Self-adaptive management method, device and equipment for soft melting dripping performance of sintered ore

Abstract

The invention provides a self-adaptive management method, device and equipment for the soft-melting dripping performance of sinter, and relates to the technical field of soft-melting dripping performance management.The method comprises the steps that current-moment sintering raw material components, current-moment sintering raw material proportions and current-moment sintering process parameters are collected, and current-moment sinter theoretical components are calculated; calculating a current-moment sintering ore soft melting dropping temperature theoretical value corresponding to the current-moment sintering ore theoretical component, inputting the current-moment sintering ore soft melting dropping temperature theoretical value into the accurate prediction model, and outputting a current-moment sintering ore soft melting dropping temperature actual value; according to the method, the theoretical value of the sintering ore soft melting dropping temperature at the current moment and the quantitative influence of the sintering process parameters at the current moment on the actual value of the sintering ore soft melting dropping temperature at the current moment are determined, the sintering process parameters at the current moment are adjusted, and optimization of the sintering ore soft melting dropping temperature is achieved. And through combination with sintering process parameters, the obtained actual value of the sintering ore soft melting dropping temperature at the current moment is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of sintered ore soft melting dripping performance management, and in particular to a sintered ore soft melting dripping performance adaptive management method, device and equipment. Background Art

[0002] The soft-melting dripping performance of sintered ore has a significant impact on blast furnace smelting and directly determines the stable and smooth operation of the lower high-temperature zone of the blast furnace. The soft-melting dripping performance of sintered ore can only be obtained through specific experimental equipment, with low data frequency and great difficulty in data acquisition. After problems occur in sintering production, it often takes a day or even several days to test and analyze the soft-melting dripping performance of sintered ore through sampling and experiments, resulting in direct economic losses of tens of thousands of tons of sintered ore.

[0003] At present, most of them optimize the performance of sintered ore through experimental mechanism research, thermodynamic calculation and other methods. For example, by improving the soft melting dripping performance of sintered ore on the basis of mechanism experimental research; obtaining the MgO content of sintered ore that can optimize the soft melting dripping performance of sintered ore through sintering cup experiment, sintered ore soft melting dripping performance detection and blast furnace industrial test; using thermodynamic calculation of the liquid phase amount-temperature relationship corresponding to the sintering batching scheme, combined with the melting characteristic test experiment to obtain the optimal sintering batching scheme, etc.

[0004] However, the above methods are mostly based on theory or experiments, and have a low degree of integration with actual on-site production, resulting in poor application value of the final result data. Summary of the invention

[0005] The present invention provides a method, device and equipment for adaptively managing the soft melting dripping performance of sintered ore, which are used to solve the defect in the prior art that the degree of integration between theoretical experiments and actual production is low, resulting in poor application value of result data.

[0006] In a first aspect, the present invention provides a method for adaptively managing sintered ore soft melting dripping performance, comprising: Collect the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the current sintering ore theoretical composition; Based on thermodynamic theory, calculating a theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; Input the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into the accurate prediction model, and output the actual value of the soft melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; Determine the quantitative influence of the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft melting dripping temperature of the sintered ore at the current moment, adjust the sintering process parameters at the current moment, and optimize the soft melting dripping temperature of the sintered ore.

[0007] A method for adaptively managing sintered ore soft melting dripping performance provided by the present invention also includes: Obtain the actual composition of the sintered ore at the last moment before the current moment and the theoretical composition of the sintered ore at the last moment, and predict the actual value of the soft melting dripping temperature of the sintered ore at the last moment through an accurate prediction model; Compare the actual composition of the sintered ore at the previous moment with the theoretical composition of the sintered ore at the previous moment, combine the actual value of the soft melting dripping temperature of the sintered ore at the previous moment with the accurate prediction model, feedback the impact of the composition deviation on the actual value of the soft melting dripping temperature of the sintered ore, adjust the raw material composition and calculate the correction coefficient.

[0008] According to a method for adaptively managing the soft melting dripping performance of sintered ore provided by the present invention, before collecting the sintering raw material composition, the sintering raw material ratio and the sintering process parameters at the current moment, the method further includes: Determine the theoretical value samples of sinter ore soft melting dripping temperature, the sintering process parameter samples and the actual value samples of sinter ore soft melting dripping temperature; The prediction model is constructed by using the theoretical value samples of the sintered ore soft melting dripping temperature, the sintering process parameter samples and the actual value samples of the sintered ore soft melting dripping temperature for training.

