WO2024060287A1 - Procédé de prédiction de température de haut-fourneau, équipement terminal et support d'enregistrement - Google Patents

Procédé de prédiction de température de haut-fourneau, équipement terminal et support d'enregistrement Download PDF

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Publication number
WO2024060287A1
WO2024060287A1 PCT/CN2022/122622 CN2022122622W WO2024060287A1 WO 2024060287 A1 WO2024060287 A1 WO 2024060287A1 CN 2022122622 W CN2022122622 W CN 2022122622W WO 2024060287 A1 WO2024060287 A1 WO 2024060287A1
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blast furnace
parameters
furnace temperature
model
sequence length
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PCT/CN2022/122622
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Chinese (zh)
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余成明
叶理德
严晗
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中冶南方工程技术有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

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  • the invention relates to the field of blast furnace ironmaking, and in particular to a blast furnace temperature prediction method, terminal equipment and storage medium.
  • blast furnace ironmaking is an important component of the entire steel industry. It plays a very important role in both the development of the entire industry and the overall energy conservation and emission reduction of the industry. Whether the blast furnace ironmaking conditions are running smoothly is related to whether the entire ironmaking process is efficient and energy-saving.
  • the blast furnace temperature is an important indicator for identifying the blast furnace conditions. Generally, the furnace temperature can be controlled to ensure the smooth progress of the blast furnace conditions. Establishing a reliable and accurate furnace temperature prediction model to guide blast furnace ironmaking workers in furnace temperature control is not only a theoretical research, but also has great guiding significance for the production practice of the steel industry.
  • the furnace temperature prediction models currently used are mainly for furnace ironmaking researchers to calculate the mechanism from the perspective of heat balance and material balance in the blast furnace production process, and thereby derive the corresponding mechanism model; or rely on the accumulation of a large amount of practical experience. Based on the generated expert knowledge, the researchers developed corresponding qualitative reasoning types. These methods are mainly derived from physical processes or experience and are interpretable, but they still cannot accurately explain the changes in furnace temperature under complex blast furnace production conditions.
  • the present invention proposes a blast furnace temperature prediction method, terminal equipment and storage medium.
  • a method for predicting blast furnace temperature comprises the following steps:
  • S1 Collect the values of various control parameters and status parameters of the blast furnace during the historical time period
  • S3 Determine the sequence length of the LSTM network based on the degree of autocorrelation of the molten iron temperature in the blast furnace; determine the input dimensions of each cycle unit in the LSTM network based on the obtained characteristic parameters;
  • step S4 Based on the determined sequence length and input dimension, extract training data from the data collected in step S1 to form a training set;
  • S5 Construct an LSTM network model based on sequence length and input dimensions, take the characteristic parameters of the blast furnace at a certain moment as input, and the blast furnace temperature at that moment as the output of the model, and train the model;
  • step S1 also includes preprocessing the collected data.
  • the preprocessing includes: performing normal distribution verification on various parameters, eliminating parameters that do not conform to the normal distribution, and performing anomaly analysis on the parameters that conform to the normal distribution. Value elimination and missing value completion are performed, and finally the values of all parameters that conform to the normal distribution are standardized to make the value ranges of different parameters consistent in magnitude.
  • the method for obtaining the characteristic parameters in step S2 is to use the principal component analysis method to map all parameters into a low-dimensional subspace through linear transformation to obtain the dimensionally reduced characteristic parameters.
  • step S3 the autocorrelation function ACF and the partial autocorrelation function PACF are used to jointly determine the molten iron temperatures at the first t moments that are most relevant to the current molten iron temperature.
  • the sequence length is the number of molten iron temperatures that are most relevant to the current molten iron temperature. t.
  • a blast furnace temperature prediction terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor.
  • the processor executes the computer program, the above embodiments of the present invention are implemented. steps of the method.
  • a computer-readable storage medium stores a computer program.
  • the computer program is executed by a processor, the steps of the method described above in the embodiment of the present invention are implemented.
  • the present invention adopts the above technical solution to improve the accuracy of prediction of the existing blast furnace temperature, and solves the problem that the existing system does not fully accurately simulate the complex production process in the blast furnace when inferring the furnace temperature through the heat balance process or based on empirical rules. The result is not as expected.
  • Figure 1 shows a flow chart of Embodiment 1 of the present invention.
  • Figure 2 is a comparison diagram between the model prediction results and the actual results in this embodiment.
  • An embodiment of the present invention provides a blast furnace temperature prediction method, as shown in Figure 1.
  • the method includes the following steps:
  • S1 Collect the values of various control parameters and status parameters of the blast furnace during the historical time period.
  • control parameters include blast furnace hot air temperature, oxygen enrichment rate, etc.
  • state parameters include top temperature, top pressure, etc.
  • this embodiment also includes preprocessing the collected data.
  • the preprocessing includes: performing normal distribution verification on various parameters, eliminating parameters that do not conform to the normal distribution, and performing verification on the parameters that conform to the normal distribution. Outlier elimination and missing value completion are performed, and finally the values of all parameters that conform to the normal distribution are standardized to make the value ranges of different parameters consistent in order of magnitude to speed up the network learning process.
  • S2 Perform lagged correlation analysis on all parameters to filter out characteristic parameters from all parameters.
  • the specific lag correlation analysis algorithm is:
  • Mutual information entropy is used to measure the dimensionless statistic (generally unit is bit) that a random variable
  • the degree of reduction in uncertainty in Y can therefore be used as a measure of the degree of correlation between X and Y.
  • Principal component analysis is used to map data to a dimensionality reduction method in a low-dimensional subspace through linear transformation, while preventing information loss as much as possible.
  • Subtract the respective average values from each dimensional feature calculate the covariance matrix, and obtain the eigenvalues and eigenvectors of the covariance matrix. Sort the eigenvalues from large to small, and select the eigenvectors corresponding to the largest k eigenvalues.
  • the original data is converted into a new space constructed by k feature vectors.
