WO2021203912A1 - Procédé de prédiction en ligne de paramètres dans un processus de conversion de cuivre basé sur un four de soufflage à fond d'oxygène - Google Patents

Procédé de prédiction en ligne de paramètres dans un processus de conversion de cuivre basé sur un four de soufflage à fond d'oxygène Download PDF

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WO2021203912A1
WO2021203912A1 PCT/CN2021/080600 CN2021080600W WO2021203912A1 WO 2021203912 A1 WO2021203912 A1 WO 2021203912A1 CN 2021080600 W CN2021080600 W CN 2021080600W WO 2021203912 A1 WO2021203912 A1 WO 2021203912A1
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model
slag
prediction
data
neural network
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Chinese (zh)
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张哲铠
黎敏
李兵
吴金财
张官祥
董择上
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中国恩菲工程技术有限公司
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    • 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"
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22B—PRODUCTION AND REFINING OF METALS; PRETREATMENT OF RAW MATERIALS
    • C22B15/00—Obtaining copper
    • C22B15/0026—Pyrometallurgy
    • C22B15/0028—Smelting or converting
    • C22B15/003—Bath smelting or converting
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • This application relates to the field of metallurgy, and specifically, to an online prediction method of copper blowing process parameters in an oxygen bottom blowing furnace.
  • the blowing process is an extremely important process in the copper smelting process. It takes hot copper matte, flux and other raw materials (electrolysis residue) through the oxygen-enriched air at the bottom to generate oxidative exothermic reaction to form blister copper.
  • the refining process provides raw materials.
  • the oxygen bottom-blowing copper conversion furnace is the core equipment of the "new oxygen-enriched bottom-blowing copper conversion process" with complete independent intellectual property rights in China, and it is also the equipment for the copper conversion process commonly used in China, and the conversion process is continuous. The characteristics of instantaneity require corresponding control technology to further exert its technological characteristics, and to ensure stable product quality and optimized furnace conditions.
  • the copper blowing process is a very complex process. It is a multi-input and multi-output system.
  • the three parameters of blister copper grade, iron-silicon ratio in slag and slag temperature are important parameters for investigating the copper blowing process of oxygen bottom blowing furnace, reflecting the state of oxygen bottom blowing copper blowing process.
  • the blister copper grade refers to the mass fraction of copper in the blister copper
  • the slag iron to silicon ratio refers to the mass ratio of iron to silicon dioxide in the slag.
  • Their detection methods all use sampling during the blister copper and slag discharge process. Then, a fluorescence analyzer was used to test the composition of the sample, and the slag iron to silicon ratio and the blister copper grade value were calculated.
  • the key parameter in the oxygen bottom blowing copper blowing process is the melt temperature. However, due to its harsh production environment, the melt temperature cannot be directly measured online.
  • the slag temperature is generally used as an indirect characterization parameter of the melt temperature in production.
  • the copper blowing process requires the three important parameters of blister copper grade, iron-silicon ratio in the blowing slag, and slag temperature to be kept in an appropriate range and the fluctuations are as small as possible. Therefore, it is necessary to establish a corresponding and reliable prediction model for bottom blowing Three important parameters in the blowing process are predicted to provide guidance for the on-site staff in their decision-making and operation.
  • This application aims to solve one of the technical problems in the related technology at least to a certain extent. For this reason, one purpose of this application is to propose an online method for predicting the parameters of the copper blowing process in an oxygen bottom blowing furnace.
  • the prediction method can significantly improve the accuracy of the prediction results, and effectively solve the problems of poor adaptability of existing prediction models and methods and unsatisfactory actual operation effects.
  • the research methods for the parameter prediction model of the oxygen bottom blowing copper blowing process mainly adopt the mechanism modeling method or the data-driven intelligent modeling method, but these two methods have their own shortcomings: 1) The model is dedicated In terms of performance, as long as the objects of the model are different, the structure and parameters of the mechanism model are very different, and the portability of the model is poor; 2) The entire modeling process of the mechanism model requires a lot of manpower and material resources, regardless of Is it the study of the essential kinetics of the reaction, the determination of various equipment models, the characterization of the heat and mass transfer effects of the device in practical applications, the estimation of a large number of parameters (including test equipment and devices), each step is very difficult; 3 ) Mechanism models are generally composed of algebraic equations, differential equations, and even partial differential equations.
  • the present application proposes an online prediction method for the parameters of the copper blowing process in an oxygen bottom blowing furnace.
