level simulation sub-model using the first-level sub-strategy of the simulation scenario.

[0033] It can be understood that in the above embodiment, by constructing multiple auxiliary simulation fields and setting calling sub-strategies according to different simulation requirements, multiple types of simulation sub-models can be quickly constructed to meet the different simulation requirements of the DC submerged arc furnace, improve the simulation efficiency and simulation accuracy of the DC submerged arc furnace, and provide data support for the optimization process, efficiency improvement and fault diagnosis of the DC submerged arc furnace.

[0034] In a preferred embodiment of the present application, when setting the simulation operation parameters of the first-level simulation model according to the grid division strategy, the following steps are included: Establish multiple simulation sub-areas based on the grid division strategy; Establish a simulation sub-area sequence C, C=(c1,c2…c i …c r ), where c iis the ith simulation sub-region; r is the number of simulation sub-regions; Generate simulated demand values ​​for each simulated sub-area; Establish a simulation demand value sequence G, G=(g1,g2…g i …g r ), where g i is the simulated demand value of the ith simulation sub-area; According to the simulated demand value sequence G, the simulated resource ratio of each simulated sub-area is set, and the initial simulation strategy of the first-level simulation model is generated according to the ratio of all simulated resources; An initial simulation result is obtained according to the initial simulation strategy, and it is determined whether to generate a correction instruction based on the initial simulation result.

[0035] Specifically, computer software is used to generate the grid of the first-level simulation sub-model, and the grid types include structured grid, unstructured grid, etc. And a single grid is set as a single simulation sub-region.

[0036] Specifically, the larger the simulated demand value, the larger the corresponding simulated resource ratio. The mapping relationship between the simulated demand value and the simulated resource ratio can be set according to the historical simulation parameters.

[0037] Specifically, simulation resources refer to resources such as hardware devices used in the simulation process.

[0038] Specifically, when generating the simulated demand value for each simulated sub-area, it includes: Set c in sequence according to the number of simulation sub-areas C i Simulate sub-regions for the target; Generate the simulated demand value g of the target simulation sub-area according to the real-time demand parameters; g=e1*Q1*[ β 1i *k i ]+e2*Q2*[ β 2i *t i ]; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of demand evaluation indicators; β 1i is the influencing factor of the i-th demand evaluation index; k i is the reference value of the demand evaluation index of the target simulation sub-area generated based on the real-time simulation demand; θ3 is the number of grid evaluation indicators; β 2i is the influencing factor of the i-th grid evaluation index; t i is the reference value of the evaluation index of the i-th grid in the target simulation sub-area.

[0039] Specifically, all parameters in the model are normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter in the model is in the same value range.

[0040] Specifically, demand evaluation indicators include but are not limited to the influence of the simulation sub-area on the simulation results, the auxiliary degree of the simulation operation status in the simulation sub-area, the proportion of demand data appearing in the simulation data corresponding to the simulation sub-area, and other parameters. The demand data refers to the demand data required to be obtained based on real-time simulation needs. For example, when simulating fault types, the demand data is fault characteristic data.

[0041] Specifically, the grid evaluation indicators include but are not limited to the area of ​​the simulation sub-area, the distance of the simulation sub-area from the center position, the number of areas that the simulation sub-area may affect, and other parameters.

[0042] Specifically, the larger the simulation demand value is, the greater the simulation complexity in the current simulation sub-area is, and the more important the simulation data is.

[0043] It can be understood that in the above embodiment, corresponding grid division strategies are generated according to different categories of simulation sub-models, and by analyzing each grid, the corresponding simulation resource ratio is generated to improve the efficiency of hardware resource utilization and thereby improve the simulation efficiency of the DC electric arc furnace.

[0044] In a preferred embodiment of the present application, when determining whether to generate a correction instruction based on the initial simulation result, the method includes: Generate simulation evaluation values ​​for each simulation sub-area based on the initial simulation results; Establish a simulation evaluation value series D, D=(d1,d2…d i …d r ), where d i is the simulation evaluation value of the ith simulation sub-area; Generate a modified evaluation value f according to the simulated evaluation value sequence D; f=e3*Q3*[ α i *h i ]+e4*Q4*[ Y(i)*s i *(d i -d') 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; is the number of outcome indicators; α i is the influencing factor of the i-th result indicator; hi is the reference value of the i-th result indicator generated based on the initial simulation results; si is a weighting coefficient generated based on the simulation demand value of the i-th simulated sub-region; Y(i) is a selection coefficient. If (d i - d') > 0, Y(i) = 0; if (d i - d') < 0, Y(i) = 1; d' is the threshold of the simulation evaluation value; Judge whether to generate a correction instruction according to the corrected evaluation value f.