[0009] According to a method for adaptively managing sintered ore soft melting dripping performance provided by the present invention, the method of determining a theoretical value sample of sintered ore soft melting dripping temperature, a sintering process parameter sample and an actual value sample of sintered ore soft melting dripping temperature comprises: Collect historical sintered ore soft melting dripping test data, historical sintered ore composition data and historical sintering process parameters of steel enterprises; Through time index alignment and data frequency integration, the collected historical sintered ore soft melting dripping experimental data, historical sintered ore composition data, and historical sintering process parameters are matched to obtain sintered ore soft melting dripping temperature theoretical value samples, sintering process parameter samples, and sintered ore soft melting dripping temperature actual value samples.

[0010] According to a method for adaptively managing sintered ore soft melting dripping performance provided by the present invention, after constructing the prediction model, the method further includes: When the prediction accuracy of the prediction model exceeds a preset value, determining that the prediction model is an accurate prediction model; The accurate prediction model is analyzed to obtain the theoretical value of the sintered ore soft melting dripping temperature and the ranking and influence trend of the sintering process parameters on the sintered ore soft melting dripping temperature.

[0011] A method for adaptively managing sintered ore soft melting dripping performance provided by the present invention also includes: When the prediction accuracy of the prediction model does not exceed a preset value, determining that the prediction model is an inaccurate prediction model; Controllable sintering process parameters are added to the sintering process parameters to improve the accuracy of the inaccurate prediction model and provide feedback on the impact of uncontrollable parameters.

[0012] According to a method for adaptively managing sintered ore soft melting dripping performance provided by the present invention, after adjusting the sintering process parameters at the current moment, the method further includes: Based on the quantitative impact, multiple decision-making suggestions are fed back to provide data support for production review and enterprise optimization.

[0013] According to a method for adaptively managing sintered ore soft melting dripping performance provided by the present invention, the sintered ore soft melting dripping temperature includes: softening start temperature, softening end temperature, melting start temperature and dripping temperature.

[0014] In a second aspect, the present invention further provides a sintered ore soft melting dripping performance adaptive management device, comprising: The collection module is used to collect the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the current sintering ore theoretical composition; A calculation module, for calculating, based on thermodynamic theory, a theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; A prediction module, used for inputting the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into an accurate prediction model, and outputting the actual value of the soft melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; The optimization module is used to determine the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the quantitative influence of the sintering process parameters at the current moment on the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, adjust the sintering process parameters at the current moment, and realize the optimization of the soft-melting dripping temperature of the sintered ore.

[0015] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for adaptively managing the soft melting dripping performance of sintered ore as described in any one of the above-mentioned methods is implemented.

[0016] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for adaptively managing the soft melting dripping performance of sintered ore as described in any one of the above-mentioned methods.

[0017] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for adaptively managing the soft melting dripping performance of sintered ore.

[0018] The present invention provides an adaptive management method, device and equipment for the soft melting dripping performance of sintered ore, which collect the sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the theoretical composition of the sintered ore at the current moment; based on the thermodynamic theory, calculate the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; input the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into an accurate prediction model, and output the actual value of the soft melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; determine the quantitative influence of the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft melting dripping temperature of the sintered ore at the current moment, adjust the sintering process parameters at the current moment, and optimize the soft melting dripping temperature of the sintered ore. Compared with a simple theoretical experimental method, the actual value of the soft melting dripping temperature of the sintered ore at the current moment obtained by combining with the sintering process parameters and using the accurate prediction model constructed by integrating mechanism and data is more accurate, and the application value of the final result data is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of the process of the adaptive management method of sintered ore soft melting dripping performance provided in this embodiment; Figure 2 2 is a schematic diagram of the structure of the sintered ore soft melting dripping performance adaptive management device provided in this embodiment; Figure 3 It is a schematic diagram of the structure of the electronic device provided in this embodiment. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Figure 1 It is a schematic flow chart of the adaptive management method of sintered ore soft melting dripping performance provided in this embodiment.

[0023] like Figure 1 As shown, an adaptive management method for sintered ore soft melting dripping performance provided by an embodiment of the present invention mainly includes the following steps: 101. Collect the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the current sintering ore theoretical composition.