  • each principal component is a linear combination of the original variables, and the principal components are uncorrelated with each other. The number of principal components is much less than the number of original variables, and most of the information of the original variables is retained.
  • the parameter lag sequence set ⁇ X 1 ,...,X n ⁇ is obtained.
  • the principal component analysis method PCA is used to reduce the dimensionality of the original n input feature sequences to obtain a new feature input sequence ⁇ X 1 ',...,X k ' ⁇ , how many characteristic parameters are obtained, then how many input dimensions are there.
  • S3 Determine the sequence length of the LSTM network based on the degree of autocorrelation of the molten iron temperature in the blast furnace; determine the input dimensions of each cycle unit in the LSTM network based on the obtained characteristic parameters.
  • the molten iron temperature at the previous t moments that is most correlated with the current molten iron temperature can be determined, thereby determining the input length t of the network.
  • the autocorrelation function ACF and the partial autocorrelation function PACF are used to determine the most concerning t lagging effects of the molten iron temperature itself.
  • ACF describes the degree of correlation between the current value of a series and its past values, while PACF finds the correlation of the residual (which remains after removing the effects already explained by previous lags) to the next lag value.
  • the lag sequence set corresponding to the sequence X t of a certain parameter at time t is ⁇ X t-1 ,...,X tm ⁇ .
  • training data is extracted from the data collected in step S1 to form a training set.
  • the training data includes the value of the characteristic parameter of each sampling point within a period of time, and the temperature of the molten iron at each sampling point.
  • the value of the characteristic parameter is used as the input of the model, and the temperature of the molten iron is used as the output of the model.
  • S5 Construct an LSTM network model based on the sequence length and input dimension, take the characteristic parameters of the blast furnace at a certain time (t time) as input, and the blast furnace temperature at that time (t time) as the output of the model, and train the model.
  • LSTM Long Short-Term Memory
  • the uniqueness of LSTM is that it has a storage unit dedicated to memory, which is protected by some gate neurons. These gate neurons are different from ordinary neurons in that they have two states: on and off. When they are in the on state, the connection weight is 1; otherwise, the weight is 0.
  • the simplest LSTM contains the following three gate neurons with different functions: keep (save gate/forget gate): When the save gate is open, the contents of the storage unit memory are not cleared; when the save gate is turned off, the previously memorized contents are cleared.
  • LSTM introduces a gate mechanism to control the circulation and loss of features.
  • the network input at time t is the input feature
  • the unit state C t at time t is:
  • f t is called the forgetting gate, indicating which dimensions of C t-1 are used to calculate C t-1 ;
  • C t ' represents the unit state update value, which is composed of the input data X t and hidden node h t-1 through a neural network layer gets.
  • i t is the input gate. Like f t , it is also a vector whose elements are in the interval [0,1]. It is also calculated from X t and h t-1 .
  • W is the network weight and b is the bias term.
  • the long short-term memory neural network can not only remember short-term information, but also solve the problem of losing the ability to capture long-term dependencies due to gradient explosion in ordinary recurrent networks through the gate structure.
  • the loss function of the model is set as the root mean square error RMSE, and its calculation formula is:
  • the collected characteristic parameters at the current moment need to be input into the trained model, and the model predicts and outputs the blast furnace temperature corresponding to the current moment based on the input state at the previous moment.
  • the comparison diagram between the model prediction results and the actual results in this embodiment is shown in Figure 2.
  • the embodiments of the present invention improve the accuracy of prediction of the existing blast furnace temperature, and solve the problem that the existing system does not fully accurately simulate the complex production process in the blast furnace when inferring the furnace temperature through the heat balance process or based on empirical rules, resulting in inconsistent results. Anticipated questions.
  • the invention also provides a blast furnace temperature prediction terminal device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor.
  • a blast furnace temperature prediction terminal device which includes a memory, a processor and a computer program stored in the memory and executable on the processor.
  • the processor executes the computer program, the present invention is realized. The steps in the above method embodiment of the first embodiment of the invention.
  • the blast furnace temperature prediction terminal device can be a computing device such as a desktop computer, notebook, PDA, cloud server, etc.
  • the blast furnace temperature prediction terminal device may include, but is not limited to, a processor and a memory.
  • a processor and a memory.
  • the above-mentioned composition structure of the blast furnace temperature prediction terminal equipment is only an example of the blast furnace temperature prediction terminal equipment and does not constitute a limitation on the blast furnace temperature prediction terminal equipment. It may include more or less than the above. components, or a combination of certain components, or different components.
  • the blast furnace temperature prediction terminal device may also include input and output devices, network access devices, buses, etc., which are not limited in the embodiment of the present invention.
  • the so-called processor can be a central processing unit (Central Processing Unit, CPU), or other general-purpose processor, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit ( Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general processor can be a microprocessor or the processor can be any conventional processor, etc.
  • the processor is the control center of the blast furnace temperature prediction terminal equipment and uses various interfaces and lines to connect the entire blast furnace temperature Predict various parts of the end device.
  • the memory may be used to store the computer program and/or module, and the processor implements the blast furnace by running or executing the computer program and/or module stored in the memory, and calling data stored in the memory.
  • the memory may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system and at least one application required for a function; the stored data area may store data created based on the use of the mobile phone, etc.
  • the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , Flash Card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
  • non-volatile memory such as hard disk, memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , Flash Card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
  • the present invention also provides a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program.
  • the computer program is executed by a processor, the steps of the above method in the embodiment of the present invention are implemented.
  • the module/unit integrated in the blast furnace temperature prediction terminal equipment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium.
  • the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented.
  • the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form.
  • the computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory) and software distribution medium, etc.

