Coking index monitoring analysis method, system and equipment based on small coke oven and medium

Abstract

The invention discloses a coking index monitoring analysis method, system and equipment based on a small coke oven and a medium. Acquiring initial coal blending ratio data and corresponding experimental regulation data; controlling an experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and acquiring experimental process data and experimental result data in real time; generating comprehensive result data according to the initial coal blending ratio data, the experimental adjustment data, the experimental process data and the experimental result data; and after multiple experiments are carried out, determining a target coal blending ratio based on comprehensive result data corresponding to each experiment. According to the method, the initial numerical value of the experiment is set, various data parameters in the process are measured, the characteristic parameters of coke after the experiment is finished are detected, and the target coal blending ratio is scientifically determined according to analysis and statistics of result data; and meanwhile, the characteristic parameters of the coke under the coal blending ratio are ensured to have good correlation with the large coke oven.

Description

Technical Field

[0001] The present invention relates to the field of coke production, and particularly to a method, a system, a computing device and a computer storage medium for monitoring and analyzing coking indexes based on a small coke oven. Background Art

[0002] The iron and steel industry is an important basic industry of the national economy. However, with the strengthening of environmental protection awareness and the adjustment of the energy structure, the traditional coking technology can no longer meet the needs of modern iron and steel production due to its heavy pollution and low production efficiency.

[0003] Meanwhile, the market's requirements for the quality of coke are getting higher and higher, and the incoming washed coal is required to have the characteristics of low ash, low sulfur and good coking property. This has led to a significant increase in the production cost of enterprises. In this regard, in order to reduce costs, each iron and steel enterprise is looking for a way to reasonably optimize the coal blending ratio, and through a reasonable coal blending ratio, the overall production cost is reduced while ensuring the coke quality and environmental protection requirements. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method for monitoring and analyzing coking indexes based on a small coke oven, and a corresponding system, a computing device and a computer storage medium for monitoring and analyzing coking indexes based on a small coke oven.

[0005] According to one aspect of the present invention, there is provided a method for monitoring and analyzing coking indexes based on a small coke oven, the method comprising: Obtaining initial coal blending ratio data and corresponding experimental adjustment data; Controlling the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtaining experimental process data and experimental result data in real time; Generating comprehensive result data according to the initial coal blending ratio data, the experimental adjustment data, the experimental process data and the experimental result data; After multiple experiments are carried out, determining the target coal blending ratio based on the corresponding comprehensive result data of each time.

[0006] In the above solution, the obtaining of the initial coal blending ratio data and the corresponding experimental adjustment data further includes: The initial coal blending ratio data is the mixing ratio of different coals preset by the user; the experimental adjustment data at least includes the coke oven temperature, the furnace internal pressure and the load pressure.

[0007] In the above solution, the controlling of the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtaining the experimental process data and the experimental result data in real time further includes: Starting the experiment based on the initial coal blending ratio data and the corresponding experimental adjustment data; During the experiment, control the operating parameters of the small coke oven with a load based on the experimental adjustment data; Complete the experimental process according to the operating parameters and obtain the experimental process data in real time; among them, the experimental process data at least includes the temperature, pressure, oxygen content and poisonous gas data in the furnace.

[0008] In the above solution, controlling the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, obtaining the experimental process data and the experimental result data in real time, further includes: After the experiment, conduct a composition analysis on the experimental products to determine the coke ratio of coke in the experimental products; Based on the weight of the experimental raw materials, combined with the weight of coke in the experimental products, calculate the coke yield; Analyze the coke in the experimental products to determine the coke characteristic parameters; among them, the coke characteristic parameters at least include the coke mechanical strengths M25 and M10 and the coke hot strength indexes CRI and CSR; Integrate the coke ratio, coke yield and coke characteristic parameters to obtain the experimental result data.

[0009] In the above solution, after conducting multiple experiments, determining the target coal blending ratio based on the corresponding comprehensive result data of each time, further includes: Conduct multiple experiments by setting different initial coal blending ratio data and the corresponding experimental adjustment data; According to the corresponding comprehensive result data of each time, generate a target experimental data table according to the changes in the initial coal blending ratio data and the corresponding experimental adjustment data; Determine the target coal blending ratio from the target experimental data table according to the user's needs.