  • the prediction method includes:
  • the slag silicon-to-iron ratio neural network model and the slag temperature neural network model between the target parameters and the input parameters, the predicted value of the blister copper grade, the predicted value of the slag silicon-to-iron ratio and the slag temperature neural network model are established.
  • the intelligent coordinator is suitable for blister copper grade prediction value, slag silicon-to-iron ratio prediction value, slag temperature prediction value and blister copper grade, slag-silicon-to-iron ratio and output based on the respective output of the mechanism model and the data-driven model.
  • the deviation between the actual measured value of the slag temperature, the weighting coefficient of the mechanism model and the data-driven model in the mixed model is calculated, and the weighting coefficient, the predicted value of the mechanism model, and the data-driven Model prediction value, output the final prediction value of blister copper grade, slag silicon-to-iron ratio and slag temperature.
  • the three important parameters of blister copper grade, iron-silicon ratio in the blowing slag, and slag temperature during the bottom-blowing blowing process are investigated to establish respectively
  • the mechanism model and data-driven model of the bottom blowing furnace predict three important parameters, and on this basis, design a suitable intelligent coordinator to integrate the two, and compare the prediction results of the integrated hybrid model with the actual production results.
  • the prediction method can fully combine the advantages of the mechanism model and the data-driven model, maximize the strengths and avoid the weaknesses, significantly improve the accuracy of the prediction results, and effectively solve the problems of poor adaptability and unsatisfactory actual operation effects caused by the modeling of existing prediction methods. It has great significance and value in theory and practical application.
  • the method for online prediction of copper blowing process parameters in an oxygen bottom blowing furnace may also have the following additional technical features:
  • the material balance model is established based on a material balance equation
  • the energy balance model is established based on an energy balance equation
  • the multiphase balance model is established based on a multiphase balance equation
  • the METCAL software or the METSIM software is used to simultaneously solve the material balance equation, the energy balance equation and the multiphase balance equation
  • the mechanism model is established in combination with the process characteristics of the copper bottom blowing process.
  • the blister copper grade neural network, the slag silicon-to-iron ratio neural network, and the slag temperature neural network each independently include a plurality of artificial neurons, and the artificial neurons include but are not limited to Copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolytic residual rate and oxygen enrichment rate in the copper bottom blowing process.
  • the modeling mechanism of BP neural network is used in combination with the industrial production big data of the copper bottom blowing process and the blister copper grade neural network, the slag silicon to iron ratio neural network, and the slag temperature neural network to establish the method.
  • the data-driven model is used in combination with the industrial production big data of the copper bottom blowing process and the blister copper grade neural network, the slag silicon to iron ratio neural network, and the slag temperature neural network to establish the method.
  • the hybrid model is corrected periodically or in real time based on actual production results.
  • calibrating the hybrid model includes: comparing the final predicted value of the blister copper grade, slag silicon-to-iron ratio, and slag temperature output by the hybrid model with actual measured values: if the error is Within the expected range, keep the weighting coefficient in the hybrid model unchanged; if the error is outside the expected range, return the final predicted value to the smart coordinator and adjust the weighting coefficient, repeat the above operation, Until the error is reduced to the expected range.
  • the intelligent coordinator adopts a method of fuzzy division of input variable regions and synthesis to calculate the weighting coefficients of the mechanism model and the data-driven model prediction method.
  • f 1 is used to represent the prediction result output by the mechanism model
  • f 2 is used to represent the prediction result output by the data-driven model
  • ⁇ (x) is used to represent the data-driven model in the
  • the weighting coefficient in the hybrid model uses (1- ⁇ (x)) to represent the weighting coefficient of the mechanism model in the hybrid model
  • the prediction result of the output of the intelligent coordinator is:
  • y represents the prediction result
  • the prediction result includes the prediction value of blister copper grade, the prediction value of slag silicon-to-iron ratio and the prediction value of slag temperature
  • the weighting coefficient ⁇ (x) of the data-driven model in the hybrid model is:
  • the selection range of the input variable includes but not limited to copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolytic residual rate and oxygen enrichment rate; a, b, c, d are based on actual conditions
  • the characteristic parameter corresponding to the input variable obtained from the technical data of industrial production, and the characteristic parameter determines the membership function of the input variable.
  • the weighting coefficient ⁇ (x) of the data-driven model in the hybrid model is:
  • ⁇ i is the membership function calculated from the input variable i and its corresponding characteristic parameters a, b, c, d, ⁇ i is the weight coefficient of the input variable i in the j input variables, and ⁇ i is the empirical value Sure.