[0024] In a specific implementation process, the soft melting dripping performance of sintered ore greatly affects the stable and smooth operation of the blast furnace. Therefore, accurate soft melting dripping performance data of sintered ore is required to provide good data support for enterprise decision-making. The moment when the soft melting dripping performance data of sintered ore needs to be determined is defined as the current moment, which can be any moment. The data such as the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters can be collected through sensors, big data or system reading, and then the data is calculated to obtain the theoretical composition of the sintered ore at the current moment.

[0025] 102. Based on the theory of thermodynamics, calculate the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment.

[0026] The current theoretical composition of the sintered ore is input into the thermodynamic theory formula for calculation to obtain the corresponding theoretical value of the soft melting dripping temperature of the sintered ore at the current moment. The specific method can be: The liquid phase amount corresponding to the sinter ore soft melting dripping temperature at each historical moment is extracted, and the average of the liquid phase amounts at all historical moments is taken. When the sinter ore liquid phase amount of the sinter ore theoretical composition reaches the average at the current moment, the corresponding thermodynamic calculation temperature is determined as the theoretical value of the sinter ore soft melting dripping temperature.

[0027] 103. Input the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into the accurate prediction model, and output the actual value of the soft melting dripping temperature of the sintered ore at the current moment. The accurate prediction model is constructed by integrating mechanism and data.

[0028] Specifically, a prediction model is first constructed, and then the prediction model is calibrated to obtain an accurate prediction model. The way to construct a prediction model is: collect historical sinter soft melting dripping experimental data, historical sinter composition data and historical sintering process parameters of steel enterprises; through time index alignment and data frequency integration, the collected historical sinter soft melting dripping experimental data, historical sinter composition data, and historical sintering process parameters are matched. Based on the theory of thermodynamics, the theoretical value of the sinter soft melting dripping temperature corresponding to all historical sinter composition data is calculated. The sinter soft melting dripping temperature includes the softening start temperature, the softening end temperature, the melting start temperature and the dripping temperature. The theoretical value sample of the sinter soft melting dripping temperature, the sintering process parameter sample and the actual value sample of the sinter soft melting dripping temperature are obtained.

[0029] Then, samples of theoretical values ​​of sintered ore soft melting dripping temperature, samples of sintering process parameters and samples of actual values ​​of sintered ore soft melting dripping temperature are used for training to construct a prediction model. Since the prediction model collects sintering process data of steel enterprises through big data technology, and is trained by calculating the theoretical value of sintered ore soft melting dripping temperature through thermodynamics and sintered ore composition, the data source is the data resources of the entire sintering process. The influence of sintering process parameters is added when establishing the sintered ore soft melting dripping temperature prediction model, which is closely integrated with the actual sintering production, has high processing efficiency and strong real-time performance.

[0030] When the prediction accuracy of the prediction model exceeds the preset value, the prediction model is determined to be an accurate prediction model; the accurate prediction model is analyzed to obtain the theoretical value of the soft melting dripping temperature of the sintered ore and the ranking and influence trend of the sintering process parameters on the soft melting dripping temperature of the sintered ore, which are used to summarize the empirical rules. When the prediction accuracy of the prediction model does not exceed the preset value, the prediction model is determined to be an inaccurate prediction model; controllable sintering process parameters are added to the sintering process parameters to improve the accuracy of the inaccurate prediction model, and uncontrollable parameters and the theoretical impact are fed back to provide suggestions for the management to optimize the sintering process.

[0031] Among them, the uncontrollable parameters and their theoretical impact are fed back to provide suggestions for the management to optimize the sintering process, including: when the uncontrollable parameter is the use of a new ore type, after the ore type of the sintering ore raw material changes, if the theoretical composition of the sintered ore remains unchanged, the soft-melting dripping performance of the sintered ore changes significantly, the assimilation temperature and liquid phase fluidity of the new ore type are tested. When the assimilation temperature is too high and / or the liquid phase fluidity is poor, causing the soft-melting dripping performance of the sintered ore to deteriorate, the suggestion of reducing the ratio to control the soft-melting dripping temperature of the sintered ore is fed back to the management system.

[0032] When the uncontrollable parameter is a large fluctuation in the basicity deviation, it affects the slag phase formation in the soft melting dripping process of the sintered ore, and feedback is given to the management system to suggest that the composition and content of the sintering flux need to be adjusted to control the quality fluctuation of the sintering flux.