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Abstract

La présente invention concerne un procédé de prédiction de température de haut-fourneau, un équipement terminal et un support d'enregistrement. Le procédé consiste à : collecter des valeurs de divers paramètres de commande et paramètres d'état d'un haut-fourneau dans une période historique ; exécuter une analyse de corrélation avec retard sur tous les paramètres pour obtenir des paramètres de caractéristiques à partir de tous les paramètres ; déterminer une longueur de séquence d'un réseau selon le degré d'autocorrélation d'une température de fer fondu dans le haut-fourneau ; déterminer des dimensions d'entrée d'unités récurrentes dans le réseau selon les paramètres de caractéristiques obtenus ; sur la base de la longueur de séquence et des dimensions d'entrée déterminées, extraire des données d'entraînement de données collectées pour former un ensemble d'entraînement ; élaborer un modèle de réseau LSTM sur la base de la longueur de séquence et des dimensions d'entrée, et entraîner le modèle à l'aide des paramètres de caractéristiques du haut-fourneau à un certain moment en tant qu'entrée et d'une température de haut-fourneau audit moment en tant que sortie du modèle ; et prédire la température de haut-fourneau au moyen du modèle entraîné. La présente invention améliore la précision de prédiction de température de haut-fourneau existante.
PCT/CN2022/122622 2022-09-21 2022-09-29 Procédé de prédiction de température de haut-fourneau, équipement terminal et support d'enregistrement WO2024060287A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118095110A (zh) * 2024-04-28 2024-05-28 中国矿业大学 一种燃煤机组炉温获取方法、系统、设备及存储介质

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210365784A1 (en) * 2020-05-19 2021-11-25 Zhejiang University Method for deriving fault diagnosis rules of blast furnace based on deep neural network
CN114015825A (zh) * 2021-11-09 2022-02-08 上海交通大学 基于注意力机制的高炉热负荷异常状态监测方法
CN114626303A (zh) * 2022-03-18 2022-06-14 山东莱钢永锋钢铁有限公司 一种基于神经网络的高炉炉温预测及指导操作的方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7308476B2 (ja) * 2019-02-28 2023-07-14 国立大学法人 奈良先端科学技術大学院大学 情報処理装置、情報処理方法、および情報処理プログラム
CN110378541A (zh) * 2019-08-08 2019-10-25 上海交通大学 一种风功率短期多步预测方法及装置
CN110555247A (zh) * 2019-08-16 2019-12-10 华南理工大学 一种基于多点传感器数据和BiLSTM的结构损伤预警方法
CN112465223A (zh) * 2020-11-26 2021-03-09 中冶南方工程技术有限公司 一种高炉炉温状态的预测方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210365784A1 (en) * 2020-05-19 2021-11-25 Zhejiang University Method for deriving fault diagnosis rules of blast furnace based on deep neural network
CN114015825A (zh) * 2021-11-09 2022-02-08 上海交通大学 基于注意力机制的高炉热负荷异常状态监测方法
CN114626303A (zh) * 2022-03-18 2022-06-14 山东莱钢永锋钢铁有限公司 一种基于神经网络的高炉炉温预测及指导操作的方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHAO YANG-YANG; XIA LIANG; JIANG XIN-GUO: "Short-term Metro Passenger Flow Prediction Based on EMD-LSTM", JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING, CN, vol. 20, no. 04, 15 August 2020 (2020-08-15), CN, pages 194 - 204, XP009553404, ISSN: 1671-1637 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118095110A (zh) * 2024-04-28 2024-05-28 中国矿业大学 一种燃煤机组炉温获取方法、系统、设备及存储介质

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