[0010] In the above solution, the method further includes: Establish a poisonous gas generation model based on deep learning and train the poisonous gas generation model by constructing a training set to obtain a trained poisonous gas generation model; Obtain the initial coal blending ratio data and the corresponding experimental adjustment data of the current experiment, and collect the current released component data according to the sensors set at the preset positions; Input the current initial coal blending ratio data, experimental adjustment data and released component data into the trained poisonous gas generation model to predict the type and generation time of poisonous gases generated during the subsequent process of the current experiment, and obtain the prediction result; Based on the prediction result, control the corresponding safety device of the small coke oven to be turned on or off.

[0011] In the above solution, the method further includes: The poisoning gas generation model determines the difference between the predicted value and the true value through the mean square error loss function MSE during the training process. The mean square error loss function is where is the number of poisoning gas types; is the th sample data; is the predicted value of the th sample data, is the actual value of the th sample data; For each type of poisoning gas, calculate the difference between the corresponding predicted value and the actual value, and determine the adjustment coefficient corresponding to each type of poisoning gas ; where is the adjustment coefficient of the th type of poisoning gas; Generate a model correction coefficient based on the adjustment coefficient, and correct the model to complete the model training. The model correction coefficient is where and are the average actual value and the average predicted value of the sample data of the th type of poisoning gas respectively; is the learning rate.

[0012] According to another aspect of the present invention, a coking index monitoring and analysis system based on a small coke oven is provided, including: an initial data determination module, an experimental operation module, a data processing module, and a target determination module; where The initial data determination module is used to obtain the initial coal blending ratio data and the corresponding experimental adjustment data; The experimental operation module is used to control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtain the experimental process data and the experimental result data in real time; The data processing module is used to generate comprehensive result data according to the initial coal blending ratio data, the experimental adjustment data, the experimental process data, and the experimental result data; The target determination module is used to determine the target coal blending ratio based on the corresponding comprehensive result data after multiple experiments.

[0013] According to still another aspect of the present invention, a computing device is provided, including: a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to a coking index monitoring and analysis method based on a small coke oven as described above.

[0014] According to another aspect of the present invention, there is provided a computer storage medium, in which at least one executable instruction is stored, and the executable instruction causes a processor to perform operations corresponding to a coking index monitoring and analysis method based on a small coke oven as described above.

[0015] According to the technical solution provided by the present invention, initial coal blending ratio data and corresponding experimental adjustment data are obtained; the experimental process is controlled based on the initial coal blending ratio data and the corresponding experimental adjustment data, and experimental process data and experimental result data are obtained in real time; comprehensive result data is generated according to the initial coal blending ratio data, experimental adjustment data, experimental process data, and experimental result data; after multiple experiments are carried out, the target coal blending ratio is determined based on the corresponding comprehensive result data for each time. Based on the technical solution provided by the present invention, by obtaining the initial coal blending ratio data and the corresponding experimental adjustment data, the initial operating parameters of the current experiment are determined, and the experimental process is controlled according to the experimental adjustment data to keep it within a preset reaction range, effectively reducing the risk of errors in experimental results caused by parameter changes, ensuring the stability of the experiment, and thus improving the accuracy of experimental results; by analyzing the experimental products, the coke yield and corresponding characteristic parameters are determined, and the actual quality of the produced coke is accurately judged, which helps the subsequent scientific selection of production conditions and coal blending ratio; based on multiple experiments with controlled variables, the experimental data corresponding to each experiment are obtained, and an experimental data table is comprehensively generated. Therefore, based on user requirements, the target coal blending ratio that meets their needs can be intuitively and clearly selected, and the subsequent production process under different requirements can be better scientifically guided. In addition, by constructing a poisonous gas generation model, the generation time and generation amount of poisonous gas during the experimental process are accurately predicted, and accordingly, the corresponding pollution treatment device is controlled to start running in advance, better ensuring the safety of the experimental operation, reducing the environmental pollution finally generated by the experiment, and at the same time, predicting the generation and emission of pollutants in the actual production process and giving effective guidance.

[0016] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification and the drawings.