  • Fig. 1 is a schematic diagram of a hybrid model of an online prediction method for blister copper grade, slag silicon-to-iron ratio and slag temperature according to an embodiment of the present application.
  • Fig. 2 is a schematic diagram of a neuron model structure according to an embodiment of the present application.
  • Fig. 3 is a schematic diagram of a BP neural network model structure according to an embodiment of the present application.
  • Fig. 4 is a flowchart of an online prediction method for parameters of copper blowing process in an oxygen bottom blowing furnace according to an embodiment of the present application.
  • this application proposes an online method for predicting parameters of the copper blowing process in an oxygen bottom blowing furnace.
  • the prediction method includes: establishing the prediction value of blister copper grade and the prediction of slag-iron-silicon ratio based on raw material input conditions and based on a material balance model, an energy balance model, and a multi-phase balance model.
  • Bottom-blown converting furnace mechanism model based on the predicted value and slag temperature; according to actual production data and based on the blister copper grade neural network model, the slag silicon-to-iron ratio neural network model and the slag temperature neural network model between the target parameters and the input parameters, Establish a bottom-blown converting furnace data-driven model about the predicted value of blister copper grade, the predicted value of slag silicon-to-iron ratio, and the predicted value of slag temperature; use the intelligent coordinator to integrate the mechanism model and the data-driven model to obtain the predicted value of blister copper grade , Bottom-blown converting furnace mixing model with predicted value of slag ferrosilicon ratio and predicted value of slag temperature, using the hybrid model to output the final predicted value of blister copper grade, slag ferrosilicon ratio and slag temperature during the copper bottom blowing process.
  • the intelligent coordinator is suitable for the predicted value of blister copper grade, the predicted value of slag silicon to iron ratio, the predicted value of slag temperature and the actual measured value of blister copper grade, the slag silicon to iron ratio and the slag temperature based on the respective output of the mechanism model and the data-driven model.
  • the deviation between the computer model and the data-driven model in the mixed model, and according to the weighted coefficient, the predicted value of the mechanism model and the predicted value of the data-driven model, the final output of blister copper grade, slag silicon-to-iron ratio and slag temperature Predictive value.
  • the prediction method can fully combine the advantages of the mechanism model and the data-driven model, maximize the strengths and avoid the weaknesses, significantly improve the accuracy of the prediction results, and effectively solve the problems of poor adaptability and unsatisfactory actual operation effects caused by the modeling of existing prediction methods. It is of great significance and value in practical application.
  • the material balance model is established based on the material balance equation
  • the energy balance model is established based on the energy balance equation
  • the multiphase balance model is established based on the multiphase balance equation, using METCAL software or METSIM software
  • the material balance equation, energy balance equation and multi-phase balance equation are solved simultaneously and combined with the process characteristics of the copper bottom blowing process to establish a bottom blowing furnace mechanism model.
  • the material balance equation is:
  • Var represents a variable
  • Con represents a constant
  • M represents a material
  • C represents a component of the material
  • E represents an element of the component
  • X represents a mole fraction
  • E c, e represents a specific element in a specific component
  • the energy balance equation is:
  • This equation represents that the heat of the input material and the heat of the output material are equal in the bottom blowing process, that is, energy conservation.
  • the multiphase balance equation is:
  • G is the total Gibbs free energy of the system, Is the standard generating Gibbs free energy of pure substance c component in phase p; ⁇ pc is the activity factor of component c in phase p; ⁇ pc is the mole fraction of component c in phase p ; C p is p The number of components in the phase; T is the temperature; R is the universal constant of the gas; N pc is the number of moles of the c component in the p phase.
  • This equation indicates that the Gibbs free energy of the system reaches the minimum during the blowing process, that is, the system reaches a steady state.
  • Gaussian method can be used to solve the multivariate linear equations for each element of each component, for example, for a specific element of a specific component
  • the reliable third-party software such as METCAL, METSIM, etc. can be used to establish the bottom blowing furnace mechanism model, and calculate and predict the blister copper grade and blowing
  • the ratio of iron to silicon in the slag and the temperature of the slag are the three important parameters of the copper bottom blowing process.
  • the blister copper grade neural network, the slag silicon-to-iron ratio neural network, and the slag temperature neural network each independently include a plurality of artificial neurons
  • the artificial neurons include but are not limited to the copper bottom blowing process Copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolytic residual electrode rate and oxygen enrichment rate.
  • the data-driven model can be processed by the method of artificial neural network.
  • Artificial neural network is a nonlinear information processing system composed of a large number of interconnected processing units that imitates the biological structure and related functions of the human brain. It consists of artificial neurons.