[0045] Specifically, all parameters in the model are normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter in the model is within the same value range.

[0046] Specifically, when judging whether to generate a correction instruction according to the corrected evaluation value f, it includes: Preset the first corrected evaluation value threshold F1 and the second corrected evaluation value threshold F2, and F1 < F2; If f < F1, no correction instruction is generated; If F1 < f < F2, generate a first-level correction instruction; If f > F2, generate a second-level correction instruction.

[0047] Specifically, the first-level correction instruction refers to correcting the simulation resource ratio of each current simulated sub-region to generate a new simulation resource allocation strategy. The second-level correction instruction refers to optimizing and correcting the grid division strategy of the current first-level simulation sub-model.

[0048] Specifically, continuously optimize the resource allocation according to the single simulation result to improve the simulation accuracy of the DC submerged arc furnace.

[0049] Based on another preferred embodiment of a DC submerged arc furnace smelting simulation method based on multi-field coupling optimization in any one of the above preferred embodiments, in this preferred embodiment, a DC submerged arc furnace smelting simulation method based on multi-field coupling optimization is provided, including: The central control unit is used to establish an initial structure model according to the equipment parameters of the DC submerged arc furnace and generate multiple auxiliary field models; The simulation unit is used to obtain the real-time simulation demand and generate a call sub-strategy according to the real-time simulation demand; The central control unit includes: The first processing module is used to generate a first-level simulation model according to the call sub-strategy; The second processing module is used to set the grid division strategy of the first-level simulation model and set the simulation operation parameters of the first-level simulation model according to the grid division strategy; The third processing module is used to establish a sequence A of auxiliary field models, A = (a1, a2…a i …a n ), where, ai is the i-th auxiliary field model; n is the number of auxiliary field models; The fourth processing module is used to obtain historical simulation parameters of the DC submerged arc furnace; Establish multiple simulation scenarios based on historical simulation parameters; Establish a simulation scenario sequence B, B=(b1,b2…b i …b m ), where b i is the i-th simulation scenario; m is the number of simulation scenarios; Establish first-level sub-strategies for each simulation scenario.

[0050] In a preferred embodiment of the present application, the simulation unit is further used to: Set b in sequence according to the simulation scenario sequence B i Simulate scenarios for your goals; Generate a matching evaluation value p between the real-time simulation requirements and the target simulation scenario; p= η i *(j i -j' i ); Among them, θ1 is the number of demand characteristic indicators; η i is the reference value of the i-th demand characteristic indicator; j i is the reference value of the i-th demand characteristic index generated based on real-time simulation demand; j' i is the reference value of the i-th demand characteristic indicator in the target simulation scenario; Generate matching evaluation values ​​between each simulation scenario and real-time simulation requirements in sequence; Establish matching evaluation value sequence P, P=(p1,p2…p i …p m ), where p i is the matching evaluation value between the real-time simulation demand and the i-th simulation scenario; Set the matching evaluation value sequence P to p max The first-level sub-strategy of the corresponding simulation scenario is the calling sub-strategy.

[0051] In a preferred embodiment of the present application, the second processing module is further configured to: Establish multiple simulation sub-areas based on the grid division strategy; Establish a simulation sub-region sequence C, C = (c1, c2…ci…cr), where ci is the i-th simulation sub-region; r is the number of simulation sub-regions; Set c in sequence according to the number of simulation sub-areas C i Simulate sub-regions for the target; Generate the simulated demand value g of the target simulation sub-area according to the real-time demand parameters; g=e1*Q1*[ β 1i *k i ]+e2*Q2*[ β 2i *t i ]; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of demand evaluation indicators; β 1i is the influencing factor of the i-th demand evaluation index; k i is the reference value of the demand evaluation index of the target simulation sub-area generated based on the real-time simulation demand; θ3 is the number of grid evaluation indicators; β 2i is the influencing factor of the i-th grid evaluation index; t i is the reference value of the evaluation index of the i-th grid in the target simulation sub-area; Generate simulated demand values ​​for each simulated sub-area; Establish a simulated demand value sequence G, G = (g1, g2…gi…gr), where gi is the simulated demand value of the i-th simulation sub-area; According to the simulated demand value sequence G, the simulated resource ratio of each simulated sub-area is set, and the initial simulation strategy of the first-level simulation model is generated according to the ratio of all simulated resources; An initial simulation result is obtained according to the initial simulation strategy, and it is determined whether to generate a correction instruction based on the initial simulation result.

[0052] According to the first concept of this application, by constructing multiple auxiliary simulation fields and setting calling sub-strategies according to different simulation requirements, multiple types of simulation sub-models can be quickly constructed to meet the different simulation requirements of DC electric arc furnaces, improve the simulation efficiency and accuracy of DC electric arc furnaces, and provide data support for the optimization process, efficiency improvement and fault diagnosis of DC electric arc furnaces.