[0017] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 It shows a schematic flowchart of a coking index monitoring and analysis method based on a small coke oven according to an embodiment of the present invention; Figure 2 It shows a schematic flowchart of an experimental operation and data acquisition method based on a small coke oven according to an embodiment of the present invention; Figure 3 It shows a schematic flowchart of a prediction method for the generation of poisonous gases according to an embodiment of the present invention; Figure 4 It shows a structural block diagram of a coking index monitoring and analysis system based on a small coke oven according to an embodiment of the present invention; Figure 5 It shows a schematic structural diagram of a computing device according to an embodiment of the present invention. Detailed Embodiments

[0019] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0020] Figure 1 It shows a schematic flowchart of a coking index monitoring and analysis method based on a small coke oven according to an embodiment of the present invention. The method includes the following steps: Step S101, obtain the initial coal blending ratio data and the corresponding experimental adjustment data.

[0021] Preferably, the initial coal blending ratio data is the mixing ratio of different coals preset by the user; the experimental adjustment data at least includes the coke oven temperature, the furnace internal pressure, and the load pressure. Among them, the load pressure is the pressure applied to the experimental sample in the furnace.

[0022] Preferably, the initial coal blending ratio data further includes the weights corresponding to different coals and the overall weight of the sample.

[0023] Step S102, control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtain the experimental process data and the experimental result data in real time.

[0024] Step S103, generate comprehensive result data according to the initial coal blending ratio data, the experimental adjustment data, the experimental process data, and the experimental result data.

[0025] Preferably, the experimental result data at least includes the components contained in the experimental product, the corresponding weights, and the proportions.

[0026] Step S104, after conducting multiple experiments, determine the target coal blending ratio based on the comprehensive result data corresponding to each experiment.

[0027] Specifically, conduct multiple experiments by setting different initial coal blending ratio data and corresponding experimental adjustment data; According to the comprehensive result data corresponding to each experiment, generate a target experimental data table according to the changes in the initial coal blending ratio data and the corresponding experimental adjustment data; Determine the target coal blending ratio from the target experimental data table according to user requirements.

[0028] Preferably, each experiment is completed based on the control variable method by setting different initial coal blending ratios or experimental adjustment data; the experimental process data and experimental result data generated from each experiment are sorted out and listed together to generate a target experimental data table; The user can determine the coal blending ratio that best meets the requirements according to the required components and their proportions.

[0029] According to the coking index monitoring and analysis method based on a small coke oven provided in this embodiment, obtain the initial coal blending ratio data and the corresponding experimental adjustment data; control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtain the experimental process data and experimental result data in real time; generate comprehensive result data according to the initial coal blending ratio data, experimental adjustment data, experimental process data, and experimental result data; after conducting multiple experiments, determine the target coal blending ratio based on the comprehensive result data corresponding to each experiment. Through the coking index monitoring and analysis method based on a small coke oven provided in this embodiment, by obtaining the initial coal blending ratio data and the corresponding experimental adjustment data, determine the initial operating parameters of the current experiment, and control the experimental process according to the experimental adjustment data to keep it within the preset reaction range, effectively reducing the risk of errors in experimental results caused by parameter changes, ensuring the stability of the experiment, and thus improving the accuracy of experimental results; based on multiple experiments with controlled variables, obtain the experimental data corresponding to each experiment, and comprehensively generate an experimental data table. Therefore, based on user requirements, the target coal blending ratio that meets their needs can be intuitively and clearly selected, and the subsequent production process under different requirements can be better scientifically guided.

[0030] Figure 2 Fig. shows a flowchart of an experimental operation and data acquisition method based on a small coke oven according to an embodiment of the present invention; As Figure 2 shown, the method includes the following steps: Step S201, start the experiment based on the initial coal blending ratio data and the corresponding experimental adjustment data.

[0031] Step S202, during the experiment, control the operating parameters of the load small coke oven based on the experimental adjustment data.

[0032] Specifically, based on the experimental data, control the temperature and pressure inside the load small coke oven and control the coal charging and / or coke pushing mechanism.

[0033] Preferably, heat the coal cake by three-stage heating; use temperature sensors and pressure sensors to collect the temperature and pressure inside the furnace, and based on the collected temperature and pressure data, adjust and control the heating system to ensure the stability of the furnace pressure and temperature.

[0034] Step S203, complete the experimental process according to the operating parameters and obtain the experimental process data in real time.