  • the schematic structure of the neuron model is shown in Figure 2.
  • the neuron model has R inputs, and each input is connected to the next layer through a weight w, where f represents the input/output relationship
  • w represents the input/output relationship
  • S j is the output function
  • b j is the threshold
  • w j,i are the connection weights
  • x 0 b j
  • w j,0 -1
  • p and y are the input and output of the neuron, respectively.
  • f(wp+b) is the transfer function.
  • the modeling mechanism of the BP neural network can be used in combination with the industrial production big data of the copper bottom blowing process and the blister copper grade neural network, the slag silicon to iron ratio neural network, and the slag temperature neural network.
  • BP neural network is currently one of the most widely used and successful artificial neural networks. It is a reverse learning algorithm for multi-layer networks. Its learning process consists of two processes: forward propagation of signals and backward propagation of errors.
  • BP neural network can learn a large number of input-output mapping relationships without revealing the explicit mathematical equations of this mapping relationship.
  • the BP neural network is composed of an input layer, an intermediate layer or a hidden layer and an output layer.
  • the input sample data is passed in from the input layer, and then passed through the hidden layer processing, information transformation, and learning. Output layer. If the output does not match expectations, the error will be passed back to the hidden layer and input layer in some form, and the error will be allocated to each layer in the process, as the basis for modifying the weights and thresholds of various places.
  • the weights and thresholds are continuously adjusted until the output error is reduced to an acceptable level or reaches the predetermined number of learning times.
  • the modeling mechanism of the BP neural network and combining the big data of industrial production of the copper bottom blowing process to construct a neural network model of the copper bottom blowing process, and using the production big data to train and optimize the model, it can further improve the
  • the three important parameters of blister copper grade, the ratio of iron to silicon in the slag and the temperature of the slag are the reliability of prediction.
  • the intelligent coordinator can use the method of fuzzy dividing the input variable area and comprehensively calculate the weighting coefficient of the bottom blowing furnace mechanism model and the bottom blowing furnace data-driven model prediction method, that is, through The intelligent coordinator integrates the two prediction models based on fuzzy division.
  • the neural network model obtains greater weight, and its compensation effect is more ensured Prediction accuracy; when the bottom-blowing furnace copper blowing balance is disturbed and the working conditions are unstable, the mechanism model gets more weight, so that the intelligent integrated hybrid prediction model can guarantee the global fitting ability of the bottom-blowing furnace copper blowing process .
  • the intelligent coordinator to intelligently integrate the two models, not only the reliability of the integrated hybrid model is greatly enhanced, but also the artificial neural network formed by the combination of a large number of neurons will also show similarity to the human brain.
  • the characteristics make the hybrid model have adaptive and self-organizing capabilities, which can significantly improve the accuracy of the prediction results.
  • f 1 can be used to represent the predicted results of the bottom-blown converting furnace mechanism model output
  • f 2 can be used to represent the predicted results of the bottom-blown converting furnace data-driven model output
  • ⁇ (x) can be used to represent the bottom-blown converting furnace data-driven
  • y represents the prediction result.
  • the prediction results include the predicted value of blister copper grade, the predicted value of slag silicon-to-iron ratio and the predicted value of slag temperature.
  • the value of the weighting coefficient ⁇ (x) of the data-driven model in the mixed model is obtained by the membership function.
  • the relationship between ⁇ (x) and the membership function is
  • x represents a certain input variable, such as copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolytic residual rate and oxygen enrichment rate, etc.;
  • a, b, c, d are characteristic parameters, and different input variables correspond to different The characteristic parameters a, b, c, and d of each characteristic parameter can be obtained by referring to the historical record of actual industrial production to obtain the reference initial value of the membership function parameter of each input variable, and using industrial data to optimize it, for example, when using copper matte grade
  • the copper matte grade and its corresponding characteristic parameters a, b, c, d can be:
  • characteristic parameters a, b, c, and d are not fixed and can be adjusted according to actual production.
  • the values of characteristic parameters a, b, c, and d are different for different production conditions. It's not all the same.
  • the weighting coefficient of the data-driven model in the mixed model can be used to predetermine the input variables and their corresponding characteristic parameters a, b, c, d, obtain the membership function ⁇ i value of each input variable, and then use the weighted average method to calculate The final weighting coefficient ⁇ (x) value, namely
  • ⁇ i is the membership function calculated from the input variable i and its corresponding characteristic parameters a, b, c, d, ⁇ i is the weight coefficient of the input variable i in the j input variables, and ⁇ i is the empirical value Sure.