[0053] According to the second concept of the present application, corresponding grid division strategies are generated according to different categories of simulation sub-models, and by analyzing each grid, the corresponding simulation resource ratio is generated to improve the efficiency of hardware resource occupancy, thereby improving the simulation efficiency of the DC electric arc furnace. At the same time, the resource allocation is continuously optimized according to the results of a single simulation to improve the simulation accuracy of the DC electric arc furnace.

[0054] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. A DC submerged arc furnace smelting simulation method based on multi-field coupling optimization, characterized in that: including: Establish an initial structure model according to the equipment parameters of the DC submerged arc furnace and generate multiple auxiliary field models; Generate a call sub-strategy according to the real-time simulation requirements and generate a first-level simulation model according to the call sub-strategy; Set the grid division strategy of the first-level simulation model and set the simulation operation parameters of the first-level simulation model according to the grid division strategy; Among them, when generating multiple auxiliary field models, it includes: Establish auxiliary field model sequence A, A=(a1,a2…a i …a n ), where a i is the i-th auxiliary field model; n is the number of auxiliary field models.

2. The DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to claim 1, characterized in that: When generating a call sub-strategy according to the real-time simulation requirements, it includes: Obtain the historical simulation parameters of the DC submerged arc furnace; Establish multiple simulation scenarios according to the historical simulation parameters; Establish a simulation scenario sequence B, B=(b1,b2…b i …b m ), where b i is the i-th simulation scenario; m is the number of simulation scenarios; Establish the first-level sub-strategies for each simulation scenario; Generate the matching evaluation values between the real-time simulation requirements and each simulation scenario; Establish matching evaluation value sequence P, P=(p1,p2…p i …p m ), where p i is the matching evaluation value between the real-time simulation demand and the i-th simulation scenario; Set the matching evaluation value sequence P to p max The first-level sub-strategy of the corresponding simulation scenario is the calling sub-strategy.

3. The DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to claim 2, characterized in that: When generating the matching evaluation values between the real-time simulation requirements and each simulation scenario, it includes: Set b in sequence according to the simulation scenario sequence B i Simulate scenarios for your goals; Generate the matching evaluation value p between the real-time simulation requirements and the target simulation scenario; p= or i *(j i -j' i ); Among them, θ1 is the number of demand characteristic indicators; η i is the reference value of the i-th demand characteristic indicator; j i is the reference value of the i-th demand characteristic index generated based on real-time simulation demand; j' i is the reference value of the i-th demand characteristic indicator in the target simulation scenario; Generate the matching evaluation values between each simulation scenario and the real-time simulation requirements in sequence.

4. The DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to claim 3, characterized in that: When setting the simulation operation parameters of the first-level simulation model according to the grid division strategy, it includes: Establish multiple simulation sub-regions according to the grid division strategy; Establish a simulation sub-area sequence C, C=(c1,c2…c i …c r ), where c i is the ith simulation sub-region; r is the number of simulation sub-regions; Generate the simulation demand values for each simulation sub-region; Establish a simulation demand value sequence G, G=(g1,g2…g i …g r ), where g i is the simulated demand value of the ith simulation sub-area; Set the simulation resource proportion for each simulation sub-region according to the simulation demand value sequence G, and generate the initial simulation strategy of the first-level simulation model according to the total simulation resource proportion; Obtain the initial simulation result according to the initial simulation strategy, and judge whether to generate a correction instruction according to the initial simulation result.

5. The DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to claim 4, characterized in that: When generating the simulation demand values for each simulation sub-region, it includes: Set c in sequence according to the number of simulation sub-areas C i Simulate sub-regions for the target; Generate the simulation demand value g of the target simulation sub-region according to the real-time demand parameters; g=e1*Q1*[ β 1i *k i ]+e2*Q2*[ β 2i *t i ]: Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of demand evaluation indicators; β 1i is the influencing factor of the i-th demand evaluation index; k i is the reference value of the demand evaluation index of the target simulation sub-area generated based on the real-time simulation demand; θ3 is the number of grid evaluation indicators; β 2i is the influencing factor of the i-th grid evaluation index; t i is the reference value of the evaluation index of the i-th grid in the target simulation sub-area.