[0035] Preferably, the experimental process data includes at least the temperature, pressure, oxygen content, and poisonous gas data inside the furnace.

[0036] Furthermore, the poisonous gas data can include the types of poisonous gases and the corresponding concentrations for each type. Among them, the types of poisonous gases can at least include hydrogen sulfide, ammonia, carbon monoxide, and benzene. Users can select and set the corresponding sensors according to actual needs to obtain the relevant data of other types of poisonous gases, which is not limited here.

[0037] Step S204, after the experiment, conduct a component analysis on the experimental product to obtain the experimental result data.

[0038] Specifically, based on the component analysis of the experimental product, determine the coke ratio of coke in the experimental product; Based on the weight of the experimental raw materials and combined with the weight of coke in the experimental product, calculate the coke yield; Conduct an analysis on the coke in the experimental product to determine the coke characteristic parameters; among them, the coke characteristic parameters at least include the coke mechanical strengths M25 and M10, and the coke thermal strength indexes CRI (Coke Reactivity Index) and CSR (Coke Strength Retention); Integrate the coke ratio, coke yield, and coke characteristic parameters to obtain the experimental result data.

[0039] Preferably, obtain the total weight and coke weight of the experimental product, and obtain the coke ratio through the ratio of the coke weight to the total weight of the experimental product; The mechanical strengths M25 and M10 of coke can be measured by a coke mechanical strength determination drum machine; Use a high-temperature reactor for heating and then conduct a drum test to measure the CRI and CSR data.

[0040] According to the above method, by controlling the experimental process, it can be kept within the preset reaction range, effectively reducing the risk of errors in experimental results caused by parameter changes, ensuring the stability of the experiment, and further improving the accuracy of experimental results; by obtaining process data, the changes of various parameters in the experimental process can be effectively understood, guiding the production process, and understanding the pollutants that may be generated in this process, facilitating subsequent cleaning treatment; by analyzing the experimental products, the coke yield and corresponding characteristic parameters are determined, accurately judging the actual quality of the produced coke, which helps to scientifically select the production conditions and coal blending ratio subsequently.

[0041] Figure 3 Fig. shows a schematic flow chart of a prediction method for the generation of poisonous and harmful gases according to an embodiment of the present invention; As Figure 3 shown, the method includes the following steps: Step S301, establish a poisonous and harmful gas generation model based on deep learning and train the poisonous and harmful gas generation model by constructing a training set to obtain a trained poisonous and harmful gas generation model.

[0042] Specifically, a training set is constructed based on the initially collected coal blending ratio data, experimental adjustment data, and experimental process data.

[0043] Preferably, during the training process of the poisonous and harmful gas generation model, the difference between the predicted value and the true value is determined by the mean square error loss function MSE, and the mean square error loss function is where is the number of types of poisonous and harmful gases; is the th sample data; is the th predicted value of the sample data, is the th actual value of the sample data; For each type of poisonous and harmful gas, calculate the difference between the corresponding predicted value and the actual value, and determine the adjustment coefficient corresponding to each type of poisonous and harmful gas; where is the adjustment coefficient of the th type of poisonous and harmful gas; Generate a model correction coefficient based on the adjustment coefficient to correct the model to complete the model training, and the model correction coefficient is where and are respectively the average actual value and the average predicted value of the sample data of the th type of poisonous and harmful gas; is the learning rate.

[0044] Step S302, obtaining the initial coal blending ratio data of the current experiment and the corresponding experimental adjustment data, and collecting the current released component data according to the sensor set at the preset position.

[0045] Specifically, the sensor can be selected according to needs to measure specific pollution components.

[0046] Step S303, input the current initial coal blending ratio data, experimental adjustment data and released component data into the trained poisonous gas generation model, predict the type and generation time of the poisonous gas generated in the subsequent process of the current experiment, and obtain the prediction result.

[0047] Step S304: Based on the prediction result, the safety device corresponding to the small coke oven is controlled to be turned on or off.

[0048] Preferably, based on the prediction results, the safety device is opened in advance and closed later, and the pollutant components are measured again using the sensor before closing, and after confirming that there are no corresponding pollutants, the safety device is confirmed to be closed.