[0033] The accurate prediction model integrates metallurgical thermodynamics with data, realizes the integration of mechanism and data, enhances the interpretability of the data model, and facilitates the rapid understanding of field engineers. The model analysis results are closely combined with the actual situation on site, and multiple decision-making suggestions are fed back to provide data support for production review and enterprise optimization direction planning.

[0034] 104. Determine the quantitative influence of the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, adjust the sintering process parameters at the current moment, and optimize the soft-melting dripping temperature of the sintered ore.

[0035] Specifically, the specific implementation process of determining the quantitative impact is as follows: based on the accurate prediction model, the sample data is input into the accurate prediction model, and the machine learning interpretable analysis algorithm is used to obtain the theoretical value of the sintering ore soft melting dripping temperature and the specific numerical impact of the sintering process parameters on the expected value of the model. The specific impact values ​​corresponding to the sintering process parameters are sorted, and the sintering process parameters and their specific impact values ​​that affect the sintering ore soft melting dripping temperature exceeding the preset value are obtained. The sintering process parameters are adjusted according to the impact value, and the other parameters input into the accurate prediction model are kept unchanged to achieve the optimization of the sintering ore soft melting dripping temperature.

[0036] By fully mining the historical data information of the sintering process, the rough trend law summary is changed to an accurate quantitative relationship summary, which improves the accuracy of the analysis. In the historical data modeling stage and the actual production optimization stage, based on the performance of the model and the analysis results of the model, decision-making suggestions are fed back from different angles to provide data support for the optimization of the sintering ore soft melting dripping temperature, sintering production review and optimization direction planning of steel enterprises.

[0037] Furthermore, on the basis of the above-mentioned embodiment, the present embodiment also includes: obtaining the actual composition of the sintered ore at the previous moment before the current moment and the theoretical composition of the sintered ore at the previous moment, and predicting the actual value of the soft melting dripping temperature of the sintered ore at the previous moment through an accurate prediction model; comparing the actual composition of the sintered ore at the previous moment and the theoretical composition of the sintered ore at the previous moment, combining the actual value of the soft melting dripping temperature of the sintered ore at the previous moment with the accurate prediction model, feedback the influence of the composition deviation on the actual value of the soft melting dripping temperature of the sintered ore, adjust the raw material composition and calculate the correction coefficient.

[0038] Specifically, the specific method of adjusting the raw material composition and calculating the correction coefficient is: When the sintered ore has a TFe content of 0.05, the theoretical TFe content of the sintered ore is the same as the actual TFe content of the sintered ore. However, in actual production, fluctuations in raw materials and equipment may cause the theoretical TFe content of the sintered ore to be different from the actual TFe content of the sintered ore. Determine the theoretical TFe content of the sintered ore and the corresponding actual TFe content of the sintered ore, and determine the raw material TFe correction coefficient through the theoretical TFe content of the sintered ore and the corresponding actual TFe content of the sintered ore. Multiply the raw material TFe correction coefficient by the calculated results of the proportion and composition to obtain the adjusted theoretical TFe content of the sintered ore. As the theoretical TFe content of the sintered ore and the actual TFe content of the sintered ore are continuously updated, the raw material TFe correction coefficient will also be adjusted accordingly.

[0039] For example, the theoretical TFe content of sintered ore is 56.5%, and the corresponding actual TFe content of sintered ore is 56%. At this time, the raw material TFe correction factor is calculated as 1-(56.5-56) / 56.5. When calculating the theoretical TFe content of sintered ore, it is necessary to multiply the raw material TFe correction factor based on the calculation results of the proportion and composition.

[0040] At the same time, the specific way of feeding back the quantitative influence of the sinter composition deviation on the sinter soft melting dripping temperature at the previous moment is: Step 1: Calculate the amount of sintered ore liquid phase corresponding to the theoretical composition of the sintered ore including the TFe content and the corresponding theoretical value of the sintered ore soft melting dripping temperature, and obtain the specific numerical influence Influence1 of the theoretical value of the sintered ore soft melting dripping temperature on the expected value of the model.

[0041] Step 2: After determining the TFe deviation of the sintered ore, modify the TFe content in the theoretical composition of the sintered ore, calculate the sintered ore liquid phase amount and the corresponding theoretical value of the sintered ore soft melting dripping temperature corresponding to the theoretical composition of the sintered ore including the modified TFe content, and obtain the specific numerical influence Influence2 of the theoretical value of the sintered ore soft melting dripping temperature on the expected value of the model.