  • the weighting coefficient ⁇ (x) of the data-driven model in the mixed model is
  • the mixing model can be corrected regularly or in real time based on actual production results, thereby further improving the reliability and accuracy of the online prediction method for the copper blowing process parameters of the oxygen bottom blowing furnace of the present application.
  • calibrating the hybrid model may include: comparing the final predicted values of the blister copper grade, slag silicon-to-iron ratio, and slag temperature output by the hybrid model with the actual measured values: if the error is within the expected range If the error is outside the expected range, return the final predicted value to the smart coordinator and adjust the weighting coefficient. Repeat the above operation until the error is reduced to the expected range.
  • This method is especially suitable In order to correct the preliminarily established mixing model, the reliability and accuracy of the online prediction method for the copper blowing process parameters of the oxygen bottom blowing furnace can be further improved.
  • the flow chart of the method for online prediction of oxygen-enriched bottom blowing process parameters based on the hybrid model can be shown in Figure 4, where the real-time collection of key process parameters is performed by detecting sensors (load cells, flow Sensors, etc.) measure the actual parameters such as the amount of raw fuel and oxygen-enriched gas flow during the oxygen-enriched bottom blowing process, and transmit them to the prediction model; the key parameters for offline detection are input and output in the human-machine interface Key process parameters such as copper flow rate and composition, slag flow rate, composition and temperature; the establishment of an online prediction model refers to the use of the principles of material balance, energy balance and phase balance to establish the mechanism model of the oxygen-enriched bottom blowing process and use production Big data constructs a neural network model (data-driven model), and uses an intelligent coordinator to integrate the two to obtain a mixed model for the prediction of three important parameters of the bottom-blowing furnace blowing process.
  • sensors load cells, flow Sensors, etc.
  • the three major factors namely the grade of blister copper in the bottom blowing process, the ratio of iron to silicon in the blowing slag, and the slag temperature, are mainly investigated.
  • the bottom blowing furnace mechanism model and data-driven model are respectively established to predict the three important parameters.
  • it uses the advantages of the mechanism model of good extrapolation and strong interpretability, and on the other hand, it adopts the neural network analysis method. Analyze and predict the three important parameters of the bottom blowing process with big data.
  • the prediction method can fully combine the advantages of the mechanism model and the data-driven model, maximize the strengths and avoid the weaknesses, significantly improve the accuracy of the prediction results, and effectively solve the problems of poor adaptability and unsatisfactory actual operation effects caused by the modeling of existing prediction methods. It has great significance and value in theory and practical application.

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Abstract

La présente demande diviulgue un procédé de prédiction en ligne pour des paramètres dans un procédé de conversion de cuivre basé sur un four à oxygène à soufflage par le bas. Le procédé consiste : à établir un modèle de mécanisme de four convertisseur à soufflage par le bas selon une condition d'entrée de matière première et sur la base d'un modèle d'équilibre de matière, d'un modèle d'équilibre d'énergie et d'un modèle d'équilibre multiphase ; à établir un modèle de commande de données de four convertisseur à soufflage par le bas selon des données de production réelles et sur la base d'un modèle de réseau neuronal de qualité de cuivre brut, d'un modèle de réseau neuronal à rapport de ferrosilicium de laitier, et d'un modèle de réseau neuronal de température de laitier entre un paramètre cible et un paramètre d'entrée ; à intégrer le modèle de mécanisme et le modèle de commande de données en utilisant un coordinateur intelligent pour obtenir un modèle de mélange de four convertisseur à soufflage par le bas relatif à une valeur de prédiction de qualité de cuivre brut, une valeur de prédiction de rapport de ferrosilicium de laitier et une valeur de prédiction de température de laitier ; et à délivrer en sortie des valeurs de prédiction finale de la qualité de cuivre brut, du rapport de ferrosilicium de laitier et de la température de laitier dans un processus de conversion à soufflage par le bas de cuivre à l'aide du modèle de mélange. Le procédé de prédiction peut résoudre efficacement le problème selon lequel les modèles et les procédés de prédiction existants sont médiocres en adaptabilité et non satisfaisants en effet de fonctionnement réel, et peut améliorer significativement la précision d'un résultat de prédiction.
PCT/CN2021/080600 2020-04-10 2021-03-12 Procédé de prédiction en ligne de paramètres dans un processus de conversion de cuivre basé sur un four de soufflage à fond d'oxygène WO2021203912A1 (fr)

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