[0049] According to the above method, by constructing a toxic gas generation model, the time and amount of toxic gas generation during the experiment are accurately predicted, and the corresponding pollution treatment equipment is controlled to start operation in advance, which better ensures the safety of the experimental operation and reduces the environmental pollution ultimately generated by the experiment. At the same time, the generation and emission of pollutants in the actual production process are predicted, and effective guidance is given.

[0050] Figure 4 A structural block diagram of a coking index monitoring and analysis system based on a small coke oven according to an embodiment of the present invention is shown; like Figure 4 As shown, the system includes: an initial data determination module 401, an experiment operation module 402, a data processing module 403 and a target determination module 404; wherein, The initial data determination module 401 is used to obtain initial coal blending ratio data and corresponding experimental adjustment data.

[0051] Specifically, the initial data determination module 401 is further used to: The initial coal blending ratio data is the mixing ratio of different coals preset by the user; the experimental adjustment data at least includes the coke oven temperature, the pressure inside the oven and the load pressure.

[0052] The experimental operation module 402 is used to control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and to obtain the experimental process data and experimental result data in real time.

[0053] Specifically, the experimental operation module 402 is further used for starting the experiment based on the initial coal blending ratio data and the corresponding experimental adjustment data; controlling the operating parameters of the small coke oven based on the experimental adjustment data during the experiment; completing the experimental process according to the operating parameters and obtaining the experimental process data in real time; wherein, the experimental process data at least includes the temperature, pressure, oxygen content and poisonous gas data in the furnace.

[0054] Specifically, the experimental operation module 402 is further used for after the experiment, performing a component analysis on the experimental product to determine the coke ratio of the coke in the experimental product; calculating the coke yield based on the weight of the experimental raw materials and the weight of the coke in the experimental product; analyzing the coke in the experimental product to determine the coke characteristic parameters; wherein the coke characteristic parameters at least include the coke mechanical strengths M25 and M10 and the coke thermal strength indexes CRI and CSR; obtaining the experimental result data by integrating the coke ratio, coke yield and coke characteristic parameters.

[0055] Specifically, the experimental operation module 402 is further used for establishing a poisonous gas generation model based on deep learning and training the poisonous gas generation model by constructing a training set to obtain a trained poisonous gas generation model; (constructing a training set based on the historically collected initial coal blending ratio data, experimental adjustment data and experimental process data) obtaining the initial coal blending ratio data and the corresponding experimental adjustment data of the current experiment, and collecting the current release component data according to the sensors set at the preset positions; inputting the current initial coal blending ratio data, experimental adjustment data and release component data into the trained poisonous gas generation model to predict the type and generation time of the poisonous gas generated during the subsequent process of the current experiment, and obtaining a prediction result; controlling the corresponding safety device of the small coke oven to be turned on or off based on the prediction result.

[0056] Preferably, the experimental operation module 402 is further used for the poisonous gas generation model determines the difference between the predicted value and the true value through the mean square error loss function MSE during the training process, and the mean square error loss function is Among them, is the number of types of poisonous gases; is the th sample data; is the predicted value of the th sample data, is the th actual value of the sample data; For each type of poisonous gas, calculate the difference between the corresponding predicted value and the actual value, and determine the adjustment coefficient corresponding to each type of poisonous gas ; among them, is the adjustment coefficient of the th type of poisonous gas; Generate a model correction coefficient based on the adjustment coefficient, and correct the model to complete model training. The model correction coefficient is Among them, and are respectively the average actual value and the average predicted value of the sample data of the th type of poisonous gas; is the learning rate.

[0057] The data processing module 403 is used to generate comprehensive result data according to the initial coal blending ratio data, experimental adjustment data, experimental process data, and experimental result data.

[0058] The target determination module 404 is used to determine the target coal blending ratio based on the corresponding comprehensive result data after multiple experiments.

[0059] Specifically, the target determination model 404 is further used for, Conduct multiple experiments by setting different initial coal blending ratio data and corresponding experimental adjustment data; Generate a target experimental data table according to the corresponding comprehensive result data for each time, according to the changes in the initial coal blending ratio data and the corresponding experimental adjustment data; Determine the target coal blending ratio from the target experimental data table according to the user's needs.