[0042] The third step is to compare Influence 1 and Influence 2 to determine the specific impact of the TFe content deviation of the sintered ore on the soft melting dripping temperature of the sintered ore, and feed back the specific impact to the terminal platform. The terminal platform receives the engineer's instructions. When the engineer's instructions indicate that the TFe content deviation of the sintered ore has a greater impact on the soft melting dripping temperature of the sintered ore, the feedback is that it is necessary to organize and invest special forces to solve the large deviation of the TFe content of the sintered ore.

[0043] Based on the same general inventive concept, the present invention also protects an adaptive management device for the soft-melting dripping performance of sintered ore. The adaptive management device for the soft-melting dripping performance of sintered ore described below and the adaptive management method for the soft-melting dripping performance of sintered ore described above can refer to each other.

[0044] Figure 2 It is a schematic diagram of the structure of the adaptive management device for sintered ore soft melting dripping performance provided in this embodiment.

[0045] like Figure 2 As shown, this embodiment provides a sintered ore soft melting dripping performance adaptive management device, comprising: The collection module 201 is used to collect the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the current sintering ore theoretical composition; The calculation module 202 is used to calculate the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment based on the thermodynamic theory; Prediction module 203, used to input the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into the accurate prediction model, and output the actual value of the soft melting dripping temperature of the sintered ore at the current moment. The accurate prediction model is constructed by integrating mechanism and data; The optimization module 204 is used to determine the current theoretical value of the sintered ore soft melting dripping temperature and the quantitative influence of the current sintering process parameters on the current actual value of the sintered ore soft melting dripping temperature, adjust the current sintering process parameters, and optimize the sintered ore soft melting dripping temperature.

[0046] Figure 3 It is a schematic diagram of the structure of the electronic device provided in this embodiment.

[0047] like Figure 3 As shown, the electronic device may include: a processor (processor) 310, a communication interface (Communications Interface) 320, a memory (memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call the logic instructions in the memory 330 to execute the adaptive management method of the soft melting dripping performance of the sintered ore, which method includes: collecting the sintering raw material composition, the sintering raw material ratio and the sintering process parameters at the current moment, and calculating the theoretical composition of the sintered ore at the current moment; based on the thermodynamic theory, calculating the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; inputting the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into the accurate prediction model, and outputting the actual value of the soft melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; determining the quantitative influence of the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft melting dripping temperature of the sintered ore at the current moment, adjusting the sintering process parameters at the current moment, and realizing the optimization of the soft melting dripping temperature of the sintered ore.

[0048] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0049] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the adaptive management method of the soft-melting dripping performance of sintered ore provided by the above-mentioned methods. The method includes: collecting the sintering raw material composition, the sintering raw material ratio and the sintering process parameters at the current moment, and calculating the theoretical composition of the sintered ore at the current moment; based on the thermodynamic theory, calculating the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; inputting the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into an accurate prediction model, and outputting the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; determining the quantitative influence of the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, adjusting the sintering process parameters at the current moment, and realizing the optimization of the soft-melting dripping temperature of the sintered ore.

[0050] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the adaptive management method for the soft-melting dripping performance of sintered ore provided by the above-mentioned methods, the method comprising: collecting the sintering raw material composition, the sintering raw material ratio and the sintering process parameters at the current moment, and calculating the theoretical composition of the sintered ore at the current moment; based on the thermodynamic theory, calculating the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; inputting the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into an accurate prediction model, and outputting the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; determining the quantitative influence of the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, adjusting the sintering process parameters at the current moment, and optimizing the soft-melting dripping temperature of the sintered ore.

[0051] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0052] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptively managing the dripping performance of sintered ore soft melting, characterized in that: include: Collect the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the current sintering ore theoretical composition; Based on thermodynamic theory, calculating a theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; Input the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into the accurate prediction model, and output the actual value of the soft melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; Determine the quantitative influence of the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment on the actual value of the soft melting dripping temperature of the sintered ore at the current moment, adjust the sintering process parameters at the current moment, and optimize the soft melting dripping temperature of the sintered ore.