6. The DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to claim 5, characterized in that: When judging whether to generate a correction instruction according to the initial simulation result, it includes: Generate the simulation evaluation values for each simulation sub-region according to the initial simulation result; Establish a simulation evaluation value series D, D=(d1,d2…d i …d r ), where d i is the simulation evaluation value of the ith simulation sub-area; Generate the correction evaluation value f according to the simulation evaluation value sequence D; f=e3*Q3*[ α i *h i ]+e4*Q4*[ Y(i)*s i *(d i -d') 2 ]; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; is the number of outcome indicators; α i is the influencing factor of the i-th result indicator; hi is the reference value of the i-th result indicator generated based on the initial simulation results; s i is the weighting coefficient generated based on the simulated demand value of the ith simulation sub-area; Y(i) is the selection coefficient. If (d i -d')>0,Y(i)=0; if(d i -d')<0,Y(i)=1;d' is the threshold of simulation evaluation value; Judge whether to generate a correction instruction according to the correction evaluation value f.

7. The DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to claim 6, characterized in that: When judging whether to generate a correction instruction according to the correction evaluation value f, it includes: Preset the first correction evaluation value threshold F1 and the second correction evaluation value threshold F2, and F1 < F2; If f < F1, do not generate a correction instruction; If F1 < f < F2, generate a first-level correction instruction; If f > F2, generate a second-level correction instruction.

8. A DC submerged arc furnace smelting simulation system based on multi-field coupling optimization, adopting the DC submerged arc furnace smelting simulation method based on multi-field coupling optimization according to any one of claims 1 to 7, characterized in that: including: A central control unit, used to establish an initial structure model according to the equipment parameters of the DC submerged arc furnace and generate multiple auxiliary field models; A simulation unit, used to obtain the real-time simulation requirements and generate a call sub-strategy according to the real-time simulation requirements; The central control unit includes: A first processing module, used to generate a first-level simulation model according to the call sub-strategy; A second processing module, used to set the grid division strategy of the first-level simulation model and set the simulation operation parameters of the first-level simulation model according to the grid division strategy; The third processing module is used to establish the auxiliary field model sequence A, A=(a1, a2…a i …a n ), where a i is the i-th auxiliary field model; n is the number of auxiliary field models; A fourth processing module, used to obtain the historical simulation parameters of the DC submerged arc furnace; Establish multiple simulation scenarios according to the historical simulation parameters; Establish a simulation scenario sequence B, B=(b1,b2…b i …b m ), where b i is the i-th simulation scenario; m is the number of simulation scenarios; Establish the first-level sub-strategies for each simulation scenario.

9. The DC submerged arc furnace smelting simulation system based on multi-field coupling optimization according to claim 8, characterized in that: The simulation unit is also used for: Set b in sequence according to the simulation scenario sequence B i Simulate scenarios for your goals; Generate the matching evaluation value p between the real-time simulation requirements and the target simulation scenario; p= or i *(j i -j' i ); Among them, θ1 is the number of demand characteristic indicators; η i is the reference value of the i-th demand characteristic indicator; j i is the reference value of the i-th demand characteristic index generated based on real-time simulation demand; j' i is the reference value of the i-th demand characteristic indicator in the target simulation scenario; Generate the matching evaluation values between each simulation scenario and the real-time simulation requirements in sequence; Establish matching evaluation value sequence P, P=(p1,p2…p i …p m ), where p i is the matching evaluation value between the real-time simulation demand and the i-th simulation scenario; Set the matching evaluation value sequence P to p max The first-level sub-strategy of the corresponding simulation scenario is the calling sub-strategy.

10. The DC submerged arc furnace smelting simulation system based on multi-field coupling optimization according to claim 9, characterized in that: The second processing module is also used for: Establish multiple simulation sub-regions according to the grid division strategy; Establish a simulation sub-region sequence C, C = (c1, c2…ci…cr), where ci is the i-th simulation sub-region; r is the number of simulation sub-regions; Set c in sequence according to the number of simulation sub-areas C i Simulate sub-regions for the target; Generate the simulated demand value g of the target simulation sub-area according to the real-time demand parameters; g=e1*Q1*[ β 1i *k i ]+e2*Q2*[ β 2i *t i ]: Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of demand evaluation indicators; β 1i is the influencing factor of the i-th demand evaluation index; k i is the reference value of the demand evaluation index of the target simulation sub-area generated based on the real-time simulation demand; θ3 is the number of grid evaluation indicators; β 2i is the influencing factor of the i-th grid evaluation index; t i is the reference value of the evaluation index of the i-th grid in the target simulation sub-area; Generate simulated demand values ​​for each simulated sub-area; Establish a simulated demand value sequence G, G = (g1, g2…gi…gr), where gi is the simulated demand value of the i-th simulation sub-area; According to the simulated demand value sequence G, the simulated resource ratio of each simulated sub-area is set, and the initial simulation strategy of the first-level simulation model is generated according to the ratio of all simulated resources; An initial simulation result is obtained according to the initial simulation strategy, and it is determined whether to generate a correction instruction based on the initial simulation result.

Images