[0060] The coking index monitoring and analysis system based on a small coke oven provided by this embodiment includes: an initial data determination module, an experimental operation module, a data processing module, and a target determination module. Among them, the initial data determination module is used to obtain the initial coal blending ratio data and the corresponding experimental adjustment data. The experimental operation module is used to control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtain the experimental process data and the experimental result data in real time. The data processing module is used to generate comprehensive result data according to the initial coal blending ratio data, the experimental adjustment data, the experimental process data, and the experimental result data. The target determination module is used to determine the target coal blending ratio based on the comprehensive result data corresponding to each experiment after multiple experiments. Through the coking index monitoring and analysis system based on a small coke oven provided by this embodiment, by obtaining the initial coal blending ratio data and the corresponding experimental adjustment data, the initial operation parameters of the current experiment are determined, and the experimental process is controlled according to the experimental adjustment data to keep it within the preset reaction range, effectively reducing the risk of errors in the experimental results caused by parameter changes, ensuring the stability of the experiment, and thus improving the accuracy of the experimental results. By analyzing the experimental products, the coke yield and the corresponding characteristic parameters are determined, and the actual quality of the produced coke is accurately judged, which helps the subsequent scientific selection of production conditions and coal blending ratio. Based on multiple experiments with controlled variables, the experimental data corresponding to each experiment are obtained, and an experimental data table is comprehensively generated. Thus, based on the user's needs, the target coal blending ratio that meets their needs can be intuitively and clearly selected, and the production process under different subsequent needs can be better scientifically guided. In addition, by constructing a poisonous gas generation model, the generation time and generation amount of poisonous gas during the experiment are accurately predicted, and accordingly, the corresponding pollution treatment device is controlled to start running in advance, better ensuring the safety of the experimental operation, reducing the environmental pollution generated by the experiment, and at the same time, predicting the generation and emission of pollutants in the actual production process and giving effective guidance.

[0061] The present invention also provides a non-volatile computer storage medium, which stores at least one executable instruction, and the executable instruction can execute a coking index monitoring and analysis method based on a small coke oven in any of the above method embodiments.

[0062] Figure 5 The structural schematic diagram of a computing device according to an embodiment of the present invention is shown. The specific implementation of the computing device in the specific embodiment of the present invention is not limited.

[0063] As Figure 5 shown, the computing device may include: a processor 502, a communications interface 504, a memory 506, and a communication bus 508.

[0064] Wherein: The processor 502, the communication interface 504, and the memory 506 communicate with each other through the communication bus 508.

[0065] The communication interface 504 is used to communicate with network elements of other devices such as clients or other servers.

[0066] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the above-mentioned embodiments of the coking index monitoring and analysis method based on a small coke oven.

[0067] Specifically, the program 510 may include program code, and the program code includes computer operation instructions.

[0068] The processor 502 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the computing device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0069] The memory 506 is used to store the program 510. The memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0070] The program 510 is specifically used to cause the processor 502 to execute a coking index monitoring and analysis method based on a small coke oven in any of the above method embodiments. For the specific implementation of each step in the program 510, reference may be made to the corresponding steps and units in the above-mentioned embodiments of the coking index monitoring and analysis method based on a small coke oven, which will not be elaborated here. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding process descriptions in the foregoing method embodiments, which will not be elaborated here.

[0071] The algorithms and displays provided herein are not inherently related to any specific computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. Based on the above description, the structure required to construct such systems is obvious. In addition, the present invention is not directed to any specific programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the description of the specific language above is to disclose the best mode of the present invention.

[0072] In the description provided herein, numerous specific details are set forth. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0073] Similarly, it should be understood that in order to streamline this disclosure and assist in understanding one or more of the various inventive aspects, in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment, as reflected in the claims. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0074] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from those of the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0075] In addition, those skilled in the art will understand that although some of the embodiments described herein include certain features included in other embodiments but not others, the combination of features of different embodiments is meant to be within the scope of the invention and forms different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0076] Each component embodiment of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for performing part or all of the methods described herein. Such a program for implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for monitoring and analyzing coking indicators based on a small coke oven, comprising: Obtaining initial coal blending ratio data and corresponding experimental adjustment data; Control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtain the experimental process data and experimental result data in real time; Generate comprehensive result data based on initial coal blending ratio data, experimental adjustment data, experimental process data and experimental result data; After conducting multiple experiments, the target coal blending ratio is determined based on the corresponding comprehensive result data of each experiment.