2. The method for adaptively managing sintered ore soft melting dripping performance according to claim 1, characterized in that: Also includes: Obtain the actual composition of the sintered ore at the last moment before the current moment and the theoretical composition of the sintered ore at the last moment, and predict the actual value of the soft melting dripping temperature of the sintered ore at the last moment through an accurate prediction model; Compare the actual composition of the sintered ore at the previous moment with the theoretical composition of the sintered ore at the previous moment, combine the actual value of the soft melting dripping temperature of the sintered ore at the previous moment with the accurate prediction model, feedback the impact of the composition deviation on the actual value of the soft melting dripping temperature of the sintered ore, adjust the raw material composition and calculate the correction coefficient.

3. The method for adaptively managing sintered ore soft melting dripping performance according to claim 1, characterized in that: Before collecting the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, the method further includes: Determine the theoretical value samples of sinter ore soft melting dripping temperature, sintering process parameter samples and actual value samples of sinter ore soft melting dripping temperature; The prediction model is constructed by using the theoretical value samples of the sintered ore soft melting dripping temperature, the sintering process parameter samples and the actual value samples of the sintered ore soft melting dripping temperature for training.

4. The method for adaptively managing sintered ore soft melting dripping performance according to claim 3, characterized in that: The method for determining the theoretical value sample of the sintered ore soft melting dripping temperature, the sintering process parameter sample and the actual value sample of the sintered ore soft melting dripping temperature includes: Collect historical sintered ore soft melting dripping test data, historical sintered ore composition data and historical sintering process parameters of steel enterprises; Through time index alignment and data frequency integration, the collected historical sintered ore soft melting dripping experimental data, historical sintered ore composition data, and historical sintering process parameters are matched to obtain sintered ore soft melting dripping temperature theoretical value samples, sintering process parameter samples, and sintered ore soft melting dripping temperature actual value samples.

5. The method for adaptively managing sintered ore soft melting dripping performance according to claim 3, characterized in that: After the prediction model is constructed, the following steps are also included: When the prediction accuracy of the prediction model exceeds a preset value, determining that the prediction model is an accurate prediction model; The accurate prediction model is analyzed to obtain the theoretical value of the sintered ore soft melting dripping temperature and the ranking and influence trend of the sintering process parameters on the sintered ore soft melting dripping temperature.

6. The method for adaptively managing sintered ore soft melting dripping performance according to claim 5, characterized in that: Also includes: When the prediction accuracy of the prediction model does not exceed a preset value, determining that the prediction model is an inaccurate prediction model; Controllable sintering process parameters are added to the sintering process parameters to improve the accuracy of the inaccurate prediction model and provide feedback on the impact of uncontrollable parameters.

7. The method for adaptively managing sintered ore soft melting dripping performance according to any one of claims 1 to 6, characterized in that: After adjusting the sintering process parameters at the current moment, the method further includes: Based on the quantitative impact, multiple decision-making suggestions are fed back to provide data support for production review and enterprise optimization.

8. The method for adaptively managing sintered ore soft melting dripping performance according to any one of claims 1 to 6, characterized in that: The sintered ore soft melting dripping temperature includes: softening start temperature, softening end temperature, melting start temperature and dripping temperature.

9. A sintered ore soft melting dripping performance adaptive management device, characterized in that: include: The collection module is used to collect the current sintering raw material composition, the current sintering raw material ratio and the current sintering process parameters, and calculate the current sintering ore theoretical composition; A calculation module, for calculating, based on thermodynamic theory, a theoretical value of the soft melting dripping temperature of the sintered ore at the current moment corresponding to the theoretical composition of the sintered ore at the current moment; A prediction module, used for inputting the theoretical value of the soft melting dripping temperature of the sintered ore at the current moment and the sintering process parameters at the current moment into an accurate prediction model, and outputting the actual value of the soft melting dripping temperature of the sintered ore at the current moment, wherein the accurate prediction model is constructed by integrating mechanism and data; The optimization module is used to determine the theoretical value of the soft-melting dripping temperature of the sintered ore at the current moment and the quantitative influence of the sintering process parameters at the current moment on the actual value of the soft-melting dripping temperature of the sintered ore at the current moment, adjust the sintering process parameters at the current moment, and realize the optimization of the soft-melting dripping temperature of the sintered ore.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for adaptively managing the soft melting dripping performance of sintered ore as described in any one of claims 1 to 8 is implemented.

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