2. The method according to claim 1, characterized in that The obtaining of the initial coal blending ratio data and the corresponding experimental adjustment data further includes: The initial coal blending ratio data is the mixing ratio of different coals preset by the user; the experimental adjustment data at least includes the coke oven temperature, the pressure inside the oven and the load pressure.

3. The method according to claim 1, characterized in that The controlling of the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtaining the experimental process data and the experimental result data in real time, further includes: Start the experiment based on the initial coal blending ratio data and the corresponding experimental adjustment data; During the experiment, the operating parameters of the small coke oven under load are controlled based on the experimental adjustment data; The experimental process is completed according to the operating parameters and the experimental process data is obtained in real time; wherein the experimental process data at least includes the temperature, pressure, oxygen content and poisonous gas data in the furnace.

4. The method according to claim 1, characterized in that The controlling of the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and obtaining the experimental process data and the experimental result data in real time, further includes: After the experiment, the experimental product is analyzed for its composition to determine the proportion of coke in the experimental product; The coke yield is calculated based on the weight of the experimental raw materials and the weight of the coke in the experimental product; Analyze the coke in the experimental product to determine the coke characteristic parameters; the coke characteristic parameters at least include the coke mechanical strength M25 and M10 and the coke hot strength index CRI and CSR; The experimental result data are obtained by comprehensively considering the coke ratio, coke yield and coke characteristic parameters.

5. The method according to claim 1, characterized in that After conducting multiple experiments, determining the target coal blending ratio based on the comprehensive result data corresponding to each experiment further includes: Conduct multiple experiments by setting different initial coal blending ratio data and corresponding experimental adjustment data; According to the corresponding comprehensive result data of each time, according to the changes of the initial coal blending ratio data and the corresponding experimental adjustment data, a target experimental data table is generated; According to user needs, the target coal blending ratio is determined from the target experimental data table.

6. The method according to claim 1, characterized in that The method further comprises: Establishing a poisonous gas generation model based on deep learning and training the poisonous gas generation model by constructing a training set to obtain a trained poisonous gas generation model; Obtaining the initial coal blending ratio data of the current experiment and the corresponding experimental adjustment data, and collecting the current release component data according to the sensor set at the preset position; Input the current initial coal blending ratio data, experimental adjustment data and released component data into the trained toxic gas generation model, predict the type and generation time of the toxic gas generated in the subsequent process of the current experiment, and obtain the prediction result; Based on the prediction results, the corresponding safety devices of the small coke oven are controlled to be opened or closed.

7. The method according to claim 6, characterized in that The method further comprises: The poisonous gas generation model determines the difference between the predicted value and the true value through the mean square error loss function MSE during the training process. The mean square error loss function is in, The number of poisonous gas types; For the Sample data; For the The predicted value of sample data, For the The actual value of the sample data; For each type of toxic gas, calculate the difference between the corresponding predicted value and the actual value, and determine the adjustment coefficient corresponding to each type of toxic gas ;in, For the Adjustment coefficient for each type of toxic gas; The model correction coefficient is generated based on the adjustment coefficient, and the model is corrected to complete the model training. The model correction coefficient is in, and Respectively The average actual value and average predicted value of sample data of toxic gas types; is the learning rate.

8. A coking index monitoring and analysis system based on a small coke oven, comprising: Initial data determination module, experiment operation module, data processing module and target determination module; wherein, The initial data determination module is used to obtain initial coal blending ratio data and corresponding experimental adjustment data; The experimental operation module is used to control the experimental process based on the initial coal blending ratio data and the corresponding experimental adjustment data, and to obtain the experimental process data and experimental result data in real time; The data processing module is used to generate comprehensive result data according to the initial coal blending ratio data, experimental adjustment data, experimental process data and experimental result data; The target determination module is used to determine the target coal blending ratio based on the comprehensive result data corresponding to each experiment after multiple experiments.

9. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to a coking index monitoring and analysis method based on a small coke oven as described in any one of claims 1-7.

10. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute operations corresponding to a method for monitoring and analyzing coking indicators based on a small coke oven as described in any one of claims 1 to 7.

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